WO2022018680A1

WO2022018680A1 - Micro-grid, energy management system and method

Info

Publication number: WO2022018680A1
Application number: PCT/IB2021/056634
Authority: WO
Inventors: Christo MYBURGH
Original assignee: U Energy Ltd
Priority date: 2020-07-22
Filing date: 2021-07-22
Publication date: 2022-01-27

Abstract

This invention relates generally to energy management methods and systems for managing supply of electrical energy to loads in micro-grids supplied with electrical energy generated by electrical energy generation source/s, and to a micro-grid. The method described herein comprises determining an amount of electrical energy generated by the electrical energy generation source/s and stored in a rechargeable power storing device/s. The generated electrical energy is supplied to the loads via controllable circuit breaker modules in communication with at least one processor which monitors consumption of electrical energy by the loads by way of the circuit breaker module; and determines whether or not electrical energy supplied to the load/s should be stopped or not. The method further comprises controlling the controllable circuit breaker module/s associated with the load/s to switch on or off supply of electrical energy to the load/s in response to receiving a suitable instructions from the processor.

Description

MICRO-GRID, ENERGY MANAGEMENT SYSTEM AND METHOD

FIELD OF INVENTION

This invention relates to a micro-grid and an energy management system and method, particularly to an islanded micro-grid and an energy management system and method.

BACKGROUND OF INVENTION

Much of the electricity used around the world is generated and/or distributed by power utility organisations which are private, public, or parastatal organisations wherein the electricity which is distributed is primarily generated on a large scale using one or a combination of renewable and/or non-renewable sources. The electricity generated is usually fed into an electricity grid, often referred to as a “mains grid”, “mains”, “grid” or the like which is accessible by anyone requiring electricity after engaging with suitable organisations and/or municipal bodies that purchase electricity from said organisations.

Due to various factors, there is a global movement to move “off-grid” and to remove reliance on a municipal grid. The reasons for doing so are varied and range from reliability issues, so-called grid-tied setups to ethical reasons in that more people prefer to rely solely on renewable and/or “clean” energy which has a low environmental footprint.

Renewable or clean energy or electricity in this sense includes, but is not limited to, electricity generated by suitable photovoltaic cells from solar energy, by suitable wind turbines from wind energy, by suitable hydro-electric generators from flowing fluid, by geothermal generators from geothermal energy, and the like. Electricity generated by renewable energy is usually stored and utilized from batteries, in the form of direct current (DC) to power various electrical devices.

Most electronic devices in grids operate on DC but since most utilities distribute power via Alternating Current (AC), these devices are equipped with suitable AC-DC converters to convert the incoming AC to DC. The conversion from AC to DC power often results in power losses, therefore, straining the main power grids.

It follows that when using electricity stored in batteries from renewable energy sources as mentioned above, DC power from the batteries is converted back to AC power. The AC power is again converted back to DC power for those household appliances that operate on DC power. The inventors have found that generating AC power and converting it to DC power is counterintuitive and results in significant amounts of energy losses. The energy management system and artificial intelligence developed to consider the energy losses in the “generation-to-load” journey.

Traditional micro-grid technologies, rely on other energy sources outside of the installed battery capacity for occasions where the batteries have run out of power and/or there are problems with the renewable energy source. For example, where there is no wind or sunshine to generate electricity to recharge batteries, the grid is looked to as a fallback for the provision of electricity.

It follows that where the grid is unreliable and/or not desired and/or not available, there is no fallback and thus there would be no electricity to distribute to a target network.

The present invention seeks to address the aforementioned difficulties and/or to provide an alternate energy management system.

SUMMARY OF INVENTION

According to a first aspect of the invention there is provided a micro-grid for providing electrical power to one or more loads via suitable electrical connection points, the micro-grid being isolated from a mains power grid, the micro-grid comprising: at least one electrical energy generation source, wherein the at least one electrical energy generation source is from renewable energy; at least one rechargeable power storing device arranged to store at least some of the electrical energy generated by the electrical energy generation source and arranged to provide AC and/or DC power to the load or plurality of loads; and an energy management system that is arranged to selectively distribute AC and/or DC power stored in the rechargeable power storing devices to the load or plurality of loads.

In an embodiment, the power distributed to the loads or plurality of loads may be based on a load schedule, where loads assigned as critical loads may be provided with power first, and loads assigned as non-critical or non-essential may not be provided with power or may be provided with power when there is either enough power in the rechargeable power storing devices or excess electrical power to provide power to both the critical and non-critical loads all of the loads.

Accordingly, the energy management system may comprise a database including a list of the loads and their respective load ratings, wherein each load is assigned a level of priority. The database may also comprise solar and wind predictions as well as the load predictions/energy consumption from suitable artificial intelligence algorithms which employ trained neural networks,

For example, the loads may be grouped into a plurality of priority groups, for example, at least five priority groups, with the priority of the loads being arranged in descending order of priority wherein the loads in the first group being critical loads and those in the last group (i.e., the 5^th group) being non-critical loads.

In an embodiment, the energy management system may use artificial intelligence algorithms which use outputs of trained neural networks, to predict or forecast the generational capacity/energy generated by the electrical energy generation source/s; and based on predicted or forecast generational capacity/energy generated by the electrical energy generation source/s and a local grid user defined load priority list, the energy management system is configured to determine whether the electrical energy generated and stored in the rechargeable power storing device is adequate to sustain the expected power usage by the load/s.

The energy management system may be configured to apply a suitable optimization algorithm which determines or calculates distribution of the energy to the load or loads according to a user defined /predefined order of priority.

The energy management system may also bring the shed appliances back online as generational capacity increases again as the weather becomes favourable for renewable generation.

In an embodiment, the expected power usage may be based on the energy consumption ratings from a controllable circuit breaker module which comprises a sense and control board (SCB) that has a data communication interface with the energy management system or remote control/switching of each of the loads.

The energy management system may be responsible for all operations of the micro-grid and these may include, forecasting the generating capacity, forecasting the load usage, optimizing the load shedding, measuring all micro-grid system parameters, controlling all breakers, load control, and energy storage control.

Energy storage in the micro-grid may comprise various systems that may include but are not limited to, batteries, electric vehicles (EV), pumped storage, and thermal storage. With EVs, the present invention permits excess power to be shunted to EV battery storage. Moreover, EV energy storage may be transferred to the Micro Grid described herein.

In one example embodiment, the energy management system may be configured to monitor the power usage over a period of time, store usage data indicative of the power usage over a period of time; and train a suitable consumption neural network which models a power usage profile of a home based on historic power use. In other words, the energy management system may be configured to use data indicative of historic electrical energy usage over time to train a suitable machine- based learning module or neural network which is configured to predict future power usage once trained.

The artificial intelligence algorithm which makes up the forecasting algorithms for the local micro-grid energy management system may be the load forecasting algorithm which may use the consumption or load forecasting neural network.

The consumption neural network may be configured to predict or forecast how much power may be consumed or drawn from the micro-grid at any given specified time and/or date and/or in a specific time period. The ability to know when the user or household consumes power and when it does not is a powerful capability of the energy management system described herein. In this way, the energy management system may be configured to plan and potentially start load shedding if the energy management system forecasts or predicts that there may be a high-power usage/consumption period coming up where there is a low electrical energy generation predicted or forecast for the same period and/or when battery storage capacity or state of charge of the energy storage/batteries and other energy stores might be lower than usual. “Load shedding” in the context of the present disclosure may be understood to mean the disconnection of one or more loads from drawing electrical energy from the micro-grid in accordance with the optimisation algorithm. The optimisation algorithm, implemented by the energy management system makes use of the user defined priority schedule to shed loads in an automated fashion by way of the controllable circuit breaker modules.

It will be understood that on the other hand, when there exists a forecasted or predicted period of high renewable generating capacity and the energy management system predicts low local grid usage, the energy management system may control the micro-grid to store energy and/or allow micro-grid power usage.

These aforementioned steps may be determined by an optimization algorithm which is implemented by the EMS.

In an embodiment, the energy management system may be arranged to collect real-time meteorological data and/or collect data from suitable sensors associated with the electrical energy generation source/s value of a measured parameter associated with the electrical energy generation source, and accordingly determine the amount of electrical energy or power that can be produced by the electrical energy generation source in real-time. The measured parameters may be solar irradiation or sunlight, wind speed, flow rate of water, or the like.

The intelligent decision-making abilities of the energy management system may come from the built-in artificial intelligence algorithms and/or neural networks that it uses to make those decisions.

The system uses three machine learning algorithms to forecast the solar generational capacity, wind generational capacity, as well as the micro-grid load usage. The results of the algorithms are then fed into the energy management system optimization algorithm and used to make decisions on when to shed what and how to control the micro-grid.

For example, when the electrical energy generation source is a wind turbine, the sensor may be an anemometer that is arranged to measure the wind speed and direction, and based on the design parameters associated with the wind turbine, the energy management system may be arranged to determine the total power that can be produced by the wind turbine based on the wind speed in a predefined period, and in addition, the energy management system may be arranged to determine the electrical power expected to be generated by the wind turbine based on weather data collected from a weather forecast system. The weather data may have provided the wind speed expected for the period in which power usage is expected.

The wind prediction artificial intelligence algorithm may be used to forecast the wind velocities over a given time period in order to predict how much power can be generated by wind turbines for the micro-grid. Forecasting the generative capacity of the wind turbines allows the energy management system to include the electricity generated by the wind turbine in the decision-making process and optimization routines.

Also, for example, if the electrical energy generation source is a solar photovoltaic panel, the sensor may be a solar radiation sensor or sun load sensor, to determine the solar radiation in the area occupied by the solar photovoltaic panels and based on the orientation of the solar panels and number of solar panels in that area, the energy management system may be arranged to determine the electrical energy expected to be produced from the solar photovoltaic panels. Similarly, the solar generation capacity forecasting algorithm is used to predict the expected amount of electrical power expected to be produced from the sun for the period of power generation.

Knowing the amount of solar power that should be generated in the micro-grid at a certain period allows the energy management system to intelligently plan the power flow in the grid, whether the grid needs to store more energy for a future date and shed load usage or allow maximum usage of all loads.

In an embodiment, when the energy generated by the electrical energy generation source (or arranged to be generated by the electrical energy generation source) is equivalent to a predefined percentage of the total energy required to provide the desired power to the load, the energy management system is accordingly arranged to store the generated electrical energy in the power storage devices, and selectively distribute the electrical power from the rechargeable power storing devices to the loads according to a predefined order of priority of the loads.

In an embodiment, when the energy generated by the electrical energy generation sources (or arranged to be generated by the electrical energy generation sources) is less than a predefined percentage of the total energy required to provide the desired power to the load, the energy management system is accordingly arranged to store the generated electrical energy in the rechargeable power storing devices, and provide power from the power storing devices selectively to the loads according to a predefined order of priority of the loads.

In an embodiment, the energy management system may determine the extent of charging of the rechargeable power storing devices, and if the power storage devices are charged to maximum capacity, the energy management system may be arranged to direct/shunt the excess electrical energy to other non-critical loads to avoid overcharging of the rechargeable power storing devices. For example, one of the loads connected to the micro-grid may comprise a plurality of hot water tanks or geysers which are in communication with one another. The plurality of hot water tanks may be used to store additional hot water to provide additional hot water when one of the pluralities of hot water tanks runs out of hot water. Alternatively, the micro-grid may comprise a hot water storage system comprising at least one hot water storage tank for supplementing a geyser load associated with a structure to which the micro-grid is connected. Preferably, if there is excess electrical energy from the energy generation sources, the excess electrical energy may be directed to the plurality of hot water tanks or the hot water storage system to maintain the temperature of the water in the tank at the desired temperature.

In another embodiment, one of the loads connected to the micro-grid may comprise an ice cooling system that is arranged to cool water or freeze water. Preferably, if there is excess electrical energy from the energy generation sources, the excess electrical energy may be directed to the cooling system.

In another embodiment, one of the loads connected to the micro-grid may comprise a pool pump. Preferably, if there is excess electrical energy from the energy generation sources, the excess electrical energy may be directed to the pool pump.

In another embodiment, one of the loads connected to the micro-grid may comprise an atmospheric water generator for extracting water from humid air. Preferably, if there is excess electrical energy from the energy generation sources, the excess electrical energy may be directed to the atmospheric water generator.

In an embodiment, the energy management system may be arranged to continuously monitor, and store in a database, the power usage of the loads, typically the loads may be connected to a structure via controllable circuit breakers with a sense and control board that has a data communication interface with the energy management system, to determine the power usage profile of the structure, and according to determine the power usage during off-peak and peak hours. Accordingly, based on the expected average power usage using the power usage profile during off-peak and peak hours, the energy management system may be arranged to determine the amount of power required per day and shunt off excess electrical energy to non-critical loads.

In an embodiment, the energy management system can be arranged to: collect meteorological data, from a meteorological system that continuously provides real-time and forecast meteorological data; based on the forecast meteorological data, determine the expected power to be produced by the energy generation source at a future time and/or date and/or period; compare the expected produced power at the future time and/or date and/or period to the power usage of the structure during off- peak and peak time and/or date and/or period; determine whether the expected power to be produced at the future date is equivalent to the power usage profile of the structure; if the expected power to be produced at the future date based on the forecast meteorological data is less than the power usage profile of the structure, prioritize the recharging of the rechargeable power storing devices so that at the future date, the power storing devices can have enough electrical energy to distribute electrical power to the loads according to a predefined order of priority of the loads for a predefined period, preferably 48 hours.

In an embodiment, while prioritizing the recharging of the rechargeable power storing devices, the energy management system may be arranged to distribute electrical power to the loads according to a predefined order of priority so that the power generated in real-time can sustain the loads and be used to recharge the rechargeable power storing devices.

In an embodiment, if the expected power to be produced at the future date based on the forecast meteorological data is less than the power usage profile of the structure, the energy management system may also be arranged to change, in real- time, the order in which the electrical energy is distributed to the loads and thereby prioritizes the recharging of the rechargeable power storing devices to ensure that enough electrical power is generated and stored in the rechargeable power storing devices by the forecasted period. Accordingly, the controllable circuit breaker modules may provide information to the energy management system so it may be arranged to switch off non-critical loads and optimize the energy stored in real-time in the rechargeable power storing devices to critical loads or prioritize the energy stored in real-time in the power storing devices to loads according to a predefined order of priority. In an embodiment, the energy management system may be arranged to report and display on a user device associated with the structure or loads, in real-time, the amount of energy produced, and the list of loads prioritized for the electrical energy produced in real-time.

In an embodiment, the energy management system may be arranged to collect from the end-user device, a custom list of loads that are critical and non-critical, and the energy management may be arranged to distribute electrical energy according to the user-defined custom list or based on the artificial intelligence algorithms for the user behaviour usage.

In an embodiment, the electrical energy generation sources may include one or more solar photovoltaic panels, wind turbines, and hydropower units.

In an embodiment, the micro-grid may include a suitable charging module (i.e., a charger controller) for controlling the charging and discharging of the rechargeable power storing unit.

In an embodiment, the micro-grid may include a suitable direct current (DC) circuit or distribution board to which is connected or connectable DC loads via controllable circuit breaker module and connection points fitted to the structure.

In an embodiment, the micro-grid may also comprise an inverter to convert the DC current to alternating current (AC). The micro-grid may therefor also comprise an AC circuit or distribution board.

In an embodiment, the micro-grid may also comprise an AC board to which is connected AC loads via controllable circuit breaker module and connection points.

In an embodiment, the micro-grid system may comprise a gas system to provide gas to gas appliances fitted to the structure, such as a gas stove.

According to a second aspect of the invention, there is provided a computer- implemented energy management method for managing and arranging the distribution of electrical energy to a load or loads which are disconnected from a power grid, the method including: determining, by at least one processor, the amount of electrical energy generated by at least one electrical energy generation source that generates electrical energy from renewable energy and energy stored in at least one rechargeable power storing device; and selectively distributing, by at least one processor, power stored in the power storing device to the load or plurality of loads. The energy management system may be an embedded software and controller used to manage all the activities of the local micro-grid. The energy management system is responsible for the complete day-to-day functioning of the entire micro-grid.

The energy management system may be configured to take in data from the three forecasting algorithms, measures all system operating parameters, takes in data from the Human Machine Interface, controls the renewable energy generators, controls the loads in the micro-grid, and optimizes the load shedding and storage in the micro- grid to ensure the two days of autonomy.

In an embodiment, the method may include: monitoring, by means of the at least one processor, the energy generated by the electrical energy generation source and stored in the power storing device, based on the expected power usage by the load or loads in a predefined period; determining, by means of the at least one processor, whether the energy stored in the power storing device is adequate to sustain the expected power usage by the load(s); and distributing, by means of the at least one processor, the electrical energy stored in the power storing device to the load or loads according to a predefined order of priority.

In an embodiment, the expected power usage may be based on the energy consumption ratings from a controllable circuit breaker module which is communicably coupled to the energy management system or remote control/switching of each of the loads.

In an embodiment, the method includes collecting real-time meteorological data, and collecting from suitable sensors associated with the electrical energy generation source that generates energy that is stored in the power storing devices, a value of a measured parameter of the electrical energy generation source, and accordingly determine the amount of energy or power that can be produced in real- time by the electrical energy generation source.

For example, when the electrical energy generation source is a wind turbine, the sensor may be an anemometer that is arranged to measure the wind speed and direction, and based on the design parameters associated with the wind turbine, the method may include determining the total power that can be produced by the wind turbine based on the wind speed in a predefined period, and in addition, determining the electrical power expected to be generated by the wind turbine based on real-time weather data collected from a weather forecast system, which weather data may have provided the wind speed expected for the period of power generation.

Also, for example, if the electrical energy generation source is a solar photovoltaic panel, the sensor may be a solar radiation sensor or sun load sensor, to determine the solar radiation in the area occupied by the solar photovoltaic panels and based on the orientation of the solar panels and number of solar panels in that area, the method may include determining the electrical energy expected to be produced from the solar photovoltaic panels. Similarly, based on the expected percentage of sun expected for the day, the method includes determining the amount of electrical power expected to be produced from the sun for the period of power generation.

In an embodiment, when the energy generated by the electrical energy generation sources (or arranged to be generated by the electrical energy source) is equivalent to a predefined percentage of the total energy required to provide the desired power to the load or plurality of loads, the method may include the step of storing the generated electrical energy in the power storage devices, and selectively distributing the electrical power from the power storage devices to the loads according to a predefined order of priority.

In an embodiment, when the energy generated by the electrical energy generation source (or arranged to be generated by the electrical energy source) is less than a predefined percentage of the total energy required to provide the desired power to the load or plurality of loads, the method may include storing the generated electrical energy in the power storing device, and providing power from the power storing device selectively to the loads according to a predefined order of priority. In an embodiment, the method may include determining the extent of charging of the rechargeable power storing device, and if the power storage device is charged to maximum capacity, the method may include directing the excess electrical energy to other non-critical loads to avoid overcharging of the rechargeable power storing device.

In an embodiment, the method may include continuously monitoring, and storing in a database, the power usage of the load connected to a structure, such as a building, a cell phone tower, etc., to determine the power usage profile of the structure, and accordingly determining the power usage of the structure during off-peak and peak hours.

Accordingly, based on the expected average power usage using the power usage profile of the structure during off-peak and peak hours, the method may include the step of determining the amount of power required per day and shunt off excess electrical energy to non-critical loads.

In an embodiment, the method may further include the steps of: collecting meteorological data, from a meteorological system that continuously provides real-time and forecast meteorological data; based on the forecast meteorological data, determining the expected power to be produced by the energy generation source at a future time and/or date and/or period; comparing the expected power to be produced to the power usage profile of the structure during off-peak and peak hours; determining whether the expected power to be produced is equivalent to the DC power usage profile of the structure; if the expected power to be produced based on the forecast meteorological data is less than the power usage profile of the structure, prioritizing the recharging of the rechargeable power storing device so that during the forecasted period, the power storing device can have enough electrical energy to distribute electrical power to the loads according to a predefined order of priority of the loads for a predefined period, preferably 48 hours.

In an embodiment, if the expected power to be produced based on the forecast meteorological data is less than the power usage profile of the structure, the method may include the step of changing, in real-time, the order in which the electrical energy is distributed to the loads and thereby prioritize the recharging of the rechargeable power storing device to ensure that enough electrical power is generated and stored in the rechargeable power storing device by the forecasted period. Accordingly, the method may include the step of switching off non-critical loads and distributing the electrical energy stored, in real time, in the rechargeable power storing device to critical loads or distributing the electrical energy stored in the power storing device to loads according to a predefined order of priority.

In an embodiment, the method may include reporting and displaying on an end user device associated with the structure, in real time, the amount of energy produced and the list of loads prioritized for the electrical energy produced in real time.

-user device, a custom list of loads that are critical and non-critical.

According to a third aspect of the invention, there is provided a computer- readable medium storing instructions thereon to manage and distribute electrical energy to a load that is disconnected from a power grid, wherein when the instructions are executed, they cause at least one processor to perform the operations of: determining the amount of electrical energy generated by at least one electrical energy generation source that generates electrical energy from renewable energy and energy stored in at least one rechargeable power storing device; and selectively distributing power stored in the power storing device to the load. selectively distributing power stored in the power storing device to the load.

In an embodiment, the processor may be arranged to perform the operations of: monitoring the energy generated by the electrical energy generation source and stored in the power storing device, based on the expected power usage by the load or loads in a predefined period; determining whether the energy stored in the power storing device is adequate to sustain the expected power usage by the load(s); and distributing the electrical energy stored in the power storing device to the load or loads according to a predefined order of priority.

In an embodiment, the expected power usage may be based on the energy consumption ratings of each of the loads.

In an embodiment, the processor may be arranged to perform the operation of collecting real-time meteorological data or collecting, from suitable sensors associated with the electrical energy generation source that generate energy that is stored in the power storing device, a value of a measured parameter of the electrical energy generation source, and accordingly determine the amount of energy or power that can be produced in real-time by the electrical energy generation source.

For example, when the electrical energy generation source is a wind turbine, the sensor may be an anemometer that is arrange to measure the wind speed and direction, and based on the design parameters associated with the wind turbine, the processor may be arranged to perform the operation of determining the total power that can be produced by the wind turbine based on the wind speed in a predefined period, and in addition, determining the electrical power expected to be generated by the wind turbine based on real-time weather data collected from a weather forecast system, which weather data may have provided the wind speed expected for the period of power generation.

Also, for example, if the electrical energy generation source is a solar photovoltaic panel, the sensor may be a solar radiation sensor or sunload sensor, to determine the solar radiation in the area occupied by the solar photovoltaic panels; and based on the orientation of the solar panels and number of solar panels in that area, the processor may be arranged to perform the operation of determining the electrical energy expected to be produced from the solar photovoltaic panels. Similar, based on the expected percentage of sun expected for the day, the processor may be arranged to perform the operation of determining the amount of electrical power expected to be produced from the sun for the period of power generation.

In an embodiment, when the energy generated by the electrical energy generation source (or arranged to be generated by the electrical energy source) is equivalent to a predefined percentage of the total energy required to provide the desired power to the load, the processor may be arranged to perform the operation of storing the generated electrical energy in the power storage device, and selectively distributing the electrical power from the power storage device to the loads according to a predefined order of priority.

In an embodiment, when the energy generated by the electrical energy generation source (or arranged to be generated by the electrical energy source) is less than a predefined percentage of the total energy required to provide the desired power to the load, the processor may be arranged to perform the operation of storing the generated electrical energy in the power storage device, and providing power from the power storage device selectively to the loads according to a predefined order of priority. In an embodiment, the processor may be arranged to perform the operation of determining the extent of charging of the rechargeable power storing device, and if the power storage device is charged to maximum capacity, the processor may be arranged to perform the operation of directing/shunting the excess electrical energy to other non- critical loads connected to the structure to avoid overcharging of the rechargeable power storing device. For example, a hot water tank with suitable circuitry may be connected to the structure, and preferably, if there is excess electrical energy from the energy generation source, the excess electrical energy may be directed to the water tank to maintain the temperature of the water in the tank at the desired temperature.

In an embodiment, the processor may be arranged to perform the operation of continuously monitoring, and storing in a database, the power usage of the structure to determine the power usage profile of the structure, and accordingly determining the power usage of the structure during off-peak and peak hours.

Accordingly, based on the expected average power usage using the power usage profile of the structure during off-peak and peak hours, the processor may be arranged to perform the operation of determining the amount of power required per day and shunt off excess electrical energy to non-critical loads.

In an embodiment, the processor may further be arranged to perform the operation of: collecting meteorological data, from a meteorological system that continuously provides real-time and forecast meteorological data; based on the forecast meteorological data, determining the expected power to be produced by the energy generation source; comparing the expected electrical power to be produced to the power usage profile of the structure during off-peak and peak hours; determining whether the expected electrical power to be produced is equivalent to the power usage profile of the structure; if the expected electrical power to be produced based on the forecast meteorological data is less than the power usage profile of the structure, prioritizing the recharging of the rechargeable power storing device so that during the forecasted period, the power storing device can have enough electrical energy to distribute electrical power to the loads according to a predefined order of priority of the loads for a predefined period, preferably 48 hours. In an embodiment, if the expected power to be produced based on the forecast meteorological data is less than the power usage profile of the structure, the processor may be arranged to perform the operation of changing, in real-time, the order in which the electrical energy is distributed to the loads and thereby prioritize the recharging of the rechargeable power storing device to ensure that enough electrical power is generated and stored in the rechargeable power storing device by the forecasted period. Accordingly, the processor may be arranged to perform the operation of switching off non-critical loads and distributing the electrical energy stored in the rechargeable power storing device to critical loads or distributing the electrical energy in the power storing device to loads according to a predefined order of priority.

The energy management system aims to interact with the smart grid environment as well as with the end-user, a supervisory system is proposed. The proposed supervisory system is a centralized approach where all the available data about the micro-grid system is concentrated in one main system.

In an embodiment, the processor may be arranged to perform the operation of reporting and displaying on a user device associated with the structure, in real-time, the amount of energy produced, and the list of loads prioritized for the electrical energy produced in real-time.

In an embodiment, the processor may be arranged to perform the operation of collecting from the end-user device, a custom list of loads that are critical and non- critical.

According to another aspect of the invention, there is provided an energy management method for managing supply of electrical energy to loads in a micro-grid supplied with electrical energy generated by one or more electrical energy generation source/s, wherein the method comprises: determining an amount of electrical energy generated by the one or more electrical energy generation source/s and stored in one or more rechargeable power storing device/s of the micro-grid; supplying electrical energy generated by the one or more electrical energy generation sources and/or stored in the one or more rechargeable power storing device/s to the loads via controllable circuit breaker modules in communication with at least one processor; monitoring consumption of electrical energy by the loads by way of the controllable circuit breaker modules, wherein the controllable circuit breaker modules are configured to measure electrical energy consumption of the respective loads and communicate data indicative of said electrical energy consumption to at least one processor; determining whether or not electrical energy supplied to one or more of the loads should be stopped or not; and controlling one or more controllable circuit breaker module/s associated with one or more load/s to switch on or off supply of electrical energy to the one or more load/s in response to receiving a suitable instructions from the at least one processor.

According to another aspect of the invention, there is provided a micro-grid system comprising: an energy management system as described herein; one or more electrical energy generation source/s; one or more rechargeable power storing device/s; and a plurality of controllable circuit breaker modules communicatively coupled to the energy management system, wherein each controllable circuit breaker module comprises: a processor; a communication module for facilitating communication with the energy management system; a measurement unit configure to measure at least the electrical energy consumed by a load; and a circuit breaker controllable in response to a suitable signal to break an electrical connection to a load.

The measurement unit may comprise suitable sensors and/or sensing circuitry to measure the electrical energy consumed by the load. According to another aspect of the invention, there is provided a controllable circuit breaker module comprising: a processor; a sensing and measurement unit coupled to the processor, wherein the sensing and measurement unit is configured at least to measure electrical energy consumed by a load; a communication module coupled to the processor to enable the controllable circuit breaker module to communicate data to and from the controllable circuit breaker module; and a circuit breaker controllable to break an electrical connection to a load in response to receiving a suitable control signal.

The controllable circuit breaker module may be a DC controllable circuit breaker module. The DC controllable circuit breaker module may comprise a DC-DC converter. The DC controllable circuit breaker module may comprise a DC load shunt.

The controllable circuit breaker module may be an AC controllable circuit breaker module. The AC controllable circuit breaker module may comprise a AC-DC converter. The AC controllable circuit breaker module may comprise a current transformer.

According to another aspect of the invention, there is provided a distribution board comprising at least one DC distribution board comprising a plurality of DC controllable circuit breaker modules as described herein; and at least one AC distribution board comprising a plurality of AC controllable circuit breaker modules as described herein.

According to yet another aspect of the invention, there is provided an energy management system for managing supply of electrical energy to loads in a micro-grid supplied with electrical energy generated by one or more electrical energy generation source/s, wherein the system comprises: at least one memory storage device; and at least one processor coupled to the at least one memory storage device, wherein the at least one processor is configured to: determine an amount of electrical energy generated by the one or more electrical energy generation source/s and stored in one or more rechargeable power storing device/s of the micro-grid; control supply of electrical energy generated by the one or more electrical energy generation sources and/or stored in the one or more rechargeable power storing device/s to the loads via controllable circuit breaker modules in communication with at least one processor; monitor consumption of electrical energy by the loads by way of the controllable circuit breaker modules, wherein the controllable circuit breaker modules are configured to measure electrical energy consumption of the respective loads and communicate data indicative of said electrical energy consumption to at least one processor; determine whether or not electrical energy supplied to one or more of the loads should be stopped or not; and control one or more controllable circuit breaker module/s associated with one or more load/s to switch on or off supply of electrical energy to the one or more load/s.

BRIEF DESCRIPTION OF DRAWINGS

The objects of this invention and the manner of obtaining them, will become more apparent, and the invention itself will be better understood, by reference to the following description of embodiments of the invention taken in conjunction with the accompanying diagrammatic drawings, wherein:

Figure 1 shows a schematic diagram of an example embodiment of a micro-grid in accordance with an example embodiment of the invention;

Figure 2 shows a schematic diagram of a DC distribution board in accordance with an example embodiment of the invention;

Figure 3(a) shows a plan schematic diagram of a DC contactor circuit breaker in accordance with an example embodiment of the invention; Figure 3(b) shows another schematic diagram of a DC contactor circuit breaker in accordance with an example embodiment of the invention;

Figure 3(c) shows a plan view of a sense and control unit or board forming part of the contactor circuit breaker of Figures 3(a) and 3(b); Figure 4 shows a schematic diagram of an AC distribution board in accordance with an example embodiment of the invention;

Figure 5(a) shows a plan schematic diagram of an AC contactor circuit breaker in accordance with an example embodiment of the invention;

Figure 5(b) shows another schematic diagram of an AC contactor circuit breaker in accordance with an example embodiment of the invention;

Figure 5(c) shows a plan view of a sense and control unit or board forming part of the contactor circuit breaker of Figures 5(a) and 5(b);

Figure 6 shows another schematic diagram of a network incorporating an energy management system in accordance with the invention; Figure 7 shows a conceptual block diagram illustrating at least interconnected subsystems, and components and parameters that flow between them, of an energy management system in accordance with an example embodiment of the invention;

Figure 8 shows a high-level block diagram of a process of operation of an energy management system in accordance with an example embodiment of the invention;

Figure 9 shows a high-level block flow diagram of a method in accordance with an example embodiment of the invention;

Figure 10 shows another block flow diagram of a method in accordance with an example embodiment of the invention; and

Figure 11 shows a diagrammatic representation of a machine in the example form of a computer system in which a set of instructions for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. DETAILED DESCRIPTION OF AN EXAMPLE EMBODIMENT

The following description of the invention is provided as an enabling teaching of the invention. Those skilled in the relevant art will recognise that many changes can be made to the embodiment described, while still attaining the beneficial results of the present invention. It will also be apparent that some of the desired benefits of the present invention can be attained by selecting some of the features of the present invention without utilising other features. Accordingly, those skilled in the art will recognise that modifications and adaptations to the present invention are possible and can even be desirable in certain circumstances and are a part of the present invention. Thus, the following description is provided as illustrative of the principles of the present invention and not a limitation thereof.

It will be appreciated that the phrase “for example,” “such as”, and variants thereof describe non-limiting embodiments of the presently disclosed subject matter. Reference in the specification to “one example embodiment”, “another example embodiment”, “some example embodiment”, or variants thereof means that a particular feature, structure, or characteristic described in connection with the embodiment(s) is included in at least one embodiment of the presently disclosed subject matter. Thus, the use of the phrase “one example embodiment”, “another example embodiment”, “some example embodiment”, or variants thereof does not necessarily refer to the same embodiment(s).

Unless otherwise stated, some features of the subject matter described herein, which are, described in the context of separate embodiments for purposes of clarity, may also be provided in combination in a single embodiment. Similarly, various features of the subject matter disclosed herein which are described in the context of a single embodiment may also be provided separately or in any suitable sub- combination.

Referring to Figure 1 of the drawings, a schematic circuit diagram of a micro- grid (MG) in accordance with an example embodiment of the invention is generally indicated by reference numeral 10. For ease of explanation, reference to the circuit diagram will be understood to refer to the micro-grid 10. Moreover, the micro-grid 10 may be referred to as a micro-grid system or network 10. The micro-grid 10 is arranged to provide electrical energy to power loads in an electrical system of a structure, such as a building in the form of a house, farmstead, etc. In some example embodiments, the micro-grid 10 may be arranged to provide electrical energy to loads connected to an electrical system a mobile telephone tower, or the like. In this regard, it follows that the micro-grid 10 may be employed to provide electrical energy in any application which may comprise a load and thus it will be evident to those skilled in the art that the micro-grid may be scaled for the specific application. For ease of explanation, the present example embodiment will be described with reference to the micro-grid 10 being used to provide electrical energy to power a structure in the form of a house but nothing should preclude the present invention from having use in other application, for example, more energy hungry applications such as an apartment block, housing estate, or the like.

Moreover, though the micro-grid 10 described herein finds application in off- grid applications wherein there is no mains or municipal electricity supply, nothing precludes the present invention from being incorporated into electrical systems which are tied to a mains or municipal power grid.

The micro-grid 10 as described herein is an islanded off-grid micro-grid 10 which comprises at least one renewable energy source, such as a renewable energy source comprising photovoltaic/solar panel/s, a wind turbine, a hydropower system, or the like which harnesses natural energy sources such as sunlight, wind, geothermal energy, flowing water, or the like, to be able to generate electrical energy or electricity for the micro-grid. For ease of explanation, unless otherwise stated or unless evident to those skilled in the art, the terms “electrical energy”, “power”, “electricity”, “electrical power”, “current” may be used interchangeably herein to refer to the energy provided by the micro-grid 10 to loads connected thereto.

The number of renewable energy generation sources required for providing electrical energy to loads in the structure is typically dependent on the expected power demand for the loads in the structure in question. For example, a structure in the form of a house or an apartment can be arranged to be powered by one renewable energy generation source, such as a single photovoltaic solar panel system. Larger applications which require more electrical energy, for example, structures such as telephone towers or commercial buildings or a building comprising a plurality of flats/apartments may require more than one renewable energy generation source. In Figure 1 of the drawings, the micro-grid 10 comprises a plurality of renewable energy generation sources 12. The sources 12 are renewable in that they are reliant on a natural non-combustible energy sources and have little to no carbon emissions. In this regard, the sources 12 are driven to generate electrical energy from natural energy sources such as sunlight, wind, water, and geothermal sources of energy.

It follows that the sources 12 comprise a photovoltaic (PV) system 16 comprising a plurality of photovoltaic solar panels 18 which are arranged in a predefined order, typically on a surface that is expected to capture a maximum percentage of incident sun rays. The plurality of photovoltaic solar panels 18 are arranged to produce direct current from sunlight in a conventional fashion. The photovoltaic system 16 comprises a combiner box 20 to which the plurality of photovoltaic solar panels 18 are connected. The combiner box 20 is arranged to collect the total electrical energy generated by the plurality of photovoltaic solar panels 18. The combiner box 20 is connected to a charger controller 40 via a first DC switch 22.

The plurality of renewable energy generation sources 12 includes a wind system 24 comprising at least one wind turbine 26 that may be erected on top of, or adjacent to the structure. The wind turbine is 26 is configured to generate alternating current (AC), wherein the AC current produced by the wind turbine 26 is arranged to be converted to direct current (DC) by a suitable (Maximum Power Point Tracking) MPPT controller 28, or any suitable power converter. It will be noted that in some example embodiments, the wind turbine output may be in the form of DC current. The wind system 24 is connected to the charger controller 40 via a second DC switch 30.

In the illustrated example embodiment, the plurality of renewable energy generation sources 12 includes a hydroelectric power system 32 comprising a hydro turbine 34 that may in one example embodiment be fitted in a pumped water storage system associated with the structure to which the micro-grid 10 is arranged to provide electrical power. The system 32 may be configured to generate electrical energy by way of the turbine 34 being fed with water from the water storage system. In some example embodiments, the turbine 34 may be driven by water flowing in a river, dam, or the like. The alternating current produced by the hydro turbine 34 is arranged to be converted to direct current (DC) by a maximum power point tracking (MPPT) controller 36 or any suitable power converter. The hydro system 32 is connected to the charger controller 40 via a third DC switch 38. It will be appreciated that nothing should preclude further alternate or renewable energy sources to provide electrical energy to the micro-grid 10.

The charger controller 40 is connected to battery system 44 via a fourth DC switch 42. The battery system 44 comprises a plurality of rechargeable power storing devices 46, such as conventional batteries. The battery system 44 also comprises a battery monitoring device 48 that is arranged to monitor the battery capacity and state of charge. In addition, the battery system 44 also comprises an electric vehicle (EV) system 50, which comprises additional rechargeable power storing device (not shown) for use in a charging power storing device of an electric vehicle (not shown).

The micro-grid 10 advantageously comprises an energy management system (EMS) 100 which controls the operation of the micro-grid 10 in a manner described herein. As shown in Figure 1 , a first power monitor 52 is provided between the first DC switch 22 and the charger controller 40. Similarly, second, third, and fourth power monitors 54, 56, 58, respectively, are provided between the second, third, and fourth DC switches 30, 38, 42, and the charger controller 40. The first, second, third, and fourth power monitors 52, 54, 56, 58 are arranged to be communicatively coupled with the (EMS) 100, and communicate the power generated by each of the plurality of renewable electrical energy generation sources 12 with a power monitor module (PMM) 102 of the energy management system 100, as shown in Figure 1 . In this way, the EMS 100 is configured to receive information indicative of the power generated by the sources 12 as well as the power stored in the battery system 44. The EMS 100 may continuously monitor or measure an amount of energy generated by the sources 12 or may monitor or measure the amount of energy stored by the battery system 44 as will be described herein.

The micro-grid 10 comprises a first sensor 60, such as a thermocouple or a suitable solar/light detector, which is associated with the photovoltaic system 16 to measure ambient light conditions and/or temperature at or adjacent a geographical location of the system 16. The micro-grid 10 also comprises a second sensor 62, such as an anemometer, which is associated with the wind system 24 to measure ambient wind conditions at or adjacent a geographical location of the system 24. Lastly, the micro-grid 10 also comprises a flowrate/pressure sensor or sensors 64 associated with the hydro system 32 to measure flowrate/pressure of water at or adjacent a geographical location of the system 16. The first, second, and third sensors 60, 62, 64 are in communication with an energy management system 100, and the signal input module 104 of the energy management system 100 is arranged to collect the sensed data from the sensors 60, 62, 64. Though not illustrated, it will be appreciated that the module 104 may comprise suitable analogue to digital (ADC) module/s. In this regard, it will be understood by those skilled in the art that the micro-grid 10 comprises suitable drivers, electronics, electricals, etc. associated with the operation of the micro-grid 10 which are not shown nor described for brevity.

In some example embodiment, the micro-grid 10 also comprises occupancy sensors to determine if the house to which the micro-grid 10 is coupled is occupied and the number of occupants for load management at any particular period of time.

The micro-grid 10 further comprises a distribution board arrangement 65 comprising a direct current (DC) distribution board 66 and an alternating current (AC) distribution board 90. The board 65 advantageously enables the micro-grid 10 to power both AC and DC loads, both appliances and circuits, simultaneously.

The board 66 is connected via suitable electrical wires to the charger controller 40. The DC distribution board 66 comprises a suitable electronic circuit and first, second, third, and fourth controllable DC circuit breaker modules 74, 76, 78, 80.

The DC distribution board 66 is arranged to be coupled to loads such as a refrigerator 68, geyser 70, laundry machine 72, and plugs 73, which are arranged to operate on DC current. Each connection point on the DC distribution board 66 to which is connected a load is associated with a dedicated controllable DC circuit breaker module 74, 76, 78, 80. For example, the connection point for refrigerator 68 is associated with a first controllable DC circuit breaker module 74; the connection point for the geyser 70 is associated with the second controllable DC circuit breaker module 76; and the connection points for the laundry machine 72, and plugs 73, are associated with the third and fourth controllable DC circuit breaker module 78, 80, respectively. Each module 74 to 80 comprises a sense and control board 74.1 , 76.1 , 78.1 , 80.1 , respectively coupled to the modules 74 to 80.

The DC distribution board 66 may be better described with reference to Figure 2 of the drawings where an example embodiment of the board 66 is illustrated in more detail. The board 66 comprises a suitable main breaker unit 300 comprising suitable electrical circuitry to connect and disconnect the board 66 to the battery system 44 via the charge controller 40 (not illustrated in Figure 2) in a conventional fashion. Looking at the DC circuit breaker module 74, which is substantially similar to the modules 76 to 80 (the latter not being illustrated), the DC circuit breaker module 74 typically comprises a circuit breaker 302, and a sense and control unit 304 which is configured to control the circuit breaker 302 via a contactor coil 306 so as to control power supplied to a load L, for example, the refrigerator 68 as illustrated in Figure 1 .

In some example embodiments, the module 74 may be matched to the current and trip characteristics of the load L. For example, the refrigerator may have a higher current rating and trip characteristics as opposed to that of a lighting connection point. It follows that the controllable DC circuit breaker module 74 may comprise a circuit breaker 302 matched to the current characteristics of the load connection type of allowing DC flow.

The standard current characteristics may be defined as the maximum current that a breaker can continuously allow under normal operation at ambient temperature. The characteristics may further include the instantaneous tripping current under the B, C, D, K, and Z trip curves, which is the minimum current at which the circuit breaker 302 will trip instantaneously.

The class B trip characteristics trip when the current flowing in an electric circuit is 3 to 5 times the rated current. The class C trip characteristics trip when the current flowing in an electric circuit is 5 to 10 times the rated current. The class D trip characteristics trip when the current flowing in an electric circuit is 10 to 20 times the rated current. The class K trip characteristics trip when the current flowing in an electric circuit is 8 to 12 times the rated current. The class Z trip characteristics trip when the current flowing in an electric circuit is 2 to 3 times the rated current.

The current characteristics are important in protecting the appropriate/relevant circuitry of the DC board 66 and/or load L from damage during electric faults and may be selected depending on the load current and cable sizes from the circuit breaker module 74 to the load L.

The circuit breaker module 74 has an ultimate breaking capacity, wherein the ultimate breaking capacity or short circuit withstand capacity is the maximum short circuit current the circuit breaker module 302, particularly the circuit breaker 302 can interrupt safely. For example, if a circuit breaker is rated at 5A, it means the circuit breaker 302 can safely interrupt the circuit to the load L during a short circuit fault for as long as the current does not exceed the 5A rating. In the event the short circuit current exceeds the ultimate breaking capacity of 5A, the circuit breaker 302 will suffer permanent damage.

The short circuit in this scenario will have to be interrupted by the main breaker 300 which will in turn have a higher ultimate breaking capacity compared to the subcircuit breakers 302. The correct rating for each sub-circuit breaker 302 in the board 66 is important to minimize the risk and reduce any potential hazards that may arise from the fault such as fires or electrical shock.

Referring also to Figure 3(a) to (c), where a DC circuit breaker module 74 and/or parts thereof is illustrated. The sense and control unit 304 comprises a suitable processor 304.1 in operative communication with the EMS 100 via a suitable communication link, for example, a RS485 link via a RS485 communication port 304.2 and module 304.3. The unit 304 comprises a suitable DC load shunt 304.4, a voltage measurement circuit 304.5, an AC-DC converter 304.6, and an analogue to digital converter 304.7. In some example embodiments, the unit 304 may comprise or may be coupled to suitable sensors such as a temperature sensor, airflow sensor, etc. The unit 304 is conveniently configured to communicate data to the EMS 100 and receive instructions therefrom. In particular, the unit 304 is configured to generate suitable digital output signals to drive the 48VDC contactor coil 306 to switch on or switch off the connected load via a suitable driver circuit. In particular, the processor 304.1 may be configured to receive instructions from the EMS 100, via the port/s 304.2/304.3 to cause actuation of the coil 306 which opens and closes the circuit breaker 302 to switch on or switch off the connected load.

The RS485 port 304.2 may be an input port to receive and transmit commands between the sense and control unit 304 and the energy management system 100 via serial Universal Asynchronous Receiver/Transmitter (UART). The main function of UART is to transmit and receive serial data.

The DC-DC converter is a DC-DC step-down converter which will step down 48VDC incoming from the battery system 44 via the charge controller 40 to a 5VDC output to the sense and control unit 304. The incoming 48VDC is supplied through contactors for automatically switching on or switching off, the DC load.

Through the supply and contactors, the DC load shunt 304.4 is provided to extend the range of the current present to be able to accurately measure, through the voltage measurement circuit/module 304.5. It will be understood by those skilled in the art that the DC shunt is a resistive device used as a ratio conversation of the current that is directly proportional to that flowing through a wire for measurement purposes. This is similar or the same as using CT (current transformer) to step down the voltage so it is safe to measure using small electronic devices. The purpose of the DC shunt to be able to measure the current using the sense and control board 304.

The function of the DC load shunt 304.4 is used whenever the current to be measured is too large to pass through the unit 304 and is likely to cause damage thereto. The DC load shunt 304.4 generates a low resistance path for an electrical current by enabling an alternative path for the current to flow.

The physical wiring distance from the energy management system 100 to the board 66 may be 250 meters max, and the micro-grid 10 may comprise up to 50 controllable circuit breaker modules located on one or more boards 66.

The electrical constraints as the contactor coil rating at 48Vdc, DC controllable circuit breaker module Max load as 3000W / 48Vdc / 62.5A.

The IO parameters constraints as the input signal energizing of a 48Vdc controllable circuit breaker module coil, DC output signal monitoring the load current up to a max of 62.5Adc, resolution 0.1 A, data comms interface RS485, opt isolated, half-duplex, daisy-chained, a proprietary protocol, the energy management system master, controllable circuit breaker module slave - repeater for more than 32 devices.

As alluded to above, the unit 304 may receive inputs from sensors. The sensory inputs may comprise a temperature probe/thermocouple having a range of operation of between -15 to 100 degrees Celsius. The temperature probe may be a modular unit that can handle hot water and ice storage mounting environment, digitized output to the processor 304.1 or analogue signals via the ADC 304.7. The processor 304.1 may be communicatively/electrically coupled to a plurality of probes, for example, up to 10 (PV, pumped storage, geothermal, gas bottle, swimming pool water, geyser outlet).

It will be understood by those skilled in the art with reference to Figures 3(a) and (b) that the circuit breaker 302 may be similar to conventional circuit breakers in that it comprises suitable terminals 302.1 , 302.2 connectable to a power source and load, respectively, and a mechanical actuator 302.3 to bring about actuation of the circuit breaker 302. However, a key difference is that the contactor coil 306 is controllable by the processor 304.1 of the unit 304.

The modules 74 to 80 described herein may be used to provide data to the energy management system 100 to better optimize the energy usage stored in the battery system 44. These conditions will give the entire system intelligence in when the switching process occurs and further still provide protection and data analysis on the occurrence of faults and user behaviour of each specific power system.

Returning to Figure 1 , it will be understood that the DC distribution board 66 is also electrically connected to the onsite energy management system 100 to provide power to the energy management system 100. The DC power distributed to the DC board 66 to power the energy management system 100 is collected by an energy management power module 106. As mentioned above, the first, second, third, and fourth controllable DC circuit breaker modules 74, 76, 78, 80 are communicatively coupled with the energy management system 100 via suitable wires (RS485 transmission) or wirelessly to receive commands from the energy management system 100 to either automatically switch on or switch off the loads associated therewith and/or at the same time transmit the load conditioning and monitoring data associated with the load.

As shown in Figure 1 , the energy management system 100 has a DC input module 108 for receiving input connectors 82 from the DC distribution board 66, and a DC output module 110 connected to output connectors 84 which are arranged to transmit commands to the controllable circuit breaker modules 74 to 80 associated with the loads connected to the DC board 66 via the input connectors 82.

The controllable circuit breakers 74 to 80 compliment the use and online connectivity for remote access, data logging, and energy management system integration. The controllable circuit breaker modules 74 to 80 may be connected to the energy management system 100 to collect the energy characteristics such as the voltage, the current flowing in the circuit, and the power consumption of the load connected to the controllable circuit breaker module 74 to 80.

In one example embodiment, the module 74 to 80 may be configured to receive signals transmitted from external sources such as smartphone applications to assess the status of the controllable circuit breakers 74 to 80 and either switch on or switch off the connected load remotely. As mentioned, the micro-grid 10, particularly the arrangement 65 also comprises an AC distribution board 90 which is connected to the DC distribution board 66 via an inverter 86 and a first AC switch 88.

The AC distribution board 90 is arranged to be coupled to loads such as lights 92 and plugs 94, which are arranged to operate on AC. Each connection point on the AC distribution board 66 to which is connected a load is associated with a dedicated controllable AC circuit breaker module. For example, the connection point for the lights 92 is associated with a controllable AC circuit breaker module 96; and the connection point for the plugs 94, are associated with another controllable AC circuit breaker module 98. As shown in Figure 1 , the energy management system 100 has an AC input module 112 for receiving input connectors 97 from the AC distribution board 90, and an AC output module 114 connected to output connectors 99 which are arranged to transmit commands to the controllable AC circuit breaker modules 96, 98 associated with the loads connected to the AC board 90 via the input connectors 99.

Turning also to Figure 4 of the drawings, the AC controllable circuit breaker modules 96, 98 and 101 are illustrated. The modules 96, 98, and 101 are similar to the module 74 described above. The modules 96, 98 and 101 are substantially similar to each other. Taking module 96 for ease of explanation, the module 96 comprises a circuit breaker 310, and a sense and control unit 312 configured to control the circuit breaker 310 via a contactor coil 306.

Referring also to Figure 5(a) to (c), where an AC circuit breaker module 96 and/or parts thereof is illustrated. The sense and control unit 312 comprises a suitable processor 312.1 in communication with the EMS 100 via the RS485 module or link 312.2. The unit 312 comprises a suitable current transformer 312.4, a voltage measurement circuit 312.5, and an AC-DC step-down converter 312.6 which will step down 220VAC (110VAC in other countries) incoming from the inverter 86 a 5VDC output to the sense and control unit 312. The incoming 220VAC will be supplied through contactors to automatically switch on or switch off the AC load. The unit 312 further comprises an analogue to digital converter 312.7. The components of the unit 312 are substantially similar to the unit 304 although it will be understood by those skilled in the art these units differ in their use for dealing with DC and AC loads and currents. Through the supply and contactors, a current transformer (CT) 312.4 will be present to reduce the AC current from a high value to a low value in the secondary windings to accurately measure through a voltage measurement circuit embedded on the sense and control unit 312. The function of the CT 312.4 is used whenever the current to be measured is too large to pass through a circuit, in this case, the sense, and control unit 312. The CT 312.4 produces a magnetic field on the core which induces AC on the secondary windings that are proportional to the AC on the primary windings.

The digital output from the sense and control unit 312 is configured to drive the 220VAC (110VAC in other countries) contactor coil to switch on or switch off the connected load via a driver circuit. This is achieved by receiving the RS485 input signal from the energy management system 100 via module 312.2.

There may be 0 - 50 (single and 3-phase) controllable circuit breaker modules located in one or more remotely located distribution boards 90, and the useability as one common slave board to be adapted for AC or DC use, the slave boards needed to provide for multiple controllable circuit breaker modules to be connected and, AC and DC Loads to be split into separate slave boards.

The electrical constraints as the contactor coil rating at AC Max load (per phase) as 6900W / 230V / 30A, safety measures to isolate the data comms interface.

The IO parameters constraints as the input signal energizing of an AC output signal (per phase) monitoring the load current up to a max of 30Aac, resolution 0.1 A, data comms interface RS485, opt isolated, half-duplex, daisy-chained, proprietary protocol, the energy management system master, controllable circuit breaker module slave - repeater for more than 32 devices.

The electrical constraints being the anemometer Sensor Output voltage 0 - 2Vdc typical, match to chosen model, the flow meter sensor output voltage of 5Vdc pulse typical, match to chosen model, occupancy Sensor to reed relay passive infra- red, interface with GPIO on the energy management system.

The IO parameters constraints being the data comms interface as RS485, half- duplex, opt isolated, daisy-chained, a proprietary protocol to a sensor slave board, the wind speed, litres per minute, temperature as the sensor output will be digitized for transmission over data comms interface. In one example embodiment, the board 65 may comprise a universal Printed Circuit Board (PCB) that can be used for both DC and AC switching by populating with only the relevant components with the addition of the optional temperature and flowrate sensor circuitry that can be populated when needed with or without contactor circuitry.

The sense and control unit software provided on the relevant processors may have pre-stored data indicative of the hardware capabilities at start-up, e.g., AC or DC, temperature, flow, to be configured by way of DIP switches.

Turning back to Figure 1 of the drawings, the energy management system 100 further comprises suitable hardware 116, a human-machine interface 118, and an operating system 120 which comprises a processor 122 and a memory device 124, which will be described in more detail further below. In addition, the energy management system 100 further comprises a hot and cold water monitor 126 for monitoring hot and cold water in a hot water system of the micro-grid 10, as will be described below. Furthermore, the energy management system 100 further comprises a remote access control module 128 for receiving commands/instructions remotely from an end-user device, as will be described below.

The human-machine interface of the energy management system 100 may be in the form of a smartphone application with connectivity with Bluetooth and through Wi-Fi to a router that is used for Internet access for the energy management system 100. Internet access is needed at least for remote diagnostics and support. Protection of interface swopped connections should not damage the unit. ESD protection of IO interfaces. Surge protection, isolation requirements of comms interfaces like RS232, RS485.

The micro-grid 10 further comprises a gas system 130 for providing gas to gas stoves and gas heaters in the building.

The micro-grid 10 further comprises an atmospheric water generator 132 for harvesting water in a water harvesting tank 134. The outlet of the water harvesting tank 134 may be connected to a water line of the structure to provide potable drinking water.

In addition, the micro-grid 10 further comprises a hot water storage system 136 comprising a hot water tank 138 that may be in communication with a geyser 70 fitted in the structure. The hot water storage system 136 may also be fitted in the structure and situated adjacent to the geyser 70, and the outlet of the hot water tank 138 may be connected to a hot water pipe leading out of the geyser 70.

The micro-grid 10 also comprises an ice storage system 140 comprising an ice storage container 142. The ice storage system 140 may be connected to an air conditioner system connected to the structure, to provide cooled/cold air in the structure.

The micro-grid 10 also comprises a greywater storage system 144, and a geothermal system 146.

In use, the renewable electrical energy generation sources 12 are arranged to produce electrical power which is stored in the battery storing system 44. The thermocouple 60, anemometer 62, and the pressure and flow rate sensors 64 communicate relevant information concerning the renewable electrical energy generation sources 12 with the energy management system 100. As will be described further below when making reference to the energy management system 100 illustrated in Figure 6, it will be appreciated that the electrical power from the battery system 44 is distributed to loads, both AC and DC loads, according to a predefined order of priority which is associated with the amount of energy demanded by the load(s) in real-time, and the amount of energy produced/or that can be produced by the renewable energy generation sources and the amount of energy that can be supplied by the battery system 44.

Turning attention to Figure 6 of the drawings, there is provided a network 150 incorporating the energy management system 100, as shown in Figure 1. The energy management system 100 is preferably located on-site and connected to loads housed in a structure, such as a building or a cell phone tower. The energy management system 100 is arranged to be in communication with remote endpoint devices, such as an administrator endpoint device 152 and a user endpoint device 154, via a communication network 156.

The communications network 156 may comprise one or more different types of communication networks. The system 100 is capable of communicating between different devices regardless of their supporting communication protocol over the network 156. In this regard, the communication networks may be one or more of the Internet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), various types of telephone networks (e.g., Public Switch Telephone Networks (PSTN) with Digital Subscriber Line (DSL) technology) or mobile networks (e.g., Global System Mobile (GSM) communication, General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), and other suitable mobile telecommunication network technologies), or any combination thereof. It will be noted that communication within the network may be achieved via suitable wireless or hard- wired communication technologies and/or standards (e.g., wireless fidelity (Wi-Fi®), 4G, long-term evolution (LTE™), WiMAX, 5G, and the like). In some example embodiments, the energy management system 100 may be coupled to other elements of the communications network 156 via dedicated communication channels, for example, secure communication networks in the form of encrypted communication lines (e.g., SSL (Secure Socket Layer) encryption).

The sense and control units of the circuit breaker modules 74 to 80, 96 and 98 may comprise protocol characteristics, master-slave relationship, and follow a request- response messaging, and hardware assigned slave station addresses. The characteristics of the master-slave principle may only be 1 master connected to the network 156, only the master may initiate communication and send requests to the slaves, and the master can address each slave individually or all slaves simultaneously using address 0. The slaves can only send replies to the master slaves cannot initiate communication to the master of other slaves and slaves do not respond to broadcasts.

The communications interface includes a 3-wire serial connection, RxD Received Data, TxD Transmit Data, and 0V signal. The data format will be 8 bits, 1 stop bit, and even parity, and a baud rate of 19200. The physical layer will be an RS485 interface that will be opt isolated as a half-duplex, 2 signal wires ground and shielded with a daisy chain connection.

The energy management system 100 may include one or more of a backend (e.g., a data server), a middleware (e.g., an application server), and a front-end (e.g., a client computing device, such as the endpoint devices 152, 154 having a graphical user interface (GUI) or a Web browser through which a user can interact with example implementations of the subject matter described herein).

As mentioned before, the energy management system 100 comprises a processor 122 that is coupled to a memory device 124 (including transitory computer memory and/or non-transitory computer memory), which are configured to perform various data processing and communication operations associated with the system 100 as contemplated herein.

The processor 122, like any other processor referred to herein such as the processor 304.1 may be one or more microcontrollers or processors in the form of programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processor 122, as well as any computing device, referred to herein, may be any kind of electronic device with data processing capabilities including, by way of non-limiting example, a general processor, a graphics processing unit (GPU), a digital signal processor (DSP), a microcontroller, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other electronic computing device comprising one or more processors of any kind, or any combination thereof. For brevity, steps described as being performed by the system 100 may be steps that are effectively performed by the processor 122 and vice versa unless otherwise indicated.

It will be appreciated that the memory device 124 may be in the form of computer-readable medium including system memory and including random access memory (RAM) devices, cache memories, non-volatile or back-up memories such as programmable or flash memories, read-only memories (ROM), etc. In addition, the memory device 124 may be considered to include memory storage physically located elsewhere in the energy management system 100, e.g., any cache memory in the processor 122 as well as any storage capacity used as virtual memory, e.g., as stored on a mass storage device.

It will be appreciated that the computer programs executable by the processor 122 or any other processor referred to herein may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other units suitable for use in a computing environment. The computer program may, but need not, correspond to a file in a file system. The program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a mark-up language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). The computer program can be deployed to be executed by one processor 122 or by multiple processors, even those distributed across multiple locations, for example, in different servers and interconnected by the communication network 156.

The computer programs may be stored in the memory device 124 or a memory provided in the processor 122. Though not illustrated or discussed herein, it will be appreciated by those skilled in the field of the invention that the energy management system 100 may comprise a plurality of logic components, electronics, driver circuits, peripheral devices, etc., not described herein for brevity.

In use, the processor 122 is configured to determine the amount of electrical energy stored in at least one rechargeable power storing device 46 and based on the power stored in the power storing devices 46, which power is communicated with the system 100 via the power monitor module 102, the processor 122 is accordingly configured to selectively distribute DC power stored in the power storing device to the load or plurality of loads, including the AC loads as well.

The energy management system 100 is arranged to receive sensor inputs from the first, second, and third sensors 60, 62, 64 to measure the power ratings at each energy generation source 16, 24, 32. The values are stored and displayed on the energy management system 100 as the power, voltage, and current ratings. The five power rating inputs include the battery rating from the battery system 44, electric vehicle rating from the EV system 50, solar rating from the photovoltaic solar panel system 16, wind rating from the wind system 24, and hydro rating from the hydro system 32. The power ratings are used to compute the total generated power in the micro-grid 10. The model for the power rating will visualize the real-time power at each generation source 16, 24, 32 with the total generated power. The measured values will be used to compare to the load consumption data, as will be described below, to calculate how much power is made available to the battery management system 100, and any excess power generated to be distributed to the least prioritized items. The energy management system 100 will continuously monitor and control the state of the generation sources and the power flow to the battery system 44 and loads.

The memory device 124 typically comprises a list of DC loads and AC loads which are connected to the energy management system 100 via the DC input and AC input ports 108, 112, respectively. Each load has a consumption rating from the controllable circuit breaker modules 74 to 80, or 96, 98 which can be used to determine the amount of electrical power required to keep the load switched on for a predefined period.

The memory device 122 has at least five groups which are arranged in descending order of priority of the loads, where the loads in the first group are critical loads, and those in the last, fifth group are non-critical loads.

The processor 122 is configured to monitor the energy generated by the electrical energy generation sources 12 or the energy expected to be generated by the electrical energy generation sources 12 over a predefined period, and the processor is also arranged to monitor the energy stored in the power storing devices 46, as mentioned above to determine the total power generated and stored by the micro-grid 10. Based on the expected power usage of the loads, which may be based on the consumption ratings of each of the loads, the processor 122 is configured to determine whether the total generated energy is adequate to sustain the expected DC power usage by the load(s) and distribute the electrical energy stored in the power storing device to the load or loads according to a predefined algorithm that is based on the order of priority of the loads and the energy generated by the power grid 10.

In general, the processor 122 is configured to collect, in real-time, meteorological data, or collect from the first, second, and third sensors 60, 62, 64, a value of a measured parameter of the electrical energy generation source, and accordingly determine the amount of energy or DC power that can be produced in real- time or over a period by the electrical energy generation source.

For example, with regard to the wind system 24, the anemometer 62 is arranged to measure the wind speed and direction thereof and based on the design parameters associated with the wind turbine 26, the data from the anemometer 62 is transmitted to the energy management system 100 which collects and stores the anemometer data. The processor 122 is configured therefore to determine the total DC power that can be produced by the wind system 24 in real-time. In addition, or alternatively, the energy management system 100 may be in communication with a remote meteorological system (not shown) via the communication network 206, and the processor 122 may be arranged to determine the electrical power expected to be generated by the wind turbine based on real-time wind data collected from the meteorological system. In one example embodiment, the processor 122 is configured to implement a wind forecasting algorithm, for example, developed in the python programming language. The wind forecasting algorithm may be a machine learning algorithm in the form of a Long-Short-Term Memory (LSTM) neural network(NN). The LSTM neural network is ideally suited to sequential prediction problems such as this wind forecasting algorithm. It will be understood by those skilled in the art that the NN algorithms described herein make use of the opensource library Keras, wherein Keras wraps the efficient numerical computation libraries of Tensorflow (GoogleTM) and TheanoTM which allows the definition and training of neural net models. Tools from the opensource sci-kit learn library are also used in the creation of the various NN models.

The processor 122 is configured to implement the wind prediction algorithm and uses an Application Program Interface (API) that connects to a source of weather data or in other words the remote meteorological system mentioned herein, wherein the weather data includes wind data. This source of weather data / remote meteorological system may be a weather website whereby the wind data for a specific location is automatically sent via the API to the processor 122 for use thereby in the implementation of the wind prediction algorithm.

The received weather data, particularly wind data is automatically stored in a local database or memory device 124 and is pre-processed to enable it to be used by the machine learning algorithm contemplated herein.

The processor 122 uses 7 unique features in the wind forecasting algorithm to predict the wind capacity at or for a given time and location. These unique features for this algorithm include wind speed, dew point, temperature, pressure, relative humidity, wind direction, and global horizon irradiance.

The API will request the data for the specific location of the local micro-grid 10 from the weather source via the network 156. As much historical wind data as possible for the location is used in order to increase the accuracy of the machine learning algorithm. Once the wind data has been pre-processed it is separated into testing and training datasets and processed by the LSTM NN.

The NN takes a time to train thus in example embodiments wherein the NN takes a few minutes, the NN will only be trained once per day. Once the NN has trained on the dataset in order to get a new wind velocity forecast the new weather conditions are simply fed into the algorithm via the weather API and predictions can be made instantaneously.

The workflow of the algorithm operating on the embedded EMS 100, particularly the processor 122 comprises the steps of: training the LSTM NN once per day from a database of weather features; using the trained LSTM NN to make wind forecasts every 10 minutes and determining the potential generative capacity of the wind turbine based on the forecasts and/or using the updated weather conditions downloaded via API ; feeding these wind forecasts to the energy management system 100; and updating the database of weather features to be used to retrain the network once per day.

It follows from the foregoing that the EMS 100, particularly the processor 122 is configured to predict or forecast wind conditions in the geographical area of the wind turbine/s 26 using Al based algorithms trained on historic weather data including wind data, and based on the prediction or forecast, predict or forecast an amount of power that can be produced via the wind turbine/s 26 at a specific date or period of time. This is useful as it at least attempts to mitigate difficulties due to uncertainty of power production by the wind turbine/s 26.

In some example embodiments, it will be appreciated that the processor 122 and system 100 may not necessarily forecast the weather or wind for a particular period of time, for example, a week or a couple days but merely receives a prediction or forecast from the weather source which it then uses to determine the wind generation capacity of the wind system 24 for the said period of time.

Also, for example, with regard to the photovoltaic system, the thermocouple 60 is arranged to determine the solar radiation in the area occupied by the solar photovoltaic panels 18 and based on the orientation of the solar panels and number of solar panels in that area, the processor 122 is configured to determine the electrical energy expected to be produced from the solar photovoltaic panels. The energy management system 100 may optionally be in communication with the meteorological system (not shown) to collect data on the expected percentage of sun expected for the day, and the processor 122 may accordingly determine the amount of electrical power expected to be produced from the photovoltaic system 16.

For the solar generational capacity in the micro-grid, the processor 122 is configured to implement a machine learning algorithm to forecast the weather, particularly ambient light conditions in the geographical location of the PV panels 18. Like the wind prediction or forecasting algorithms mentioned herein, the algorithm is developed in the python coding language as there are many machine learning libraries available which decreases the coding effort and complexity of the task.

To begin with, the processor 122 collects weather data from a weather source / the meteorological system, for example, in the form of an independent opensource weather website such as www.openweather.org. This data is read into the system 100 via an Application Program Interface (API) which allows the processor 122 to collect weather data from the weather source over the Internet.

The weather data is then stored in a database or memory device 124 for easy manipulation and access. To initialize, the processor 122 is configured to collect weather data, particularly solar weather data indicative of ambient sunlight at the geographic location of the PV panels 18, from the current date to as far back as possible.

The processor 122 is configured to request the weather data via the API, the website will then transmit the relevant weather data over the network 156. This may be automatic and periodic and/or may be in an ad hoc fashion as and when required. The weather data, particularly the solar weather data may include 10 different features including, solar radiance, hourly relative humidity, hourly sky conditions, hourly visibility, hourly dry bulb temperature, sin of the azimuth angle, cos of the azimuth angle, sin of the zenith angle, cos of the zenith angle and hourly dew point temperature.

The aforementioned features are all highly pertinent in the solar generation output of a solar/PV panel 18. The processor 122 is configured to generate a covariance matrix of the aforementioned features, the covariance matrix illustrates the covariance between the various features.

Once the weather data is received via the API and loaded into a database or memory store 124, it is pre-processed. The ML algorithm which the processor 122 employs for this task is a sequential neural network (NN). Like the algorithms for the wind prediction or forecasting mentioned above, the NN are coded in Python and make use of the opensource library Keras, Keras wraps the efficient numerical computation libraries of TensorflowTM (GoogleTM) and TheanoTM which allows the definition and training of neural net models.

Tools from the opensource sci-kit learn library are also used in the creation of the various NN models. The preferred NN model uses 3 neural layers with the rectified linear unit as the activation function and 200 epochs. The output of the model is the prediction, depending on the weather inputs, of the solar power generational capacity.

The NN model learns what the generational capacity is of the solar panels with regards to the input features for the specific geographical location, when the algorithm is running on the energy management system 100 the new weather features will be input into the model and the output will be the predicted solar capacity.

The processor 122 is also configured to update the training database using new weather data and retrain the model on the new weather data at a predefined time period to ensure that the model is as accurate as possible. Since training the solar prediction algorithm takes time which is dependent on the computing power of the processor 122, training the NN model with the new data will take place once a day and the predictions will occur around every 10 minutes.

Once the Machine learning model has trained on the data set all that is required to make a solar power prediction is to input the current conditions (the features), received via API back into the model to get the predicted solar power from the solar panels 18.

This predicted solar power is then used by the energy management system 100 in the manner described herein.

Also, for example, with regard to the hydro system 32, the data transmitted by the flowrate and pressor sensors 64 coupled to the hydro turbine 34, to the processor 122 may enable the processor 122 to determine the amount of energy expected to be produced by the hydro-system 32.

As mentioned previously, the power monitors 52, 54, 56 associated with the energy generation sources 12 may be arranged to monitor the power produced by the energy generation sources 12 in real-time and communicate the power generated with the energy management system 100 to provide the system 100 with information concerning the amount of power produced by each of the energy generation sources 12.

When the total electrical energy generated by the electrical energy generation sources 12 and the energy saved in the battery system 44 is equivalent to a predefined percentage of the total energy required to provide the desired DC power to all of the loads 68, 70, 72, 74, 9, 94, the processor 122 is arranged to store the generated electrical energy in the power storing devices 46 while distributing the energy from the power storing devices 46 to the loads 68, 70, 72, 74, 9, 94 according to a load distribution algorithm that is based on a predefined order of priority of the loads and the total energy generated by the micro-grid 10.

For example, if the required 15-70% of the total energy is produced from the photovoltaic system 16, then the generated energy will be transferred to the battery system 44. The battery system 44 will transpose the energy to the necessary loads according to a predefined algorithm that distributes electrical energy from the battery system 44 to the loads based on the order of priority of the loads, the capacity of charge of the batteries 46, and the amount of energy that is produced by the energy generation sources 12 and which energy will be stored in the batteries 46.

However, when the energy generated by the electrical energy generation sources 12 is less than a predefined percentage of the total energy required to provide the desired DC power to the loads 68, 70, 72, 74, 9, 94, the processor 122 is arranged to instruct the charger controller 40 to charge the power storing devices 46, and the processor 122 is further arranged to simultaneously cause the charger controller to discharge electrical energy from the power storing devices 46 and distribute the power to loads according to a load distribution algorithm that is based on a predefined order of priority of the loads. Typically, based on the load distribution artificial intelligence algorithm, the processor 122 would command the relevant controllable circuit breaker modules 74, 76, 78, 80, 96, 98 associated with the loads in the load distribution algorithm to be switched on, and the processor 122 will automatically cause the controllable circuit breaker modules 74 to 80, and 96, 98 to switch off the rest of the other loads. The primary function is to interrupt the current flow in the event of a short- circuit or overload or any electric fault to prevent electrical injuries and/or fires until the fault is safely assessed and resolved. If bad weather is anticipated, then the system 100 will reduce power usage for non-critical loads to account for 2 days autonomy. For example, if 0-15% of the energy is produced from the PV system 16, a comparison between the energy generated by the PV system 16 and wind system 24 is made. If a full 5-10% of the total power is generated from the wind system 24, then the system 100 is arranged to distribute energy to the loads according to a predefined algorithm. However, if power generated from wind system 24 is less than 10% of the total power, then the system 100 will priorities the recharging of the power storing devices 46 and distribute power to the loads according to yet another algorithm that is arranged to prioritize the recharging of the power storing devices to full capacity.

The processor 122 is configures to predict or forecast load usage based on historic power consumed by the loads operatively connected to the micro-grid 10. The processor is configured to predict or forecast load usage by making use of a load forecasting algorithm which is similar to the wind prediction algorithm in that the load usage forecasting algorithm uses an LSTM NN ML algorithm to train and predict or forecast load usage.

Unlike the wind prediction algorithm, in implementing this algorithm, the processor 122 does not access data via an API. Instead, a training database is preloaded with a dataset of typical household power consumption for the specific size of the household that the micro-grid 10 is going to be installed.

An example of a typical usage pattern for a household grid would include high usage in the morning and the late afternoon and evening. Using this preloaded training database, the data is pre-processed, and the LSTM is trained. Once the LSTM has been trained on the data, predictions can be made regarding future household electricity use.

Advantageously, the processor 122 is configured to continually update and train based on actual power usage of the micro-grid. In this way, the EMS 100 is able to customize itself to the specific micro-grid which it operates on. In other words, the processor starts the prediction or forecasting of load use based on a priori data but is continually retrained by the implementation of a load usage forecasting algorithm to be able to be tailored specifically to a micro-grid in which it is deployed as every household will have its usage patterns depending on when they use specific appliances, how often, and for how long. In this regard, all appliance power usage profiles are collected by the energy management system 100 over time in use and the load usage prediction algorithm is retrained this new data. This means that the algorithm becomes more accurate the longer it runs on a specific micro-grid 100 as it conveniently learns the usage patterns of the occupants.

The machine learning algorithm implemented by the processor 122 only uses two features, the combined power usage of the household and the time and date, in order to predict at what time and on what day the electricity load usage in the micro- grid will be.

In particular, the processor 122 implementing the load usage prediction algorithm is configured to: train a LSTM neural network on the preloaded power usage data; make predictions using the trained LSTM on the load usage in the micro-grid in a periodic fashion, for example, every 10 mins; continuously store load usage data collected from the micro-grid while operating; use the stored load usage data to retrain the LSTM neural network in a periodic fashion, for example, once per day.

The database or memory device 124 is updated with load usage data in real- time as the occupants of the micro-grid switch on/off appliances, otherwise, this reverts to the 10min cycle.

In summary, the system 100, particularly the processor 122 is configured to implement i) solar; ii) wind; and load usage forecasting Al neural networks which are trained once per day and are trained with data captured and stored in the database or memory device 124 from the weather source, in the case of the solar and wind data, and from the micro-grid energy usage in the case of the load use data every 10 minutes.

As mentioned previously, the processor 122 is arranged to monitor the extent of charging of the rechargeable power storing devices 46. If the power storing devices 46 are charged to maximum capacity, the processor 122 is arranged to use another load distribution algorithm that is based on a fully charged power storing devices 46, and accordingly cause the controllable circuit breaker modules 74 to 80, and 96, 98 of other loads, typically non-critical loads, which may have been switched off, to switch on those loads, so as to direct the excess electrical energy to other non-critical loads to avoid overcharging of the rechargeable power storing devices 46. For example, the excess electrical energy generated may be transferred to the EV system 50, and or the hot water storage system 136 and the atmospheric water generator system 132.

In another example embodiment of the invention, the processor 122 is arranged to continuously monitor, and store in the memory device 124, the DC power usage of the loads in the structure, such as a house, to determine the DC power usage profile (i.e., energy profile) of the house. The energy profile comprises detailed information on the generated energy from the renewable energy generation sources 12, and the energy consumption of the loads. The energy profile information is arranged to be stored on a SQL database of the memory device 124 and is arranged to be presented on the monitor/dashboard of the system 100 through historical reporting. The processor 122 is accordingly configured to determine the DC power usage of the structure during off-peak and peak hours, typically using the energy profile of the structure. Accordingly, based on the expected average DC power usage using the DC power usage profile of the structure during off-peak and peak hours, the processor 122 is configured to determine the amount of DC power required per day, and can accordingly shunt off excess electrical energy to the non-critical loads as mentioned above.

As alluded to herein but stated in a different manner, the processor 122 is also arranged to collect meteorological data, from a meteorological system (not shown), and based on the forecast meteorological data, the processor 122 is arranged to determine the expected DC power to be produced by the energy generation sources 12 on a future date. The processor 122 is then configured to compare the expected DC power to be produced and stored in the power storing devices 46 by the future date, to the DC power usage profile of the structure during off-peak and peak hours, and thereafter determine whether the expected DC power to be produced and stored in the power storing devices 46 on the future date is equivalent to the DC power usage profile of the structure. Typically, if the expected DC power to be generated and stored in the power storing devices 46 on the future date based on the forecast meteorological data is less than the DC power usage profile of the structure, the processor 122 is configured to prioritize the recharging of the rechargeable power storing devices 46 so that during the forecasted period (i.e. on the future date), the power storing devices 46 can have enough electrical energy to distribute electrical power to critical and/or non- critical loads according to yet another load distribution algorithm, and so that the power storing devices 46 can also allow the loads to be manually switched on as and when desired by the user.

Typically, when the expected DC power to be produced and stored in the power storing devices 46 by the future date, based on the forecast meteorological data, is less than the DC power usage profile of the structure, the processor 122 is configured to change, in real-time, from one load distribution algorithm to another suitable load distribution algorithm, in which the electrical energy will be distributed to the loads according to the new load distribution algorithm, and the processor 122 is arranged to command the charger controller to prioritize the recharging of the power storing devices 46 to ensure that enough electrical power is generated and stored in the rechargeable power storing devices 46 by the forecasted period, and which stored energy can last for a predefined period, preferably 48 hours.

If, however, the total power generated by the grid 10 (i.e., the power expected to be generated by the energy generation sources 12, and the energy stored in the power storing devices 46) is above 100%, then the processor 122 is arranged to use another load distribution algorithm where the excess power can be distributed to non- critical loads such as the ice storage system 140 and the hot water storage system 136.

Typically, the energy management system 100 is configured to monitor the temperature and water level using a temperature sensor (not shown) and proximity sensor (not shown) positioned inside the hot water tank 138. The hot water storage system will be an extension of the geyser. If the temperature of the hot water in the hot water tank 138 reaches the setpoint, then the heating element (not shown) thereof stops. When the temperature falls below a dead band, the heating element (not shown) turns on to maintain the water in the hot water tank 138 at a steady temperature. A timer (not shown) integrated into the energy management system 100 will be set such that the circulation of the water in the hot water tank 138 by a water circulation pump (not shown) occurs at predefined intervals. Typically, during load peak hours, the circulation of water is activated to maintain a constant temperature throughout the tank 138. If it is during load off-peak hours, the circulation pump (not shown) is not activated. The heating of the water may be most active during load peak- hours, thus maintaining a constant temperature is essential. Any excess power generated from the energy sources 12 and battery system 44 will allow for the heating of the hot water storage tank. The ice water storage system 140 may be incorporated with the HVAC (Heating Ventilation and Air Conditioning) system. This will allow temperature control in the structure (e.g., residential home) for space cooling. It is preferred that the ice storage system 140 will be power-dependent on the energy management system 100.

The processor 122 is further configured to report and display on an end-user device 154 associated with the structure, for example, a house, in real-time, the amount of energy produced, and the list of loads prioritized (according to the load distribution algorithm selected based on the energy stored in the power storing devices 46 and the power generated or expected to be generated by the electrical energy generation sources 12).

Based on the report of the amount of energy produced or expected to be produced by the energy generation sources in a particular period, the user associated with the building structure may be desirous to use a different loads distribution algorithm, and may accordingly wish for the energy management system 100 to distribute power to some loads which may have been switched off by the system 100 and may wish to switch off some loads which may be switched on according to the load distribution algorithm used by the energy management system 100. Accordingly, upon receiving a list of loads which the user may wish to switch on real-time, the processor 122 is configured to switch on the desired loads and use a new load distribution algorithm that is based on the loads selected by the user, while at the same time managing the energy stored in the power storing devices 46 to ensure that enough power is always available in the power storing devices 46 to sustain the critical and non-critical loads in the house for a period of at least 48 hours.

The energy management system 100 is intelligently arranged to determine the amount of energy generated by the energy generation sources, and the amount of energy stored in the power storing devices 46, to determine the total amount of energy produced by the micro-grid 10 in real-time. Based on the loads connected to the AC and DC distribution boards 66, 90, and the energy that can be consumed by the loads according to the list of loads stored in the memory device 124 and according to the order of priority of the loads, the energy management system 100 is arranged to effectively determine which loads can be supplied with energy available in real-time, and by using a weather forecasting algorithm can determine the amount of energy that can be produced by the micro-grid 10 on bad weather days, and based on this data the system 100 can automatically switch off loads which may have been previously switched on, in order to prioritize the recharging of the power storing devices 46 so that there will be enough energy stored in the power storing devices 46 for at least 48 hours during the period in which the energy that is arranged to be produced by the energy generation sources will be minimal. In addition, the energy management system 100 is further arranged to determine when the total energy generated by the energy generation sources 12 and the energy stored in the battery system 44 is in excess and can deploy another load distribution algorithm in which less critical loads are switched on in real-time in order to shunt off excess electrical power to the non-critical loads and protect the power storing devices 46 from overcharging. The micro-grid 10 incorporates a hot water storage system 136 in which excess electrical energy can be transferred to the hot water storage system 136 to warm up water in the hot water tank which may be a pool, etc. in some example embodiments. In some example embodiments, the use of the hot water storage system 136 is to supplement a hot water heater or geyser which may have been erected in the structure, so that the structure can always have an excess supply of hot water, even during the days on which the total energy generated by the micro-grid 10 is not enough to enable the switching on of the geyser 70. The micro-grid 10 also includes the ice storage system 140 that is coupled with the FIVAC system (not shown), so that any excess energy can also be transferred to the ice storage system 140. It is preferred that the excess energy is transferred first to the hot water storage system 136, followed by the excess energy being transferred to the ice storage system 140 and then the water generation system 132.

Referring to Figure 7 of the drawings, interconnected subsystems, and components and parameters that flow between them, of the energy management system 100 is generally indicated by reference numeral 400. The components may be understood to be software components, hardware components, or a combination of both hardware and software components wherein the software components are provided or implemented by the processor 122 as will be understood by those skilled in the field of invention. Though described independently, it will be noted that there may be overlap between the components and modules as will be evident to those skilled in the art.

Forecasting algorithms 402 provide the energy management system 100 with predictions about the amount of power that should be generated in the following week via Solar and Wind sources as well as how much power the grid should consume at any given time in the preceding week.

The system parameters 404 are measurable variables of the micro-grid such as the State of Charge (SOC) of the batteries in the battery system 44, the power consumption of each appliance/load, the power generated from the renewable energy sources, and every connected system that provides information to the energy management system 100.

The system 100 also comprises suitable state of charge estimation algorithms 412, to estimate the state of charge of the batteries 46 of the battery system 44, and suitable measurement algorithms 414 to process signals received from suitable sensors, etc. to determine measurements used by the system 100.

A Human Machine Interface (HMI) 406 allows the grid user to define or override their load priority lists for their micro-grid, it also shows them all relevant information such as power usage and availability.

The operational algorithm 408 is the algorithm that may control every subsystem.

The load shedding optimization algorithm 410 runs the actual optimization routine to ensure that the optimal control logic is adhered to and that the micro-grid 10 conserves enough power to secure autonomy for a predetermined amount of time, for example, two-days.

On a high level, the main objective of the energy management system 100 is to ensure that there is always capacity of the micro-grid 10 to provide power to the loads for a period of time, for example, two-days of autonomy. This is determined by the optimization algorithm 410, but the high-level process/method can be seen in Figure 8 and is generally indicated by reference numeral 500.

In Figure 8, the processor 122 is configured to calculate or determine a detailed power generation and power usage forecast schedule for a predetermined forecasted period of time, for example, one week in advance, at block 52, by applying suitable forecasting algorithms 402.

In particular, the forecast schedule is calculated or determined by the processor 122 by calculating or determining an amount of power expected to be generated via one or more renewable energy sources 12, at block 504. In other words, the processor 122 is configured to forecast the amount of power expected to be generated via one or more renewable energy sources 12. The processor 122 may be configured to calculate or determine the forecast schedule comprising information indicative of power expected to be generated by the renewable energy source 12 by applying suitable machine learning algorithms.

Moreover, the forecast schedule is calculated or determined by the processor 122 by calculating or determining an amount of power expected to be used or in other words the power usage of the micro-grid 10, at block 504, for the predetermined forecasted period of time of one week. . In other words, the processor 122 is configured to forecast the amount of power expected to be used in or by the micro- grid. The processor 122 may be configured to calculate or determine the forecast schedule comprising information indicative of power usage expected for one week based on previous or historic micro-grid 10 power usage, or information indicative thereof. This may be done by the processor 122 applying suitable machine-learning algorithm/s to information indicative of previous or historic micro-grid 10 power usage.

The processor 122 is configured to process the forecast schedule to calculate or determine whether load shedding and energy storage efforts of the energy management system 100 needs to be addressed to curb power demand shortfall or if the micro-grid can still operate in an unmanaged manner, at block 506. The processor 122 may achieve this by applying a suitable optimisation algorithm.

The processor 122 is configured to calculate or determine the actual power generated and the actual power usage in or by the micro-grid, at block 510. The processor 122 may achieve this by way of suitable sensors in the micro-grid 10 to which the system 100 and particularly the processor 122 is communicatively coupled.

The processor 122 is configured to compare the forecasted power generation and forecasted power usage to real-time grid generation and usage for the forecast period of time of a week, , at block 510.

In response to the comparison, the energy management system 100, particularly the processor 122 makes immediate adjustments to the component/s of the micro-grid 10, at block 512, to mitigate any discrepancies. The processor 122 is configured to switch on/off any loads or generators via a wired or wireless portal, at block 516, if necessary, after applying the optimisation algorithm, at block 514. As alluded to herein, the processor 122 may be configured to generate suitable control signals which may be transmitted to the DC and AC circuit breaker modules 74, 76, 78, 80 and 96, 98 respectively to switch on/off loads. The cycle 500 then repeats itself.

The ability of the energy management system 100 to accurately load shed loads such as appliances and control different elements in the islanded micro-grid 10 comes from forecasted data from artificial intelligence machine learning forecasting and prediction algorithms 402. These algorithms coupled with the optimization algorithm mean the energy management system 100 can continually keep the micro- grid at an optimal state and secure a predetermined period, for example, two-days of autonomy wherever possible.

As mentioned above, the memory device 124 may store a priority list which may be a shunt priority list. When the micro-grid 10 experiences a state of having the initial energy storage devices at maximum capacity the energy management system must either halt power generation or shunt this excess power capacity to various subsystems in the grid. As an example, in a micro-grid, there may be additional hot water storage, a pumped storage scheme, electric vehicle, or other thermal storage such as ice storage as described above.

The user, via the suitable HMI 406 in Figure 7, can then set up a power shunting priority list whereby they can prioritize where the energy management system shunts excess power and in what order. As mentioned herein, the HMI 406 may include a software application operating on a mobile computing device such as a Smartphone of a user which the user may use to interact with the system 100 to receive data associated with the micro-grid 10 and provide preferences and/or instruction for the operation thereof, for example, the prioritizing of loads and shunting priority as described herein.

Additionally, the user has the ability to be alerted, for example, via the HMI 406 to this excess power capacity state of the micro-grid and can also choose to rather use the excess power to supply certain appliances above the other options.

Needing to automatically connect and disconnect both load generating and consuming components/systems in the micro-grid 10 means that the energy management system 100 needs the ability to physically create or destroy electrical connections between the micro-grid 10 and loads such as appliances.

This ability is afforded to the energy management system 100 via the DC/AC contactor circuit breakers 74 to 80, and 96, 98 described herein.

When the micro-grid 10 is under power constraints and energy-consuming devices need to be shed from the system as per the load priority list, the energy management system 100 may send suitable commands to the circuit breakers to switch off that particular load / appliance. The same load / appliance may be brought back online and connected to the micro-grid using the circuit breakers once the energy management system 100 deems it affordable.

Referring now to Figure 9 of the drawings where a high-level flow diagram of an energy management method in accordance with an example embodiment of the invention is generally indicated by reference numeral 180. The energy management method 180 is arranged to manage and distribute electrical energy to a load that is disconnected from a power grid. It will be appreciated that the example method 180 may be implemented by systems and means not described herein. However, by way of a non-limiting example, reference will be made to the method 180 as being implemented by way of the energy management system 100, as described above.

Accordingly, the method 180 comprises the steps of: 182: determining, by at least one processor 122, the amount of electrical energy generated by at least one electrical energy generation source that generates electrical energy from renewable energy and energy stored in at least one rechargeable power storing device; and 184: selectively distributing, by the at least one processor 122, DC power stored in the power storing device 46 to the load or plurality of loads.

Referring now to Figure 10 of the drawings where a flow diagram of a method in accordance with an example embodiment of the invention is generally indicated by reference numeral 600.

The method 600 may be a flow diagram of the methodology which the EMS 100 as herein described may employ to control the micro-grid 10 and to allow the micro-grid 10 to provide power to loads for at least two days of power supply or autonomy. Though described with reference to the system 100 and the micro-grid 10, it will be appreciated the methodologies described herein may be applied to other systems and micro-grids, mutatis mutandis. Moreover, though described with reference to a simulation in the diagram, it will be noted that the method 600 may be implemented, mutatis mutandis, in the real world.

At the outset, it will be noted that the EMS 100 is configured to control the micro- grid 10 in the manner described herein with a number of variables at play in accordance with the number of time periods N_s during operation thereof, for example, power from the supply side from different sources of Renewable Energy Generators (REGs) 12 and power consumed by loads the operation period of the micro-grid 10.

Mathematically, these variables may be written as an optimal control problem which the system 100 addresses where the control objective is the maximization of the total daily energy produced and to minimize the critical loads. This part of the problem may be characterized by a first objective function, which may be expressed by:

, wherein P_L is the power consumption of an individual load in the micro-grid; w is the weighting of the load according to the load priority list; P_G is the power generated by the individual power generation sources; i is the time horizon; N_s is the number of time periods considered for the optimization study of the hybrid system. As alluded to herein, the load priority list is an integral part of the control of the micro-grid 10 by the EMS 100. As mentioned, the priority list is a user defined list of what that user deems are the most important power consuming appliances connected to the micro-grid 10. Equation 1 may thus be considered as the minima of sum of the difference between the weighted power drawn by the load connected to the micro-grid 10 and the power generated by the energy sources 12 over the time period considered, for example, two days. The weighting being based on the load priority list.

In this regard, the load priority list may be initialized with standard weights and the user can choose whether to alter these weights to suit their needs. Appliances that are weighted lower on the list will be targeted by the optimization algorithm 410 (Figure 7) to be shed before appliances that carry a higher weighting. A second objective function is characterized by the excess power generated ( P_epg ) which is a parameter which gives the excess in power generated and unutilized by the micro-grid 10. This value can vary due to the variation of hourly average demand, sun insolation, wind velocity and state of charge (SOC) of the battery bank. At a specific time i, the excess power generated may be expressed by:

, wherein P_epg is the excess power generation (EPG) needed by the micro-grid 10 in the case of load-shedding situation; P_RER is the total power generated by the renewable energy sources 12; P_Dem is the power consumed by the connected loads during a specific time of the day; b is the coefficient factor applied to demand side for additional unforeseen future load capacity in the micro-grid 10; SOC_max is the maximal SOC capacity of the battery system 44; SOC(i - 1) is measured value of the SOC after a certain period of time; and is the efficiency of the bidirectional converter. The bi-

directional converter is a DC/DC converter.

Using the equation 2 above for the excess power generation, the relative excess power generated ( R_epg ) may be calculated as follows:

To respond to the difficulties caused by load-shedding issues into the micro- grid 10, the second objective function (equation 2) can be based on the maximization of the amount of relative excess power generated. To make sure that in the case of power shortage, the value of the remaining power will still respond correctly in balancing of the micro-grid (and ensuring at least two days of autonomy). Thus, the objective function may be written as follows:

Substituting Obj₁ (equation 1) into Obj₂ (equation 4) will solve the micro-grid optimization system under a load-shedding scheme 410 (Figure 7). The multi- objective function of the system is characterized by the summation of these two equations and can be expressed as follows:

The objective functions in above are characterized by the following constraints. The power generated at the common point of connection should be greater or equal to the total power demand at the same point. The power balance is characterized by an excess of power reserve from the RERs 12 and the power reserve can be regarded as the difference between the supply and the demand sides of the micro-grid system 10. This power balance can be considered as a system of two equations combined into one, depending on the need at the consumer side of the system and the use of the RERs 12. The power balance equation of the combined specific time can be written as follows:

, wherein P_Hydro(k) ^is the power from the micro-hydropower systems 32 at a specific time, P_PV(k) is the power delivered by the PV system 16, P_WT(k) is the power from the wind turbine system 24, P_EV(k ) is the power delivered or consumed by the electric vehicle and P_B(k) is the power delivered or consumed by the battery banks 46.

In the event of using hot water storage or shunting excess power to an ice storage system more variables can easily be included in the above equation 6. The index n^NC regarded as the total number of non-critical loads which will be assigned with weights from the priority list of the specific micro-grid m^c is the total number of critical loads, which depends on the settings for the specific grid and user settings, normally set to at least two such as phone/Wi-Fi power and a light. The SOC of the batteries 46 in the battery system 44 can be expressed in the discrete time domain as follows:

, wherein C_n is the nominal capacity of the battery system 44. The SOC is expressed in terms of its initial value, SOC(O), by the following equation:

The lower and upper limits on the SOC of the battery bank are expressed as follows:

The wind turbine is modelled with the following equations:

The PV panel 18 is modelled with the following. The output power of the PV panel depends on the characteristics of the solar cell itself, as well as on the external irradiance and temperature conditions, according to the following equations:

, wherein T_a is the ambient temperature of the site, T_n is the nominal operating temperature of the PV cell 18, T_c is the temperature of the PV cell 18, K_v is the voltage temperature coefficient, K_i is the current temperature coefficient, V_oc is the open circuit voltage, I_SC is the short circuit current, V_max is the voltage at maximum power point, l_max is the current at maximum power point, FF is the fill factor, V is the voltage, I is the current, G is the solar irradiance, P_s,out is the solar cell output power.

The above equations describe the electrical behavior of the PV cells 18. The more solar irradiance reaches the cell, the more current flows through it.

The constraint to ensure that there is at least 2 days of autonomy in the system is seen below. This equation states that the generated power P_G , is equal to the battery power P_B when there is no renewable energy generated by either the solar or wind generators and that this battery power needs to last for at least the specified 2 days for which the micro-grid is required to provide some power to the grid in an optimized fashion.

To solve the above optimization problem an optimization algorithm needs to be selected and implemented. To solve this problem the processor 122 implements a conventional Particle Swarm Optimization (PSO) algorithm. The PSO is a metaheuristic algorithm that does not rely on gradients thus the algorithm does not need to evaluate any type of gradient or Hessian matrix during solving and thus the problem does not need to be differentiable in the problem space.

The algorithm solves the optimization problem by initializing a population of candidate solutions (particles) over the search space and iteratively tries to find an optimal function value while dynamically changing the particles velocity and position. Each particle updates itself with its best position every iteration and the particle with the best overall cost is updated as the global best particle every iteration. The velocities of the particles are then updated using this information and a new iteration is run. The PSO algorithm continues running until the maximum number of iterations is met or if the convergence of the particles reaches some predefined convergence criteria.

Turning back to Figure 10 of the drawings, the method 600 comprises receiving real-world measurements of the power generated by the sources 12. These are evaluated together with their predicted counterparts at the same time and/or date associated with obtaining the rea-world measurements. Similarly, the power consumption values are obtained in real-time and evaluated with its predicted counterpart.

The method 600 repeats itself in regular intervals, for example, 10 minutes and as mentioned herein, the real-time or real-world values of the power generated by the sources 12, power consumed, and state of charge of the battery system 44 is stored in the memory device 124 and may be used to train the relevant neural networks accordingly every 24 hours.

In any event, the method 600 is associated with the actions which the EMS 100 has to take in managing the load usage to ensure autonomy for a predetermined period of time, for example, 2 days. To this end, the parameters referred to below may be forecasts by the EMS 200.

The method 600 comprises forecasting the state of charge of the battery system 44, at block 620. The method 600 comprises determining, at block 622, if the total power generated by the energy sources 12 is greater than the total power consumption of the loads of the micro-grid 10.

If the total power generated is greater than the total power consumption then the method 600 comprises determining if the state of charge of the battery system 44 is greater than the maximum capacity of the battery system 44, at block 624. If it is greater, then the batteries are fully charged and the method 600 comprises shunting the excess power generated at block 632. In particular, the method 632 shunts the excess power in accordance with a shunt priority list, at block 632, which may comprise shunting the excess power to heat water to store the excess power as hot water, storing the excess power in bi-directional batteries of electric vehicles, and the like, at block 634 or shunting the excess power to certain predetermined appliances, at block 636.

If the state of charge of the battery system 44 is less than the maximum state of charge of the battery system 44, at block 624, the method 600 comprises charging the batteries with the excess power.

If the total power generated is the same as the total power consumption, then there is equilibrium and there is nothing further to be done.

If the total power generated is the same as the total power consumption, then there is equilibrium and there is nothing further to be done, at block 628.

If the total power generated is less than the total power consumption then the method 600 comprises determining if the state of charge of the battery system 44 is less than the maximum capacity of the battery system 44, at block 640. If it is not less then, the battery system 44 is discharged to meet the deficit power requirements, at block 642. The method 600 implements load shedding optimisation, at block 646, as described herein by the application of the suitable algorithms by the EMS 100 and the load priority list, at block 648, as described herein. If the state of charge of the battery system 44 is less than the maximum state of charge of the battery system 44, at block 640, then it would indicate that the battery system 44 is completely drained and all loads are shed, at block 644, with the EMS controlling the circuit breaker modules as described herein, at block 650.

Referring now to Figure 11 of the drawings which shows a diagrammatic representation of a machine in the example of a computer system 200 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In other example embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked example embodiment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or ridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated for convenience, the term “machine” shall also be taken to include any collection of machines, including virtual machines, that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

In any event, the example computer system 200 includes a processor 202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 204, and a static memory 206, which communicate with each other via a bus 208. The computer system 200 may further include a video display unit 210 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 200 also includes an alphanumeric input device 212 (e.g., a keyboard), a user interface (Ul) navigation device 214 (e.g., a mouse, or touchpad), a disk drive unit 216, a signal generation device 218 (e.g., a speaker) and a network interface device 220.

The disk drive unit 216 includes a non-transitory machine-readable medium 222 storing one or more sets of instructions and data structures (e.g., software 224) embodying or utilized by any one or more of the methodologies or functions described herein. The software 224 may also reside, completely or at least partially, within the main memory 204 and/or within the processor 202 during execution thereof by the computer system 200, the main memory 204 and the processor 202 also constituting machine-readable media. The software 224 may further be transmitted or received over a network 226 via the network interface device 220 utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Although the machine-readable medium 222 is shown in an example embodiment to be a single medium, the term "machine- readable medium" may refer to a single medium or multiple medium (e.g., a centralized or distributed memory store, and/or associated caches and servers) that store the one or more sets of instructions. The term "machine-readable medium" may also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such a set of instructions. The term "machine-readable medium" may accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals.

While the invention has been described in detail with respect to a specific embodiment and/or example thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing may readily conceive of alterations to, variations of, and equivalents to these embodiments. Accordingly, the scope of the present invention should be assessed as that of the claims and any equivalents thereto, which claims shall be appended hereto upon completion of this patent application.

It will be appreciated that the present invention addresses, ameliorates or solves problems and difficulties noted by the Inventor:

- residential, indigenous, and small power tariffs are subsidized by large power user tariffs

- residential metering, billing and collections are costly, including challenges with tampering and occurrences of non-payment

- residential power grid infrastructure limits EV charging potential in residential suburbs and delay the EV evolution

- smart metering with trading and wheeling inflates utility operating expenses and tariffs along with new access to grid tariffs - more renewable energy sources with grid-tie at small loads, reduce utility energy sales and increases grid instability

- more renewables with grid-tie at small loads increase the end-user energy costs; reduce disposable income and economic activity. By removing small loads, utility operating expenses are reduced and grid stability is improved which may accelerate EV and economic activity; whilst achieving emission goals and targets.

The present invention advantageously addresses both the morning and evening generation demand peaks, as well as mid-day “duck effect” on the power grid.

Claims

1 . An energy management method for managing supply of electrical energy to loads in a micro-grid supplied with electrical energy generated by one or more electrical energy generation source/s, wherein the method comprises: determining electrical energy generated by the one or more electrical energy generation source/s and stored in one or more rechargeable power storing device/s of the micro-grid; supplying electrical energy generated by the one or more electrical energy generation sources and/or stored in the one or more rechargeable power storing device/s to the loads via controllable circuit breaker modules in communication with at least one processor; monitoring consumption of electrical energy by the loads by way of the controllable circuit breaker modules, wherein the controllable circuit breaker modules are configured to measure electrical energy consumption of the respective loads and communicate data indicative of said electrical energy consumption to at least one processor; determining whether or not electrical energy supplied to one or more of the loads should be stopped or not; and controlling one or more controllable circuit breaker module/s associated with one or more load/s to switch on or off supply of electrical energy to the one or more load/s in response to receiving a suitable instructions from the at least one processor.

2. A method as claimed in claim 1 , wherein the method comprises controlling the one or more controllable circuit breaker module/s to switch on or off supply of electrical energy to the one or more load/s in accordance with a load priority list or schedule, wherein loads identified in the load priority list or schedule are ranked in order of priority.

3. A method as claimed in claim 2, wherein the method comprises determining whether or not electrical energy supplied to one or more of the load/s should be stopped or not by determining whether or not the micro-grid is capable of providing electrical energy to the loads, wherein if the micro-grid is not capable of providing electrical energy to the loads, the method comprises controlling the one or more controllable circuit breaker module/s to switch on or off supply of electrical energy to the one or more load/s in accordance with the load priority list.

4. A method as claimed in either claim 2 or 3, wherein determining whether or not electrical energy supplied to one or more of the load/s should be stopped or not comprises: determining an amount of electrical energy which the micro-grid is capable of providing to the loads based on the determined amount of electrical energy generated by the one or more electrical energy generation source/s and/or stored in one or more rechargeable power storing device/s; determining an amount of electrical energy consumed by the loads, based on the monitored consumption of electrical energy by the loads; and determining whether the amount of determined electrical energy consumed by the loads exceeds the determined amount of electrical energy which the micro-grid is capable of providing, wherein if the amount of determined electrical energy consumed by the loads exceeds the determined amount of electrical energy which the micro-grid is capable of providing then the method comprises controlling one or more controllable circuit breaker module/s to shed one or more load/s in accordance with the load priority list.

5. A method as claimed in any one of claims 2 to 4, wherein the method comprises generating the load priority list from input from a user, wherein the load priority list is a user priority list.

6. A method as claimed in either claim 2 or 3, wherein the method comprises: forecasting total electrical energy expected to be generated by the one or more electrical energy generation sources and stored in the one or more power storing device/s at a future time, date, and/or period of time by way of a suitably trained neural network ; forecasting electrical energy consumption of the loads operatively connected to the micro-grid at the future time, date, and/or period of time by way of a suitable neural network; determining, by way of the at least one processor, if one or more controllable circuit breaker modules should be operated to shed one or more load/s in accordance with the load priority list by comparing the forecasted total energy expected to be generated and the forecasted electrical energy consumption so as to preserve autonomy of the micro-grid to provide electrical energy to certain loads for a specific period of time.

7. A method as claimed in claim 6, wherein the specific period of time is two days.

8. A method as claimed in claim 4 or 6, wherein the method comprises storing excess electrical energy in one or more of suitable storage means selected from a group comprising the one or more rechargeable power storing device/s comprising batteries, bi-directional electrical vehicle batteries, and thermally.

9. A method as claimed in claim 1 or claim 2, wherein the method comprises: collecting renewable energy data associated with one more or more source/s of energy used by the one or more electrical energy source/s to generate electrical energy from a suitable data source and/or from one or more suitable sensors; using the collected renewably energy data train a neural network to forecast or predict future renewable energy data at a future time, date, and/or period; using the trained neural network to forecast or predict future renewable energy data associated with the renewable source of energy at a future time, date, and/or period; and using the predicted or forecasted renewable energy data to determine electrical energy to be generated by the one or more electrical energy sources at the future time, date, and/or period, wherein the determined electrical energy to be generated is a forecasted or predicted electrical energy to be generated by the one or more electrical energy sources at the future time, date, and/or period.

10. A method as claimed in claim 9, wherein the method comprises collecting the renewable energy data and storing the same at periodic intervals during a day; and training the neural network with the collected renewable energy data at predetermined intervals.

11. A method as claimed in claim 9 or 10, wherein the method comprises using the forecasted electrical energy to be generated at a future time, date, and/or period to determine an amount of electrical energy available at the future time and/or date and/or period to charge the rechargeable power storing device/s.

12. A method as claimed in any one of the preceding claims, wherein the one or more electrical energy source/s is/are configured to generate electrical energy from renewable energy source/s, wherein the one or more electrical energy source/s is/are selected from a group comprising photovoltaic cells, wind turbines, hydro-electric generators, and geo-thermal devices.

13. A method as claimed in claim 12 when dependent on any of claims 9 to 11 , wherein the renewable energy data comprises one or more of weather data comprising one or both of , fluid flow data, and geothermal data.

14. A method as claimed in any one of claims 9 to 11 , wherein the method comprises storing the renewable energy data used to train the neural network, as well as a time and/or date stamp associated with the renewable energy data, in a suitable memory device.

15. A method as claimed in claim 12, wherein the method comprises: collecting weather data associated with a geographical location of the micro-grid from a weather data source and/or from one or more suitable sensors located adjacent the geographical location of the one or more electrical energy sources; using the collected weather data to train a neural network provided by the at least one processor to forecast or predict the weather data at the geographical location of the one or more electrical energy sources; using the trained neural network to forecast or predict the weather data at the geographical location of the one or more electrical energy sources; and using the predicted or forecasted weather data to determine the electrical energy to be generated by the one or more electrical energy sources at a future time, date, and/or period, wherein the determined electrical energy to be generated is a forecasted or predicted electrical energy to be generated by the one or more electrical energy sources.

16. A method as claimed in claim 15, wherein in the case of the electrical energy source being in the form of photovoltaic cell/s, the weather data comprises data indicative of sunlight in the geographical location of the photovoltaic cell/s, and wherein in the case of the electrical energy source being in the form of wind turbine/s, the weather data comprises data indicative of wind conditions in the geographical location of the wind turbine/s.

17. A method as claimed in any one of the preceding claims, wherein the method comprises: measuring electrical energy consumption of the loads operatively connected to the micro-grid by way of the controllable circuit breaker module; using data indicative of the measured electrical energy consumption to train a neural network provided by the at least one processor to forecast or predict the electrical energy consumption of the loads operatively connected to the micro-grid at a future time, date, and/or period; and using the trained neural network to determine the electrical energy consumption of the loads operatively connected to the micro-grid at a future time, date, and/or period, wherein the determined electrical energy consumption is a forecasted or predicted electrical energy consumption.

18. A method as claimed in claim 17, wherein the method comprises storing the measured electrical energy consumption, or data indicative thereof, as well as a time and/or date stamp associated therewith in a suitable memory device, wherein the method comprises determining the electrical energy to be consumed at a particular time and/or date, or period of time in the future.

19. An energy management system for managing supply of electrical energy to loads in a micro-grid supplied with electrical energy generated by one or more electrical energy generation source/s, wherein the system comprises: at least one memory storage device; and at least one processor coupled to the at least one memory storage device, wherein the at least one processor is configured to: determine electrical energy generated by the one or more electrical energy generation source/s and stored in one or more rechargeable power storing device/s of the micro-grid; control supply of electrical energy generated by the one or more electrical energy generation sources and/or stored in the one or more rechargeable power storing device/s to the loads via controllable circuit breaker modules in communication with at least one processor; monitor consumption of electrical energy by the loads by way of the controllable circuit breaker modules, wherein the controllable circuit breaker modules are configured to measure electrical energy consumption of the respective loads and communicate data indicative of said electrical energy consumption to at least one processor; determine whether or not electrical energy supplied to one or more of the loads should be stopped or not; and control one or more controllable circuit breaker module/s associated with one or more load/s to switch on or off supply of electrical energy to the one or more load/s.

20. A system as claimed in claim 19, wherein the at least one processor is configured to control the one or more controllable circuit breaker module/s to switch on or off supply of electrical energy to the one or more load/s in accordance with a load priority list or schedule, wherein loads in the load priority list or schedule are ranked in order of priority.

21. A system as claimed in claim 20, wherein the at least one processor is configured to determine whether or not the micro-grid is capable of providing electrical energy to the loads, wherein if the micro-grid is not capable of providing electrical energy to the loads, the processor is configured to control the one or more controllable circuit breaker module/s to switch on or off supply of electrical energy to the one or more load/s in accordance with the load priority list.

22. A system as claimed in either claim 20 or 21 , wherein the at least one processor is configured to : determine an amount of electrical energy which the micro-grid is capable of providing to the loads, based on the determined amount of electrical energy generated by the one or more electrical energy generation source/s and stored in one or more rechargeable power storing device/s; determine an amount of electrical energy consumed by the loads, based on the monitored consumption of electrical energy by the loads; and determine whether the amount of determined electrical energy consumed by the loads exceeds the determined amount of electrical energy which the micro-grid is capable of providing, wherein if the amount of determined electrical energy consumed by the loads exceeds the determined amount of electrical energy which the micro-grid is capable of providing then the at least one processor is configured to control one or more controllable circuit breaker module/s to shed one or more load/s in accordance with the load priority list.

23. A system as claimed in any one of claims 20 to 22, wherein the at least one processor is configured to receive input from a user indicative of the load priority list or schedule such that said load priority list or schedule is user defined.

24. A system as claimed in either claim 20 or 21 , wherein the at least one processor is configured to : forecast total electrical energy expected to be generated by the one or more electrical energy generation sources and stored in the one or more power storing device/s at a future time, date, and/or period of time; forecast electrical energy consumption of the loads operatively connected to the micro-grid at the future time, date, and/or period of time; determine if one or more controllable circuit breaker modules should be operated to shed one or more load/s in accordance with the load priority list by comparing the forecasted total energy expected to be generated and the forecasted electrical energy consumption so as to preserve autonomy of the micro-grid to provide electrical energy to certain loads for a specific period of time.

25. A system as claimed in claim 24, wherein the specific period of time is two days.

26. A system as claimed in claim 22 or 24, wherein the at least one processor is configured to store excess electrical energy in one or more of suitable storage means selected from a group comprising batteries including bi-directional electrical vehicle batteries, and thermally.

27. A system as claimed in any one of claims 19 to 26, wherein the at least one processor is configured to: collect renewable energy data associated with a renewable source of energy used by the one or more electrical energy source/s to generate electrical energy from a suitable data source and/or from one or more suitable sensors; use the collected renewably energy data train a neural network to forecast or predict future renewable energy data at a future time, date, and/or period; use the trained neural network to forecast or predict future renewable energy data associated with the renewable source of energy at a future time, date, and/or period; and use the predicted or forecasted renewable energy data to determine electrical energy to be generated by the one or more electrical energy sources at the future time, date, and/or period, wherein the determined electrical energy to be generated is a forecasted or predicted electrical energy to be generated by the one or more electrical energy sources at the future time, date, and/or period.

28. A system as claimed in claim 27, wherein the at least one processor is configured to collect the renewable energy data and storing the same at periodic intervals during a day; and train the neural network with the collected renewable energy data at predetermined intervals.

29. A system as claimed in claim 27 or 28, wherein the at least one processor is configured to use the forecasted electrical energy to be generated at a future time, date, and/or period to determine an amount of electrical energy available at the future time and/or date and/or period to charge the rechargeable power storing device/s.

30. A system as claimed in any one of claims 19 to 29, wherein the one or more electrical energy source/s is/are configured to generate electrical energy from renewable energy source/s, wherein the one or more electrical energy source/s is/are selected from a group comprising photovoltaic cells, wind turbines, hydro-electric generators, and geo-thermal devices.

31. A system as claimed in claim 30 when dependent on any of claims 27 to 29, wherein the renewable energy data comprises one or more of weather data, fluid flow data, and geothermal data.

32. A system as claimed in any one of claims 27 to 29, wherein the at least one processor is configured to store the renewable energy data used to train the neural network, as well as a time and/or date stamp associated with the renewable energy data, in the at least one suitable memory device.

33. A system as claimed in claim 30, wherein the at least one processor is configured to: collect weather data associated with a geographical location of the micro-grid from a weather data source and/or from one or more suitable sensors operatively located adjacent the geographical location of the one or more electrical energy sources; use the collected weather data to train a neural network provided by the at least one processor to forecast or predict the weather data at the geographical location of the one or more electrical energy sources; use the trained neural network to forecast or predict the weather data at the geographical location of the one or more electrical energy sources; and use the predicted or forecasted weather data to determine the electrical energy to be generated by the one or more electrical energy sources at a future time, date, and/or period, wherein the determined electrical energy to be generated is a forecasted or predicted electrical energy to be generated by the one or more electrical energy sources.

34. A system as claimed in claim 33, wherein in the case of the electrical energy source being in the form of photovoltaic cell/s, the weather data comprises data indicative of sunlight in the geographical location of the photovoltaic cell/s, and wherein in the case of the electrical energy source being in the form of wind turbine/s, the weather data comprises data indicative of wind in the geographical location of the wind turbine/s.

35. A system as claimed in any one of claims 19 to 34, wherein the at least one processor is configured to: measure electrical energy consumption of the loads operatively connected to the micro-grid by way of the controllable circuit breaker module; use data indicative of the measured electrical energy consumption to train a neural network provided by the at least one processor to forecast or predict the electrical energy consumption of the loads operatively connected to the micro-grid at a future time, date, and/or period; and use the trained neural network to determine the electrical energy consumption of the loads operatively connected to the micro-grid at a future time, date, and/or period, wherein the determined electrical energy consumption is a forecasted or predicted electrical energy consumption.

36. A system as claimed in claim 35, wherein the at least one processor is configured to store the measured electrical energy consumption, or data indicative thereof, as well as a time and/or date stamp associated therewith in a suitable memory device, wherein the system is configured to determine the electrical energy to be consumed at a particular time and/or date, or period of time in the future.

37. A micro-grid system comprising: an energy management system as claimed in any one of claims 19 to 36; one or more electrical energy generation source/s; one or more rechargeable power storing device/s; and a plurality of controllable circuit breaker modules communicatively coupled to the energy management system, wherein each controllable circuit breaker module comprises: a processor; a communication module for facilitating communication with the energy management system; a sensing and measurement unit configured to measure at least the electrical energy consumed by a load; and a circuit breaker controllable in response to a suitable signal to break an electrical connection to a load.