WO2021195796A1 - Système et procédé de commande et de gestion de demande énergétique dans des systèmes électriques - Google Patents

Système et procédé de commande et de gestion de demande énergétique dans des systèmes électriques Download PDF

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Publication number
WO2021195796A1
WO2021195796A1 PCT/CL2021/050017 CL2021050017W WO2021195796A1 WO 2021195796 A1 WO2021195796 A1 WO 2021195796A1 CL 2021050017 W CL2021050017 W CL 2021050017W WO 2021195796 A1 WO2021195796 A1 WO 2021195796A1
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Prior art keywords
demand
information
electrical loads
unit
energy
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PCT/CL2021/050017
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English (en)
Spanish (es)
Inventor
Matías Alejandro NEGRETE PINCETIC
Daniel Eduardo OLIVARES QUERO
Alvaro Hugo LORCA GÁLVEZ
Marcelo Alejandro SALGADO BRAVO
Aldo Maximiliano SAAVEDRA ADASME
Rafael Ignacio RODRIGUEZ ARAYA
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Pontificia Universidad Catolica De Chile
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Publication of WO2021195796A1 publication Critical patent/WO2021195796A1/fr

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    • 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
    • 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
    • H02J4/00—Circuit arrangements for mains or distribution networks not specified as ac or dc

Definitions

  • the invention relates to a system and a method for controlling and managing energy demand in electrical systems.
  • the invention refers to a process of information exchange, coordination and control of the distributed demand in different industries, offices or residential homes, according to the different signals that a demand aggregation unit, or aggregator of demand, delivery to each of the participants of an electrical system.
  • the invention incorporates information, communication and automation technologies, with advanced monitoring, control, optimization and autonomous operation capabilities. This is mainly aimed at reducing variability and increasing the efficiency of control and management of energy demand in electrical systems.
  • the invention proposes a solution in which the aggregation of the demand contemplates the exchange of information on the flexibility of the clients and the electrical system.
  • Said flexibility is represented by a band of demand trajectories, where the aggregator is capable of delivering information on the flexibility required by the electricity system through the width of the demand band generated by the band of demand trajectories delivered to the customer. If the size of the demand band is wide, the aggregator provides flexibility to the customer, giving freedom in the energy demanded by the customer at a higher cost. Whereas, if the demand band is narrow, the aggregator requires flexibility from the client, thus accessing, for example, lower costs in its energy consumption.
  • the aggregation unit can deliver offers of displacement and reduction of demand and of the demand band, with which the client will be able to access better conditions, economic or otherwise, for the same energy consumed. .
  • the solution in WO2018207226A1 seeks to quantify influence factors that are monitored to stay between certain preset values, making the distribution of the power adjustment request based on said monitoring. Therefore, the solution in WO2018207226A1 does not reflect the flexibility involved in the system, since it does not generate an energy resource management that reflects the existing flexibility in both energy supply and energy demand.
  • the publication WO2019243524A1 of MOIXA ENERGY HOLDINGS LIMITED proposes systems to optimize and manage the flexibility of multiple local assets of distributed energy storage resources.
  • the solution in WO2019243524A1 focuses on a problem where the loads are mainly batteries, implications associated with the energy billing / pricing, smart contracts, and implements machine learning techniques to recognize patterns of demand behavior, to coordinate how flexibility in energy resources can be programmed, shared or managed.
  • there is no relevant processing capacity at the local level preventing a proper identification of the load flexibility existing in an energy system and making it difficult to manage energy demand based on said flexibility.
  • the proposed solution focuses on energy storage loads, whose flexibility management is simple compared to other types of loads.
  • the invention seeks to design and implement the technological infrastructure necessary for the management and coordination of flexible electrical consumptions. These consumptions are characterized by presenting a certain slack in the way of consuming electricity over time, without impacting the quality of the service obtained.
  • An example of flexible consumption can be a pump associated with a pool filter that needs to run 3 hours a day to fulfill the service of keeping the pool clean. Said service will be fulfilled with the same effectiveness by consuming electricity consecutively or by distributing the hours of electricity consumption throughout the day in any other way.
  • Other examples of flexible electrical consumptions include, at the industrial level, industrial water pumps, desalination plants, aluminum manufacturing, and at the residential level, boilers, refrigeration and charging of electric vehicles, among others.
  • the problem to be solved by the invention is to extract the flexibility from different loads in the network by means of the development of an appropriate technological infrastructure and suitable control models, enabling the decentralized management of different types of loads. and delivering a coordinated response, satisfying the flexibility requirements of the system, without impacting the service obtained by customers.
  • the invention relates to a system and a method for controlling and managing energy demand in electrical systems.
  • the energy demand management and control system in electrical systems comprises: one or more demand aggregation units arranged in communication with an electrical network of an electrical system, wherein each demand aggregation unit comprises at least one central server, and wherein each demand aggregation unit has associated an amount of energy available for consumption by users of the electrical system; one or more pluralities of local coordination units, wherein each plurality of local coordination units is arranged in communication with at least one corresponding demand aggregation unit, and wherein each local coordination unit comprises at least one local server; one or more pluralities of terminal nodes (NT), wherein each plurality of terminal nodes is arranged in communication with at least one corresponding local coordination unit, wherein each terminal node has the ability to monitor and / or control the operation of loads electrical connected to it; one or more controllable or flexible electrical loads, wherein each controllable electrical load is arranged in communication with at least one corresponding terminal node, and where each controllable electrical load has associated information about its demand flexibility; and one or more
  • each local coordination unit is configured to: collect information from the controllable electrical loads to quantify the energy demand of controllable electrical loads; and collect information on non-controllable electrical loads to model the energy demand of non-controllable electrical loads;
  • each local coordination unit is configured to generate information on the energy requirement of the electrical loads.
  • Said information on the energy requirement comprises: information on the available controllable electrical loads; information on the consumption of non-controllable electrical loads per unit of time, said information on the consumption of non-controllable electrical loads, comprising consumption data from the previous day and / or prediction data for non-controllable demand, corresponding to the operation of non-controllable electrical charges during a prediction time period; a value of the total energy required by the controllable electrical loads during a predetermined period of time; the maximum feasible power consumption of the controllable electrical loads per unit of time during a predetermined period of time; and information on specific electrical loads;
  • each demand aggregation unit is configured to: receive the information on the energy requirement from each local coordination unit; Based on the information on the energy requirement, generate a band of demand trajectories for a first period of time of operation, for each local coordination unit, where the band of demand trajectories generated by each demand aggregation unit comprises a trajectory of minimum demand and a trajectory of maximum demand for a period of time of operation, where said trajectories of minimum and maximum demand define a demand band for each local coordination unit; and send said band of demand trajectories to each local coordination unit, where said band of demand trajectories is capable of meeting the energy requirement of the electrical loads and, simultaneously, minimizing deviations of the energy demand with respect to the quantity energy available for each unit of demand aggregation.
  • each local coordination unit is configured to schedule the operation of the available controllable electrical loads, adjusting the energy consumption of said controllable electrical loads to the received demand path band. Furthermore, each local coordination unit is configured to calculate energy demand deviation information with respect to the received demand path band and to send said demand deviation information to the corresponding demand aggregation unit, both during the scheduling of the operation of the available controllable electrical loads as during the monitoring of the operation of scheduled electrical loads.
  • each demand aggregation unit is configured to: analyze the demand deviation information from the plurality corresponding local coordination units; assess whether the amount of available energy is sufficient to meet the energy demand; and based on said evaluation, each demand aggregation unit is configured for one or more of: o determine the need to have additional energy from the electricity grid, or determine the option of delivering additional energy to the electricity grid, and or generate a new band of demand trajectories for a second period of operation time following the first period of operation time, for one or more of the corresponding local coordination units to be determined based on the information on the energy requirement.
  • the method of control and management of energy demand in electrical systems comprises the steps of: a) monitor electrical loads by each terminal node so that each local coordination unit: i. collects information from controllable electrical loads to model the power demand of controllable electrical loads; ii. collects information from non-controllable electrical loads to model the energy demand of non-controllable electrical loads; b) generate, by each local coordination unit and by modeling the energy demand of electrical loads, information on the energy requirement of electrical loads, where such information on the energy requirement includes: i. information on available controllable electrical loads; ii.
  • information on the consumption of non-controllable electrical loads per unit of time comprising consumption data from the previous day and / or prediction data for non-controllable demand, corresponding to the operation of non-controllable electrical charges during a prediction time period; iii. a value of the total energy required by the controllable electrical loads during a predetermined period of time; iv. the maximum feasible power consumption of the controllable electrical loads per unit of time during a predetermined period of time; e v.
  • each demand aggregation unit calculates energy demand deviation information from the received demand path band; and ii. sends said demand deviation information to the corresponding demand aggregation unit, both during the scheduling of the operation of the available controllable electrical loads and during the monitoring of the operation of the scheduled electrical loads; and g) based on said demand deviation information received from each local coordination unit, the method further comprises that each demand aggregation unit: i. analyze the demand deviation information from the plurality of corresponding local coordination units; ii. assess whether the amount of available energy is sufficient to meet the energy demand; and iii.
  • each demand aggregation unit is configured to identify the latent demand flexibility of each local coordination unit. Said identification of latent flexibility occurs based on the energy requirement information and based on the demand deviation information received from each local coordination unit.
  • Other alternative modalities of the invention comprise that two or more local coordination units, belonging to the one or more pluralities of local coordination units of the preferred modality, are grouped according to the behavior of the energy demand of the electrical loads associated. The grouping of the two or more local coordination units is considered independently by the corresponding demand aggregation unit.
  • Other alternative embodiments of the invention comprise that each demand aggregation unit is configured to characterize the energy demand of the plurality of corresponding local coordination units. Said characterization of the energy demand occurs based on the information of the energy requirement. The characterization of energy demand is in an aggregate form and is called aggregate demand information.
  • each local coordination unit is configured to: minimize the deviations of the energy demand with respect to the band of demand trajectories received from the corresponding demand aggregation unit; and in case of demand deviations, communicate them to the corresponding demand aggregation unit.
  • the information of the controllable electrical loads, collected by each local coordination unit comprises one or a combination of two or more of: quantity of controllable loads; total operating time of each electrical load for 24 hours; information on the type of controllable load, for example, whether the electrical load is interruptible or uninterruptible; window of availability of each electrical load; estimated consumption of each electrical load every 1 hour; and estimated consumption of each electrical load during 24 hours.
  • the information on the controllable electrical loads, collected by each local coordinating unit comprises one or a combination of two or more of: average operating temperature of the electrical load ; acceptable temperature range of an enclosure where the electrical load operates; target room temperature for a given period of time; and window of unavailability.
  • each local coordination unit collects historical consumption information, and that, to schedule the operation of the controllable electrical loads available, each local coordination unit combines the band of received demand paths with one or more of: a prediction of the ambient temperature during the prediction time period, in the event of associated thermal electrical loads; information on the thermal properties of an enclosure, in the event of associated thermal electrical loads; and information on the energy requirement of electrical loads.
  • each local coordination unit can be configured to, based on the band of received demand trajectories, determine operation points per unit of time for each of the controllable electrical loads, during a first scheduling period of time.
  • the operating points can be determined by the hour
  • the first scheduling period of time can correspond to the next 24 hours
  • the determination of the operating points can also include meteorological information for the scheduling period of time.
  • each local coordination unit is also configured to evaluate the total energy demand of the scheduling during a second period of time of lower scheduling. duration to the first scheduling period of time, recalculating the energy demand and updating the scheduling of the controllable electrical loads.
  • each local coordination unit is configured to: communicate the information of the controllable electrical loads available to the corresponding demand aggregation unit, wherein said communication is executed each time a change in controllable electrical loads connected to the at least one corresponding terminal node; communicate the information on the consumption of the non-controllable electrical loads to the corresponding demand aggregation unit every 1 hour, said consumption information comprising prediction data of the non-controllable demand which in turn comprise a path of the non-controllable demand.
  • controllable for the next 24 hours, with hourly resolution communicate, at least once every 24 hours, the value of the total energy required by the controllable electrical loads during the next 24 hours; communicate, at least once every 24 hours, the maximum feasible consumption power of the controllable electrical loads, said maximum feasible consumption power comprising a trajectory of the maximum feasible power consumption during the next 24 hours, with hourly resolution; each time a band of demand trajectories is received from the corresponding demand aggregation unit, acquire a prediction of ambient temperature and global solar radiation for the next 24 hours, with hourly resolution, from a meteorological server on an internet network or from the corresponding demand aggregation unit; and schedule the operation of the controllable electrical loads each time a band of demand trajectories is received from the corresponding demand aggregation unit
  • an alternative embodiment of the invention contemplates that the minimum and maximum demand trajectories generated by each demand aggregation unit are for a period of operation time corresponding to the next 24 hours.
  • the band of demand trajectories is generated at least once every 24 hours, preferably every 1 or 6 hours.
  • demand deviations occur when the energy demand is outside the demand band.
  • Free customers Possibility of obtaining better energy prices to satisfy your needs and the ability to respond to flexibility requirements of the system with a reward in its costs.
  • Generators Ability to coordinate generation with demand, reducing the cycling of generators, reducing generation and reviewing in real time the fulfillment of contractual contracts with some of its clients.
  • the distributor can keep control of the loads of its clients so that it is able to respond to contingencies in the lines or monitor energy consumption in real time.
  • Fig. 1 shows a diagram of the centralized and hierarchical structure of the proposed network architecture according to an embodiment of the invention.
  • Fig. 2 shows a diagram of the interactions between the different elements that make up the architecture of a demand aggregation unit according to one embodiment of the invention.
  • Fig. 3 shows a diagram with the information flows and processes carried out by the local coordination unit.
  • Fig. 4 shows an example of the aggregate demand path band according to one embodiment of the invention.
  • Fig. 5a and Fig. 5b show an example of proposed demand path bands and proposed load scheduling, respectively, in accordance with one embodiment of the invention.
  • the demand response service is provided by the operator of the flexible loads, or aggregation unit or aggregator, to the electrical system.
  • the aggregation unit has various loads at its disposal, geographically distributed, and which evolve over time according to the needs of consumers. At a certain point in time, the aggregation unit, according to the requests it receives from the electrical system operator, must determine the level of available load. It should be noted that the distributed nature of the loads imposes a challenge for the service infrastructure, since to make a joint decision it is necessary to: i) centralize the network diagnostic information, ii) process it, iii) generate a response and, finally , iv) translate this response into concrete actions for each of the charges at your disposal.
  • a centralized and hierarchical structure is the one that best adjusts to the service requirements and is under which the solution of the invention is implemented, as shown in Fig. 1.
  • the structure of the service is made up of three levels: i) actuators and sensors housed in a device called a terminal node, ii) local coordination units comprising local servers and iii) the aggregation unit comprising a central server. According to one embodiment of the invention, for each of these levels some of the design assumptions and specific requirements are:
  • the loads associated with a consumer are limited in an enclosure.
  • the enclosure is considered an indoor environment and of a certain local scope, for example, from 10 to 100 meters.
  • the Terminal Nodes are adjacent to the loads and inside the enclosure.
  • the loads are duly compensated with a power factor equal to one. and.
  • Each enclosure has a local server inside.
  • the enclosure has an internet connection or has cellular coverage.
  • Terminal Nodes In Terminal Nodes that manage thermal loads, there is information on the temperature of the environment in which it operates. g. The Terminal Nodes are designed in a modular way, which allows the incorporation of new sensors and actuators as the enclosure evolves. h. Each Local Server establishes a bidirectional communication with each Terminal Node of the network. i. Each Local Server is capable of serving multiple Terminal Nodes, at least 10 Terminal Nodes. j. The Terminal Node-Local Server Network is capable of connecting to the Local Server with multiple Terminal Nodes, at least with 10 Terminal Nodes. k. The Local Server connects to the internet. l. The internet connection solution is modular. m. The data transmission time is reduced, for example, less than one second. n. The Central Server can simultaneously handle more than a million connections with Local Servers.
  • the elements of the architecture of each Terminal Node are divided into those associated with the equipment to be controlled and those associated with the operation of the Terminal Node.
  • the elements associated with the equipment to be controlled can be divided, in turn, into two groups: sensors and actuators.
  • the group of sensors may have the following elements: i) current sensor, ii) voltage sensor, iii) controller for human-machine interface of the equipment to be controlled, and iv) specific sensors for the load in question.
  • the actuator group may have the following elements: i) on / off type actuators and ii) on / off type actuators controllers.
  • each Terminal Node has a modular operating architecture, which allows its flexibility to be maximized by incorporating various components as the load system connected to it evolves.
  • each Local Server is based mainly on an embedded computer with a processing capacity greater than that of the Terminal Nodes, which houses the models of local optimization of the demand supply service.
  • each Local Server has a wireless communications transceiver module, for example, WiFi, configured as an access point, to connect with the Terminal Nodes.
  • a wired communications transceiver module for example, Ethernet
  • a second wireless communications transceiver module to establish communication with the Central Server.
  • the Local Server feeds its electronics through a power source, using an AC / DC converter if necessary.
  • each Local Server must have the ability to communicate locally with the Terminal Nodes and remotely with the Central Server.
  • local communication occurs through a first wireless interface, which is implemented with a protocol that maintains topics with the sensor data with retention, that is, it keeps in memory the last correctly received data. Then, the information received is processed by an intermediate layer, called Local Communications Middleware. Local communication also works in the opposite direction, that is, from the Local Server to each Terminal Node.
  • remote communication occurs through a wired interface, for example Ethernet, or a second wireless interface.
  • the latter is commanded through a microcontroller configured as a gateway, which is commanded through a serial interface.
  • the communication protocol implemented offers a bidirectional exchange of information between the Central Server and the Local Server.
  • Remote communication like local communication, operates bi-directionally.
  • the architecture of the Central Server is based on a protocol that allows the reading, writing and exchange of information from and to the Local Servers. Additionally, the architecture includes: i) a database to store the historical information of the system and ii) a data application layer with a data interface to serve a model application layer, a frontend and a backend.
  • the model application layer executes the global optimization models of the system, the frontend allows users to access diagnostic information about their consumption and the backend allows an external connection with the application layer and with the database.
  • the loads can provide different types of flexibility.
  • movable loads which, to provide their service, only depend on a certain number of hours of daily operation, regardless of whether they are continuous or not, or at what time of day they operate. Examples of this type of loads are found in irrigation systems or in hydraulic pumps for swimming pools, pools or ponds.
  • time multiplexing flexibility is available in all equipment that operates in duty cycles, that is, in each operating cycle the load is active for a portion of the time and inactive for the rest of the cycle.
  • each load alone does not represent a source of flexibility for the system.
  • the start time of the work cycles of this type of loads it is possible to control the added instantaneous power that they demand from the network.
  • sensors and controllers are contemplated for: (i) air conditioning equipment, (ii) refrigeration equipment, (iii) hydraulic motors, (iv) lighting fixtures and (v) lighting equipment. battery charging.
  • air conditioning equipment ii
  • refrigeration equipment iii
  • hydraulic motors iv
  • lighting fixtures iv
  • lighting equipment iv
  • battery charging iv
  • the proposed system due to its modularity, can be adapted to operate with other types of loads.
  • a distributed demand management, aggregation and scheduling system has 4 levels:
  • Demand aggregation generation of demand trajectories bands for each participant of the aggregation unit and the implementation of the network requirements. It can be called as the actions to be taken and demand management of the aggregation unit.
  • FIG. 2 shows the interactions between each of these elements of the architecture of the proposed solution, the information that flows between said elements and their respective tasks.
  • Each of the levels implies an interaction with the demand aggregation unit, both at the energy management level and the economic interactions with the local coordination units and the electrical system network (grid), where the unit Aggregation is configured to ensure compliance with contracts, and generate flexible and uncontrollable demand modeling, with which you can define different flexibility services that you can later deliver to the network.
  • the aggregation unit complies with the review of different contracts and demand offers.
  • the interaction between the aggregation unit and each local coordination unit is defined by the band of demand trajectories that the aggregation unit delivers as a suggestion of electricity consumption with which the local coordination units propose a schedule of flexible loads, in such a way as to reduce deviations with respect to the demand band defined by said demand path band.
  • the band of demand trajectories is made up of a series of hourly demand blocks that are capable of satisfying the energy requirements of each local coordination unit and that can have different patterns, according to the indications of the aggregation unit. to adapt to both specific demand generation profiles and demand release requirements.
  • the size of the delivered demand block delimits both the flexibility available from the aggregation unit and that required from each local coordination unit. For example, if the aggregation unit delivers a large demand block, it implies that the aggregation unit can take care of the variations in demand from the corresponding local coordination unit, while if the demand block sent is very limited, it means that the aggregation unit requires the flexibility of the local coordination unit in order to reach a specific energy consumption, so deviations from the block will be penalized since these will affect the operation of the aggregation unit. It should be noted that this attribute of flexibility delivery can be valued in proportion to the width of the demand band, where a wide demand band has a higher cost than a limited one.
  • the aggregation unit has 3 main tasks (indicated in Fig. 2), which are:
  • a model of an aggregation unit is built capable of capturing the latent flexibility and at the same time, meeting the demand needs of each of the local coordination units, evaluating models of coordination of flexible loads and considering a new objective function with the ability to maximize the latent flexibility in the local coordination units or minimize the costs of the energy demanded by the local coordination units.
  • models such as time series, Gaussian processes, Markov models and neural networks to model flexibility, uncontrollable demand and the ability of each local coordination unit to respect the band of trajectories generated by the unit of aggregation, in order to improve the models of generation of bands of feasible trajectories for the local coordination units.
  • the local coordination units must be able to respond to the requirements of both the aggregation unit and the rules established by the different smart contracts, in order to follow the trajectory band sent by the aggregation unit.
  • the proposed models include models capable of scheduling the operation of the loads of the local coordination unit from the previous day and models that evaluate the state of the local coordination unit in real time, for the scheduling and monitoring of demand, in What can be described as a 2-stage model, where the objective of the first stage is to minimize deviations in demand, while the objective of the second is to minimize peaks and demand above the expected value. so that the consumption of each local coordination unit is as close to that proposed by the first stage of the model. It is possible to integrate additional restrictions according to the requirements of smart contracts.
  • the proposed architecture is limited to four parts that interact with each other: aggregation unit, local coordination units, smart contracts and finally communication mechanisms. Some of these parts are detailed below in attention to the solution proposed in the present invention.
  • the objective of the aggregation unit is to deliver to each of the participants (local coordination units) bands of demand trajectories that are capable of meet the energy requirements while minimizing the acquisition of additional energy to the hourly energy acquisition contracts that the aggregation unit has with different generators or market agents, energy that does not involve any additional cost for the daily operation of the aggregation unit.
  • the aggregation unit has said contracts, that is, it has a pre-established amount of available energy.
  • the aggregation unit for the generation of the demand trajectories that make up the demand band for each unit of local coordination, the aggregation unit requires at least the following information to be able to apply its optimization models:
  • the aggregation unit has all the information of each of the loads, this implies that it is possible to deliver a more accurate and easy-to-follow band of demand trajectories, but to receive and generate models in a specific way. for each of the loads, it increases the computational complexity of the models and restricts the scalability of the aggregation unit to such an extent that it would not be able to deliver a feasible result, generating problems for both the local coordination units, when receiving bands of demand trajectories that are not able to follow, and to the same aggregation unit, which must acquire additional energy or recalculate the scheduling of the more flexible local coordination units in order to stay within the total aggregate demand (and the problem still persists of high computational complexity).
  • the present challenge in the design of the aggregation unit problem is to find the balance between the minimum level of information necessary for each of the local coordination units, which at the same time allows them to generate feasible demand trajectories and extract the latent flexibility in each one of them.
  • a first cluster or group includes different households with increases in consumption at dawn and dusk
  • a second cluster or group includes offices with high consumption at noon, complementing both consumptions and presenting different types of response to demand requirements.
  • Another task of the aggregation unit is to characterize the demand of the local coordination units in an aggregate way. Initially, the aggregation unit receives predictions of uncontrollable demand from each of the local coordination units, even so, the prediction independently presents a higher percentage of error than the aggregate prediction of the local coordinating units. To do this, it is possible to use artificial neural network (ANN) models or Gaussian processes to predict total consumption and uncontrollable demand, working together with clusters of local coordination units, where there may be different models for each cluster that Capture the specific properties exhibited by the local coordinating units.
  • ANN artificial neural network
  • the aggregation unit is responsible for formulating and evaluating the smart contracts made with each of the local coordination units, defining the interactions between the different local coordination units and the aggregation unit itself, through a bounded set of information.
  • the structure and properties of the local coordination unit seeks to replicate the functions of a Home Management Energy System (HEMS), performing tasks of evaluation, monitoring and scheduling of flexible loads under the approach of minimizing the deviations of the trajectory band demand sent by the aggregation unit.
  • HEMS Home Management Energy System
  • the local coordination unit it is necessary for the local coordination unit to be able to collect information on the loads and how they can provide flexibility to the system, while through monitoring it is able to build models for the prediction of the demand of the loads.
  • uncontrollable loads which is hampered by the erratic actions of the inhabitants.
  • the purpose of modeling controllable loads and non-controllable demand is to delimit and simplify the information that the local coordination unit reports to the aggregation unit, so that it can generate the demand path bands.
  • Load availability window (process start and end time).
  • controllable loads that can be considered as tasks, in addition to other loads that can be modeled as batteries, such as pumps and electric vehicles.
  • thermal loads require additional characteristics for their modeling. These can be: Average operating temperature.
  • the simplification of these parameters according to the type of load allows the aggregation unit to reduce the computational complexity of the control model to be proposed, so that the local coordination units do not inform the aggregation unit about certain types of charges individually, grouping them into a small set of parameters corresponding to the required demand and the power to be consumed per hour.
  • the local coordination unit Based on the information of each of the loads plus the historical information on household consumption obtained, for example, by monitoring methods, the local coordination unit has the task of generating demand prediction curves both in the short term as for a full day. It is possible that this prediction does not present a high level of precision at the hourly and sub-hourly level due to the difficulty of modeling the behavior of the inhabitants of the home, but if it should model in a good way the total daily energy consumption in each home. .
  • the relevance of this process is to model the load scheduling based on the uncontrollable demand estimate in order not to exceed the maximum demand limits due to actions of the local coordination unit, together with delivering a prediction of Hourly and daily demand to the aggregation unit to determine the demand trajectory band of the local coordination unit for the next 24 hours.
  • the local coordination unit upon receiving a new band of demand trajectories from the aggregation unit, must be able to schedule the available loads in order to minimize demand deviations. According to one modality, this process can occur at intervals of at least 1 hour up to once every 24 hours, or in response to some requirement of the aggregation unit.
  • the aggregation unit requires the following information, according to the properties of the model and the characterization of each of the loads:
  • the local coordination unit is responsible for reporting any deviation in demand to the aggregation unit so that the aggregation unit can collect the deviations from the set of local units. of coordination and analyze whether the acquisition of additional energy or the re-scheduling of a set of local coordination units is necessary.
  • each local coordination unit solves two problems with different analysis times, in order to reduce the computational cost of the scheduling and monitoring of the operation. of loads throughout the day. These problems are:
  • Schedule Problem Scheduling of available loads according to the trajectory bands available for the local coordination unit and the meteorological information for the next 24 hours, with a time interval of 1 hour per block. Operation points are determined that each of the loads will fulfill throughout the day.
  • Sub-schedule problem The scheduling information of the loads of the schedule problem is extracted in order to implement the results and evaluate the total demand in the local coordination unit in short time intervals, with an evaluation horizon of 2 hours.
  • the objective is to maintain the demand proposed by the hourly problem and minimize the consumption peaks that can occur due to the scheduling of multiple loads in the same interval. It should be noted that only scheduling a limited set of loads reduces the complexity of the model.
  • the main reason for the division of the problem is the computational power available.
  • the division of the control model into stages allows to effectively manage the scheduling of the loads and the monitoring of the demand with an adequate temporal resolution. Otherwise, a model with the same capabilities as the one exposed would generate a problem that would not be able to be evaluated unless high-power processors or operation heuristics were used that would limit the optimality of the problem.
  • the local coordination unit is able to recalculate the demand and make changes in the scheduling at each time step, so the local coordination unit is able to respond to changes in demand or requests to reduce consumption in a short period of time, ensuring that each of the loads operates during the stipulated time and respecting the comfort restrictions. Therefore, the sub-schedule problem extends the capacities of the local coordination unit by being able to validate the proposed schedules through demand and make predictions of the total demand for the next hour, information that can be used to improve the scheduling models and participating in demand response markets of 15 minutes or one hour.
  • Fig. 3 concisely describes the different processes of the local coordination unit based on the hourly and sub-hourly model, in addition to the information that the local coordination unit needs from the aggregation unit to be able to implement your control models.
  • the communication mechanisms describe the interaction between local coordination units and the aggregation unit, considering the economic and operation rules described in smart contracts.
  • the information exchanged by the local coordination units and the aggregation unit is listed below: a.
  • Flexible loads the local coordinating unit, based on the information available, determines the amount and type of loads available to control.
  • the information on flexible loads is reported to the aggregation unit whenever there is a change in the list of available loads, such as the change of an air conditioner or the acquisition of a new electric vehicle.
  • the information reported corresponds to a list of parameters for each type of load under the domain of the local coordination unit.
  • the parameters include, but are not limited to: i) a time window of operation and ii) maximum power consumption.
  • Uncontrollable demand the local coordination unit generates and reports a prediction of the uncontrollable demand every 1 hour, in order to facilitate the scheduling of loads to the aggregation unit.
  • the prediction consists of a trajectory of the next 24 hours, with hourly resolution. That is, an array of 24 values.
  • Total daily controllable energy the local coordinating unit informs the aggregation unit of the total energy required by its controllable loads for one day of operation. The total energy required is a scalar number.
  • Maximum consumption power the local coordination unit informs the aggregation unit of the maximum feasible consumption power. The maximum power consumption is reported by a vector of 24 values corresponding to the 24 hours of a day. and.
  • Meteorological Information the local unit of coordination requires a prediction of ambient temperature and global solar radiation (GHI). This information can be requested from a meteorological server with the ability to predict temperature and irradiance for the next 24 hours. The meteorological information then corresponds to an arrangement of 24 points for each variable and is requested each time it receives a band of demand trajectories.
  • GPI global solar radiation
  • F. 24-hour trajectory bands From the flexible load information and the uncontrollable demand prediction, the aggregation unit will generate a minimum and maximum demand trajectory band for the local coordination unit. Each trajectory consists of 24 points associated with the next 24 hours from the aggregation unit to each local coordination unit. This information will be sent every hour. g.
  • Deviations from the trajectory the local coordination unit upon receiving the trajectories of minimum and maximum demand will analyze the feasibility of the follow-up. At the beginning of each hour, the local coordination unit will propose the scheduling of the available loads in order to adjust the demand to the allowed demand band. In the event of a demand deviation outside the permitted demand band, the local coordination unit will report the value of the deviation to the aggregation unit.
  • the local coordinating unit reports the type and quantity available of flexible loads.
  • the local coordination unit reports the amount of energy required to maintain temperature within the comfort range, along with a forecast of uncontrollable demand.
  • the aggregation unit sends a first band of demand trajectories to the local coordination unit, two vectors of 24 parameters each with the maximum and minimum consumption allowed for the next 24 hours. To generate the trajectories, it uses optimization models and historical information on feasible trajectories.
  • the local coordination unit optimizes the load scheduling at the local level and informs the aggregation unit of deviations from the maximum and minimum values allowed. This is done using a 24 hour vector. This stage is repeated each time a band of demand trajectories is received by the aggregation unit, which occurs at least once a day, and can be repeated every 4 or 6 hours.
  • the local coordination unit informs each hour of the consumed demand and an estimate of this for the next hour.
  • An EV Electric Vehicle of a set of 13 electric vehicles with different characteristics and states of charge.
  • Standard test case 112 cases that cover 4 months of operation and have the loads indicated above.
  • Case of multiple charges 28 cases that consider 3 interruptible charges, 4 uninterruptible charges of one hour, 3 charges of 2 hours and 4 charges lasting more than 3 hours. The purpose of this test is to stress the model with a high number of available loads
  • Fig. 4 shows the sum of the demand of the 112 cases studied with respect to the band of aggregate demand trajectories sent by the aggregation unit to the different local coordination units.
  • the model proposed for the local coordination unit had no problems in scheduling the different loads, despite the notable increase in the number of elements to schedule in this test case.
  • the average time in which the model was able to schedule the loads is 4.79 seconds, 3 times longer than the standard case but it is still a low response time. While the maximum time recorded in which the proposed cases reach convergence was 11.76 seconds, in line with the values obtained in the previous case.
  • the schedule and the trajectory proposed for one of the cases evaluated in Figs. 5a and 5b where the band of demand trajectories proposed by the aggregation unit for one of the cases evaluated (Fig. 5a) and the load scheduling proposed by the local coordination unit (Fig. 5b).
  • the time model is capable of scheduling the 17 proposed loads according to the path sent, in addition to simplifying the future problem of the local coordination unit in sub-hourly time intervals, where it only has to schedule a limited set of loads that does not exceed 7 loads (block 19, 7 scheduled loads) instead of the 17 loads available in the time problem, which means that in the worst case you have to evaluate less than half of the originally considered loads.
  • the proposed model is capable of evaluating the flexibility of the loads and temporarily distributing its operation according to the band of demand trajectories sent by the aggregation unit. Its resolution time is low, this being an average of 3 seconds, in addition to the fact that in no case evaluated did it exceed 15 seconds, so the proposed model is capable of giving a quick response that allows the charges to be quickly re-scheduled before a change in restrictions, available loads or a new requirement of the aggregation unit.
  • the proposed model demonstrates its ability to schedule multiple loads, complying with the numbers expected to be found in households and even numbers that may correspond to a specific division of an industry, assuming that each load evaluated can be an industrial process.
  • the proposed solution allows both the distributed control of different flexible loads with which the aggregation unit has contracts, which can be used for different services:
  • the implementation of each one of them only depends on the control and optimization models to be implemented both in the demand aggregation unit and in the local coordination unit. For example, it is possible to implement a primary or secondary frequency response system by establishing a merit list of available loads as notified by the local coordinating unit to the aggregation unit, while the latter only finally indicates the decrease in demand that is expected from each client. Therefore, the weight of the different control systems provides the different service and business opportunities based on the latent flexibility of the network.
  • An indirect service of the demand aggregation unit is the determination of failures in the electrical distribution networks, by means of the analysis of the loss of communication with different local coordination units with which the system has a contract. Or that the same local coordination units deliver a notification of the drop in electricity consumption in the event that they have a power supply system independent of the network. With this, it speeds up the work of restoring the network service to the distributors.
  • An important modification in the operation of the system of the aggregation unit may be the possibility of offering different types of energy contracts according to a modeling of the flexibility of each one of the clients. In this way, the aggregation unit can deliver different operating and energy management conditions that are better adapted to both the customer's needs while seeking to reduce the total costs of the service.
  • a new variation of the service is the prediction of demand based on the study and the models developed to understand the behavior of the different market agents.
  • the aggregation unit can previously determine possible demand peaks or flexibility requirements and adapt its operation to carry out regulation offers.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

L'invention concerne un système et un procédé de commande et de gestion de demande énergétique dans des systèmes électriques, comprenant un échange d'informations, de coordination et de commande de demande répartie selon des signaux qu'une unité d'agrégation de demande donne à chaque participant du système. L'agrégation de la demande prend en compte l'échange d'informations sur la flexibilité des clients et le système électrique, représentée par une bande de trajectoires de demande dans la largeur de la bande de demande donnée au client.
PCT/CL2021/050017 2020-04-01 2021-03-22 Système et procédé de commande et de gestion de demande énergétique dans des systèmes électriques WO2021195796A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003055031A2 (fr) * 2001-12-20 2003-07-03 Enel Distribuzione S.P.A. Systeme pour l'acquisition eloignee de la consommation electrique et pour la telecommande de cibles reparties d''utilisateurs, egalement de type a usage domestique
WO2014022596A1 (fr) * 2012-07-31 2014-02-06 Causam Energy, Inc. Système, procédé et appareil pour un réseau d'énergie électrique et une gestion de réseau des éléments de réseau
US9188109B2 (en) * 2012-02-16 2015-11-17 Spyros James Lazaris Virtualization, optimization and adaptation of dynamic demand response in a renewable energy-based electricity grid infrastructure
CN110809844A (zh) * 2018-02-05 2020-02-18 比吉有限责任公司 用于电气设施的负荷管理的方法和设备

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003055031A2 (fr) * 2001-12-20 2003-07-03 Enel Distribuzione S.P.A. Systeme pour l'acquisition eloignee de la consommation electrique et pour la telecommande de cibles reparties d''utilisateurs, egalement de type a usage domestique
US9188109B2 (en) * 2012-02-16 2015-11-17 Spyros James Lazaris Virtualization, optimization and adaptation of dynamic demand response in a renewable energy-based electricity grid infrastructure
WO2014022596A1 (fr) * 2012-07-31 2014-02-06 Causam Energy, Inc. Système, procédé et appareil pour un réseau d'énergie électrique et une gestion de réseau des éléments de réseau
CN110809844A (zh) * 2018-02-05 2020-02-18 比吉有限责任公司 用于电气设施的负荷管理的方法和设备

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