WO2019158575A1 - Système et procédé de mise en œuvre d'une combinaison de ressources d'énergie hétérogènes pour des services de réseau rapide - Google Patents

Système et procédé de mise en œuvre d'une combinaison de ressources d'énergie hétérogènes pour des services de réseau rapide Download PDF

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Publication number
WO2019158575A1
WO2019158575A1 PCT/EP2019/053528 EP2019053528W WO2019158575A1 WO 2019158575 A1 WO2019158575 A1 WO 2019158575A1 EP 2019053528 W EP2019053528 W EP 2019053528W WO 2019158575 A1 WO2019158575 A1 WO 2019158575A1
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Prior art keywords
power
electrical power
electrical
resources
time
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PCT/EP2019/053528
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English (en)
Inventor
Colin Jones
Ioannis LYMPEROPOULOS
Tomasz GORECKI
Luca FABIETTI
Faran QURESHI
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Ecole Polytechnique Federale De Lausanne (Epfl)
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Publication of WO2019158575A1 publication Critical patent/WO2019158575A1/fr

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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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]
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • 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
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Definitions

  • the present invention relates to a method for provisioning fast regulating services to a power grid operator. More precisely, a novel methodology is described which allows the coordination of a network of heterogeneous resources that collectively follow a regulation signal, such as an automatic generation control (AGC) signal, received from a power grid operator.
  • AGC automatic generation control
  • the present invention also relates to a corresponding system for implementing the method.
  • the proposed new solution has the following advantages: ⁇
  • ⁇ The coordination of heterogeneous resources or sources at different time-scales enables the access to the ancillary market for resources that would otherwise be limited either by physical constraints, e.g. commercial buildings, or by economic costs, e.g. energy or electrical storage systems (ESSs).
  • ESSs energy or electrical storage systems
  • the exploitation of a more diverse set of resources has the potential to improve the overall operations of the power network from both an economic, environmental (by minimising emissions) and reliability (having sufficiently energy) point of view.
  • the model-based predictive approach allows precisely assessing and declaring a combined or aggregated baseline and power flexibility that the network of resources (i.e. the ASP) can offer to the power grid operator.
  • a forecasting module configured to predict future power requests improves the tracking performance and maximises the amount of electric power the ASP can be provide.
  • a system for controlling a set of electrical power resources as recited in claim 21.
  • Figure 1 is a simplified example block diagram illustrating elements which are useful for understanding the teachings of the present invention according to an embodiment of the present invention
  • Figure 2 is a simplified example block diagram illustrating a planning module of Figure 1 in more detail
  • Figure 3 is a simplified example block diagram illustrating a high-level controller of a tracking module of Figure 1 in more detail
  • Figure 4 is a simplified example block diagram illustrating a low-level controller of the tracking module of Figure 1 in more detail
  • Figure 5 is a flow chart illustrating the operation of the high-level controller according to an example of the present invention
  • Figure 6 is a flow chart illustrating the operation of the low-level controller according to an example of the present invention.
  • Figures 7 and 8 show example graphs for various parameters used in the present invention and obtained from a real system experiment.
  • “x and/or y” means“one or both of x and y.”
  • “x, y, and/or z” means any element of the seven-element set ⁇ (x), (y), (z), (x, y), (x, z), (y, z), (x, y, z) ⁇ .
  • “x, y and/or z” means “one or more of x, y, and z.”
  • the term“comprise” is used herein as an open-ended term. This means that the object encompasses all the elements listed, but may also include additional, unnamed elements. Thus, the word“comprise” is interpreted by the broader meaning "include”, “contain” or "comprehend”.
  • two new resources are added, namely a first electrical power resource characterised by a first power ramp rate and a first electrical energy storing capability, and a second electrical power resource characterised by a second power ramp rate and a second electrical energy storing capability.
  • the first power ramp rate is higher than the second electrical power ramp rate, and optionally the second electrical energy storing capability is greater than the first electrical energy storing capability.
  • Potential candidates for the new resources are e.g. ESSs, power plants, buildings, such as large commercial buildings, and more specifically their heating, ventilation and air conditioning (HVAC) systems.
  • HVAC heating, ventilation and air conditioning
  • ESSs are very well suited for ancillary services since they are highly controllable devices that exhibit very high ramp rates, i.e. their power injection parameter or value (which in this case can be positive or negative) can be adjusted very quickly to a desired value.
  • a ramp rate may be defined to be the increase or reduction in (power) output (or input) per time unit (usually minute) and is usually expressed as megawatts per minute (MW/min) or kilowatts per minute (kW/min). It is to be noted that in the present description the term “ramp rate” could be replaced with“electrical power response frequency”, which is the frequency at which the electrical power of an electric resource can be modified (e.g. once every 15 minutes).
  • the electrical power response frequency By using the electrical power response frequency, then for the first electrical power resource this value may be between 0.2 Hz and 1000 Hz or more specifically between 1 Hz and 100 Hz.
  • the electrical power response frequency For the second electrical power resource, the electrical power response frequency may be between 0.05 mHz and 0.03 Hz or more specifically between 0.1 mHz and 1 mHz.
  • ancillary services may require large energy throughput (i.e. large energy storage capability), which prevents ESSs from providing ancillary services economically, due to the large capital cost of the storage capacity required to fulfil the service.
  • a large number of buildings may be connected to the network and can store large amount of energy in the form of thermal energy, i.e. the HVAC power consumption can be shifted over time without significantly impacting the building indoor temperature.
  • the power consumption of a standard HVAC system cannot be modulated at very high frequencies due to physical limitations of the equipment.
  • these two types of resources are complementary.
  • the ESSs are power-intensive devices (their power injection can be changed very rapidly) with restrictive energy limitations.
  • buildings are energy-intensive devices (i.e. they can store lots of energy) with restrictive power limitations (i.e. their power injection cannot be changed rapidly).
  • Each type of resource could not provide ancillary services due to economic and technical limitations, respectively, but have the potential to be operated together to provide power economically and reliably, and in turn improve the overall efficiency of the network.
  • a method is provided to coordinate, at different time scales, a network of heterogeneous energy resources to exploit the synergies between them and achieve improved performance.
  • the providers, or more specifically the ASPs are required to commit, ahead-of-time, to a nominal or default power profile, referred to as a baseline power profile, and a power flexibility value around this baseline profile.
  • a power baseline is calculated for a given period of time (e.g. one day) and for every energy resource and/or for groups of energy resources (single group forming e.g. an ASP) in the network and may indicate the electric power consumed and/or produced depending on the type of the energy resource.
  • the power baseline for a given energy source may be different for different days.
  • the power flexibility value is a fixed or constant value for the given period of time and expressed in kilowatts.
  • the proposed hardware components of the present invention are explained next with reference to Figure 1.
  • the present embodiment comprises:
  • This resource could for example be an ESS 10 (as in Figure 1 ), an electrical boiler or a hydro-power plant.
  • This resource could for example be an HVAC system of a controllable building 1 1 (as in Figure 1 ), a backup diesel generator or a slower hydro-power plant.
  • the present embodiment further comprises a local measurement and controller unit or module 13, referred to simply as a local controller, at each resource, i.e. connected to each resource.
  • a central processing unit physical or in the cloud
  • different dedicated networks are present for the information exchange.
  • a dedicated secure channel is used to receive the real-time power profile request (also known as a tracking request) from the power grid operator 15, which is hereinafter also referred to as a transmission system operator (TSO), but it could instead be a vertically integrated utility or any other suitable unit.
  • TSO transmission system operator
  • Another secure dedicated channel is used by a planning module 17 to declare the baseline power profile and the power flexibility to an energy market 19 and an ancillary service market 21 , respectively.
  • a tracking module 23 makes use of a secure dedicated channel to interact with energy markets, by placing and filling energy trades in real-time trades on the real-time markets (or intraday markets) 25.
  • an internal secure network between the local controllers and both the planning and tracking modules is also used.
  • the purpose of a forecasting module 27 also shown in Figure 1 is explained later.
  • a gateway module 29, or simply a gateway, is provided between the local controllers 13 and the planning and tracking modules 17, 23.
  • the proposed software components are explained next.
  • the software may be stored in the central processing unit or elsewhere e.g. in a distributed manner.
  • the present embodiment comprises the following software components or modules:
  • a mathematical model for each resource in the network of resources is used to obtain a prediction for the energy usage of each resource over a predetermined period of time.
  • the model is generally based on several relevant parameters. For example, in the case of a building, it could include at least one of the following parameters: building material(s) used, geometrical properties, geographical location of the building, weather prediction etc. 2)
  • a forecasting module or tool that determines a forecast for all relevant uncertain quantities or parameters.
  • the forecasting tool can forecast the future prices of energy over a predetermined period of time. Alternatively, or in addition, it may forecast the future requests from the TSO.
  • a planning module that determines both the aggregate baseline power profile as well as the power flexibility for the network of resources over a predetermined period of time.
  • a tracking module which coordinates the network of resources in order to respond to the request(s) coming from the TSO.
  • the tracking module performs a multi-timescale coordination of all resources in order to meet the constraints at each individual resource while responding to the service request.
  • SoC state of charge
  • Et +i f ⁇ E t s , P , (1 )
  • E t denotes the SoC of the ESS at time step t where t measures time in intervals of the fastest time scale required (in our case, generally in the order of 1 to 10 seconds)
  • P t s denotes its power injection
  • /( ⁇ ) is the map, typically non-linear, describing the time evolution of the SoC as a function of the current SoC and its power injection. In general, /( ⁇ ) accounts for both conversion as well as temporal losses.
  • Ramp constraints for the ESS can also be considered as in Equation 5 but ESS generally can ramp in a sub-second time-scale which is much faster than the typical time-response required for the provision of regulation services (seconds), which may allow us to neglect these ramp constraints as will be the case in the following.
  • the power plant can be typically characterised by a minimum and maximum production level expressed in kW and the maximum rate at which the unit can modify its production level. More specifically: rmin ⁇ R ⁇ ⁇ Pmax and (4) xpu ⁇ pu
  • a large-scale consumer model such as a commercial building, is in general a resource capable of storing and releasing energy (typically in the form of thermal energy) with both power and ramp limitations.
  • the large-scale consumer model, or more specifically its consumption unit could also be characterised by a slower response time, i.e. its power consumption can be adjusted at a much slower time-scale (e.g. 15 minutes).
  • E£ denotes the equivalent SoC of the consumption unit at time t
  • P t c its power injection
  • g( ⁇ ) is the map describing the relation between the current SoC and its power consumption to the SoC at the next time instant, t + 1.
  • the map g( ⁇ ) is, in general, based on several relevant parameters. For instance, in the case of a commercial building, it could comprise at least one of the following parameters: building material(s) used, geometrical properties, geographical location, weather predictions etc.
  • the energy constraints can be expressed as:
  • an additional constraint encodes the fact that the power input to the slow resources can only change at a slow rate so that:
  • Equation 9 states that the power consumption of the resource can be modulated once every 15 minutes.
  • the planning module 17 shown in Figure 2 is described next in more detail for the minimal setup of one power-intensive and energy-constrained resource, such as the ESS, and one resource with slower power response, such as the HVAC system of a commercial building.
  • one power-intensive and energy-constrained resource such as the ESS
  • one resource with slower power response such as the HVAC system of a commercial building.
  • the purpose of the planning module is twofold:
  • This baseline power profile defines a power output of the ASP as a function of time, at a pre- determined time step (interval), for example 15 minutes, as required by the TSO or another external entity such as a power flexibility aggregator.
  • This baseline power profile can also be used to purchase power on the power markets directly.
  • the baseline power profile may include one operating baseline profile for the ESS (or more specifically to its battery) and one baseline profile for the HVAC system, or only one aggregate baseline profile describing the total power injection of the system as the sum of the power injection for the two resources.
  • the method provides a description of possible alterations in power injections achievable by the system, i.e. the power flexibility. These possible alterations can be described as a set of possible power injection trajectories achievable by the system, meaning that they satisfy the operating characteristics of the two resources, as previously described where the models of the resources were described.
  • an optimisation problem (a first optimisation problem) is solved which involves the mathematical models and constraints of the resources and that uses a cost function.
  • the optimisation problem is in this example solved by an optimisation unit or module 31 , or an optimiser for short, as shown in Figure 2.
  • Resource models may be stored in a first data storage unit 33 or first database, while a second data storage unit 35 or a second database may be used for storing historical TSO requests.
  • both the resource models and the historical TSO requests may be used as inputs for the optimiser 31 .
  • the first and second data storage units could be merged into one single data storage unit.
  • the optimisation problem can thus be formulated as follows: min E ⁇ jpianning ⁇ (10a)
  • Equation 10c states that the resources should be able to track with high probability the power request from the TSO by modifying their power profile accordingly.
  • the planning module takes care of transferring both the baseline power profile and the flexibility to the energy market and the ancillary service market, respectively.
  • the Forecasting module or tool 27 encompasses different algorithms and methods that allow the prediction over a pre-determ ined time-frame of all relevant quantities.
  • the forecasting module provides the possible future TSO requests based on different information such as the recently recorded requests, historical patterns, time of the day etc.
  • the forecasting module 27 may provide the predictions of other quantities affecting the resources under control, such as weather, occupancy patterns, energy prices etc.
  • the predictions obtained by the forecasting module are transmitted (e.g. continuously or at given time instants) to the tracking module 23 during real-time operation.
  • the tracking module 23 is activated at the time of delivery of the service, i.e. when the commitment of both the baseline power profile and flexibility have been settled and communicated to the various responsible entities (the baseline profile to the energy market 19, and the flexibility to the AS market 21 ).
  • the tracking module 23 receives from the planning module 17 the committed power profile b and flexibility y.
  • a two-time scales control routine i.e. a hierarchical control routine
  • HL controller 37 running at a lower temporal resolution, e.g. 15 minutes
  • LL low level controller 39 running at a (much) faster rate equivalent to the delivery requirements of the service, e.g. 5 seconds.
  • HL controller 37 running at a lower temporal resolution, e.g. 15 minutes
  • LL low level
  • the HL controller 37 receives from the forecasting module 27 the predictions of all relevant parameters or quantities. It also receives from the local controllers 13 the current status of each resource in the ASP system. Based on this information, it decides or determines an operating point, i.e. a power setpoint, of the slow resource for the next slow-time interval (15 minutes in this example) indicating how much the slow resource consumes energy during this time interval. Depending on the agreement between the ASP and the TSO or depending on the particular regional market, the HL controller 37 may concurrently decide to acquire (purchase) or give away (sell) energy in order to modify the pre-committed baseline. This could be done for economic, environmental and/or reliability reasons for instance.
  • an operating point i.e. a power setpoint
  • a dedicated interface to a real-time energy market may consult available open bids and based on the environmental and/or reliability aspects and/or current price of energy on sale, may fill the corresponding bids to purchase power.
  • the HL might decide to increase/decrease the baseline power profile. It could also decide to place a trade to guarantee a proper functioning of at least one of the resources. For example, it could decide to increase/decrease the baseline power profile in order to reset the SoC of the ESS to a convenient level.
  • the baseline can be modified during the delivery phase according to the regulation established by the power grid operator. This modification may be determined in the HL optimisation problem (a second optimisation problem) solved by an HL optimiser 41 , which may or may not be the same physical unit as the optimiser 31.
  • the objective function J re ai-time can capture, as in the planning module, several costs (e.g. environmental and/or economic) that will depend on the specific case under analysis.
  • the term AGC k denotes the prediction, as provided by the forecasting module, of the average power request from the TSO for the slow time step k.
  • the term l k denotes the energy that the HL controller can decide to purchase/sell, for instance on the intraday market.
  • the HL controller fills (optionally immediately) the bid on the intraday market to purchase or sell the energy.
  • the HL controller transmits the operating setpoint of the slow resource and the updated baseline to the LL controller.
  • the LL controller 39 receives the current request from the TSO 15. Additionally, at each or given slow iterations k, it receives the current baseline power profile together with the current operating point for the slow resource over the next slow-time interval. The latter is transmitted to the local controller at the slow resource which is in charge of following this setpoint as close as possible. However, in general, small fluctuations from this setpoint can be experienced due to inherent uncertainties. Thus, the actual power profile at the slow resource is expressed as:
  • the LL controller 39 computes (optionally continuously) and communicates to the local controller of the fast resource the operating setpoint. More specifically, in order to guarantee the provision of the service, the LL controller determines the power injection at the fast resource as follows:
  • P t s is the power injection at the ESS at the fast time-scale
  • AGC t the TSO power request at the current iteration
  • P t c the power injection for the slow-resource at the current fast iteration
  • the terms ( b k + 1 k ) denote the baseline power profile and the intraday trade, respectively.
  • the flow charts of Figure 5 and 6 summarise some of the method steps carried out by the HL and LL controllers. In the above example, the processes explained in these flow charts are run in parallel.
  • ASM stands for ancillary service market
  • EM stands for energy market
  • IM stands for intraday market.
  • the process starts in step 51 , where data is collected. More specifically, this step may involve collecting the historical AGC signals or TSO signals and resource models describing the resources.
  • the planning problem (Eq.10) is solved by using the collected data.
  • a combined power baseline profile for a first duration of time and a power flexibility value for a second duration of time may be determined.
  • the first time duration may equal the second time duration, or these time durations may be different.
  • the first time duration may be longer than the second time duration.
  • the HL control problem (Eq. 11 ) is solved by using the predicted AGC signal, the models of the resources in the set and the current status of the resources in the set.
  • the predicted AGC signal and the current status of the resources in the set are first determined if they have not been previously determined.
  • the steps 53 and 55 may be carried out substantially parallel.
  • the most recent baseline ( b k + 1 k ) is sent to the LL controller 39.
  • the power setpoint P k is sent to the local controller 13 of the power electric resource having the lower ramp rate (i.e. the building in this example).
  • the process waits for the next iteration of the HL controller 37.
  • step 63 a counter for k is incremented by 1.
  • step 65 the status of all the power electrical resources in the set (ESS, building) is measured or determined.
  • step 67 the AGC signal over prediction horizon is predicted by using the forecasting module 27.
  • other parameters affecting the operation of the electrical power resources may be predicted to coordinate the first and second electrical power resources. Such predicted parameters may be e.g. future electricity prices and/or future weather conditions.
  • the process starts in step 71 , where the baseline profile b k + l k is obtained from the HL controller for the current iteration.
  • the current tracking request AGC t is received from the grid operator (TSO) 15.
  • the most updated measurement P t c is obtained for the power injection of the power electrical resource having the smaller ramp rate.
  • the power injection setpoint for the ESS is computed as detailed in Eq. 13 by using the current baseline profile, the received tracking request and the current power injection of the power electrical resource having the smaller ramp rate.
  • the current setpoint is sent to the local controller of the ESS.
  • step 81 the process waits for the next iteration of the LL controller 39.
  • step 83 a counter for t is incremented by 1 and the process then continues in step 71.
  • the electrical power resources 10, 11 of the set are coordinated so that a sum of power injected from or into the electrical power resources of the set substantially matches the requested power profile over a third period of time to follow the requested power profile over the third period of time.
  • the graphs of Figures 7 and 8 show simulation results for an ASP consisting of a commercial building served by a single heat pump (HP), and an ESS.
  • HP heat pump
  • FIG. 7 shows the pre-computed baseline power profile as computed by the planning module 17, the AGC request scaled by the bidded flexibility or capacity, and the power realisation (or power delivery) of the set of resources, i.e. the heat pump and the ESS, during real- time operation.
  • the tracking module 23 is able to optimally coordinate the two resources so as to perfectly track or follow the received AGC signal. In other words, the AGC signal and the tracking signals are overlapping. This is done while, at the same time, respecting the physical limitation(s) of each resource.
  • the present invention provides an apparatus or system and a computer-implemented method for the coordination of a set of heterogeneous resources for the provision of fast regulating services to the TSO.
  • the main benefits of the present invention with respect to the state-of-the-art are twofold: 1 ) On one side it allows the participation to these fast regulating services for resources characterised by high response times (e.g. commercial buildings) for which it would be impossible to participate.
  • the present invention allows exploiting the synergies between these complementary resources in order to boost the overall performance of the system, in particular by offering operating reserves at a cheaper overall cost, in a more environment-friendly manner (in terms of optimised emissions) and/or more reliably.

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Abstract

La présente invention concerne un procédé de commande d'un ensemble de ressources d'énergie électrique dans un réseau électrique géré par un opérateur de réseau électrique. L'ensemble comprend une première ressource d'énergie électrique ayant une première fréquence de réponse d'énergie électrique et une première capacité de stockage d'énergie électrique, et une seconde ressource d'énergie électrique ayant une seconde fréquence de réponse d'énergie électrique et une seconde capacité de stockage d'énergie électrique, la première fréquence de réponse d'énergie électrique étant supérieure à la seconde fréquence de réponse d'énergie électrique. Le procédé comprend les étapes consistant : (a) à déterminer pour les ressources d'énergie électrique de l'ensemble un profil de ligne de base d'énergie combiné pendant une première durée et une valeur de flexibilité d'énergie pendant une deuxième durée, la valeur de flexibilité d'énergie indiquant la quantité d'énergie demandée auprès de l'ensemble qui est autorisée à s'écarter du profil de ligne de base d'énergie ; (b) à recevoir un signal de demande d'énergie en provenance de l'opérateur de réseau électrique, le signal de demande d'énergie dépendant du profil de ligne de base d'énergie combiné et de la valeur de flexibilité d'énergie, le signal de demande d'énergie indiquant un profil d'énergie demandé auprès de l'ensemble ; et (c) à coordonner les ressources d'énergie électrique dans l'ensemble de telle sorte qu'une somme d'énergie injectée depuis ou dans les ressources d'énergie électrique dans l'ensemble corresponde sensiblement au profil d'énergie demandé sur une troisième durée pour suivre le profil d'énergie demandé sur la troisième durée.
PCT/EP2019/053528 2018-02-14 2019-02-13 Système et procédé de mise en œuvre d'une combinaison de ressources d'énergie hétérogènes pour des services de réseau rapide WO2019158575A1 (fr)

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CN110752614A (zh) * 2019-11-07 2020-02-04 山东大学 一种储能系统控制方法及系统
CN114151207A (zh) * 2021-11-03 2022-03-08 中山嘉明电力有限公司 一种燃机机组快速变负荷控制方法
JP7442417B2 (ja) 2020-10-15 2024-03-04 株式会社日立製作所 需給調整支援装置、需給調整装置並びに方法

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CN110752614A (zh) * 2019-11-07 2020-02-04 山东大学 一种储能系统控制方法及系统
CN110752614B (zh) * 2019-11-07 2021-05-07 山东大学 一种储能系统控制方法及系统
JP7442417B2 (ja) 2020-10-15 2024-03-04 株式会社日立製作所 需給調整支援装置、需給調整装置並びに方法
CN114151207A (zh) * 2021-11-03 2022-03-08 中山嘉明电力有限公司 一种燃机机组快速变负荷控制方法
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