Operation mode design method and device for energy storage participating in electric power spot market, medium and product

Abstract

The invention discloses an operation mode design method and device for participation of energy storage in an electric power spot market, a medium and a product, and relates to the field of wind power and pumped storage combined energy storage systems, and the method comprises the steps: constructing a combined transaction model for participation of an energy storage system composed of pumped storage and wind power generation in the electric power spot market and a frequency modulation auxiliary service market; analyzing the uncertainty of wind power generation in the joint transaction model, and constructing to obtain an uncertainty set; constructing a joint declaration and scheduling strategy decision model of the energy storage system; and constraining a joint declaration and scheduling strategy decision model based on the uncertainty set, and generating an operation mode design strategy of the energy storage participating in the electric power spot market. The invention aims to design a multi-energy joint market participation mechanism aiming at emerging market subjects and continuously changing market demands, and has the advantages of high energy utilization rate, wide application prospect and the like.

Description

Technical Field

[0001] The present application relates to the field of wind power and pumped storage combined energy storage systems, and in particular to a method, equipment, medium and product for designing an operation model for energy storage to participate in the electricity spot market. Background Art

[0002] As the contradiction between energy supply and environmental protection becomes increasingly prominent, the power system is gradually shifting towards a smart grid. The electricity spot market can accurately reflect the short-term supply and demand relationship and the spatiotemporal value of power resources and achieve the optimal allocation of power resources. Energy storage resources can effectively realize the conversion, storage and utilization of electric energy, which helps to improve the flexibility of power system operation and promote the consumption of renewable energy. In the electricity spot market environment, energy storage stores or purchases off-peak electricity at a lower price, and then discharges or sells it at a higher price during peak load periods. At the same time, the production, operation and maintenance costs of energy storage are gradually reduced. However, the current operation model of the electricity spot market is not fully designed in combination with energy storage, and has disadvantages such as low energy utilization and narrow application prospects. Summary of the invention

[0003] The purpose of this application is to provide a method, equipment, medium and product for designing an operation model for energy storage to participate in the electricity spot market, which has the advantages of high energy utilization and broad application prospects.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In the first aspect, the present application provides a method for designing an operation model for energy storage to participate in the electricity spot market, including:

[0006] Constructing a joint trading model for energy storage systems to participate in the electricity spot market and the frequency regulation auxiliary service market; the energy storage system consists of pumped storage and wind power generation;

[0007] Analyzing the uncertainty of wind power generation in the joint trading model and constructing an uncertainty set;

[0008] Construct a joint declaration and dispatch strategy decision model for energy storage systems;

[0009] Based on the uncertainty set, the joint declaration and dispatch strategy decision model is constrained to generate an operation mode design strategy for energy storage participating in the electricity spot market.

[0010] Optionally, in a joint trading model in which the energy storage system participates in the electricity spot market and the frequency regulation ancillary service market, when there is excess wind power, the actual output of the energy storage system is greater than the bid power, and the pumped storage system stores the excess energy;

[0011] When the energy storage system participates in the frequency regulation auxiliary service market, pumped storage, as an energy storage resource, provides the frequency demand required by the dispatch center by changing the operating state; in the frequency regulation auxiliary service market, the energy storage system formulates the optimal bidding strategy based on the predicted wind power output of the next day and the market price, declares the reserved frequency regulation capacity and mileage price, and the dispatch center conducts frequency regulation pre-examination;

[0012] In the electricity spot market, the energy storage system declares an upper limit for electric energy output and a lower limit for electric energy output.

[0013] Optionally, the uncertainty of wind power generation in the joint transaction model is analyzed to construct an uncertainty set, including:

[0014] The IGDT model is used to represent the uncertainty in the pumped storage process in order to construct a decision model;

[0015] Determining an expected electricity price deviation coefficient corresponding to an expected power generation rate, solving the decision model to obtain an uncertainty coefficient, so as to generate an uncertainty set of power generation;

[0016] Considering the uncertainty of wind power output, the Wass metric method is used to construct the fuzzy set of wind power prediction error;

[0017] Construct the fuzzy uncertainty set based on the fuzzy set of wind power prediction error;

[0018] The fuzzy uncertainty set is converted into a reasonable boundary representation, and the reasonable boundary representation is converted into an optimization problem, and the optimization problem is solved to obtain the optimal boundary and the uncertainty set of wind power output.

[0019] Optionally, the decision model is expressed as:

[0020]

[0021] Where X represents the uncertainty parameter, d represents the decision variable, F(X,d) represents the objective function, H(X,d) represents the equality constraint, and G(X,d) represents the inequality constraint; Represents the uncertainty factor in the power generation process, that is, the amplitude of fluctuation; κ z represents the deviation coefficient of the expected power generation, Z0 represents the actual power generation, represents the fluctuation range of the uncertain parameters, Indicates the predicted value of generated power.

[0022] Optionally, considering the uncertainty of wind power output, the Wass metric method is used to construct the fuzzy set of wind power prediction error, including:

[0023] The empirical distribution of actual wind power output is obtained from a set of sample historical data, and a wind power distribution containing the real distribution and satisfying The fuzzy set of N represents the empirical distribution, P represents the true distribution, and x represents the sample historical data;

[0024] The Wass metric method is used to measure the distance between the empirical distribution and the true distribution to generate a fuzzy set of wind power prediction errors.

[0025] Optionally, the joint declaration and scheduling strategy decision model is expressed as:

[0026] maxR(Z wp )=∑ ξ∈M ρ(z ξ )(Z wp );

[0027] In the formula, R(Z wp ) represents the satisfaction of user n in participating in demand response in time period t, Z wp represents the satisfaction in the electricity spot market and the frequency regulation auxiliary service market, ξ represents the scenario in which user n participates in time period t, M represents the set of scenarios ξ, ρ(z ξ ) represents the coefficient of user comfort loss function, z ξ It represents the actual benefit of user n participating in demand response in time period t, and max represents the maximum value.

[0028] Optionally, constraining the joint declaration and scheduling strategy decision model based on the uncertainty set includes:

[0029] Limit the number of bids for wind power generation to a minimum bid number and a maximum bid number;

[0030] When pumped storage participates in multi-market bidding, the sum of the power in each market is between the corresponding upper and lower operating limits, setting the base point and frequency regulation capacity constraints for pumped storage;

[0031] Constrain the running status;

[0032] When pumped storage is in operation, restrictions are placed on storage capacity;

[0033] Based on the uncertainty set, coupling constraints are imposed on the energy storage system in the power system and the frequency regulation auxiliary service system;

[0034] Based on the uncertainty set, the adjustment process of the flexible resources is characterized by using linear affine;

[0035] A DRO model is used to characterize the uncertainty of the wind power generation, and constraint conditions of the DRO model are set.

[0036] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-mentioned methods for designing an operating model for energy storage participating in the electricity spot market.

[0037] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for designing an operation model for energy storage participating in the electricity spot market.

[0038] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for designing an operation model for energy storage participating in the electricity spot market.

[0039] According to the specific embodiments provided in this application, this application has the following technical effects:

[0040] The present application provides a method, equipment, medium and product for designing an operation mode for energy storage to participate in the electricity spot market, aiming to design a multi-energy joint market participation mechanism for emerging market players and changing market demands. Among them, a joint trading model for an energy storage system consisting of pumped storage and wind power generation to participate in the electricity spot market and the frequency regulation auxiliary service market is constructed, which can realize a collaborative trading model between the energy storage and frequency regulation markets. Constructing a joint declaration and dispatching strategy decision model for the energy storage system and constraining the joint declaration and dispatching strategy decision model for the energy storage system can effectively deal with the uncertainty of wind power output, which has practical significance for improving the safety and reliability of power grid operation. When the energy storage system participates in the frequency regulation auxiliary service market, the pumped storage power station, as an energy storage resource, can provide the frequency requirements required by the dispatching center by changing the operating state to reduce the frequency deviation, thereby improving the energy utilization rate, and has a relatively broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A flow chart of a method for designing an operation mode for energy storage participating in the electricity spot market provided in one embodiment of the present application;

[0043] Figure 2 A schematic diagram of a DRO model for wind power generation uncertainty provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0047] In an exemplary embodiment, Figure 1 As shown, a method for designing an operation mode for energy storage to participate in the electricity spot market is provided. The method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, in the context of sufficient wind power and hydropower resources in Qinghai Province, the method is applied to a server as an example for explanation, including:

[0048] Step 100: Construct a joint transaction model for energy storage systems to participate in the electricity spot market and the frequency regulation auxiliary service market. A wind power and pumped storage combined system (WPCS) is composed of pumped storage and wind power generation. Among them, WPCS is connected to the external power grid through a transformer to achieve comprehensive energy interaction.

[0049] Step 101: Analyze the uncertainty of wind power generation in the joint trading model and construct an uncertainty set.

[0050] Step 102: Construct a joint declaration and dispatch strategy decision model for the energy storage system.

[0051] Step 103: Constrain the joint declaration and dispatch strategy decision model based on the uncertainty set, and generate an operation model design strategy for energy storage participating in the electricity spot market.

[0052] In another exemplary embodiment of the present application, in a joint trading model in which the energy storage system participates in the electricity spot market and the frequency regulation ancillary service market, when there is excess wind power, the actual output of the energy storage system is greater than the bid power, and pumped storage stores excess energy to avoid wasting wind energy resources.

[0053] When the energy storage system participates in the frequency regulation auxiliary service market, pumped storage, as an energy storage resource, provides the frequency demand required by the dispatch center by changing the operating state to reduce the frequency deviation. In the frequency regulation auxiliary service market, the energy storage system formulates the optimal bidding strategy based on the predicted wind power output of the next day and the market price, declares the reserved frequency regulation capacity and mileage price, and the dispatch center conducts frequency regulation pre-examination.

[0054] In the electricity spot market, the energy storage system declares an upper limit on the amount of electricity it can output and a lower limit on the amount of electricity it can output.

[0055] Since the frequency regulation ancillary service market and the electricity spot market have a strong coupling relationship, the joint participation of the frequency regulation ancillary service market and the electricity spot market requires joint optimization in resource allocation. In the frequency regulation ancillary service market, the frequency regulation system formulates the optimal bidding strategy based on the predicted wind power output of the next day and declares the reserved frequency regulation capacity and mileage price, and the dispatch center conducts frequency regulation pre-examination. In the electricity spot market, the energy storage system declares the upper and lower limits of the output. The energy storage system decision center formulates the optimal bidding strategy for capacity declaration, and the wind power and pumped storage systems will adjust their operation plans based on the data released by the decision center.

[0056] Based on the above description, the actual output of the energy storage system will be greater than the bid power. In order to avoid deviation penalties, pumped storage avoids wind curtailment by storing excess energy. When the energy storage system participates in the auxiliary frequency regulation service system, pumped storage, as an energy storage resource, can provide the frequency requirements required by the dispatching center by changing the operating state to reduce the frequency deviation. At the same time, the combined energy storage system has a fast response capability, which can compensate for the frequency modulation deviation caused by insufficient adjustment rate, and further improve the overall frequency regulation performance of the system.

[0057] In another exemplary embodiment of the present application, the operating principle of the energy storage system is that pumped storage obtains energy by converting potential energy and electrical energy, converting excess electrical energy during low-load periods into high-value electrical energy during peak periods, and realizing the transfer of time and space. The working logic is to act as an electricity user during periods of low system load, storing water from the lower reservoir to the upper reservoir. During peak electricity consumption, it acts as a generator set, releasing water back to the reservoir and supplying power to the grid. For daily regulated pumped storage power stations, there are:

[0058]

[0059] In the formula, and They represent the pumping and power generation of pumped storage in period t respectively. represents the maximum allowable power generation of pumped storage subject to reservoir capacity constraints. Since adjusting the operating conditions of pumped storage will result in energy losses, η represents the overall efficiency.

[0060] In another exemplary embodiment of the present application, the implementation process of step 101 can be described as follows:

[0061] (1) The IGDT model is used to represent the uncertainty in the pumped storage process in order to construct a decision model.

[0062] Among them, in order to more accurately predict the capacity of pumped storage and provide a basis for participating in the electricity spot market. Since the process of pumped storage is affected by many factors, the uncertainty expressed using traditional probability methods is challenging. Therefore, this application develops an IGDT model that does not rely on prior knowledge or probability distribution functions to represent the uncertainty in the pumped storage process. While achieving the expected goals, it can maximize the fluctuation range of uncertain factors. The uncertainty in the pumped storage process is expressed as:

[0063]

[0064] Where X is an uncertainty parameter, d is the decision variable, F(X,d) is the objective function, H(X,d) and G(X,d) represent equality constraints and inequality constraints respectively, min is the minimum value, and st is the constraint condition.

[0065] A common uncertainty set in IGDT models is the fractional uncertainty model, which can be expressed as:

[0066]

[0067] In the formula, Indicates the predicted value of generated power. Represents the uncertainty factor in the power generation process, that is, the amplitude of fluctuation. Indicates the fluctuation range of uncertain parameters.

[0068] A more comprehensive uncertainty considering multiple market participants can be expressed as: In the formula, and Represent the uncertainty coefficients of the electricity spot market and the frequency regulation ancillary service market respectively.

[0069] Furthermore, in order to reflect the risk preference of decision makers, the expected power generation is used as a benchmark value. The expected power generation is expressed as follows:

[0070] Z=(1-κ z )Z0.

[0071] Where Z0 and Z represent the actual power generation and predicted power respectively. z Indicates the coefficient of deviation of the expected generated power.

[0072] Consider a decision model under a risk aversion strategy. Based on this decision model, the decision maker is pessimistic about the predicted value of power generation. The decision model is expressed as:

[0073]

[0074] κ z Indicates the coefficient of deviation of the expected generated power.

[0075] (2) The decision maker determines the expected electricity price deviation coefficient κ corresponding to the expected power generation rate z , solve the decision model to obtain the uncertainty coefficient To generate the uncertainty set of generated power

[0076] (3) Considering wind power output P w,t The uncertainty of the wind power prediction error is constructed by using the Wass metric method. Characterizes the uncertainty of wind power output, and W is the fuzzy set of actual wind power output.

[0077] Based on the above description, the fuzzy set of wind power prediction error is constructed based on the Wass metric method.

[0078] In order to deal with the uncertainty in the fuzzy set of wind power forecast error, the true distribution P of wind power output is unknown, but random samples can be obtained based on historical sample data. Based on this, first, the empirical distribution P of actual wind power output is obtained from a set of sample historical data. N , and used as an estimate of the true distribution P. Then, a distribution that contains the true distribution as much as possible and satisfies The fuzziness set of x represents the sample history data. The Wass metric measures the distance between the empirical distribution and the true distribution. The distance between the empirical distribution and the true distribution can be defined as W(P N ,P):

[0079]

[0080] In the formula, the uncertain parameters and They follow the distribution P N and P, π w It is P and P N The joint distribution of .

[0081] Based on the distance between the empirical distribution and the true distribution measured by the Wass metric method, the fuzzy set of wind power prediction error can be generated.

[0082] (4) Construct a fuzzy uncertainty set based on the fuzziness set of wind power prediction error. The fuzzy uncertainty set W is as follows:

[0083] W={P∈Φ(Ξ w )W(P N ,P)≤ε(N w )}.

[0084]

[0085] In the formula, Φ(Ξ w ) represents the complete probability distribution on the support set. W can be regarded as a circle with a radius of ε(N w ), with the center being P N The ball contains all possible probability distributions and has a certain degree of confidence. w represents the coefficient. w Represents the preset confidence level. Represents the sample mean of actual wind power output historical data. N w represents the number of samples of historical data, τ represents the time of the wind power generation process, Represents a sample set.

[0086] Furthermore, in order to obtain the complete probability distribution on the support set, we need to have the data source of the support set. Based on this, the support set Ξ w It can be derived from historical sample data. First, After standardization, the normalized data is used It is indicated that the standardization is as follows:

[0087]

[0088] In the formula, ∑ w Represents the sample variance of historical data on actual wind power output.

[0089] Uncertain parameters The mean of the uncertain parameter is 0, and the variance of the uncertain parameter is taken as the uncertain parameter Assume Θ w yes The uncertainty set is:

[0090]

[0091] In the formula, l wIt is an uncertain parameter of the border. is the uncertainty parameter under the standardized sample set, S w are different sample sets after standardization.

[0092] (5) The fuzzy uncertainty set is converted into a reasonable boundary representation, and the reasonable boundary representation is converted into an optimization problem. The optimization problem is solved to obtain the optimal boundary and the uncertainty set of wind power output.

[0093] According to the characteristics described in step (4), the following formula is optimized to obtain a reasonable boundary representation:

[0094]

[0095] In the formula, and W std Represents the uncertain parameters The empirical distribution and fuzziness set of ,1 w is the uncertainty set Θ w The confidence level. w,max It is an uncertain parameter The maximum boundary of .

[0096] By building a reasonable boundary, it can ensure that the energy storage system can make more accurate and reliable decisions when facing wind power output fluctuations, thereby improving the overall operating efficiency and stability of the system. In addition, reasonable boundary settings can help reduce the economic risks and operating costs caused by wind power output forecast errors, ensuring the safe and economic operation of the power system.

[0097] The above reasonable boundary expression formula is transformed into an optimization problem that is easy to solve, and the optimal boundary l is obtained. w and uncertainty set Θ w , thereby increasing the support set for actual wind power output w , increase the support set of actual wind power output w becomes:

[0098]

[0099] Get support for increasing actual wind power output w Afterwards, the uncertainty analysis of the ambiguity in the Wass measurement process can be further performed.

[0100] In another exemplary embodiment of the present application, a joint declaration and scheduling strategy decision model is constructed based on the predetermined user incentive factors and the satisfaction of each user in participating in the demand response. The constructed joint declaration and scheduling strategy decision model is expressed as:

[0101] maxR(Z wp )=∑ ξ∈M ρ(z ξ )(Z wp ).

[0102] In the formula, R(Z wp ) represents the satisfaction of user n in participating in demand response in period t, Z wp represents the satisfaction in the electricity spot market and frequency regulation auxiliary service market, ξ represents the scenario in which user n participates in period t, M represents the set of scenarios ξ, ρ(z ξ ) represents the coefficient of user comfort loss function, z ξ represents the actual benefit of user n participating in demand response in period t, represents the settlement price scenario of the electricity spot market, represents the settlement price scenario of the frequency regulation ancillary service market, Z ξ It represents the actual benefit of user n participating in demand response in time period t. max represents the maximum value.

[0103] In another exemplary embodiment of the present application, constraining the joint declaration and scheduling strategy decision model based on the uncertainty set includes:

[0104] (1) The determination of the bidding quantity is based on the analysis and processing results of the uncertainty of wind power output. Based on this, the bidding quantity of wind power generation is limited to the minimum bidding quantity and the maximum bidding quantity.

[0105]

[0106] Where P w,t is the power generated by wind power in the electricity spot market, It is a representation of the uncertainty of wind power generation.

[0107] The process of determining the number of bids involves dealing with the uncertainty of wind power output, that is, constraining the joint application and dispatch strategy decision model through the constructed uncertainty set to generate an operation model design strategy for energy storage to participate in the electricity spot market.

[0108] (2) When pumped storage participates in multi-market bidding, the total power of each market is between the corresponding upper and lower operating limits. Based on this, the base point and frequency regulation capacity constraints of pumped storage are:

[0109]

[0110] In the formula, γ ps It represents the ratio of pumped storage frequency regulation power. and Represent the minimum and maximum limiting rated power of pumped storage respectively. represents the power generated by pumped storage in the electricity spot market during period t, represents the power generated by pumped storage at time t in the frequency regulation ancillary service market.

[0111] (3) To ensure that the pumping and power generation conditions of pumped storage do not occur at the same time, the operating state is constrained, which can be expressed as:

[0112]

[0113] In the formula, and Two variables representing the operating status of pumped storage, represents the water storage capacity of pumped storage at period t, represents the power generation of pumped storage in period t.

[0114] (4) When pumped storage is in operation, the storage capacity must be between the minimum and maximum storage capacities and the storage capacity must be balanced. Based on this, the storage capacity is restricted as follows:

[0115]

[0116] In the formula, and denote the initial and final capacities of pumped storage, respectively; Respectively represent the residual energy of pumped storage in period t, η g and η p They represent the power generation efficiency and pumping efficiency of pumped storage respectively, and Δt represents the time difference between the initial state and the final state.

[0117] (5) Capacity allocation in WPCS is the key path to achieve multi-market connection. Based on this, the energy storage system is coupled with the power system and the frequency regulation auxiliary service system based on the uncertainty set, which is:

[0118]

[0119] In the DRO model, uncertainty in wind power output occurs and the prediction error vector is P wp,t Represents the output of the WPCS declaration, specifically expressed as:

[0120]

[0121]

[0122] In the formula, represents the power output in the electricity spot market, represents the power output of the frequency regulation ancillary services market, Represents the uncertainty in wind power output.

[0123] (6) The pumped storage of WPCS is adjusted as a flexible resource. Based on the uncertainty set, the adjustment process of flexible resources is characterized by linear affine:

[0124]

[0125] In the formula, and They respectively represent the flexible adjustment of pumping power and frequency regulation power in pumped storage. and Linear affine coefficients representing the pumping and frequency regulation power of pumped storage with respect to the prediction error.

[0126] (7) The DRO model is used to characterize the uncertainty of wind power generation, and the constraints of the DRO model are set as follows:

[0127]

[0128] In the formula, represents the flexibly adjusted pumping power in pumped storage, represents the final capacity of pumped storage after flexible regulation, represents the remaining capacity of pumped storage after flexible regulation in period t, Represents the flexibly generated frequency-regulated power in pumped storage.

[0129] The DRO model is a model that represents the uncertainty of wind power generation, which can better estimate the amount of wind power storage and participate in the electricity spot market. Figure 2 As shown in the figure, the DRO model first estimates and predicts the historical data of wind power generation forecast errors to determine the uncertainty set of wind power generation output. The uncertainty set is converted into an internal optimization target, and the optimization target is merged into a single-layer optimization and a data-driven fuzzy set is constructed to obtain the constraint equation to obtain a single-layer model. The single-layer model is then solved in the generative solver to obtain relevant data on wind power generation uncertainty, thereby obtaining an overall estimate of the uncertainty of the process.

[0130] In summary, this application aims to design a multi-energy joint market participation mechanism for emerging market players and changing market demands. Based on this, this application constructs a joint trading model for an energy storage system consisting of pumped storage and wind power generation to participate in the electricity spot market and the frequency regulation auxiliary service market, and uses the coordinated optimization model IGDT-DRO (i.e., IGDT model plus DRO model) to deal with the uncertainty of pumped storage and wind power output. In addition, a joint declaration and dispatch strategy decision model for the energy storage system is constructed, and the joint model is constrained. The core is to design a collaborative trading model for the energy storage and frequency regulation markets, which effectively responds to the uncertainty of wind power output and has practical significance for improving the safety and reliability of power grid operation.

[0131] The energy storage system is a combined system consisting of pumped storage and wind power generation, in which the energy storage system is connected to the external power grid through a transformer to achieve comprehensive energy interaction. The energy storage system participates in the market as a power generation unit, and its declaration, clearance and settlement are all based on the combined force of the system. When there is a surplus of wind power, the actual output of the energy storage system will be greater than the bid power. In order to avoid deviation penalties, pumped storage will store excess energy to avoid waste. When the energy storage system participates in the frequency regulation auxiliary service market, the pumped storage power station, as an energy storage resource, can provide the frequency requirements required by the dispatching center by changing the operating state to reduce the frequency deviation.

[0132] In the frequency regulation auxiliary service market, the energy storage system formulates the optimal bidding strategy based on the predicted wind power output of the next day and declares the reserved frequency regulation capacity, and the dispatch center conducts frequency regulation pre-examination. In the electricity spot market, the energy storage system declares the upper and lower limits of the output.

[0133] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the design data of the operation mode of energy storage participating in the electricity spot market. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for designing an operation mode of energy storage participating in the electricity spot market is implemented.

[0134] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0135] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0136] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0138] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0139] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0140] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for designing an operation model for energy storage to participate in the electricity spot market, characterized in that: The method for designing the operation mode of energy storage participating in the electricity spot market includes: Constructing a joint trading model for energy storage systems to participate in the electricity spot market and the frequency regulation auxiliary service market; the energy storage system consists of pumped storage and wind power generation; Analyzing the uncertainty of wind power generation in the joint trading model and constructing an uncertainty set; Construct a joint declaration and dispatch strategy decision model for energy storage systems; Based on the uncertainty set, the joint declaration and dispatch strategy decision model is constrained to generate an operation mode design strategy for energy storage participating in the electricity spot market.

2. The method for designing an operation mode for energy storage participating in the electricity spot market according to claim 1 is characterized in that: In the joint trading model of energy storage systems participating in the electricity spot market and the frequency regulation ancillary service market, when there is excess wind power, the actual output of the energy storage system is greater than the bid power, and pumped storage stores the excess energy; When the energy storage system participates in the frequency regulation auxiliary service market, pumped storage, as an energy storage resource, provides the frequency demand required by the dispatch center by changing the operating state; in the frequency regulation auxiliary service market, the energy storage system formulates the optimal bidding strategy based on the predicted wind power output of the next day and the market price, declares the reserved frequency regulation capacity and mileage price, and the dispatch center conducts frequency regulation pre-examination; In the electricity spot market, the energy storage system declares an upper limit for electric energy output and a lower limit for electric energy output.

3. The method for designing an operation mode for energy storage participating in the electricity spot market according to claim 1 is characterized in that: The uncertainty of wind power generation in the joint trading model is analyzed, and an uncertainty set is constructed, including: The IGDT model is used to represent the uncertainty in the pumped storage process in order to construct a decision model; Determining an expected electricity price deviation coefficient corresponding to an expected power generation rate, solving the decision model to obtain an uncertainty coefficient, so as to generate an uncertainty set of power generation; Considering the uncertainty of wind power output, the Wass metric method is used to construct the fuzzy set of wind power prediction error; Construct the fuzzy uncertainty set based on the fuzzy set of wind power prediction error; The fuzzy uncertainty set is converted into a reasonable boundary representation, and the reasonable boundary representation is converted into an optimization problem, and the optimization problem is solved to obtain the optimal boundary and the uncertainty set of wind power output.

4. The method for designing an operation mode for energy storage participating in the electricity spot market according to claim 3 is characterized in that: The decision model is expressed as: Where X represents the uncertainty parameter, d represents the decision variable, F(X,d) represents the objective function, H(X,d) represents the equality constraint, and G(X,d) represents the inequality constraint; Represents the uncertainty factor in the power generation process, that is, the amplitude of fluctuation; κ z represents the deviation coefficient of the expected power generation, Z0 represents the actual power generation, represents the fluctuation range of the uncertain parameters, Indicates the predicted value of generated power.

5. The method for designing an operation mode for energy storage participating in the electricity spot market according to claim 3 is characterized in that: Considering the uncertainty of wind power output, the Wass metric method is used to construct the fuzzy set of wind power prediction error, including: The empirical distribution of actual wind power output is obtained from a set of sample historical data, and a wind power distribution containing the real distribution and satisfying The fuzzy set of N represents the empirical distribution, P represents the true distribution, and x represents the sample historical data; The Wass metric method is used to measure the distance between the empirical distribution and the true distribution to generate a fuzzy set of wind power prediction errors.

6. The method for designing an operation mode for energy storage participating in the electricity spot market according to claim 1, characterized in that: The joint declaration and scheduling strategy decision model is expressed as: maxR(Z wp )=∑ ξ∈M ρ(z ξ )(WITH wp ); In the formula, R(Z wp ) represents the satisfaction of user n in participating in demand response in period t, Z wp represents the satisfaction in the electricity spot market and the frequency regulation auxiliary service market, ξ represents the scenario in which user n participates in time period t, M represents the set of scenarios ξ, ρ(z ξ ) represents the coefficient of user comfort loss function, z ξ It represents the actual benefit of user n participating in demand response in time period t, and max represents the maximum value.

7. The method for designing an operation mode for energy storage participating in the electricity spot market according to claim 3 is characterized in that: Constraining the joint declaration and scheduling strategy decision model based on the uncertainty set includes: Limit the number of bids for wind power generation to a minimum bid number and a maximum bid number; When pumped storage participates in multi-market bidding, the sum of the power in each market is between the corresponding upper and lower operating limits, setting the base point and frequency regulation capacity constraints for pumped storage; Constrain the running status; When pumped storage is in operation, restrictions are placed on storage capacity; Based on the uncertainty set, coupling constraints are imposed on the energy storage system in the power system and the frequency regulation auxiliary service system; Based on the uncertainty set, the adjustment process of the flexible resources is characterized by using linear affine; A DRO model is used to characterize the uncertainty of the wind power generation, and constraint conditions of the DRO model are set.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for designing an operation model for energy storage participating in the electricity spot market as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for designing an operation model for energy storage participating in the electricity spot market described in any one of claims 1-7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for designing an operation model for energy storage participating in the electricity spot market described in any one of claims 1-7 is implemented.

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