NL2015490B1 - System for balancing an electricity network with a grid. - Google Patents

System for balancing an electricity network with a grid. Download PDF

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Publication number
NL2015490B1
NL2015490B1 NL2015490A NL2015490A NL2015490B1 NL 2015490 B1 NL2015490 B1 NL 2015490B1 NL 2015490 A NL2015490 A NL 2015490A NL 2015490 A NL2015490 A NL 2015490A NL 2015490 B1 NL2015490 B1 NL 2015490B1
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NL
Netherlands
Prior art keywords
grid
electricity
load
electricity grid
phase shift
Prior art date
Application number
NL2015490A
Other languages
Dutch (nl)
Inventor
Wouter Joghum Robers Egbert
Beijer David
Smit Wouter
Lucas Jorrit
Original Assignee
Peeeks B V
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Filing date
Publication date
Application filed by Peeeks B V filed Critical Peeeks B V
Priority to NL2015490A priority Critical patent/NL2015490B1/en
Priority to EP16767041.3A priority patent/EP3353872A1/en
Priority to PCT/NL2016/050605 priority patent/WO2017052362A1/en
Application granted granted Critical
Publication of NL2015490B1 publication Critical patent/NL2015490B1/en

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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/10—Energy trading, including energy flowing from end-user application to grid

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The present invention relates to a system for balancing an electricity network with a grid, at least one power source and multiple loads, the system comprising at least one load control unit, for determining a regular energy consumption of one of the power loads, determining a current state of said load, storing and/or communicating the determined values, switching the load on and off the network based on the determined state and a switching signal, at least one grid behavior model unit, for modelling grid behavior based on grid parameters and/or pricing parameters, storing a grid behavior model in a database, at least one central controller, having access to the database, for calculating a switching strategy for matching the power requested by the loads and the power available from the power source, based on the grid behavior model from the database, the current state of the at least one load and sending a switching signal to the load.

Description

System for balancing an electricity network with a grid
The present invention relates to a system for balancing an electricity network with a grid.
An electrical grid in itself cannot store energy. At any moment, the amount of electricity generated and put into the grid has to be equal to the amount of electricity drawn by energy consumers. If there is a shortage (the whole grid needs more electricity than what is generated) this can lead to a dropping of voltage in the grid, also called a brownout. Brown-outs can damage equipment connected to the grid. Also, the whole grid or parts thereof can experience black-outs: when this occurs the energy transport comes to a full stop and the grid has to be started up completely. If there is a surplus of electricity (i.e. more electricity is generated than what is consumed) this can lead to over voltage, which can also damage equipment connected to or part of the grid. Also these over voltages can result in tripping of safeguards, creating a black out.
Surpluses or shortages also have other effects on the grid besides the voltage drops or spikes. The spinning mass present in generators connected to the grid experiences a larger resistance, as the load on the grid is larger than was intended. This leads the generators to slow down, which in turns leads to a lower frequency in the grid, as the rotational speed of these generators is tied directly to the grid frequency. This poses an additional reason to keep the grid balanced: some equipment uses this grid frequency as a time keeping mechanism.
Keeping stability of the grid is not easy, and the complexity of this task is increasing. Not only is the amount of generators and consumers running in the millions, but many of the consumers are not under any form of control by the parties responsible for keeping the grid stable: any individual can at will switch on or off their light, water heater, computer or other appliance. On top of this, the amount of electricity consuming assets is ever increasing, and due to the prices of photovoltaic panels dropping, the amount of electricity generated out of control of utilities or grid operators is also increasing. Due to the increase in photovoltaic and wind generators driven by government incentives to promote clean energy generation the relative amount of controllable generation compared to controlled generation is increasing: wind and solar is strongly depending on weather circumstances.
Most countries have a national organization charged with the task to keep the national grid balanced, the national grid operator, sometimes also referred to as TSO which stands for Transmission System Operator. These grid operators use a multitude of ways to do this such as multilateral agreements with large energy consumers such as steel mills, rewards for large consumers that implement frequency-control or parties that are willing to run of their emergency power generators upon the request of the grid operator.
Another way to regulate this is by using an open imbalance market where real-time electricity prices keep the grid stable: in such a system energy consumers can choose to buy their electricity from a spot market where a price for electricity is determined multiple times per day, variations exist ranging from a price every minute to pricing per hour. This way energy consuming parties can be incentivized to schedule their consumption based on the actual state of the grid: a shortage of electricity will lead to high prices which will hopefully lead parties to postpone their electricity consuming processes, whereas surpluses will lead to low or even negative prices, leading to people receiving money for consuming electricity. If parties have processes that can be started quickly this should lead them to put these surpluses to good use.
These incentives lead to a number of companies buying their electricity straight from these spot markets. In order to get the best possible price for their electricity in order to make their processes as cheap as possible, the owners of electricity steering assets want to predict what will happen on the grid, since many assets require some time to start up or stop. Besides this prediction of the situation of the grid and prices of electricity, it is desirable to have a mechanism in place that automatically checks the process state and boundary parameters and decides to switch on or off certain assets. Having this automated will prevent human error, and allow the system to optimize faster, leading to a better match of the processes to the electricity prices, which in turn leads to the lowest costs of energy.
Currently predictions of the grid status are already being made by different parties. TSO’s, utilities, commodity-traders, large energy consumers such as steel mills already have a keen interest in predicting the grid behavior. This is done by a combination of signal processing, monitoring certain assets, weather models and behavioral models of consumers (e.g. on a hot summer day more air-conditioning will be used). All these methods have their drawbacks, as many of the information required to get accurate predictions is either not publicly available (state of certain assets in the grid), or has a low resolution (both in time and space, such as weather predictions),
It is known that in some cases net frequency is used to predict the imbalance on the system. However, a frequency is not a good quantity for a prediction, because it only shows an actual net status, but gives no indication of future behavior.
It is therefore an object of the present invention to provide a solution that provides better results in demand response, or at least to provide a useful alternative to the state of the art.
The invention thereto proposes a system for balancing an electricity network with a grid, at least one power source and multiple loads, the system comprising at least one load control unit, for determining a regular energy consumption of one of the power loads, determining a current state of said load, storing and/or communicating the determined values, switching the load on and off the network based on the determined state and a switching signal, at least one grid behavior model unit, for modelling grid behavior based on grid parameters and/or pricing parameters, storing a grid behavior model in a database, at least one central controller, having access to the database, for calculating a switching strategy for matching the power requested by the loads and the power available from the power source, based on the grid behavior model from the database, the current state of the at least one load and for sending a switching signal to the load. A switching strategy is to be understood here as a set of conditions and measures to be taken based on the conditions. In particular it determines which loads are to be switched on or off under what circumstances. A switching strategy may be optimized based on current pricing, expected future pricing and the current state of all assets in the network.
Optimization may in this case refer to a best match of supply and demand and/or a lowest price for a consumer.
The load control units are connected to an electricity consuming asset in such a way that the unit can switch the asset on or off, but also check the current state of the asset and whether it is safe to switch. Besides that the unit can also measure the energy consumption of the asset at regular intervals in order to measure the effect of switching. This whole set of information and feedback of regulating actions is passed on back to the central control unit.
The load control unit may further determine a regular energy consumption of one of the power loads and a current state of said load at regular intervals in order to measure or predict the effect of switching.
Preferably, the grid behavior model unit is coupled to at least one data gathering unit, for gathering current grid parameters such as frequency and phase shift, but also current pricing data, public usage data and environmental parameters such as weather data. This is combined in models with behavioral models of electricity consumption in the grid (such as time of year, day of week and time of day leading to certain usage patterns) to reach a prediction of energy consumption and resulting energy prices. This data will be forwarded to the central orchestration unit such that these predictions can be taken into account when determining the optimal switching strategy.
The phase shift measurement module in the system according to the invention is to improve on the predictions and speed compared to similar solutions known as the state of the art. Frequency measurement is being used, but this is a reaction on things that have happened already, so it does not provide any predictionary value. The phase shift measurement module measures the phase shift of the 50Hz signal in the grid. Extremely small changes in this signal can provide an indication of changes in the grid equilibrium and can be detected faster than the published prices provided by the TSOs, so can provide a significant advantage in a system that tries to capitalize on these imbalance markets.
This invention also covers an embodiment of this grid behavior model that is implemented through neural networks that combine the input data to a prediction. Also covered is an embodiment in which the neural network is improved by reinforcement learning where the realized prices are fed back into the system and compared to the prediction, which is then used to optimize the neural network model to improve future predictions.
The invention will now be elucidated with reference to the following figures. Herein:
Figure 1 shows a variation where the load control units are integrated within the existing control systems of the electricity consuming assets or software-only;
Figure 2 shows an embodiment wherein a meter has to measure the electrical installation;
Figure 3 shows an embodiment wherein a power using plant is coupled to two balance responsible parties;
Figure 4 shows a graph of the phase shift over a period of a year;
Figure 5 shows an embodiment of the system where a control system (3) is used to control several assets;
Figure 6 shows an embodiment of the method where information about one or more assets is communicated to a control system
Figure 7 shows an embodiment of the system where a control and/or metering device is coupled to the control system and a trading system or grid balancing system; and
Figure 8 shows an embodiment of a device that measures and determines the phase shift and actions from the measurements.
Figure 1 shows a variation where the load control units are integrated within the existing control systems of the electricity consuming assets or software-only. The system comprises a processing unit (3), connected to a controller (10) that controls an electric installation (5). The processing unit is also connected to other systems, such as energy trading systems (1) and/or grid balancing systems (2). The processing unit is connected to a database (31) of properties and/or states of the electrical installations and controllers. Also it is connected to a set of optimization rules, a database of (historic) behavior of the energy trading systems and/or grid balancing systems (33). Furthermore the processing unit is connected to a grid behavior model (34), which contains relations between measured parameters (such as phase shift of the grid) and future behavior of the grid. A particular implementation of this invention is for distributed computing. Cloud computing prices fluctuate but are very much coupled to demand and cost of computing. An owner of distributed computing hardware can choose to lower its prices when electricity prices go down. Therefore offering services at a lower price and cost than others. For this the platform can send price information to other servers that make decisions for their own price structure.
There are several markets where electric installation control can be monetized. A single electrical installation can be used for various markets. This requires the electrical installation to be split up into several virtual installations to be controlled as separate installations.
Figure 2 shows an embodiment wherein a meter 10 has to measure the electrical installation 5. This meter data is transmitted to the platform 3. The platform or the meter splits the measurements into several virtual installations that can be controlled separately. The virtual installations are communicated as single installations to servers of different markets 21, 22, 23. Control signals from the markets for the virtual installations are aggregated by the platform and sent to the electrical installation.
This is also possible where the electrical installation is a group of installations. Typically, the electrical installations are connected through a connection to a platform. In a particular embodiment, one or more electrical installations are connected to a suboptimizer, that optimizes the installation(s) and present them to the platform as an aggregated controllable system. The platform checks the availability of the electrical installation, but also the quality of the connection to the electrical installation.
Besides directly reacting to the platform’s instructions, the electrical installation receives pre-programmed control actions or strategies. This increases reliability of the system. If the connection to the platform fails, the electrical installation can resume functionality on its own. For example the day-ahead prices of electricity can be used to control the electrical installation in the case that the connection fails. Also weather data can be used to predict price fluctuations. A sensor that measures the phase and/or the frequency of the mains can be used to control the device, when the connection is not present. That sensor can give its measurements to the platform. The platform can then use multiple measurements to create a more accurate model of the state of the grid. Particularly when the grid interconnections are separated and the grid breaks into several stand-alone sub grids. The electrical installation buffers measurements and control actions to send to the platform when the connection resumes.
Based on the grid behavior model several strategies can be made for the automated energy trading. The trading strategies can be determined and optimized by back testing of strategies on historic data. The trading can be performed by a trained system, using historic data. For example a neural network, fuzzy logic or subspace identification. The frequency or the phase shift of the mains is used as a prediction of the imbalance market. With regards to the optimization of the grid, the system can measure electricity use of one or more of the electrical installations.
Figure 3 shows an example wherein a site (48) has an electrical connection and gets electricity from the grid (44) receiving electricity from BRP1 (41). However BRP2 (42) wants to control an electrical installation (46) on the site. The location/site of the electrical installation will usually have a grid connection. That grid connection may be in the portfolio of a certain Balance Responsible Party (BRP). These parties (usually electricity companies) are responsible for the balance between the electricity production and electricity use. However if the electrical installation is in the portfolio of another BRP. This means that the BRP may not be able to get the full potential out of controlling the electrical installation. Also this could result in a suboptimal balancing of the grid on a country level.
Therefore the platform can use the measurement data to make a payment happen from one BRP to another. This way the BRP of a site does not have to be the same BRP of the electrical installation on the site.
This figure shows an example wherein a site has an electrical connection and a meter (44) and gets electricity from BRP1. However BRP2 wants to control an electrical installation on the site. That is fine, however every steering action of the electrical installation is credited to BRP1. The proposed system corrects this fault by adding an additional meter (45) to the electrical installation on the site. This way the steering actions can be measured and a financial correction can be made between BRP1 and BRP2 by the control system (10). A special case of this is when there are profiled customers. Profiled customers are not measured by themselves. Usually this is done in large urban areas: Several households are combined, an estimate is made how many households get electricity from BRP1 and how many from BRP2 (and/or BRP3.. .4.. .etc). Then a ratio is agreed. The problem with this is that a steering action in a profiled customer credits not only the appropriate BRP, but also its competitors (according to the ratio). This prevents most BRPs to do this. The proposed system changes this by using the meter at a site to measure steering actions and to make financial corrections.
Figure 4 shows a graph of the phase shift over a period of a year. The horizontal axis shows the time over a year and the vertical axis shows the amount of seconds deviation. This shows that over a year’s period, the phase shift is steered towards a phase close to 0. But the deviation can be as big as 100 seconds. A structural deviation of the phase shift indicates a structural power shortage or surplus. Therefore a change in the phase shift reflects a shortage or a surplus and therefore predicts if grid stabilizing parties (e.g. TSO/DSO) or energy trading parties will start to contract electricity assets.
An embodiment uses the following rules: 1. If the phase shift is behind (meaning the average frequency has been low in the past) and there is another phase shift that puts the grid even more behind, then the grid has a shortage. The result will be that it is likely that grid balancing systems and energy trading systems will contract, deploy or buy more energy generation (or decrease use). 1. If the phase shift is behind (meaning the average frequency has been low in the past) and there is another phase shift that puts the grid less behind, then the grid is restoring. The result will be that it is likely that grid balancing systems and energy trading systems will do nothing (or less than in the previous situation). 1. If the phase shift is preceding (meaning the average frequency has been high in the past) and there is another phase shift that puts the grid even more in the lead, then the grid has a surplus. The result will be that it is likely that grid balancing systems and energy trading systems will contract, deploy or buy more energy use (or decrease generation). 1. If the phase shift is preceding (meaning the average frequency has been high in the past) and there is another phase shift that puts the grid less in the lead, then the grid is restoring. The result will be that it is likely that grid balancing systems and energy trading systems will do nothing (or less than in the previous situation).
Figure 5 shows an embodiment of the system where a control system 3 is used to control several ADR assets 5 through several regional or local service APIs/control systems 4. Control system 3 aggregates the assets to one or more services to grid balancing 2 systems and/or energy trading systems 1. The individual assets 5 may not be distinguishable by the grid balancing or trading systems 2, 1.
Figure 6 shows an embodiment of the method where information, such as state of the asset, about one or more assets 5 is communicated 8 to a control system, the availability (amount of power, duration of availability) of the group of assets is received by the control system 3. Control system 3 combines assets into groups that have most value for a certain trading systems 1 or grid balancing systems 2. The groups and their aggregated states are then communicated 6 with the trading systems 1 or grid balancing systems 2. Then the trading systems 1 or grid balancing systems 2 respond 7 what amount of power they want to deploy.
The control system 3 determines which assets need to change their power consumption of generation and communicates 9 that to the individual assets (or though sub controllers, not depicted).
Figure 7 shows an embodiment of the system where a control and/or metering device 10 is coupled to the control system 3 and a trading system 1 or grid balancing system 2.
The metering device is used to prove control actions. The metering data can be sent directly 11 to one or more trading systems 1 or grid balancing systems 2 or through the control system 6. The data can be aggregated and sorted to improve processability for the trading system 1 or grid balancing system 2.
Figure 8 shows an embodiment of a device that measures and determines the phase shift and actions from the measurements. The system is connected to one or more grids 86 through a grid connection 85. The coupling may be electric and/or magnetic or any type it can determine the grid frequency. One or more counters 84 count the cycles per second. This can be done by measuring the time between cycles or counting the number of cycles in a time period. For example a Schmitt-trigger can be used to turn the analog wave signal into a digital signal. From there the number of cycles per second can easily be determined. The system requires at least one particularly accurate clock 83. This at least one clock can, for example, be a tuned quartz clock, an atomic clock, a GPS receiver, a DCF77 receiver, an internet time server or alike. If the clock does not receive absolute time (such as GPS or DCF77) but needs to be set on time, this may be done in operation. A way to do this is to use the grid frequency as a guide. In some regions the grid frequency is increased by lOmHz in the phase shift is more than 20s behind and decreased if the phase shift is more than 20s leading. So a structural change in the grid frequency can be used to determine how much the grid is lagging. For example: A grid frequency of 50.01 Hz changes to 50Hz. This means that the grid is lagging by 20s. When the phase shift is measured this is communicated with a processing unit (82) and at least one grid behavior model. The model and/or processing unit may contain historic data about the phase shift.

Claims (9)

1. Systeem voor het balanceren van een elektrisch netwerk met een elektriciteitsnet, ten minste één energiebron en meerdere belastingen, het systeem omvattende: - ten minste één belastingsregeleenheid voor: • het bepalen van een regulier energieverbruik van één van de belastingen; • het bepalen van een huidige toestand van genoemde belasting; • het opslaan en/of communiceren van de bepaalde waarden; • het aan en uit het netwerk schakelen van de belasting op basis van de bepaalde toestand en een schakelsignaal; - ten minste één elektriciteitsnetgedragmodeleereenheid voor: • het modelleren van elektriciteitsnetgedrag op basis van elektriciteitsnetwerkparameters en/of prijsstellingsparameters; • het opslaan van een elektriciteitsnetgedragmodel in een database; - ten minste een centraal controlemechanisme dat toegang heeft tot de database voor: • het berekenen van een schakelstrategie voor het afstemmen van het door de belastingen gevraagd vermogen en het van de energiebron beschikbaar vermogen op basis van: het elektriciteitsnetgedragmodel van de database; de huidige toestand van de ten minste ene belasting; • het naar de belasting sturen van een schakelsignaal.A system for balancing an electrical network with an electricity network, at least one energy source and a plurality of loads, the system comprising: - at least one load control unit for: • determining a regular energy consumption of one of the loads; • determining a current state of said load; • storing and / or communicating the determined values; • switching the load on and off the network based on the determined state and a switching signal; - at least one electricity grid behavior modeling unit for: • modeling electricity grid behavior based on electricity grid parameters and / or pricing parameters; • storing an electricity grid behavior model in a database; - at least a central control mechanism that has access to the database for: • calculating a switching strategy for matching the power demanded by the taxes and the power available from the energy source on the basis of: the database's electricity network behavior model; the current state of the at least one load; • sending a switching signal to the load. 2. Systeem volgens conclusie 1, waarbij de elektriciteitsnetgedragmodeleereenheid aan ten minste één gegevensverzameleenheid is gekoppeld, voor het verzamen van: - huidige elektriciteitsnetparameters zoals frequentie en faseverschuiving; - huidige prijsstellingsgegevens; - openbare gebruiksgegevens en omgevingsparameters zoals weergegevens.System according to claim 1, wherein the electricity grid behavior modeling unit is coupled to at least one data collection unit, for gathering: - current electricity grid parameters such as frequency and phase shift; - current pricing data; - public usage data and environmental parameters such as weather data. 3. Systeem volgens conclusie 2, waarbij de gegevensverzameleenheid is ingericht voor het met gedragsmodellen van elektriciteitsgebruik in het elektriciteitsnet combineren van de verzamelde gegevens voor het komen tot een voorspelling van energieverbruik en resulterende energieprijzen.The system of claim 2, wherein the data collection unit is adapted to combine the collected data with behavioral models of electricity consumption in the electricity grid to arrive at a prediction of energy consumption and resulting energy prices. 4. Systeem volgens één van de voorgaande conclusies, waarbij het elektriciteitsnetgedragmodel voor het bepalen of een overschot of een tekort in het elektriciteitsnet aanwezig is, gebruik maakt van een faseverschuiving van het elektriciteitsnet.A system according to any one of the preceding claims, wherein the electricity grid behavior model for determining whether a surplus or deficit is present in the electricity grid makes use of a phase shift of the electricity grid. 5. Systeem volgens één van de voorgaande conclusies, waarbij de belastingsregeleenheid - een regulier energieverbruik van één van de belastingen bepaald; - een huidige toestand van genoemde belasting bepaald; - op gezette intervallen om het effect van schakelen te meten.A system according to any one of the preceding claims, wherein the load control unit - determines a regular energy consumption of one of the loads; - a current state of said load is determined; - at set intervals to measure the effect of switching. 6. Systeem volgens één van de voorgaande conclusies, waarbij de elektriciteitsnetgedragmodeleereenheid ten minste één neuraal netwerk omvat dat de elektriciteitsnetparameters en/of de prijsstellingsparameters combineert met een voorspelling voor het elektriciteitsnetgedragmodel.A system according to any one of the preceding claims, wherein the electricity network behavior modeling unit comprises at least one neural network that combines the electricity network parameters and / or the pricing parameters with a prediction for the electricity network behavior model. 7. Systeem volgens conclusie 5, waarbij het neurale netwerk wordt verbeterd door reinforcement-leren waarbij verzamelde elektriciteitsnetparameters en/of prijsstellingsparameters worden teruggevoerd in het systeem en worden vergelijken met de voorspelling, die dan wordt gebruikt voor het optimaliseren van het neurale netwerkmodel ter verbetering van toekomstige voorspellingen.The system of claim 5, wherein the neural network is enhanced by reinforcement learning wherein collected electricity network parameters and / or pricing parameters are fed back into the system and compared with the prediction, which is then used to optimize the neural network model to improve neural network model. future forecasts. 8. Werkwijze voor het bepalen van de toestand van een elektriciteitsnetbalans, omvattende de stappen van: a. het bepalen van een faseverschuiving over een langere tijdsperiode van bijvoorbeeld een jaar; b. het bepalen van een faseverschuiving over een kortere tijdsperiode van bijvoorbeeld een uur; c. het vergelijken van twee faseverschuivingen en bepalen van de toestand van het elektriciteitsnet op basis van een opgeslagen elektriciteitsnetgedragmodel dat een faseverschuiving in verband brengt met een toestand van een elektriciteitsnet; d. het schakelen van elektrische belastingen of opwekking volgens de bepaalde toestand van het elektriciteitsnet.A method for determining the state of an electricity grid balance, comprising the steps of: a. Determining a phase shift over a longer period of time of, for example, a year; b. determining a phase shift over a shorter period of time of, for example, one hour; c. comparing two phase shifts and determining the state of the electricity grid based on a stored electricity grid behavior model that relates a phase shift to a state of an electricity grid; d. switching electrical loads or generation according to the determined state of the electricity grid. 9. Werkwijze volgens conclusie 8, waarbij het volgende plaatsvindt: a. Als de faseverschuiving over genoemde langere periode achterloopt en de faseverandering over de kortere periode achterloopt, schakel dan elektrische belasting uit en/of opwekking aan. b. Als de faseverschuiving over genoemde langere periode voorloopt en de faseverschuiving over de kortere periode voorloopt, schakel dan elektrische belasting aan en/of opwekking uit.The method of claim 8, wherein the following occurs: a. If the phase shift lags behind said longer period and the phase change lags behind the shorter period, turn off electrical load and / or power on. b. If the phase shift takes place over the longer period mentioned and the phase shift takes place over the shorter period, switch off electrical load on and / or generate.
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