Network construction type energy storage frequency modulation auxiliary service capacity distribution and income optimization method

Abstract

The invention relates to the technical field of network construction type energy storage, in particular to a network construction type energy storage frequency modulation auxiliary service capacity distribution and income optimization method, which comprises the steps of designing a multi-stage bidding clearing process; constructing a network construction type energy storage frequency modulation auxiliary service bidding clearing double-layer game model; and proposing a profit distribution strategy based on an improved Shapley value method. The inertia support and dynamic response characteristics are quantified by constructing a cooperative scheduling framework of network-constructing type energy storage main attack frequency modulation and network-following type energy storage matching peak regulation and combining a double-track compensation mechanism; establishing a double-layer game model aiming at minimizing the power purchase cost of the power grid and maximizing the energy storage income to realize global optimization; a Shapley value method is improved, a three-stage correction factor is designed, and profit distribution is optimized from multiple dimensions of technical contribution, capacity availability and dynamic response capability.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-type energy storage technology, and in particular to a method for capacity allocation and revenue optimization of grid-type energy storage frequency regulation auxiliary services. Background Art

[0002] As renewable energy penetration continues to increase, its output volatility poses a significant challenge to grid frequency stability. Grid-connected energy storage, with its multiple advantages such as rapid response, inertia support, and voltage regulation, has become a key solution for improving system frequency stability. Therefore, establishing a market-based operating mechanism tailored to the technical characteristics of grid-connected energy storage is a key issue in optimizing the allocation of frequency regulation ancillary services resources. The trading mechanisms in the frequency regulation ancillary services market directly impact resource allocation efficiency, and mature international markets have developed differentiated development models. The PJM region in the United States utilizes a sequential clearing mechanism for energy and frequency regulation markets, integrating generators, energy storage, and demand response resources. The UK and Northern Europe have established independent ancillary services markets, prioritizing battery energy storage for rapid frequency regulation services. The Chinese market exhibits distinct regional characteristics. North China utilizes an available time compensation mechanism, while Shandong and Shanxi introduce frequency regulation mileage and performance indicator compensation. Jiangsu and other regions implement a two-part compensation system based on capacity and mileage, better reflecting the capacity value and actual contribution of frequency regulation resources. These differentiated market mechanisms reflect the varying needs of local grid characteristics and resource endowments.

[0003] However, the current frequency regulation ancillary services market still faces numerous challenges. On the one hand, existing mechanisms primarily focus on optimizing the dispatch of a single type of energy storage, lacking systematic research on capacity allocation mechanisms for grid-connected energy storage participating in the frequency regulation market. This makes it difficult to adequately compensate for the rapid response advantages of energy storage. On the other hand, mechanisms for optimizing the returns of energy storage participating in the frequency regulation market are still imperfect, dampening investment enthusiasm. Therefore, a method for capacity allocation and revenue optimization for grid-connected energy storage frequency regulation ancillary services is urgently needed. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method for capacity allocation and revenue optimization of grid-type energy storage frequency regulation auxiliary services, which effectively solves the above-mentioned problems mentioned in the background technology.

[0005] The technical solutions of the present invention are as follows:

[0006] First, a method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services is proposed. The method includes the following steps:

[0007] S1. Design a multi-stage bidding clearing process;

[0008] S2. Construct a two-tier game model for bidding and clearing grid-connected energy storage frequency regulation auxiliary services;

[0009] S3. Propose a profit distribution strategy based on the improved Shapley value method.

[0010] A further improvement of the present invention is that the specific content of the multi-stage bidding clearing process in S1 is as follows: before the day-ahead market starts, the load reports the power consumption S to the dispatching center. t ; All energy storage has declared peak shaving capacity in the past few days and peak-shaving price λ peak The dispatching agency ranks bidders based on the principle of minimizing service costs, and the price declared by the last successful bidder forms the clearing price. All successful bidders are settled at the clearing price. On the peak-shaving day, the service provider executes the dispatching instruction and ultimately receives compensation. The grid-connected energy storage power station i declares its frequency regulation price λ to the dispatching center. freq,i,t , FM capacity FM mileage The dispatching center makes transaction decisions based on the market clearing rules and takes the minimum total frequency regulation cost of grid-connected energy storage as the goal, and obtains the frequency regulation capacity clearing price λ cap and the frequency modulation mileage clearing price λ mil At the same time, the dispatching center takes the minimum total peak-shaving cost as the goal and obtains the peak-shaving clearing price λ peak Each energy storage power station adjusts its declared cost, peak-shaving and frequency-regulating capacity, and mileage based on the transaction volume and transaction price issued by the dispatching center until the market reaches equilibrium, that is, the declared volume, transaction price, and thermal power unit output plan no longer change.

[0011] A further improvement of the present invention is that the two-layer game model in S2 includes an upper layer model and a lower layer model; the construction of the upper layer model includes the following specific steps:

[0012] S21. With the goal of minimizing the cost of purchasing electricity from the power grid, an objective function is constructed. The calculation formula of the objective function is: Among them, λ cap is the capacity compensation price declared for grid-type energy storage, λ mil Mileage compensation price declared for grid-type energy storage, To build the frequency regulation capacity of the grid-type energy storage power station that won the bid, Frequency regulation mileage for the winning bid of the grid-type energy storage power station;

[0013] S22. Construct the constraint conditions of the upper model, wherein the constraint conditions include supply and demand balance constraint, network flow constraint and thermal power unit constraint; the specific formula of the supply and demand balance constraint is:

[0014]

[0015] in, For frequency regulation capacity requirements; is the frequency modulation mileage requirement; St is the load value at time t; The frequency regulation capacity of the grid-type energy storage power station i; is the frequency regulation capacity of thermal power unit j; and are the frequency regulation mileage factors of grid-type energy storage i and thermal power unit j respectively. and are the frequency regulation mileage of grid-type energy storage power station i and thermal power unit j respectively and and They represent the charging power of the grid-connected energy storage station i at time t; Represents the output of thermal power unit j at time t. and are the discharge power and charging power of energy storage i in time period t respectively.

[0016] A further improvement of the present invention is that the specific formula for the network power flow constraint in S22 is:

[0017]

[0018] Among them, P ij represents the line flow between nodes i and j; ij is the line reactance between nodes i and j; θ i,t and θ j,t represents the phase angle of nodes i and j at time t; F ij,max Indicates the maximum transmission power of the line between nodes i and j.

[0019] A further improvement of the present invention is that the thermal power unit constraint in S22 includes a unit power constraint: Frequency regulation capacity constraints: Hill climbing constraints: And the minimum start and stop time constraints: in, and They represent the upward and downward climbing rate limits of unit j, T on,j and T off,j Represent the operation and shutdown time of unit j, T on,min,j and T off,min,j They represent the minimum operating time and minimum outage time of unit j respectively.

[0020] A further improvement of the present invention is that the two-layer game model in S2 further includes a lower-layer model; the construction of the lower-layer model includes the following specific steps:

[0021] S23. With the goal of maximizing the individual benefits of each energy storage member, the objective function is constructed as follows:

[0022]

[0023] f(c) i ); where λ peak are the peak-shaving and clearing prices of energy storage; w i Assign a weight to the capacity of energy storage i; K i is the frequency regulation performance coefficient of energy storage i; P freq,t is the frequency modulation demand in period t; P max,i is the power capacity of energy storage i. f(c) i Cost of energy storage frequency auxiliary services: MC represents the marginal cost of frequency regulation per unit of energy storage;

[0024] S24. Construct the constraints of the lower model, wherein the constraints include state of charge constraints, charge-discharge switching times constraints, and peak-shaving and frequency regulation market declaration quantity constraints; the state of charge constraints include SOC dynamic update constraints: Among them, SOC i,t and SOC i,t-1 They represent the state of charge of the energy storage at time t and t-1, respectively, ch and η dis They represent the charging and discharging efficiency of the energy storage station, Es i Represents the rated capacity of energy storage station i; SOC upper and lower limit constraints: SOC i,min ≤SOC i,t ≤SOC i,max Among them, SOC max , SOC min Respectively represent the maximum and minimum state of charge of the energy storage power station; SOC initial and terminal value constraints: Among them, SOC i,0 , SOC i,T They represent the state of charge of energy storage station i at the initial time (t=0); SOC i,initial , SOC i,final They represent the termination time of energy storage station i at the end of its operation cycle (t=T);

[0025] S25. The specific content of the charge-discharge switching times constraint is: ch,i,t +u dis,i,t ≤1; where u ch,i,t and u dis,i,t are two variables of 0 and 1, which respectively indicate whether the energy storage station i is in the charging state or the discharging state at time t; the conditions for the energy storage station i to switch between charging and discharging states at time t are: Among them, z i,t Indicates whether the energy storage station i switches between charging and discharging modes at time t. i,t=1 means switching occurs, z i,t = 0 means no switching occurs; within a given time period T, the number of switching times of the charging and discharging operating conditions of the energy storage station i shall not exceed the maximum allowed number

[0026] S26. The peak-shaving and frequency-regulation market application quantity constraints include the peak-shaving market application quantity constraints of energy storage power stations, the frequency-regulation market application quantity constraints of grid-type energy storage, and the capacity constraints of energy storage systems participating in the peak-shaving and frequency-regulation markets. The peak-shaving market application quantity constraints of energy storage power stations are: C peak,min ≤λ peak,i,t ≤C peak,max ; Among them, λ peak,i,t represents the peak load quotation of energy storage power station i at time t; C peak,max and C peak,min Respectively represent the upper and lower limits of the peak-shaving price; the frequency regulation market declaration quantity constraint of the grid-type energy storage is: C freq,min ≤λ freq,i,t ≤C freq,max ; Among them, λ freq,i,t represents the frequency regulation quotation of grid-connected energy storage power station i at time t; C freq,max and C freq,min They represent the upper and lower limits of the grid-type energy storage frequency regulation price respectively; the capacity constraint of the energy storage system in the peak regulation and frequency regulation market is in, and They represent the maximum capacity of energy storage for peak regulation and frequency regulation respectively.

[0027] A further improvement of the present invention is that S3 includes the following specific steps:

[0028] S31. Introducing the Shapley value method, for an alliance S consisting of n energy storage units, the marginal contribution of member i is defined as the increase in total revenue after joining the alliance. The Shapley value calculation formula is:

[0029]

[0030] Among them, R i is the Shaplay value of energy storage member i; S is the set that does not include energy storage member i; N is the set of all energy storage members; v(S) represents the profit function of sub-alliance S; v(S∪{i}) represents the profit of each sub-alliance after joining energy storage member i;

[0031] S32, introduce technology contribution correction factor α i , Capacity availability correction factor β i and the dynamic response capability correction factor γ i The calculation formula of the technical contribution correction factor is: Ki and P cap,i They represent the technical performance indicators of energy storage i and the available power capacity of energy storage i participating in peak load regulation and frequency regulation; K j and P cap,j They represent the technical performance indicators of other energy storages except energy storage i and the available power capacity participating in peak load regulation and frequency regulation respectively; the calculation formula of the capacity availability correction factor is: Among them, T avail,i It represents the duration of time that energy storage i can provide capacity during peak load and frequency regulation, T avail,j It represents the duration of time that other energy storages except energy storage i can provide capacity during peak and frequency regulation. The calculation formula of the dynamic response capability correction factor is: Among them, R mil,i Represents the dynamic response capability index of energy storage i; R mil,j It represents the dynamic response capability index of other energy storages except energy storage i.

[0032] The technical effects of the present invention are as follows:

[0033] A method for capacity allocation and revenue optimization of frequency regulation auxiliary services for grid-type energy storage was constructed. The present invention proposes an optimized configuration in which grid-type energy storage focuses on frequency regulation and grid-following energy storage cooperates with peak regulation, so that grid-type energy storage retains more frequency regulation margin. Through the "capacity + mileage" dual-track compensation mechanism, the inertia support and dynamic response characteristics of grid-type energy storage are quantified. A frequency regulation capacity optimization allocation model based on a two-layer game is proposed. The upper layer aims to minimize the grid's electricity purchase cost, and the lower layer aims to maximize energy storage revenue. Global optimization is achieved through KKT conditions and duality theory to verify the model's effectiveness in improving system operational reliability and economy. An improved Shapley value method is proposed for revenue distribution. A three-stage correction factor is designed to quantify the actual contribution of energy storage from a multi-dimensional perspective, effectively solving the problem of underestimation of the value of fast-response resources by traditional allocation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0035] Figure 1 This is a flow chart of a method for capacity allocation and revenue optimization of a grid-connected energy storage frequency regulation auxiliary service according to Example 1 of the present invention;

[0036] Figure 2 This is a structural diagram of the double-layer optimization model of Example 1 of the present invention. DETAILED DESCRIPTION

[0037] A method for capacity allocation and revenue optimization of frequency regulation auxiliary services for grid-type energy storage was constructed. The present invention proposes an optimized configuration in which grid-type energy storage focuses on frequency regulation and grid-following energy storage cooperates with peak regulation, so that grid-type energy storage retains more frequency regulation margin. Through the "capacity + mileage" dual-track compensation mechanism, the inertia support and dynamic response characteristics of grid-type energy storage are quantified. A frequency regulation capacity optimization allocation model based on a two-layer game is proposed. The upper layer aims to minimize the grid's electricity purchase cost, and the lower layer aims to maximize energy storage revenue. Global optimization is achieved through KKT conditions and duality theory to verify the model's effectiveness in improving system operational reliability and economy. An improved Shapley value method is proposed for revenue distribution. A three-stage correction factor is designed to quantify the actual contribution of energy storage from a multi-dimensional perspective, effectively solving the problem of underestimation of the value of fast-response resources by traditional allocation methods.

[0038] Example 1:

[0039] This embodiment proposes a method for allocating capacity and optimizing revenue of grid-connected energy storage frequency regulation auxiliary services, such as Figure 1-Figure 2 As shown, the following specific steps are included:

[0040] S1. Design a multi-stage bidding clearing process;

[0041] S2. Construct a two-tier game model for bidding and clearing grid-connected energy storage frequency regulation auxiliary services;

[0042] S3. Propose a profit distribution strategy based on the improved Shapley value method.

[0043] In this embodiment, the specific content of the multi-stage bidding clearing process in S1 is: before the day-ahead market starts, the load reports the power consumption S to the dispatching center. t ; All energy storage has declared peak shaving capacity in the past few days and peak-shaving price λ peak The dispatching agency ranks bidders based on the principle of minimizing service costs, and the price declared by the last successful bidder forms the clearing price. All successful bidders are settled at the clearing price. On the peak-shaving day, the service provider executes the dispatching instruction and ultimately receives compensation. The grid-connected energy storage power station i declares its frequency regulation price λ to the dispatching center. freq,i,t , FM capacity FM mileage The dispatching center makes transaction decisions based on the market clearing rules and takes the minimum total frequency regulation cost of grid-connected energy storage as the goal, and obtains the frequency regulation capacity clearing price λ cap and the frequency modulation mileage clearing price λ mil At the same time, the dispatching center takes the minimum total peak-shaving cost as the goal and obtains the peak-shaving clearing price λ peakEach energy storage power station adjusts its declared cost, peak-shaving and frequency-regulating capacity, and mileage based on the transaction volume and transaction price issued by the dispatching center until the market reaches equilibrium, that is, the declared volume, transaction price, and thermal power unit output plan no longer change.

[0044] In this embodiment, the two-layer game model in S2 includes an upper layer model and a lower layer model; the construction of the upper layer model includes the following specific steps:

[0045] S21. With the goal of minimizing the cost of purchasing electricity from the power grid, an objective function is constructed. The calculation formula of the objective function is: Among them, λ cap is the capacity compensation price declared for grid-type energy storage, λ mil Mileage compensation price declared for grid-type energy storage, To build the frequency regulation capacity of the grid-type energy storage power station that won the bid, Frequency regulation mileage for the winning bid of the grid-type energy storage power station;

[0046] S22. Construct the constraint conditions of the upper model, wherein the constraint conditions include supply and demand balance constraint, network flow constraint and thermal power unit constraint; the specific formula of the supply and demand balance constraint is:

[0047]

[0048] in, For frequency regulation capacity requirements; is the frequency modulation mileage requirement; S t is the load value at time t; The frequency regulation capacity of the grid-type energy storage power station i; is the frequency regulation capacity of thermal power unit j; and are the frequency regulation mileage factors of grid-type energy storage i and thermal power unit j respectively. and are the frequency regulation mileage of grid-type energy storage power station i and thermal power unit j respectively and and They represent the charging power of the grid-connected energy storage station i at time t; Represents the output of thermal power unit j at time t. and are the discharge power and charging power of energy storage i in time period t respectively.

[0049] In this embodiment, the specific formula for the network power flow constraint in S22 is:

[0050]

[0051] Among them, P ijrepresents the line flow between nodes i and j; ij is the line reactance between nodes i and j; θ i,t and θ j,t represents the phase angle of nodes i and j at time t; F ij,max Indicates the maximum transmission power of the line between nodes i and j.

[0052] In this embodiment, the thermal power unit constraints in S22 include unit power constraints: Frequency regulation capacity constraints: Hill climbing constraints: And the minimum start and stop time constraints: in, and They represent the upward and downward climbing rate limits of unit j, T on,j and T off,j Represent the operation and shutdown time of unit j, T on,min,j and T off,miu,j They represent the minimum operating time and minimum outage time of unit j respectively.

[0053] In this embodiment, the two-layer game model in S2 further includes a lower-layer model; the construction of the lower-layer model includes the following specific steps:

[0054] S23. With the goal of maximizing the individual benefits of each energy storage member, the objective function is constructed as follows:

[0055] Among them, λ peak are the peak-shaving and clearing prices of energy storage; w i Assign a weight to the capacity of energy storage i; K i is the frequency regulation performance coefficient of energy storage i; P freq,t is the frequency modulation demand in period t; P max,i is the power capacity of energy storage i. f(c) i Cost of energy storage frequency auxiliary services: MC represents the marginal cost of frequency regulation per unit of energy storage;

[0056] S24. Construct the constraints of the lower model, wherein the constraints include state of charge constraints, charge-discharge switching times constraints, and peak-shaving and frequency regulation market declaration quantity constraints; the state of charge constraints include SOC dynamic update constraints: Among them, SOC i,t and SOC i,t-1 They represent the state of charge of the energy storage at time t and t-1, respectively, ch and η dis They represent the charging and discharging efficiency of the energy storage station, Es iRepresents the rated capacity of energy storage station i; SOC upper and lower limit constraints: SOC i,min ≤SOC i,t ≤SOC i,max Among them, SOC max , SOC min Respectively represent the maximum and minimum state of charge of the energy storage power station; SOC initial and terminal value constraints: Among them, SOC i,0 , SOC i,T They represent the state of charge of energy storage station i at the initial time (t=0); SOC i,initial , SOC i,finel They represent the termination time of energy storage station i at the end of its operation cycle (t=T);

[0057] S25. The specific content of the charge-discharge switching times constraint is: ch,i,t +u dis,i,t ≤1; where u ch,i,t and u dis,i,t are two variables of 0 and 1, which respectively indicate whether the energy storage station i is in the charging state or the discharging state at time t; the conditions for the energy storage station i to switch between charging and discharging states at time t are: Among them, z i,t Indicates whether the energy storage station i switches between charging and discharging modes at time t. i,t =1 means switching occurs, z i,t = 0 means no switching occurs; within a given time period T, the number of switching times of the charging and discharging operating conditions of the energy storage station i shall not exceed the maximum allowed number

[0058] S26. The peak-shaving and frequency-regulation market application quantity constraints include the peak-shaving market application quantity constraints of energy storage power stations, the frequency-regulation market application quantity constraints of grid-type energy storage, and the capacity constraints of energy storage systems participating in the peak-shaving and frequency-regulation markets. The peak-shaving market application quantity constraints of energy storage power stations are: C peak,min ≤λ peak,i,t ≤C peak,max ; Among them, λ peak,i,t represents the peak load quotation of energy storage power station i at time t; C peak,max and C peak,min Respectively represent the upper and lower limits of the peak-shaving price; the frequency regulation market declaration quantity constraint of the grid-type energy storage is: C freq,min ≤λ freq,i,t ≤C freq,max ; Among them, λ freq,i,t represents the frequency regulation quotation of grid-connected energy storage power station i at time t; C freq,max and C freq,minThey represent the upper and lower limits of the grid-type energy storage frequency regulation price respectively; the capacity constraint of the energy storage system in the peak regulation and frequency regulation market is in, and They represent the maximum capacity of energy storage for peak regulation and frequency regulation respectively.

[0059] In this embodiment, S3 includes the following specific steps:

[0060] S31. Introducing the Shapley value method, for an alliance S consisting of n energy storage units, the marginal contribution of member i is defined as the increase in total revenue after joining the alliance. The Shapley value calculation formula is:

[0061]

[0062] Among them, R i is the Shaplay value of energy storage member i; S is the set that does not include energy storage member i; N is the set of all energy storage members; v(S) represents the profit function of sub-alliance S; v(S∪{i}) represents the profit of each sub-alliance after joining energy storage member i;

[0063] S32, introduce technology contribution correction factor α i , Capacity availability correction factor β i and the dynamic response capability correction factor γ i The calculation formula of the technical contribution correction factor is: K i and P cap,i They represent the technical performance indicators of energy storage i and the available power capacity of energy storage i participating in peak load regulation and frequency regulation; K j and P cap,j They represent the technical performance indicators of other energy storages except energy storage i and the available power capacity participating in peak load regulation and frequency regulation respectively; the calculation formula of the capacity availability correction factor is: Among them, T avail,i It represents the duration of time that energy storage i can provide capacity during peak load and frequency regulation, T avail,j It represents the duration of time that other energy storages except energy storage i can provide capacity during peak and frequency regulation. The calculation formula of the dynamic response capability correction factor is: Among them, R mil,i Represents the dynamic response capability index of energy storage i; R mil,j It represents the dynamic response capability index of other energy storages except energy storage i.

[0064] Example 2:

[0065] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned method for capacity allocation and revenue optimization of a grid-connected energy storage frequency regulation auxiliary service by calling the computer program stored in the memory.

[0066] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement a method for capacity allocation and revenue optimization of a grid-type energy storage frequency regulation auxiliary service provided in the above-mentioned method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment will not be described in detail here.

[0067] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0068] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0069] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowcharts and block diagrams, as well as combinations of processes and blocks in the flowcharts or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts or block diagrams. Figure 1 A process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and boxes Figure 1 A step that specifies a function in one or more boxes.

[0071] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services, characterized by: The specific steps include: S1. Design a multi-stage bidding clearing process; S2. Construct a two-tier game model for bidding and clearing grid-connected energy storage frequency regulation auxiliary services; S3. Propose a profit distribution strategy based on the improved Shapley value method.

2. A method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services according to claim 1, characterized in that: The specific content of the multi-stage bidding clearing process in S1 is as follows: before the day-ahead market starts, the load reports the power consumption S to the dispatching center. t ; All energy storage has declared peak shaving capacity in the past few days and peak-shaving price λ peak The dispatching agency ranks bidders based on the principle of minimizing service costs, and the price declared by the last successful bidder forms the clearing price. All successful bidders are settled at the clearing price. On the peak-shaving day, the service provider executes the dispatching instruction and ultimately receives compensation. The grid-connected energy storage power station i declares its frequency regulation price λ to the dispatching center. freq,i,t , FM capacity FM mileage The dispatching center makes transaction decisions based on the market clearing rules and takes the minimum total frequency regulation cost of grid-connected energy storage as the goal, and obtains the frequency regulation capacity clearing price λ cap and the frequency modulation mileage clearing price λ mil At the same time, the dispatching center takes the minimum total peak-shaving cost as the goal and obtains the peak-shaving clearing price λ peak Each energy storage power station adjusts its declared cost, peak-shaving and frequency-regulating capacity, and mileage based on the transaction volume and transaction price issued by the dispatching center until the market reaches equilibrium, that is, the declared volume, transaction price, and thermal power unit output plan no longer change.

3. The method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services according to claim 2, characterized in that: The two-layer game model in S2 includes an upper layer model and a lower layer model; the construction of the upper layer model includes the following specific steps: S21. With the goal of minimizing the cost of purchasing electricity from the power grid, an objective function is constructed. The calculation formula of the objective function is: Among them, λ cap is the capacity compensation price declared for grid-type energy storage, λ mil Mileage compensation price declared for grid-type energy storage, To build the frequency regulation capacity of the grid-type energy storage power station that won the bid, Frequency regulation mileage for the winning bid of the grid-type energy storage power station; S22. Construct the constraint conditions of the upper model, wherein the constraint conditions include supply and demand balance constraint, network flow constraint and thermal power unit constraint; the specific formula of the supply and demand balance constraint is: in, For frequency regulation capacity requirements; is the frequency modulation mileage requirement; S t is the load value at time t; The frequency regulation capacity of the grid-type energy storage power station i; is the frequency regulation capacity of thermal power unit j; and are the frequency regulation mileage factors of grid-type energy storage i and thermal power unit j respectively. and are the frequency regulation mileage of grid-type energy storage power station i and thermal power unit j respectively and and They represent the charging power of the grid-connected energy storage station i at time t; Represents the output of thermal power unit j at time t. and are the discharge power and charging power of energy storage i in time period t respectively.

4. A method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services according to claim 3, characterized in that: The specific formula for the network power flow constraint in S22 is: Among them, P ij represents the line flow between nodes i and j; ij is the line reactance between nodes i and j; θ i,t and θ j,t represents the phase angle of nodes i and j at time t; F ij,max Indicates the maximum transmission power of the line between nodes i and j.

5. A method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services according to claim 4, characterized in that: The thermal power unit constraints in S22 include unit power constraints: Frequency regulation capacity constraints: Hill climbing constraints: And the minimum start and stop time constraints: Among them, P j RU and P j RD They represent the upward and downward climbing rate limits of unit j, T on,j and T off,j Represent the operation and shutdown time of unit j, T on,min,j and T off,min,j They represent the minimum operating time and minimum outage time of unit j respectively.

6. A method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services according to claim 5, characterized in that: The two-layer game model in S2 also includes a lower layer model; the construction of the lower layer model includes the following specific steps: S23. With the goal of maximizing the individual benefits of each energy storage member, the objective function is constructed as follows: Among them, λ peak are the peak-shaving and clearing prices of energy storage; w i Assign a weight to the capacity of energy storage i; K i is the frequency regulation performance coefficient of energy storage i; P freq,t is the frequency modulation demand in period t; P max,i is the power capacity of energy storage i. f(c) i Cost of energy storage frequency auxiliary services: MC represents the marginal cost of frequency regulation per unit of energy storage; S24. Construct the constraints of the lower model, wherein the constraints include state of charge constraints, charge-discharge switching times constraints, and peak-shaving and frequency regulation market declaration quantity constraints; the state of charge constraints include SOC dynamic update constraints: Among them, SOC i,t and SOC i,t-1 They represent the state of charge of the energy storage at time t and t-1, respectively, ch and η dis They represent the charging and discharging efficiency of the energy storage station, Es i Represents the rated capacity of energy storage station i; SOC upper and lower limit constraints: SOC i,min ≤SOC i,t ≤SOC i,max Among them, SOC max , SOC min Respectively represent the maximum and minimum state of charge of the energy storage power station; SOC initial and terminal value constraints: Among them, SOC i,0 , SOC i,T They represent the state of charge of energy storage station i at the initial time (t=0); SOC i,initial , SOC i,final They represent the termination time of energy storage station i at the end of its operation cycle (t=T); S25. The specific content of the charge-discharge switching times constraint is: ch,i,t +u dis,i,t ≤1; where u ch,i,t and u dis,i,t are two variables of 0 and 1, which respectively indicate whether the energy storage station i is in the charging state or the discharging state at time t; the conditions for the energy storage station i to switch between charging and discharging states at time t are: Among them, z i,t Indicates whether the energy storage station i switches between charging and discharging modes at time t. i,t =1 means switching occurs, z i,t = 0 means no switching occurs; within a given time period T, the number of switching times of the charging and discharging operating conditions of the energy storage station i shall not exceed the maximum allowed number S26. The peak-shaving and frequency-regulation market application quantity constraints include the peak-shaving market application quantity constraints of energy storage power stations, the frequency-regulation market application quantity constraints of grid-type energy storage, and the capacity constraints of energy storage systems participating in the peak-shaving and frequency-regulation markets. The peak-shaving market application quantity constraints of energy storage power stations are: C peak,min ≤λ peak,i,t ≤C peak,max ; Among them, λ peak,i,t represents the peak load quotation of energy storage power station i at time t; C peak,max and C oeak,min Respectively represent the upper and lower limits of the peak-shaving price; the frequency regulation market declaration quantity constraint of the grid-type energy storage is: C freq,min ≤λ freq,i,t ≤C freq,max ; Among them, λ freq,i,t represents the frequency regulation quotation of grid-connected energy storage power station i at time t; C freq,max and C freq,min They represent the upper and lower limits of the grid-type energy storage frequency regulation price respectively; the capacity constraint of the energy storage system in the peak regulation and frequency regulation market is in, and They represent the maximum capacity of energy storage for peak regulation and frequency regulation respectively.

7. A method for capacity allocation and revenue optimization of grid-connected energy storage frequency regulation auxiliary services according to claim 6, characterized in that: The S3 includes the following specific steps: S31. Introducing the Shapley value method, for an alliance S consisting of n energy storage units, the marginal contribution of member i is defined as the increase in total revenue after joining the alliance. The Shapley value calculation formula is: Among them, R i is the Shaplay value of energy storage member i; S is the set that does not include energy storage member i; N is the set of all energy storage members; v(S) represents the profit function of sub-alliance S; v(S∪{i}) represents the profit of each sub-alliance after joining energy storage member i; S32, introduce technology contribution correction factor α i , Capacity availability correction factor β i and the dynamic response capability correction factor γ i The calculation formula of the technical contribution correction factor is: K i and P cap,i They represent the technical performance indicators of energy storage i and the available power capacity of energy storage i participating in peak load regulation and frequency regulation; K j and P cap,j They represent the technical performance indicators of other energy storages except energy storage i and the available power capacity participating in peak load regulation and frequency regulation respectively; the calculation formula of the capacity availability correction factor is: Among them, T avail,i It represents the duration of time that energy storage i can provide capacity during peak load and frequency regulation, T avail,j It represents the duration of time that other energy storages except energy storage i can provide capacity during peak and frequency regulation. The calculation formula of the dynamic response capability correction factor is: Among them, R mil,i Represents the dynamic response capability index of energy storage i; R mil,j It represents the dynamic response capability index of other energy storages except energy storage i.

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