Method and device for optimizing independent energy storage power station providing frequency modulation service

Abstract

The invention discloses an optimization method and device for an independent energy storage power station providing frequency modulation service. The method comprises the following steps: establishing an operation framework for sharing a battery energy storage system, and determining functions and roles of the battery energy storage system in a power system by designing a distribution mechanism and an operation rule; the method comprises the following steps: modeling a bidding strategy of a battery energy storage system in a power market into a chance constraint optimization model, considering a bidding target and a constraint condition of a market condition, and determining a bidding strategy for maximizing the earnings of the battery energy storage system through an optimization algorithm; a bidding strategy model of a battery energy storage system is converted into a mixed integer quadratic constraint programming problem, and the optimization problem is solved by adopting a data-driven distribution robust optimization technology based on Wharisstein measurement. According to the technical scheme, the operation framework of the shared battery energy storage system is established, the optimized bidding strategy is designed, the utilization rate and economic benefits of the battery energy storage system are improved, and meanwhile the frequency stability of a power system is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy storage, and in particular to an optimization method and device for an independent energy storage power station providing frequency modulation services. Background Art

[0002] With the rapid growth of renewable energy sources (RESs) around the world, their stochastic and intermittent characteristics lead to an increased risk of frequency instability in the power system. In addition, power systems with a high penetration of RESs usually have less inertia because RESs are usually connected to the network through power electronic converters, which decouples them from the AC grid. In order to ensure the stability and reliability of the power system, RESs must be able to perform primary frequency response (PFR) in certain areas. Traditional renewable energy PFR technologies usually require load shedding operations, which will lead to reduced generation and reliability issues. Therefore, PFR methods for RESs supported by energy storage systems (ESS) have attracted great interest from industry and academia.

[0003] Renewable energy plants must provide PFR capacity that proactively changes output in real time. For example, a photovoltaic (PV) plant does not need to have PFR capacity at night. Similarly, a wind farm does not need to participate in downstream PFR when its output is low to reduce the risk of tripping from the grid. Therefore, deploying a separate ESS for each renewable energy plant may be neither efficient nor economical. In contrast, a large-scale shared ESS can perform PFR for renewable energy plants when needed while providing other ancillary services as an independent entity. Therefore, installing a shared ESS to assume the PFR obligations of multiple renewable energy plants is worth studying.

[0004] Currently, the shared application of battery energy storage systems (ESS) among multiple users has become a research hotspot. According to different ownership, ESS sharing systems can be divided into four types: private, interconnected, public, and independent. The latter two are favored because they allow energy and capacity sharing. With the exploration of interconnected ESS structures, residents can even rent part of their energy storage capacity to large facilities. At the same time, the sharing of ESS not only saves electricity costs and reduces grid investment for users, but also helps to smoothly regulate the fluctuations in power generation from distributed renewable energy and reduce household energy costs. In addition, cloud energy storage, as a typical application of independent ESS, provides services to many users through centralized energy storage facilities.

[0005] At present, the traditional methods have the following shortcomings. First, the current research on shared ESS is mainly focused on the user side, and its potential in frequency regulation on the power supply side of the power system needs to be further explored. Second, the ability of shared ESS to provide frequency regulation on the power supply side has not been fully utilized. The potential of large-scale shared ESS to participate in the power system ancillary service market and use its spare capacity to make profits has not been explored. How to properly solve the above problems has become an urgent issue to be solved in the industry. Summary of the invention

[0006] The present invention provides an optimization method, device, equipment and storage medium for an independent energy storage power station providing frequency modulation services, which are used to improve the utilization rate and economic benefits of the battery energy storage system by establishing an operating framework of a shared battery energy storage system, and at the same time enhance the frequency stability of the power system.

[0007] According to a first aspect of the present invention, there is provided a method for optimizing an independent energy storage power station providing frequency regulation services, the method comprising:

[0008] Establish an operational framework for shared battery energy storage systems, defining the functions and roles of battery energy storage systems in the power system by designing allocation mechanisms and operating rules;

[0009] The bidding strategy of battery energy storage systems in the electricity market is modeled as a chance-constrained optimization model. The bidding objectives and constraints of market conditions are considered, and the bidding strategy that maximizes the benefits of the battery energy storage system is determined through an optimization algorithm.

[0010] The bidding strategy model of battery energy storage system is transformed into a mixed integer quadratic constrained programming problem, and the data-driven distributed robust optimization technique based on Wasserstein metric is used to solve the optimization problem.

[0011] In one embodiment, it further includes:

[0012] When establishing the operating framework of the shared battery energy storage system, when the system frequency deviation exceeds the preset frequency dead zone value, the battery energy storage system adjusts the active output in proportion to the frequency deviation based on the Pf droop control mechanism to provide a primary frequency response;

[0013] After the primary frequency response, the battery energy storage system dynamically sets the target power output through a centralized automatic generation control system to provide secondary frequency control to ensure effective frequency regulation over a longer time scale.

[0014] In one embodiment, it further includes:

[0015] In the modeling process of bidding strategy, a rolling time domain optimization method is used to optimize bidding schemes within multiple hours within the decision period;

[0016] The chance-constrained optimization model is improved by incorporating the chance-constrained default risk measured by the conditional value-at-risk into the objective function.

[0017] In one embodiment, it further includes:

[0018] When solving the bidding strategy model, a data-driven distributionally robust optimization method based on Wasserstein metric is used to reformulate the stochastic programming into a distributionally robust optimization model.

[0019] The distributed robust optimization model is transformed into an effectively solvable mixed integer quadratic constrained programming problem, and solved using a preset solver.

[0020] In one embodiment, it further includes:

[0021] In the optimization model, chance constraints are used to limit the energy limit of the battery energy storage system according to the power and energy constraints of the battery energy storage system;

[0022] The opportunity constraints are transformed using piecewise linear functions and conditional value at risk.

[0023] In one embodiment, it further includes:

[0024] In the operation of a shared battery energy storage system, the battery energy storage system assumes the primary frequency response obligations of multiple renewable energy power plants and determines the required primary frequency response capacity based on the output forecast of the renewable energy power plants;

[0025] The battery energy storage system bids in the energy and ancillary service market one hour in advance, modifies the economic basis point and automatic generation control service capacity, and dynamically adjusts the operation strategy to maximize revenue and optimize frequency regulation performance.

[0026] According to a second aspect of the present invention, there is provided an independent energy storage power station optimization device providing frequency regulation services, comprising:

[0027] Establish a module for establishing an operational framework for shared battery energy storage systems, defining the functions and roles of battery energy storage systems in the power system by designing allocation mechanisms and operating rules;

[0028] An optimization module is used to model the bidding strategy of the battery energy storage system in the power market as a chance-constrained optimization model, taking into account the bidding objectives and constraints of market conditions, and determining the bidding strategy that maximizes the benefits of the battery energy storage system through an optimization algorithm;

[0029] The solution module is used to transform the bidding strategy model of the battery energy storage system into a mixed integer quadratic constrained programming problem, and solve the optimization problem using a data-driven distributed robust optimization technique based on the Wasserstein metric.

[0030] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0031] The memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned independent energy storage power station optimization methods that provide frequency regulation services is implemented.

[0032] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a computer (e.g., a processor in a computer), implement any of the above-mentioned methods for optimizing an independent energy storage power station providing frequency regulation services.

[0033] In summary, the present invention provides an optimization method and device for an independent energy storage power station that provides frequency regulation services, the method comprising: establishing an operating framework for a shared battery energy storage system, determining the function and role of the battery energy storage system in the power system by designing an allocation mechanism and operating rules; modeling the bidding strategy of the battery energy storage system in the power market as an opportunity constrained optimization model, considering the bidding objectives and constraints of market conditions, and determining the bidding strategy that maximizes the benefits of the battery energy storage system through an optimization algorithm; converting the bidding strategy model of the battery energy storage system into a mixed integer quadratic constrained programming problem, and using a data-driven distributed robust optimization technique based on the Wasserstein metric to solve the optimization problem. The technical solution of the present application improves the utilization rate and economic benefits of the battery energy storage system by establishing an operating framework for a shared battery energy storage system, designing an optimized bidding strategy, and utilizing advanced optimization techniques, while enhancing the frequency stability of the power system.

[0034] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0035] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flow chart of an optimization method for an independent energy storage power station providing frequency regulation services provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a primary frequency response provided by an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of a non-shared energy storage system and a shared energy storage system provided for an embodiment of the present invention;

[0040] Figure 4 A structural diagram of an independent energy storage power station optimization device providing frequency regulation services provided by an embodiment of the present invention;

[0041] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0044] like Figure 1 As shown, the present invention provides an optimization method for an independent energy storage power station providing frequency regulation services, and the optimization method for an independent energy storage power station providing frequency regulation services includes:

[0045] In step S11, an operation framework for sharing the battery energy storage system is established, and the functions and roles of the battery energy storage system in the power system are determined by designing allocation mechanisms and operation rules;

[0046] In step S12, the bidding strategy of the battery energy storage system in the electricity market is modeled as an opportunity constrained optimization model, the bidding target and constraints of the market conditions are considered, and the bidding strategy that maximizes the benefits of the battery energy storage system is determined through an optimization algorithm;

[0047] In step S13, the bidding strategy model of the battery energy storage system is converted into a mixed integer quadratic constrained programming problem, and the data-driven distributed robust optimization technology based on the Wasserstein metric is used to solve the optimization problem.

[0048] In one embodiment, the core steps are divided into the establishment of the operation framework, the modeling and optimization of the bidding strategy, and the frequency response and dynamic adjustment. Each layer is closely linked to each other, aiming to improve the utilization efficiency and benefits of the battery energy storage system and ensure the frequency stability of the power system.

[0049] Establishing an operation framework Design an efficient operation framework for the battery energy storage system. By designing allocation mechanisms and operating rules, clarify the functions of the battery energy storage system in the power system, that is, when and how it needs to participate in frequency regulation and market bidding, and provide a basic framework for it to provide frequency regulation services and participate in market bidding. Since it may be shared by multiple power users (such as different renewable energy power plants), the design of an allocation mechanism can ensure the reasonable allocation of system resources among users, meet the frequency regulation needs of each user, and reduce resource waste, ensure the stability of the system and the rationality of resource allocation, and provide basic data and structural support for the formulation of system bidding strategies. Based on the operation framework, focus on the bidding strategy of the battery energy storage system in the power market to ensure that the system maximizes its benefits while providing frequency regulation services. Establishment of the opportunity constraint optimization model The bidding strategy of the battery energy storage system needs to consider a variety of market conditions and possible constraints. Through the opportunity constraint optimization model, these factors can be incorporated into the bidding model to ensure that the system can not only meet the requirements of frequency regulation, but also obtain the best economic benefits in the market environment. Introduce conditional value at risk (CVaR) in the bidding process to measure the risk of opportunity constraint default. This risk measurement mechanism can enable the battery energy storage system to control possible risks while maximizing benefits, and avoid the impact of market fluctuations on bidding results. The addition of conditional risk control makes the bidding strategy more robust and adaptable. Using the rolling time domain optimization method in the modeling process means that the bidding situation in multiple time periods in the future is taken into account at the decision-making moment, but only the bidding plan for the most recent time period is executed in actual execution. This method enhances the flexibility of the model in actual execution, helps to cope with dynamic changes in the market, and dynamically adjusts the frequency response and bidding strategy during actual operation to ensure a balance between frequency regulation needs and market changes. Primary frequency response (PFR) and secondary frequency control (AGC) are when the frequency deviation exceeds a certain threshold, the battery energy storage system adjusts the active output according to the frequency deviation based on the Pf droop control mechanism to provide a primary frequency response. Afterwards, through the centralized automatic generation control system (AGC), the system continues to adjust the output power to ensure stable frequency for a longer period of time. In the energy and ancillary service market, the battery energy storage system can adjust the economic basis point (i.e., the planned charging and discharging power) and AGC capacity based on real-time data to ensure maximum benefits in the price fluctuations of the energy market one hour ago and on the same day. The bidding strategy model is transformed into a mixed integer quadratic constrained programming (MIQCP) problem using data-driven distributed robust optimization based on the Wasserstein metric. This distributed robust optimization can enhance the adaptability of the battery energy storage system under uncertain market conditions and ensure that the bidding strategy remains robust in various possible market environments.

[0050] The technical solution in this embodiment designs a bidding strategy model based on opportunity constraint optimization, which achieves a balance between maximizing benefits and controlling risks through rolling time domain optimization and conditional risk control. The battery energy storage system provides primary and secondary frequency responses and responds to market changes through dynamic adjustment strategies, thereby ensuring the robustness and profitability of the system and ensuring that the battery energy storage system maximizes its economic benefits and frequency regulation capabilities while providing frequency regulation services.

[0051] The main frequency response is essential to maintain the stability of the power system against random disturbances. Renewable energy power plants should also assume the obligation to perform PFR. When the system frequency deviation exceeds a certain value, namely the frequency dead band, PFR is activated immediately to avoid unnecessary PFR activation. It is generally designed based on the Pf droop control mechanism, such as Figure 2 As shown, where is the reference point of the PFR resource. The active output power adjustment required by PFR is proportional to the frequency deviation, as shown in the following formulas 1 and 2

[0052]

[0053] Where ΔP p is the power adjustment required by PFR, f0 is the nominal system frequency, d is the droop coefficient, P n is the rated power output of the generator, f is the real-time measured frequency, and f b (1) / (2) is the upper / lower limit of the normal working frequency band, f db is the dead band size. Figure 2 As shown, a saturation value (e.g., 10% Pn) of the PFR adjustment amplitude is set for the generator. After the output adjustment reaches this value, no further PFR is required. For renewable generators, when their output power is below a certain limit (e.g., 10% Pn according to the NCGC rule), no downward adjustment is required. In addition, PV power plants do not participate in PFR at night. PFR provides a secondary immediate response to frequency changes, typically lasting from 30 seconds to 1 minute. To ensure effective frequency adjustment at the minute level and longer, PFR is followed by secondary frequency control, implemented by a centralized AGC system that dynamically sets the target power output for the generators and ESSs providing AGC services. The target power output of the AGC resources is shown in Equation 3 and Equation 4:

[0054] P s =P0+ΔP s (3)

[0055] ΔP s =s agc *R s (4)

[0056] Among them, s agc is the AGC signal allocated to the AGC resources in real time, R s is the regulation capability of AGC resources, P0 is the base point of AGC resources, ΔP s To adjust the power required according to the real-time AGC signal.

[0057] Considering the low probability of large disturbances in the power system and the short support time of PFR, if the BESS is used only to perform PFR, the energy capacity required by the ESS is small (e.g., 5 min). However, the full power duration of popular large-scale lithium-ion BESS is generally 30 minutes to 1 hour. Therefore, using BESS to only provide PFR services for renewable energy generators will cause a huge waste of its energy capacity. In addition, the maximum PFR power adjustment required by renewable energy generators will constantly change with the changes in weather conditions and day and night changes, which will also lead to a waste of power capacity of BESS.

[0058] In order to better utilize BESS to undertake the PFR obligations of renewable energy power plants, a large-scale shared BESS is considered. In the absence of a shared BESS, each renewable energy power plant must perform PFR separately according to the system frequency. After building a shared BESS, it can help multiple power plants perform PFR, so that the power plants no longer need to respond to the system frequency. At the same time, BESS participates in the ancillary service market as an independent entity, providing frequency regulation services in the presence of spare energy and power capacity, improving utilization and revenue. For example, the day-ahead and hour-ahead markets. In order to participate in the day-ahead market, the BESS operator needs to estimate its spare capacity based on the BESS configuration parameters and PFR capacity requirements stipulated in the contract, and submit a suitable planned power curve and AGC service capacity one day before the operation day.

[0059] On the day of operation, the independent system operator (ISO) determines the PFR capacity that the BESS needs to undertake based on the output forecast of the renewable energy power station and notifies the BESS in advance. The BESS can modify its economic basis point (i.e., planned charging and discharging power) and AGC service capacity based on the required PFR capacity and real-time SoC by bidding in the energy and ancillary service market one hour in advance.

[0060] A shared BESS can be a common ESS, i.e. invested by multiple renewable energy power plants, or an independent ESS, i.e. invested by an independent profit-seeking entity. In the first case, renewable energy power plants use shared BESS to assume PFR obligations, and the benefits of providing paid AGC services are shared by all investors. In the second case, renewable energy power plants entrust shared BESS to assume their PFR obligations through a fixed contract and pay a fee agreed by both parties to BESS. This paper mainly studies the optimal operation of shared BESS. This paper introduces the bidding strategy for BESS to maximize profits in the hourly market. The day-ahead bidding strategy is not within the scope of this study.

[0061] The reference active power output of the shared BESS for simultaneous PFR and AGC can be expressed as Equation 5

[0062] P r =P0+ΔP p +ΔP s (5)

[0063] Where P0 is the base point of BESS, (1) calculated ΔP p and ΔP calculated by (4) s These are the output adjustments due to the execution of PFR and AGC, respectively.

[0064] To simplify the problem, the present invention only considers the PFR capacity change caused by day and night changes, that is, the PFR capacity of the photovoltaic power generation unit is its rated output power P during the day. n,pv , which is 0 at night, while the PFR capacity of the wind turbine is always the rated output power P n,w The shared BESS is considered a price taker in the ancillary services market. The compensation for providing AGC services is determined by the market clearing price.

[0065] In describing the advance power base point and AGC capacity provision strategy for a shared BESS that performs PFR and provides AGC services simultaneously, a horizon-backward optimization approach is used to avoid short-sighted decision making. The bidding plans in multiple hour horizons are optimized within the decision hour. The optimization is performed hourly, but only the bidding plans for the operating hours, i.e., the first hour of the horizon, are executed. A chance-constrained programming model is first proposed and then reformulated into a more solvable form by adding the risk of violating the chance constraint measured by the conditional value-at-risk (CVaR) in the objective.

[0066] The market environment is mainly based on the PJM market and can be easily adapted to other markets. After the day-ahead energy market and ancillary market have cleared bids, the power configuration and AGC capacity supply can be changed 65 minutes before the start of the operating hour through the pre-hour market competition. For ease of presentation, 60 minutes is set. In the energy and ancillary markets, if the bid one hour ago deviates from the settlement result of the previous day, it will be penalized. The energy bidding unit is set to 15 minutes, and the ancillary market bidding unit is set to 1 hour.

[0067] The goal is to maximize the net income of the shared BESS operator, that is, to minimize the total cost. The total BESS operating cost C is composed of primary and secondary frequency regulation compensation C1, battery degradation cost C2, energy market penalty C3, ancillary service market penalty C4, and battery energy deviation penalty C5:

[0068] minC=-C1+C2+C3+C4+C5 (6)

[0069]

[0070] Where t is defined as the hourly index within the optimization time range, and its length is T hours. The index i is defined as the power curve bidding interval Δt within each hour. The interval is 15 minutes, so N = 4. The revenue C1 of providing frequency regulation services consists of capacity revenue and performance revenue, which can be calculated as (7), where represents the AGC capacity bid one hour ago, μ represents the AGC expected adjustment mileage per hour, and c sc and c sp They represent AGC capacity price and performance price respectively.

[0071] Equation (8) calculates the battery degradation cost C2, where σ is the expected total discharge energy caused by subsequent AGC signals per unit AGC capacity per hour. is non-positive, the discharge power is non-negative, which can be guaranteed by the constraints (25) and (26) given in step 2-B. Since the degradation process of the battery is affected by many factors such as battery materials and usage environment, it is difficult to accurately simulate the degradation process of the battery. To calculate the degradation cost, it is assumed that the degradation cost is approximately proportional to the sum of the charging and discharging energy.

[0072] Formula (9) and Formula (10) respectively calculate the deviation between the hour-ahead energy market and ancillary service market bidding and the day-ahead market clearing results. and Among them, represents the time-of-use electricity price, r e and r s are the penalty rates for power configuration and AGC capacity adjustment respectively.

[0073] and The calculation of is shown in equation (12) and equation (13), where is the discharge / charging power planned on the previous day, r t s,da is the market clearing result of the day-ahead AGC capacity. C5 in (11) represents the penalty for the battery energy at the end of the operating day deviating from the planned final energy. It takes 1 when the nth hour of the optimization horizon corresponds to the last hour of the operating day, and takes 0 otherwise. Expected energy deviation It can be calculated by formula (14), where E t is the expected energy of the battery at the end of the hour.

[0074] Due to the random characteristics of power system frequency and AGC signals, it is necessary to strike a balance between giving full play to the potential of BESS to provide AGC services and ensuring its frequency regulation performance. Therefore, chance constraints are used to describe the energy limit of BESS.

[0075]

[0076] t∈{1,2,…,T},i∈{1,2,…,N},k∈{1,2,…,K}(17)

[0077] e max =SoC max *e n ,e min =SoC min *e n (18)

[0078] We divide each 15-minute time interval into K subintervals and denote their indices by subscription K. We set a pair of energy chance constraints at the end of each subinterval. We use To represent the random variable. In formula (16) and formula (17), To optimize the initial energy of the horizon at the nth hour. represents the BESS energy change caused by charging and discharging according to energy market bidding, executing PFR and following AGC signals. β is the confidence level, which can be the same or different for different opportunity constraints. In order to avoid overcharging / overdischarging and reduce degradation costs, BESS should be in SoC max and SoC min The permissible energy range of BESS is e max / min It can be calculated as (18), where e n is the rated energy capacity of BESS.

[0079] In formula (16) and formula (17), The calculation is as follows:

[0080]

[0081] In (19), The random variable represents the cumulative charge / discharge energy per unit PFR / AGC capacity over a period of time induced by the following system frequency / AGC signal. Here the length of the time period is N*Δt, i.e. one hour. The distribution of the random variable can be estimated from the historical frequency regulation data. (19) The second, third and fourth terms on the right-hand side represent the net energy changes caused by planned charge / discharge, executing PFR and following the AGC signal, respectively. is the charge and discharge efficiency of BESS, is the required PFR capacity. It is worth noting that when t takes 1, subscription t-1 is 0, corresponding to the decision hour. For simplicity of expression, we slightly abuse some notations. and are parameters rather than decision variables, and they represent the base point of the decision hour and the AGC capacity respectively. It is also a parameter representing the BESS energy at the beginning of the decision hour, rather than a random variable.

[0082] and The calculation formula is as follows:

[0083]

[0084] In the formula and Both represent the energy change in the i-th 15-minute interval within the t-th hour from the beginning of the t-th hour to the end of the k-th subinterval, that is, the duration interval The subscripts base, p, and s are used to distinguish the energy changes caused by maintaining the planned power, performing PFC regulation, and following the AGC signal. The first and second terms in equation (20) represent the amount of power charged and discharged according to the energy market bidding plan, respectively. Similarly, the first and second terms in brackets in (21) and (22) are the energy changes caused by PFR and AGC in The energy charged and discharged during the duration, where the random variable has the same representation as in (19).

[0085] The output power of BESS should meet the following constraints:

[0086]

[0087] where p max and p minare the maximum and minimum active power output of the BESS, respectively. Considering the impact of PFR on the stability of the power system, we use hard constraints (23) and (24) to describe the power limit of the BESS to ensure that it can perfectly perform PFR without passing the AGC signal. Constraints (25) and (26) ensure that the BESS cannot be charged and discharged at the same time. is a binary variable whose value is 1 when the BESS is in charging mode and 0 otherwise.

[0088] The AGC provided capacity should be non-negative:

[0089]

[0090] The expected energy dynamics can be expressed as:

[0091]

[0092] The nonlinear equality constraints (12), (13), and (14) can be linearized into the following inequality constraints.

[0093]

[0094] CVaR is a risk assessment metric that quantifies the conditional expectation of losses above a certain quantile. The larger CVaR between two energy opportunity constraints set at each time point can be expressed as

[0095]

[0096] Then conservatively approximate Then the expectation term is reformulated as a piecewise linear function:

[0097]

[0098] In data-driven distributed robust optimization and its Miocp approximation, considering that it is difficult to learn the accurate distribution of system frequency and AGC signal from historical data, a data-driven DRO method is used to optimize the worst case on Wasserstein fuzzy sets. The stochastic programming is first reformulated as a data-driven DRO model based on Wasserstein metric, and then further reformulated as a MIQCP model that can be effectively solved.

[0099] The goal of DRO is to find a decision that minimizes the expected loss of the worst case scenario, where the worst case scenario should take over the fuzzy set. There are two main types of fuzzy sets, namely moment fuzzy sets and metric-based fuzzy sets. The Wasserstein metric-based fuzzy sets used consist of all distributions whose Wasserstein distance to the empirical distribution is less than a given radius ò.

[0100] The Wasserstein metric dW is defined as:

[0101] d W (Q1,Q2):=inf{∫ X2 ‖x1-x2‖P(dx1,dx2)} (38)

[0102] Where Q1, Q2 are arbitrary distributions supported on the uncertainty set.

[0103] use To represent the empirical distribution of the multidimensional random variable ξ learned from a training data set containing N independent samples. The fuzzy set centered on is defined as:

[0104]

[0105] where M(Ξ) is the space of all distributions supported on Ξ. When there exists an index a>1 such that:

[0106] ∫ X exp(||x|| a )P(dx) (40)

[0107] The wasserstein fuzzy set can be obtained by appropriately choosing and N to contain the actual distribution P with a given confidence level.

[0108] An hour ago Stochastic programming can first be reformulated as the following DRO problem:

[0109]

[0110] stconstrains(23)~(34) (41)

[0111] in is learned from a dataset containing N∈ historical samples The empirical distribution of .

[0112] The range of values ​​of a random variable can be expressed as Among them C m is a matrix, d m is a vector. P1 can be restated as Equation 42

[0113]

[0114] Among them, λ, s, γ are auxiliary variables, is a random variable The ith sample of,subscript m,i',k' is the index of the subinterval,random variable sample and piecewise linear loss function.,P2 is a MIQCP problem that can be solved efficiently by,existing commercial solvers.

[0115] The technical solution in this embodiment improves the utilization rate and economic benefits of the battery energy storage system and enhances the frequency stability of the power system by establishing an operating framework for a shared battery energy storage system and designing an optimized bidding strategy.

[0116] In one embodiment, Figure 4 FIG. 1 is a block diagram of an independent energy storage power station optimization device providing frequency regulation services according to an exemplary embodiment. Figure 4 As shown, the independent energy storage power station optimization device providing frequency regulation service includes an establishment module 41, an optimization module 42 and a solution module 43.

[0117] The establishment module 41 is used to establish an operation framework for the shared battery energy storage system, and determine the function and role of the battery energy storage system in the power system by designing an allocation mechanism and operation rules;

[0118] The optimization module 42 is used to model the bidding strategy of the battery energy storage system in the power market as a chance-constrained optimization model, consider the bidding objectives and constraints of market conditions, and determine the bidding strategy that maximizes the benefits of the battery energy storage system through an optimization algorithm;

[0119] The solution module 43 is used to transform the bidding strategy model of the battery energy storage system into a mixed integer quadratic constrained programming problem, and solve the optimization problem using a data-driven distributed robust optimization technique based on the Wasserstein metric.

[0120] The establishment module 41, the optimization module 42 and the solution module 43 included in the block diagram of the independent energy storage power station optimization device providing frequency regulation services are controlled to execute the independent energy storage power station optimization method providing frequency regulation services described in any of the above embodiments.

[0121] like Figure 5 As shown, the present invention provides an electronic device 500, the electronic device comprising: a communication interface, a processor 501, and a memory 502;

[0122] Among them, the memory 502 is used to store program instructions. When the program instructions are executed by the processor 501 that is communicatively connected to the memory 502 through the communication interface, an operating framework for sharing the battery energy storage system is established, and the function and role of the battery energy storage system in the power system are determined by designing an allocation mechanism and operating rules; the bidding strategy of the battery energy storage system in the power market is modeled as an opportunity constrained optimization model, and the bidding objectives and constraints of market conditions are considered, and the bidding strategy that maximizes the benefits of the battery energy storage system is determined through an optimization algorithm; the bidding strategy model of the battery energy storage system is converted into a mixed integer quadratic constrained programming problem, and the optimization problem is solved using a data-driven distributed robust optimization technique based on the Wasserstein metric.

[0123] The present invention provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, an operating framework for a shared battery energy storage system is established, and the function and role of the battery energy storage system in the power system are determined by designing an allocation mechanism and operating rules; the bidding strategy of the battery energy storage system in the power market is modeled as an opportunity constrained optimization model, and the bidding objectives and constraints of market conditions are considered, and the bidding strategy that maximizes the benefits of the battery energy storage system is determined through an optimization algorithm; the bidding strategy model of the battery energy storage system is converted into a mixed integer quadratic constrained programming problem, and the optimization problem is solved by a data-driven distributed robust optimization technology based on the Wasserstein metric.

[0124] It should be understood that the specific features, operations and details described hereinabove about the method of the present invention may also be similarly applied to the device and system of the present invention, or, vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding parts or units of the device or system of the present invention.

[0125] It should be understood that each module / unit of the device of the present invention can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the computer device in the form of hardware or firmware or independent of the processor, or can be stored in the memory of the computer device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0126] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores computer instructions executable by the processor, and the computer instructions instruct the processor to execute each step of the method of the embodiment of the present invention when executed by the processor. The computer device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities in a broad sense. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The steps of the method of the present invention are executed by the processor.

[0127] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0128] It can be understood by a person skilled in the art that the method steps of the present invention can be completed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the computer program causes the steps of the present invention to be executed when it is executed. Depending on the circumstances, any reference to memory, storage, database or other media in this article may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (PROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0129] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization method for an independent energy storage power station providing frequency regulation services, characterized in that: include: Establish an operational framework for shared battery energy storage systems, defining the functions and roles of battery energy storage systems in the power system by designing allocation mechanisms and operating rules; The bidding strategy of battery energy storage systems in the electricity market is modeled as a chance-constrained optimization model. The bidding objectives and constraints of market conditions are considered, and the bidding strategy that maximizes the benefits of the battery energy storage system is determined through an optimization algorithm. The bidding strategy model of battery energy storage system is transformed into a mixed integer quadratic constrained programming problem, and the data-driven distributed robust optimization technique based on Wasserstein metric is used to solve the optimization problem.

2. The method for optimizing an independent energy storage power station providing frequency regulation services according to claim 1, characterized in that: Also includes: When establishing the operating framework of the shared battery energy storage system, when the system frequency deviation exceeds the preset frequency dead zone value, the battery energy storage system adjusts the active output in proportion to the frequency deviation based on the Pf droop control mechanism to provide a primary frequency response; After the primary frequency response, the battery energy storage system dynamically sets the target power output through a centralized automatic generation control system to provide secondary frequency control to ensure effective frequency regulation over a longer time scale.

3. The method for optimizing an independent energy storage power station providing frequency regulation services according to claim 1, characterized in that: Also includes: In the modeling process of bidding strategy, a rolling time domain optimization method is used to optimize bidding schemes within multiple hours within the decision period; The chance-constrained optimization model is improved by incorporating the chance-constrained default risk measured by the conditional value-at-risk into the objective function.

4. The method for optimizing an independent energy storage power station providing frequency regulation services according to claim 1, characterized in that: Also includes: When solving the bidding strategy model, a data-driven distributionally robust optimization method based on Wasserstein metric is used to reformulate the stochastic programming into a distributionally robust optimization model. The distributed robust optimization model is transformed into an effectively solvable mixed integer quadratic constrained programming problem, and solved using a preset solver.

5. The method for optimizing an independent energy storage power station providing frequency regulation services according to claim 1, characterized in that: Also includes: In the optimization model, chance constraints are used to limit the energy limit of the battery energy storage system according to the power and energy constraints of the battery energy storage system; The opportunity constraints are transformed using piecewise linear functions and conditional value at risk.

6. The method for optimizing an independent energy storage power station providing frequency regulation services according to claim 1, characterized in that: Also includes: In the operation of a shared battery energy storage system, the battery energy storage system assumes the primary frequency response obligations of multiple renewable energy power plants and determines the required primary frequency response capacity based on the output forecast of the renewable energy power plants; The battery energy storage system bids in the energy and ancillary service market one hour in advance, modifies the economic basis point and automatic generation control service capacity, and dynamically adjusts the operation strategy to maximize revenue and optimize frequency regulation performance.

7. An independent energy storage power station optimization device providing frequency regulation services, characterized in that: include: Establish a module for establishing an operational framework for shared battery energy storage systems, defining the functions and roles of battery energy storage systems in the power system by designing allocation mechanisms and operating rules; An optimization module is used to model the bidding strategy of the battery energy storage system in the power market as a chance-constrained optimization model, taking into account the bidding objectives and constraints of market conditions, and determining the bidding strategy that maximizes the benefits of the battery energy storage system through an optimization algorithm; The solution module is used to transform the bidding strategy model of the battery energy storage system into a mixed integer quadratic constrained programming problem, and solve the optimization problem using a data-driven distributed robust optimization technique based on the Wasserstein metric.

8. The independent energy storage power station optimization device providing frequency regulation service according to claim 7, characterized in that: The establishment module, the optimization module and the solution module are controlled to execute the independent energy storage power station optimization method providing frequency regulation services as described in any one of claims 1-6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the independent energy storage power station optimization method that provides frequency regulation services as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the independent energy storage power station optimization method providing frequency regulation services as described in any one of claims 1 to 6.

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