Pumped storage participated multi-element market regulation capacity optimization distribution method, system, equipment and medium

Abstract

The invention belongs to the technical field of power systems, and relates to a pumped storage participated multi-element market regulation capacity optimization distribution method, system and equipment and a medium. The method comprises the following steps of: constructing a refined capacity distribution model of a pumped storage power station in a multi-element market by taking the maximum income of the pumped storage power station when pumped storage participates in capacity distribution of main and auxiliary markets as a target; constructing a medium-and-long-term main and auxiliary market joint clearing model by taking the minimum social total cost as a target; combining the capacity distribution model and the medium-and-long-term main and auxiliary market joint clearing model, and establishing a pumped storage participated multi-element market comprehensive income capacity distribution double-layer clearing model; and solving the pumped storage participated multi-element market comprehensive income capacity distribution double-layer clearing model by adopting a particle swarm double-layer algorithm to obtain an optimal capacity distribution scheme. The problem that an existing method is difficult to provide a reasonable capacity distribution and income optimization scheme for the pumped storage power station is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power systems, and relates to a method, system, equipment and medium for optimizing allocation of pumped storage capacity participating in multi-market regulation. Background Art

[0002] In the current context of power system development, with the proposal of the "dual carbon" goal, the power system is gradually transforming into a new power system with new energy as the main body, facing a new supply and demand balance situation. Affected by natural conditions, the actual output of new energy power generation such as wind power and photovoltaic power generation has obvious randomness and volatility, which has led to a sharp increase in the demand for flexible resources in the power system. However, the power system dominated by thermal power units has insufficient operational flexibility, which has brought many challenges to the absorption of wind power and photovoltaic power and the safe and stable operation of the power system. Energy storage is an important means to smooth out power system power fluctuations. Therefore, the reasonable addition of energy storage to the new power system is an effective means to achieve stable operation of the current power system.

[0003] As a mature energy storage technology, pumped storage can ensure the safe and economic operation of the power system by providing flexible resources at different time scales. It plays an important role in peak load regulation, frequency regulation, and standby of the power system. Large-scale deployment of pumped storage power stations can better fill the flexibility gap of the power system and effectively promote the consumption of new energy. Due to the long initial construction and investment cycle and high cost of pumped storage power stations, under the current mechanism, the main cost is still recovered through the capacity electricity price approved by the government, while ensuring a certain rate of return. In this case, the value of the flexible adjustment capacity of pumped storage is difficult to quantify, and the income is relatively stable, resulting in low investment willingness of investors, which hinders the construction and development of pumped storage to a certain extent. Therefore, it is necessary to combine the new situation of power market construction under the background of dual carbon, and establish a capacity allocation strategy for pumped storage power stations in a diversified market, so as to solve the current market problems of pumped storage power stations.

[0004] The existing capacity allocation strategies for pumped-storage power stations have many deficiencies when participating in multiple markets such as the electric energy market and the ancillary service market. First, these methods usually fail to fully consider the volatility of renewable energy output and the uncertainty of rival quotations, which easily leads to deviations from the optimal capacity allocation decision and limited benefits. Secondly, due to the lack of flexibility of existing strategies in the market regulation process, pumped-storage power stations are difficult to respond quickly to changes in market demand and cannot effectively support peak-valley regulation and reduce wind and solar power abandonment, resulting in low resource utilization. In addition, in terms of the goals of revenue distribution and cost minimization, traditional methods often focus on a single goal and fail to take into account the overall interests of power stations and market members, resulting in large fluctuations in the revenue of pumped-storage power stations and weakened market competitiveness. When responding to external factors such as policy changes and market environment adjustments, existing methods also lack adaptability and are difficult to provide reasonable capacity allocation and revenue optimization solutions for pumped-storage power stations.

[0005] Therefore, a more comprehensive, flexible and efficient capacity allocation strategy is needed to improve the profitability and resource utilization efficiency of pumped storage power stations and enhance their competitiveness and sustainability in diversified markets. Summary of the invention

[0006] The purpose of the present invention is to provide a method, system, equipment and medium for optimizing the allocation of pumped storage capacity participating in multiple market regulation, which solves the problem that existing methods are difficult to provide reasonable capacity allocation and revenue optimization solutions for pumped storage power stations.

[0007] The present invention is achieved through the following technical solutions:

[0008] The present invention discloses a method for optimizing allocation of pumped storage capacity participating in multiple market regulation, comprising the following steps:

[0009] S1. With the goal of maximizing the benefits of pumped storage power stations when pumped storage participates in the primary and auxiliary markets for capacity allocation, a refined capacity allocation model for pumped storage power stations in a multi-market is constructed;

[0010] S2. With the goal of minimizing the total social cost, a medium- and long-term joint clearing model for the main and auxiliary markets is constructed;

[0011] S3. Combine the capacity allocation model with the medium- and long-term primary and auxiliary market joint clearing model to establish a double-layer clearing model for pumped storage participating in multiple market comprehensive income capacity allocation;

[0012] S4. The particle swarm double-layer algorithm is used to solve the double-layer clearing model of pumped storage participating in the multi-market comprehensive income capacity allocation and obtain the optimal capacity allocation plan.

[0013] Furthermore, in S1, a refined capacity allocation model of pumped storage power stations in a multi-market is constructed, specifically:

[0014] The operation mode of pumped storage participating in the market is divided into two stages: participating in the electric energy market and the auxiliary service market. At the same time, it complements with the new energy generating units. The total income obtained by the pumped storage power station is the sum of the income from complementing with new energy and the income from participating in the main and auxiliary markets; the auxiliary service market includes the frequency regulation and standby markets;

[0015] The objective function of maximizing the total revenue of the pumped storage power station is shown in formula (1):

[0016]

[0017] Where N w is the number of output scenarios of new energy generators; δ w is the probability of the scene appearing; R E,tis the energy market of the pumped storage power station at time t; R F,t is the energy market of the pumped storage power station at time t; R R,t is the revenue in the frequency modulation market and the reserve market; R RE,t,w represents the benefits of the pumped storage power station complementing with renewable energy at time t under scenario w; C represents the operating cost per unit time of the pumped storage power station;

[0018] in,

[0019]

[0020] R R,t =π R,cap P R,t +β R π R,t P R,t (4)

[0021]

[0022] C=C E P E.t +C R P R.t (6)

[0023] In the formula, C E represents the power cost per unit time of the pumped storage power station, C R represents the capacity cost per unit time of the pumped storage power station; P E.t Indicates the amount of electricity per unit time of the pumped storage power station, P R.t Represents the capacity of the pumped storage power station per unit time; π da,t represents the day-ahead electricity price at time t when the market is cleared, π F,cap represents the frequency regulation capacity price at time t when the market is cleared, π mile,t represents the frequency regulation mileage price at time t, R,cap represents the reserve electricity price at time t when the market is cleared; π R,t Indicates the compensation price for the additional power generation when the reserve capacity of the pumped storage power station is called upon;

[0024] represents the winning bid power generation of the pumped storage power station at time t, represents the winning bid for the pumped storage power station at time t, represents the frequency regulation capacity of the pumped storage power station that won the bid at time t, represents the down-regulation capacity of the pumped storage power station that won the bid at time t, P R,t represents the reserve capacity of the pumped storage power station that won the bid at time t;

[0025] α mile is the mileage-capacity ratio; β R is the standby call rate; P tRE,pre is the predicted output value of the new energy power station at time t; is the actual output of the new energy power station at time t under scenario w; e Represents the unit capacity price when the pumped storage power station complements the new energy power station through output; π p It represents the unit capacity price when the pumped-storage power station complements the renewable energy power station through pumping water.

[0026] Furthermore, the constraints of pumped storage units include output constraints, reservoir capacity constraints, operating state transition constraints, and unit maximum start and stop times constraints;

[0027] The expression of output constraint is:

[0028] P min ≤P λ,t ≤P max ,λ=1,2,3,4,5,6,t∈T (7)

[0029]

[0030] Where P min is the minimum allowed power generation / pumping for each period, P max is the maximum allowed power generation / pumping amount in each period; P λ,t It represents the power generation / pumping amount at time t under different states of the pumped storage power station. When λ=1, it is the pumping power; when λ=2, it is the power generation and grid connection power; when λ=3, it is the capacity participating in the upper frequency regulation market; when λ=4, it is the capacity participating in the lower frequency regulation market; when λ=5, it is the capacity participating in the reserve market; when λ=6, it is the capacity complementary to new energy; T is the number of dispatch cycle periods;

[0031] The expression of reservoir capacity constraint is:

[0032] V0=V T (9)

[0033] V min ≤V t ≤V max (10)

[0034]

[0035] Formula (9) indicates that at the beginning and end of the optimization period, the reservoir capacity of the pumped storage is equal;

[0036] Formula (10) represents the upper and lower limits of reservoir capacity;

[0037] Formula (11) represents the relationship constraint between reservoir capacity and power;

[0038] Where V tV is the reservoir capacity of the pumped storage power station at time t; min is the lower limit of storage capacity, V max is the upper limit of storage capacity; is the frequency regulation capacity that won the bid during power generation, The frequency regulation capacity awarded for pumping water; It is the spare capacity awarded in the bid for power generation; The spare capacity awarded for pumping water; The capacity of pumped storage to complement renewable energy during power generation; is the capacity of pumped storage and renewable energy complementation during pumping; α is the conversion coefficient between electricity and storage capacity; η is the power generation efficiency of the pumped storage power station;

[0039] The expression of the working state transition constraint is:

[0040]

[0041] Formula (12) indicates that the pumped storage cannot be in the working state of pumping and generating electricity at the same time;

[0042] Formulas (13) to (14) represent the conversion relationship of pumped storage working state;

[0043] In the formula, Represents that the pumped storage unit is in the power generation state at time t; It represents that the pumped storage unit is in the pumping state at time t; is the number of starts and stops of the pumped storage under power generation conditions, is the number of starts and stops of pumped storage under pumping conditions;

[0044] The expression for the maximum start and stop times constraint of the unit is:

[0045]

[0046] In the formula, The maximum number of starts and stops of pumped storage under power generation conditions; It is the maximum start and stop times of pumped storage under pumping conditions.

[0047] Furthermore, in S2, the medium- and long-term main and auxiliary market joint clearing model is constructed, and the expression is:

[0048]

[0049] In the formula, π S is the total social cost; T is the number of dispatching cycle periods; I is the number of units participating in the dispatching;

[0050] is the winning bid price of the electric energy of unit i at time t; is the frequency regulation capacity of unit i at time t; is the frequency regulation mileage of unit i at time t; is the winning bid price for the standby of unit i at time t;

[0051] α mile is the mileage-capacity ratio; is the reserve capacity price of unit i called at time t;

[0052] The electrical energy of unit i at time t, is the frequency regulation value of unit i at time t, is the scalar value of the down-regulation of unit i at time t, is the reserve bid quantity of unit i at time t;

[0053] is the startup cost of unit i at time t, u i,t is a 0-1 variable representing the start / stop status of unit i at time t, C i is the startup cost of unit i.

[0054] Furthermore, the expression of the frequency regulation capacity of unit i at time t is:

[0055] The expression of frequency regulation mileage of unit i at time t is: In the formula, This is the quotation for the frequency regulation capacity before adjustment. is the frequency regulation mileage quotation before adjustment, f s It is the frequency modulation performance index;

[0056] The constraints of the medium- and long-term main and auxiliary market joint clearing model need to consider system power balance constraints, unit ramp constraints, unit output limits, frequency regulation capacity requirements, and reserve capacity requirements, as shown in equations (25)-(29):

[0057]

[0058] Where P t D is the system load at time t; P i ramp is the climbing capacity of unit i in adjacent time periods; are the upper and lower limits of the output of unit i at time t respectively;

[0059] Formula (28) indicates that the frequency regulation capacity provided by all winning units is greater than or equal to the market demand. Formula (29) indicates the substitutability between frequency regulation service and backup service. The frequency regulation and backup capacity provided by the winning units are not less than the sum of the two demands.

[0060]

[0061] In the formula, and Respectively represent the market demand for frequency regulation capacity and reserve;

[0062] is the scalar value of the frequency modulation of unit i at time t;

[0063] is the reserve standard quantity of unit i at time t.

[0064] Furthermore, S3 is specifically as follows: the capacity allocation model of pumped storage power stations under the multi-market is used as the upper model, and the medium- and long-term primary and auxiliary market joint clearing model is used as the lower model;

[0065] The upper-level model allocates the capacity of pumped storage power stations with the goal of maximizing its own profits, taking into account the marginal cost of its own power generation or provision of auxiliary services and the trading strategy for mobile phone groups, and submits quotation to participate in the primary and auxiliary markets. It also implements optimization analysis based on the clearing price forecast of the primary and auxiliary markets, and transmits the declared quantity and price of pumped storage of the upper-level optimization results to the lower level;

[0066] The lower-level model aims to minimize the total social cost, taking into account safety constraints and price deficit phenomena, and optimizes the clearing based on the quotations of market members, and transmits the market clearing price of the lower-level optimization result back to the upper level;

[0067] Market equilibrium is achieved through repeated iterations between the upper and lower models.

[0068] Furthermore, in S4, a two-layer adaptive particle swarm algorithm is used to solve the problem, which improves the adjustment factor parameters, self-learning factor parameters and social learning factor parameters. The specific expressions are as follows:

[0069]

[0070] c1=0.5+2exp[-(4t g / T g ) 2 ] (31)

[0071] c2=2.5-2exp[(4t g / T g ) 2 ] (32)

[0072] In the formula, ω s is the initial adjustment factor; ω e is the ending adjustment factor; T g is the total number of iterations; t g is the current iteration number; ω is the adjustment factor parameter, c1 is the self-learning factor parameter, and c2 is the social learning factor parameter;

[0073] The two-layer adaptive particle swarm algorithm includes an outer layer and an inner layer; the algorithm flow is as follows:

[0074] ① Initialize the outer population size S O and the maximum number of iterations T O,d , randomly generate allocation strategies for pumped storage in the main energy market, auxiliary market and reserve market;

[0075] ② Enter the inner layer and initialize the inner population size S I and the maximum number of iterations T I,d , randomly generate the market decision of each unit i in the main energy market, auxiliary market and standby market;

[0076] ③ Use parameters ω, c1, and c2 to update the position and velocity of the inner particles;

[0077] ④ Calculate the joint clearing value of the three markets and obtain the market clearing electricity price And update the optimal solution;

[0078] ⑤ Repeat steps ③ to ④ until the maximum number of iterations of the inner layer is reached or the difference in the market decision-making quantity of each unit is less than 0.001;

[0079] ⑥ Use parameters ω, c1, and c2 to update the position and velocity of the outer particles;

[0080] ⑦ Output inner market clearing price Calculate the fitness value of the outer layer individuals, then get the profit R of pumping and storage, and update the optimal solution;

[0081] ⑧ Repeat steps ② to ⑦ to reach the maximum number of outer iterations or when the difference in the pumped storage market decision-making amount is less than 0.001, the profit is maximized, and the optimal market allocation strategy and pumped storage unit profit R are obtained.

[0082] The present invention also discloses a pumped storage energy participating in multiple market regulation capacity optimization allocation system, comprising:

[0083] The capacity allocation module is used to build a refined capacity allocation model for pumped storage power stations in multiple markets with the goal of maximizing the benefits of pumped storage power stations when pumped storage participates in the primary and auxiliary markets for capacity allocation;

[0084] The joint clearing module is used to build a medium- and long-term joint clearing model for the main and auxiliary markets with the goal of minimizing the total social cost;

[0085] The double-layer clearing module is used to combine the capacity allocation model with the medium- and long-term primary and auxiliary market joint clearing model to establish a double-layer clearing model for pumped storage participating in multiple market comprehensive income capacity allocation;

[0086] The solution module is used to solve the double-layer clearing model of pumped storage participating in the multi-market comprehensive income capacity allocation by using the particle swarm double-layer algorithm to obtain the optimal capacity allocation plan.

[0087] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for optimizing allocation of pumped storage capacity participating in multiple market regulation are implemented.

[0088] The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for optimizing allocation of pumped storage capacity participating in multi-market regulation are implemented.

[0089] Compared with the prior art, the present invention has the following beneficial technical effects:

[0090] The present invention discloses a method for optimizing the allocation of pumped storage capacity in multiple market regulation. The method aims to maximize the benefits of the pumped storage power station when the pumped storage power station participates in the main and auxiliary markets for capacity allocation, establishes a refined capacity allocation model for the pumped storage power station electric energy market and the auxiliary service market, and divides the constraints into conventional unit constraints and pumped storage unit constraints for modeling. Then, with the goal of minimizing the total social cost, a market clearing model for pumped storage participating in multiple markets in the medium and long-term market is established, and the two are combined to establish a double-layer clearing model for the comprehensive benefit capacity allocation of pumped storage participating in multiple markets.

[0091] The pumped-storage units are made to play a role of flexible and rapid adjustment. On the one hand, they follow the output of wind and solar power to ensure the execution of the power generation plan. On the other hand, according to the different mechanisms of the current electricity market, two reasonable capacity allocation mechanisms are designed for the market, so that internal and external influencing factors can be considered more accurately and reflected through simulation results. This is conducive to revealing the potential impact of various factors on the revenue of the combined system including pumped-storage units, and can also timely judge the response strategies when the external mechanisms, policies, system operating conditions, etc. change. This can help pumped-storage construction and operation units to better plan the way for pumped-storage units to participate in the market and promote the sound and healthy development of pumped-storage units in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is the overall structure of the dual-mode market participation of the pumped storage unit proposed in the present invention;

[0093] Figure 2 It is the capacity allocation two-layer model framework of pumped storage participating in multiple markets proposed by the present invention;

[0094] Figure 3 It is a flow chart of a method for optimizing allocation of pumped storage capacity participating in multi-market regulation according to the present invention;

[0095] Figure 4 The present invention is a block diagram of a pumped storage capacity optimization allocation system participating in multiple market regulation. DETAILED DESCRIPTION

[0096] In order to make the purpose, technical solution and advantages of the present invention more clear, the following is further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, not all embodiments.

[0097] The components described and shown in the drawings and embodiments of the present invention may be arranged and designed in various configurations. Therefore, the detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the claimed invention, but merely represents a selected embodiment of the present invention. Based on the drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0098] It should be noted that the terms "comprises", "includes" or any other variations are intended to cover non-exclusive inclusion, so that a process, element, method, article or equipment that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to the process, element, method, article or equipment.

[0099] The features and performances of the present invention are further described in detail below in conjunction with the embodiments.

[0100] Example 1

[0101] like Figure 3 As shown, the present invention discloses a method for optimizing allocation of pumped storage capacity participating in multi-market regulation, comprising the following steps:

[0102] S1. With the goal of maximizing the benefits of pumped storage power stations when pumped storage participates in the primary and auxiliary markets for capacity allocation, a refined capacity allocation model for pumped storage power stations in a multi-market is constructed;

[0103] S2. With the goal of minimizing the total social cost, a medium- and long-term joint clearing model for the main and auxiliary markets is constructed;

[0104] S3. Combine the capacity allocation model with the medium- and long-term primary and auxiliary market joint clearing model to establish a double-layer clearing model for pumped storage participating in multiple market comprehensive income capacity allocation;

[0105] S4. The particle swarm double-layer algorithm is used to solve the double-layer clearing model of pumped storage participating in the multi-market comprehensive income capacity allocation and obtain the optimal capacity allocation plan.

[0106] The capacity allocation model of pumped storage power stations under multiple markets refers to the fact that pumped storage, as an independent entity, participates in multiple markets such as capacity trading and auxiliary services, which can simultaneously improve the benefits of itself and other power generation systems, play a role in peak shaving and valley filling, and reduce wind and solar power abandonment. Figure 1 As shown in the figure, in order to promote pumped storage as an independent entity to reasonably participate in various markets, the operation mode of pumped storage participating in multi-level markets is divided into two stages: participating in the electric energy market and the auxiliary service market. The auxiliary service market includes the frequency regulation and standby markets, and the total income obtained by the pumped storage power station is the sum of the income from complementarity with new energy and the income from participating in the main and auxiliary markets.

[0107] The medium- and long-term main and auxiliary market joint clearing model refers to the situation in which pumped storage participates in a diversified electricity market, comprehensively considering the joint system power purchase costs and ancillary service costs of power generation entities including wind power, photovoltaic power, thermal power, etc., maximizing their profits, and ultimately giving a reasonable pumped storage clearing electricity price to establish a reasonable capacity allocation strategy.

[0108] On the one hand, the present invention enables pumped storage units to meet the development plan of the electric energy market, and on the other hand, it also enables pumped storage power stations as independent entities to fully utilize flexibility to participate in the electric energy market and auxiliary service market, providing peak load regulation, frequency regulation, standby and other services. This will improve the current optimization configuration of pumped storage power stations in the power market and make the construction of the power market more perfect.

[0109] Example 2

[0110] This paper mainly introduces the capacity allocation model of pumped storage power stations in multiple markets.

[0111] (1) Objective function

[0112] Under the dual mode of complementarity and participation in multi-level markets, pumped-storage power stations participate in the electric energy market and ancillary service market, and at the same time complement each other with new energy power generators. The complementary electricity price is agreed upon according to the Shapley value method, the pumping cost of pumped storage is reduced, and the assessment costs of new energy power generators are reduced, which becomes the income of pumped-storage power generation.

[0113] The upper objective function with the goal of maximizing the total revenue of the pumped storage power station is shown in formula (1).

[0114]

[0115] R R,t =π R,cap P R,t +β R π R,t P R,t (4)

[0116]

[0117] C=C E P E.t +C R P R.t (6)

[0118] Where N w is the number of output scenarios of new energy generators, δ w is the probability of the scene appearing; R E,t , R F,t and R R,t are the revenues of the pumped storage power station in the electric energy market, frequency regulation market and reserve market at time t, R RE,t,w represents the benefits of the pumped storage power station complementing with renewable energy at time t under scenario w; C represents the operating cost per unit time of the pumped storage power station; C E , C R P represents the power cost and capacity cost per unit time of the pumped storage power station; E.t , P R.t Represents the power and capacity of the pumped storage power station per unit time; π da,t , π F,cap , π mile,t and π R,cap They represent the day-ahead electricity price, frequency regulation capacity price, frequency regulation mileage price and reserve price at time t respectively; R,t Indicates the compensation price for the additional power generation when the reserve capacity of the pumped storage power station is called upon; and P R,t They represent the winning bid power generation, pumping capacity, upper frequency regulation capacity, lower frequency regulation capacity and reserve capacity of the pumped storage power station at time t respectively; mile is the mileage-capacity ratio; β R is the standby call rate; P t RE,pre is the predicted output value of the new energy power station at time t; is the actual output of the new energy power station at time t under scenario w; e Represents the unit capacity price when the pumped storage power station complements the new energy power station through output; π p It represents the unit capacity price when the pumped-storage power station complements the renewable energy power station through pumping water.

[0119] (2) Output Constraint

[0120] P min ≤P λ,t ≤P max ,λ=1,2,3,4,5,6,t∈T (7)

[0121]

[0122] Where P minis the minimum allowed power generation / pumping for each period, P max is the maximum allowed power generation / pumping amount in each period; P λ,t It represents the power generation / pumping amount at time t under different states of the pumped-storage power station. When λ=1, it is the pumped power; when λ=2, it is the power generation and grid-connected power; λ=3 and λ=4 are the capacities participating in the upper and lower frequency regulation markets respectively; λ=5 is the capacity participating in the reserve market; and λ=6 is the capacity complementary to new energy.

[0123] (2) Reservoir capacity constraints

[0124] Formula (9) indicates that the reservoir capacity of the pumped storage system is equal at the beginning and end of the optimization period; Formula (10) indicates the upper and lower limits of the reservoir capacity; Formula (11) indicates the relationship constraint between the reservoir capacity and power.

[0125] V0=V T (9)

[0126] V min ≤V t ≤V max (10)

[0127]

[0128] Where V t V is the reservoir capacity of the pumped storage power station at time t; min is the lower limit of storage capacity, V max is the upper limit of storage capacity; and They are the frequency regulation capacity won in the bid for power generation and water pumping respectively; and They are the spare capacity awarded in the bid for power generation and water pumping respectively; and are the complementary capacities of pumped storage and new energy during power generation and pumping respectively; α is the conversion coefficient between electricity and reservoir capacity; η is the power generation efficiency of the pumped storage power station.

[0129] (3) Working state transition constraints

[0130] Formula (12) indicates that the pumped storage cannot be in the working state of pumping and generating electricity at the same time; Formulas (13) to (14) represent the conversion relationship of the working state of the pumped storage.

[0131]

[0132] In the formula, and They represent the pumped storage unit being in power generation and pumping state at time t respectively; and are the start and stop times of pumped storage in power generation and pumping conditions, respectively.

[0133] (4) Constraints on the maximum number of unit starts and stops

[0134] Equations (15) to (16) represent the maximum start-stop times constraints for pumped storage.

[0135]

[0136] In the formula, and They are the maximum start and stop times of pumped storage under power generation and pumping conditions, respectively.

[0137] Example 3

[0138] This paper mainly introduces the joint clearing model of the main and auxiliary markets in the medium and long term market.

[0139] (1) Main energy market model

[0140] In the energy market, each power generator quotes its price based on its marginal fuel cost. Other costs such as no-load cost and start-stop cost are compensated by the power market trading agency, and capacity cost is recovered through capacity electricity price. The cost function of thermal power units, the main competitor of pumped storage power stations in the main and auxiliary markets, can be written as a quadratic function, as shown in formula (17), which is mainly composed of fixed cost and fuel cost. By taking the derivative of the total cost, the marginal cost can be obtained as shown in formula (18).

[0141] T C (P i )=a+bP i +cP i 2 (17)

[0142]

[0143] Where P i is the power output of unit i; a is the fixed cost coefficient of the unit, and b and c are the variable cost coefficients of the unit. The cost of a pumped storage unit includes fixed cost and variable cost. The variable cost mainly includes pumping cost and loss cost, which can be expressed by a univariate linear function, as shown in formula (19). Its marginal cost is shown in formula (20).

[0144]

[0145] Where a1 is the fixed cost coefficient of the unit; d is the average pumping electricity price; η1 and η2 are the loss rates of pumping and power generation processes of pumped storage, respectively.

[0146] is the winning bid price of the electricity produced by unit i at time t. The winning bid price of electricity is obtained by the sum of the marginal costs of thermal power units and hydropower units participating in the electricity market.

[0147] (2) Auxiliary Market

[0148] The auxiliary market mainly considers the frequency regulation market and the reserve market. The frequency regulation market refers to the PJM frequency regulation market rules, and the units providing frequency regulation auxiliary services need to declare the frequency regulation capacity price separately. and FM mileage price Market operators should base their decisions on the frequency modulation performance index f s The quotations are adjusted to form the final quotations, as shown in equations (21) to (22).

[0149]

[0150] In the formula, and They are the frequency regulation capacity quotation and frequency regulation mileage quotation before adjustment respectively.

[0151] (3) Market Clearing Objective Function

[0152] When the market develops to a certain stage, the clearing method will gradually change from sequential clearing to joint clearing, so as to better allocate market product resources among market players and further maximize social welfare. Therefore, a clearing method combining the electricity energy market with the frequency regulation and standby auxiliary service market is adopted, allowing high-level services to replace low-level services. The clearing results and prices of each market are obtained through one-time clearing calculation. The clearing model aims to minimize the total social cost, as shown in formula (23).

[0153]

[0154] In the formula, π S is the total social cost; T is the number of dispatching period periods; I is the number of units participating in the dispatching in the system; are the electric energy, frequency regulation capacity, frequency regulation mileage and the winning bid price of standby of unit i at time t; is the reserve capacity price of unit i called at time t; are the electric energy, up-frequency regulation, down-frequency regulation, and standby bid quantity of unit i at time t respectively; is the startup cost of unit i at time t, u i,t is a 0-1 variable representing the start / stop status of unit i at time t, C i is the startup cost of unit i.

[0155] (4) Constraints

[0156] The constraints need to consider system power balance constraints, unit ramp constraints, unit output limits, frequency regulation capacity requirements, and reserve capacity requirements, as shown in equations (25)-(29). This paper ignores power flow constraints.

[0157]

[0158] Where P t D is the system load at time t; P i ramp is the climbing capacity of unit i in adjacent time periods; are the upper and lower limits of the output of unit i at time t respectively.

[0159] Formulas (28) and (29) can prevent price deficits when the market is cleared. Formula (28) indicates that the frequency regulation capacity provided by all winning units must be greater than or equal to the market demand, and Formula (29) indicates the substitutability between frequency regulation services and backup services, that is, the frequency regulation and backup capacities provided by the winning units are not less than the sum of the two demands.

[0160]

[0161] In the formula, and Respectively represent the market demand for frequency regulation capacity and reserve; is the scalar value of the frequency modulation of unit i at time t; is the reserve standard quantity of unit i at time t.

[0162] Example 4

[0163] This paper mainly introduces the capacity allocation and clearing double-layer model of pumped storage participating in multiple markets.

[0164] By combining the two models, the capacity allocation model of pumped-storage power stations in a multi-market is used as the upper layer, and the capacity of pumped-storage power stations is allocated with the goal of maximizing their own profits. The marginal cost of their own power generation or provision of auxiliary services and the trading strategy for mobile groups are considered, and the quotation is quoted to participate in the primary and auxiliary markets. Optimization analysis is implemented based on the clearing price forecast of the primary and auxiliary markets, and the declared quantity and price of pumped storage of the upper-layer optimization results are transmitted to the lower layer.

[0165] In the present invention, power generation, pumping capacity, frequency regulation capacity and standby capacity are equivalent to power. The lower model aims to minimize the total social cost, considers safety constraints and price deficit phenomena, implements optimization and clearing based on market member quotations, and transmits the market clearing price of the lower optimization result back to the upper layer. Market equilibrium is achieved through repeated iterations between the upper and lower models.

[0166] Example 5

[0167] The model based on particle swarm double-layer algorithm solves the double-layer clearing model of capacity allocation of pumped storage participating in multiple markets with comprehensive benefits, and obtains the optimal capacity allocation plan.

[0168] Particle swarm optimization (PSO) is widely used due to its strong applicability and simple principle. From the iterative strategy of the PSO algorithm, it can clearly reflect the iterative process of the problem being solved, and the solution space is continuous, without too many parameter settings, and is easier to implement.

[0169] For the two-layer optimization algorithm proposed above, since it is not a linear model, an algorithm that can solve nonlinear problems is needed. Here, the two-layer adaptive particle swarm algorithm (SAPSO) is used to solve it, which improves the adjustment factor parameters, self-learning factor parameters and social learning factor parameters. The specific improvements and algorithm flow are as follows:

[0170]

[0171] c1=0.5+2exp[-(4t g / T g ) 2 ] (31)

[0172] c2=2.5-2exp[(4t g / T g ) 2 ] (32)

[0173] In the formula, ω s is the initial adjustment factor, which is 0.4; ω e is the ending adjustment factor, which is 0.9; T d is the total number of iterations; t d is the current iteration number.

[0174] ① Initialize the population size S of the outer SAPSO O and the maximum number of iterations T O,d , randomly generate allocation strategies for pumped storage in the main energy market, auxiliary market and reserve market.

[0175] ② Enter the inner layer and initialize the inner layer SAPSO population size S I and the maximum number of iterations T I,d , randomly generate the market decisions of each unit i in the main energy market, auxiliary market and backup market.

[0176] ③ Use parameters ω, c1, and c2 to update the position and velocity of the inner particles.

[0177] ④ Calculate the joint clearing value of the three markets and obtain the market clearing electricity price And update the optimal solution;

[0178] ⑤ Repeat steps ③ to ④ until the maximum number of iterations of the inner SAPSO is reached or the difference in the market decision-making quantity of each unit is less than 0.001.

[0179] ⑥ Use parameters ω, c1, and c2 to update the position and velocity of the outer particles.

[0180] ⑦ Output inner market clearing price Calculate the fitness value of the outer layer individuals, then get the profit R of pumping and storage, and update the optimal solution;

[0181] ⑧ Repeat steps ② to ⑦ to reach the maximum number of outer iterations or when the difference in the pumped storage market decision-making amount is less than 0.001, the profit is maximized, and the optimal market allocation strategy and pumped storage unit profit R are obtained.

[0182] Example 6

[0183] like Figure 4 As shown, the present invention discloses a pumped storage energy participating in multiple market regulation capacity optimization allocation system, comprising:

[0184] The capacity allocation module is used to build a refined capacity allocation model for pumped storage power stations in multiple markets with the goal of maximizing the benefits of pumped storage power stations when pumped storage participates in the primary and auxiliary markets for capacity allocation;

[0185] The joint clearing module is used to build a medium- and long-term joint clearing model for the main and auxiliary markets with the goal of minimizing the total social cost;

[0186] The double-layer clearing module is used to combine the capacity allocation model with the medium- and long-term primary and auxiliary market joint clearing model to establish a double-layer clearing model for pumped storage participating in multiple market comprehensive income capacity allocation;

[0187] The solution module is used to solve the double-layer clearing model of pumped storage participating in the multi-market comprehensive income capacity allocation by using the particle swarm double-layer algorithm to obtain the optimal capacity allocation plan.

[0188] like Figure 2 As shown in the figure, the capacity optimization allocation mechanism of pumped-storage power stations participating in the electric energy market and ancillary service market is demonstrated. In the medium and long-term market, various power generation entities, including pumped-storage power stations, wind power generators, photovoltaic power generators and thermal power generators, declare capacity based on the market capacity demand and rental fees, and determine the winning electricity volume and electricity price through the market clearing mechanism. The market side minimizes the total system cost by comprehensively considering the power purchase cost and ancillary service costs through a unified clearing target. Under the premise of contractual power constraints, the electric energy market and the ancillary service market allocate capacity, and further refine it into a multi-market capacity arrangement of specific wind-storage power stations, photovoltaic-storage power stations, thermal power stations and pumped-storage power stations, ensuring that the dual goals of maximizing the benefits of pumped-storage power stations and minimizing the total social cost are achieved in different market environments.

[0189] Example 7

[0190] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for optimizing the allocation of pumped storage participating in the multi-market regulation capacity are implemented. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, the network interface, and the memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0191] Example 8

[0192] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the allocation of pumped storage participating in the multi-market regulation capacity are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include a random access memory and / or a cache memory, etc. The non-volatile memory may include a read-only memory, a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0193] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0194] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the allocation of pumped storage capacity in multiple market regulation, characterized in that: The following steps are involved: S1. With the goal of maximizing the benefits of pumped storage power stations when pumped storage participates in the primary and auxiliary markets for capacity allocation, a refined capacity allocation model for pumped storage power stations in a multi-market is constructed; S2. With the goal of minimizing the total social cost, a medium- and long-term joint clearing model for the main and auxiliary markets is constructed; S3. Combine the capacity allocation model with the medium- and long-term primary and auxiliary market joint clearing model to establish a double-layer clearing model for pumped storage participating in multiple market comprehensive income capacity allocation; S4. The particle swarm double-layer algorithm is used to solve the double-layer clearing model of pumped storage participating in the multi-market comprehensive income capacity allocation and obtain the optimal capacity allocation plan.

2. A method for optimizing allocation of pumped storage capacity for multi-market regulation according to claim 1, characterized in that: In S1, a refined capacity allocation model of pumped storage power stations in a multi-market is constructed, specifically: The operation mode of pumped storage participating in the market is divided into two stages: participating in the electric energy market and the auxiliary service market. At the same time, it complements with the new energy generating units. The total income obtained by the pumped storage power station is the sum of the income from complementing with new energy and the income from participating in the main and auxiliary markets; the auxiliary service market includes the frequency regulation and standby markets; The objective function of maximizing the total revenue of the pumped storage power station is shown in formula (1): Where N w is the number of output scenarios of new energy generators; δ w is the probability of the scene appearing; R E,t is the energy market of the pumped storage power station at time t; R F,t is the energy market of the pumped storage power station at time t; R R,t is the revenue in the frequency modulation market and the reserve market; R RE,t,w represents the benefits of the pumped storage power station complementing with renewable energy at time t under scenario w; C represents the operating cost per unit time of the pumped storage power station; in, R R,t =π R,cap P R,t +b R p R,t P R,t (4) C=C E P E.t +C R P R.t (6) In the formula, C E represents the power cost per unit time of the pumped storage power station, C R represents the capacity cost per unit time of the pumped storage power station; P E.t Indicates the amount of electricity per unit time of the pumped storage power station, P R.t Represents the capacity of the pumped storage power station per unit time; π da,t represents the day-ahead electricity price at time t when the market is cleared, π F,cap represents the frequency regulation capacity price at time t when the market is cleared, π mile,t represents the frequency regulation mileage price at time t, R,cap represents the reserve electricity price at time t when the market is cleared; π R,t Indicates the compensation price for the additional power generation when the reserve capacity of the pumped storage power station is called upon; represents the winning bid power generation of the pumped storage power station at time t, represents the winning bid for the pumped storage power station at time t, represents the frequency regulation capacity of the pumped storage power station that won the bid at time t, represents the down-regulation capacity of the pumped storage power station that won the bid at time t, P R,t represents the reserve capacity of the pumped storage power station that won the bid at time t; α mile is the mileage-capacity ratio; β R is the standby call rate; is the predicted output value of the new energy power station at time t; is the actual output of the new energy power station at time t under scenario w; e Represents the unit capacity price when the pumped storage power station complements the new energy power station through output; π p It represents the unit capacity price when the pumped storage power station complements the new energy power station through pumping water.

3. A method for optimizing allocation of pumped storage capacity for multi-market regulation according to claim 2, characterized in that: Pumped storage unit constraints include output constraints, reservoir capacity constraints, operating state transition constraints, and unit maximum start and stop times constraints; The expression of output constraint is: P min ≤P λ,t ≤P max ,λ=1,2,3,4,5,6,t∈T (7) Where P min is the minimum allowed power generation / pumping for each period, P max is the maximum allowed power generation / pumping amount in each period; P λ,t It represents the power generation / pumping amount at time t under different states of the pumped storage power station. When λ=1, it is the pumping power; when λ=2, it is the power generation and grid connection power; when λ=3, it is the capacity participating in the upper frequency regulation market; when λ=4, it is the capacity participating in the lower frequency regulation market; when λ=5, it is the capacity participating in the reserve market; when λ=6, it is the capacity complementary to new energy; T is the number of dispatch cycle periods; The expression of reservoir capacity constraint is: V0=V T (9) In min ≤V t ≤V max (10) Formula (9) indicates that at the beginning and end of the optimization period, the reservoir capacity of the pumped storage is equal; Formula (10) represents the upper and lower limits of reservoir capacity; Formula (11) represents the relationship constraint between reservoir capacity and power; Where V t V is the reservoir capacity of the pumped storage power station at time t; min is the lower limit of storage capacity, V max is the upper limit of storage capacity; is the frequency regulation capacity that won the bid during power generation, The frequency regulation capacity awarded for pumping water; It is the spare capacity awarded in the bid for power generation; The spare capacity awarded for pumping water; The capacity of pumped storage to complement renewable energy during power generation; is the capacity of pumped storage and renewable energy complementation during pumping; α is the conversion coefficient between electricity and storage capacity; η is the power generation efficiency of the pumped storage power station; The expression of the working state transition constraint is: Formula (12) indicates that the pumped storage cannot be in the working state of pumping and generating electricity at the same time; Formulas (13) to (14) represent the conversion relationship of pumped storage working state; In the formula, Represents that the pumped storage unit is in the power generation state at time t; It represents that the pumped storage unit is in the pumping state at time t; is the number of starts and stops of the pumped storage under power generation conditions, The number of starts and stops of pumped storage under pumping conditions; The expression for the maximum start and stop times constraint of the unit is: In the formula, The maximum number of starts and stops of pumped storage under power generation conditions; It is the maximum start and stop times of pumped storage under pumping conditions.

4. A method for optimizing allocation of pumped storage capacity for multi-market regulation according to claim 1, characterized in that: In S2, the medium- and long-term main and auxiliary market joint clearing model is constructed, and the expression is: In the formula, π S is the total social cost; T is the number of dispatching period periods; I is the number of units participating in the dispatching; is the winning bid price of the electric energy of unit i at time t; is the frequency regulation capacity of unit i at time t; is the frequency regulation mileage of unit i at time t; is the winning bid price for the standby of unit i at time t; α mile is the mileage-capacity ratio; is the reserve capacity price of unit i called at time t; The electrical energy of unit i at time t, is the frequency regulation value of unit i at time t, is the scalar value of the down-regulation of unit i at time t, is the reserve bid quantity of unit i at time t; is the startup cost of unit i at time t, u i,t is a 0-1 variable representing the start / stop status of unit i at time t, C i is the startup cost of unit i.

5. A method for optimizing allocation of pumped storage capacity for multi-market regulation according to claim 4, characterized in that: The expression of frequency regulation capacity of unit i at time t is: The expression of frequency regulation mileage of unit i at time t is: In the formula, This is the quotation for the frequency regulation capacity before adjustment. is the frequency regulation mileage quotation before adjustment, f s It is the frequency modulation performance index; The constraints of the medium- and long-term main and auxiliary market joint clearing model need to consider system power balance constraints, unit ramp constraints, unit output limits, frequency regulation capacity requirements, and reserve capacity requirements, as shown in equations (25)-(29): In the formula, is the system load at time t; is the climbing capacity of unit i in adjacent time periods; are the upper and lower limits of the output of unit i at time t respectively; Formula (28) indicates that the frequency regulation capacity provided by all winning units is greater than or equal to the market demand. Formula (29) indicates the substitutability between frequency regulation service and backup service. The frequency regulation and backup capacity provided by the winning units are not less than the sum of the two demands. In the formula, and Respectively represent the market demand for frequency regulation capacity and reserve; is the scalar value of the frequency modulation of unit i at time t; is the reserve standard quantity of unit i at time t.

6. A method for optimizing allocation of pumped storage capacity for multi-market regulation according to claim 1, characterized in that: S3 specifically includes: taking the capacity allocation model of pumped storage power stations in a multi-market as the upper model, and the medium- and long-term primary and auxiliary market joint clearing model as the lower model; The upper-level model allocates the capacity of pumped storage power stations with the goal of maximizing its own profits, taking into account the marginal cost of its own power generation or provision of auxiliary services and the trading strategy for mobile phone groups, and submits quotation to participate in the primary and auxiliary markets. It also implements optimization analysis based on the clearing price forecast of the primary and auxiliary markets, and transmits the declared quantity and price of pumped storage of the upper-level optimization results to the lower level; The lower-level model aims to minimize the total social cost, taking into account safety constraints and price deficit phenomena, and optimizes the clearing based on the quotations of market members, and transmits the market clearing price of the lower-level optimization result back to the upper level; Market equilibrium is achieved through repeated iterations between the upper and lower models.

7. A method for optimizing allocation of pumped storage capacity participating in multiple market regulation according to claim 4, characterized in that: In S4, a two-layer adaptive particle swarm algorithm is used to solve the problem, which improves the adjustment factor parameters, self-learning factor parameters and social learning factor parameters. The specific expressions are as follows: c1=0.5+2exp[-(4t g / T g ) 2 ] (31) c2=2.5-2exp[(4t g / T g ) 2 ] (32) In the formula, ω s is the initial adjustment factor; ω e is the ending adjustment factor; T g is the total number of iterations; t g is the current iteration number; ω is the adjustment factor parameter, c1 is the self-learning factor parameter, and c2 is the social learning factor parameter; The two-layer adaptive particle swarm algorithm includes an outer layer and an inner layer; the algorithm flow is as follows: ① Initialize the outer population size S O and the maximum number of iterations T O,d , randomly generate allocation strategies for pumped storage in the main energy market, auxiliary market and reserve market; ② Enter the inner layer and initialize the inner population size S I and the maximum number of iterations T I,d , randomly generate the market decision of each unit i in the main energy market, auxiliary market and standby market; ③ Use parameters ω, c1, and c2 to update the position and velocity of the inner particles; ④ Calculate the joint clearing value of the three markets and obtain the market clearing electricity price And update the optimal solution; ⑤ Repeat steps ③ to ④ until the maximum number of iterations of the inner layer is reached or the difference in the market decision-making quantity of each unit is less than 0.001; ⑥ Use parameters ω, c1, and c2 to update the position and velocity of the outer particles; ⑦ Output inner market clearing price Calculate the fitness value of the outer layer individuals, then get the profit R of pumping and storage, and update the optimal solution; ⑧ Repeat steps ② to ⑦ to reach the maximum number of outer iterations or when the difference in the pumped storage market decision-making amount is less than 0.001, the profit is maximized, and the optimal market allocation strategy and pumped storage unit profit R are obtained.

8. A pumped storage capacity optimization allocation system participating in multiple market regulation, characterized in that: include: The capacity allocation module is used to build a refined capacity allocation model for pumped storage power stations in multiple markets with the goal of maximizing the benefits of pumped storage power stations when pumped storage participates in the primary and auxiliary markets for capacity allocation; The joint clearing module is used to build a medium- and long-term joint clearing model for the main and auxiliary markets with the goal of minimizing the total social cost; The double-layer clearing module is used to combine the capacity allocation model with the medium- and long-term primary and auxiliary market joint clearing model to establish a double-layer clearing model for pumped storage participating in multiple market comprehensive income capacity allocation; The solution module is used to solve the double-layer clearing model of pumped storage participating in the multi-market comprehensive income capacity allocation by using the particle swarm double-layer algorithm to obtain the optimal capacity allocation plan.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for optimizing allocation of pumped storage capacity participating in multi-market regulation are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the method for optimizing allocation of pumped storage capacity participating in multi-market regulation as claimed in any one of claims 1 to 7 are implemented.

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