Ant colony optimization algorithm energy storage charging and discharging optimization method based on multiple time scales

Abstract

The invention discloses an ant colony optimization algorithm energy storage charging and discharging optimization method based on multiple time scales, and the method comprises the following steps: building an energy storage station cross-market cooperation model based on an energy storage station income objective function, energy storage station charging and discharging efficiency constraint, energy storage station capacity constraint and an energy storage station energy storage state; optimizing the cross-market cooperation model of the energy storage station based on an ant colony optimization algorithm to find an optimal charging and discharging strategy; dynamically adjusting a charging and discharging plan for the optimal charging and discharging strategy based on a multi-time scale optimization strategy; through the ant colony optimization algorithm and the multi-time scale optimization strategy, the optimal charging and discharging strategy is searched and the charging and discharging strategy is optimized, so that the charging and discharging plan is dynamically adjusted, and the economic benefit of the energy storage station is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to an energy storage charge-discharge optimization method based on an ant colony optimization algorithm with multiple time scales. Background Art

[0002] With the large-scale grid connection of renewable energy and the deepening of the electricity market reform, energy storage systems play key roles in the power system, such as peak shaving and valley filling, balancing power supply and demand, and improving the stability of the power grid. There are still the following problems in the existing energy storage charge-discharge strategy optimization methods. 1. Limitations of single-time scale optimization: The existing methods in references [1-2], such as linear programming methods, mostly target single-day optimization and cannot adapt to the short-term and long-term alternating characteristics of electricity price fluctuations, resulting in limited economic benefits. 2. Insufficient algorithm efficiency and accuracy: The existing methods in references [3-4], such as dynamic programming methods, decompose charge-discharge into multiple stages, with high computational complexity and difficulty in real-time application. The method proposed in reference [5] is based on particle swarm optimization, which optimizes energy storage scheduling through global search and is prone to falling into local optima; reference [6] uses a genetic algorithm to find the optimal charge-discharge strategy by simulating the evolution process, but lacks multi-time scale optimization. 3. Lack of multi-market coordination: The existing methods in references [7-8], such as deep learning methods, combine reinforcement learning to optimize energy storage scheduling strategies, without fully considering the coordinated optimization of energy storage systems participating in the spot market and the ancillary service market.

[0003] Most current technologies do not consider issues such as charge-discharge sequence, charge-discharge efficiency, policy compensation, and multi-market integration, directly affecting economic benefits. Most of the existing optimization methods can usually only perform single-day optimization, but electricity price fluctuations show strong short-term and long-term alternating characteristics in actual applications, and single-day optimization is difficult to take into account the multi-dimensions of market changes. At the same time, the current methods lack multi-market integration. Energy storage systems can not only participate in the spot market but also in the ancillary service market. Existing optimization methods often ignore the impact of the ancillary service market on energy storage benefits and fail to effectively select which markets the energy storage system should participate in, resulting in the failure to achieve the maximization of benefits.

[0004] [1] D.K. Molzahn and I.A. Hiskens, “A Survey of Relaxations and Approximations of the Optimal Power Flow Problem”, IEEE Transactions on Power Systems, 2019.

[0005] [2] Y. Zhang, W. Tang, et al., “Optimal Operation of Battery Storage in Distribution Networks”, IEEE Transactions on Smart Grid, 2021.

[0006] [3] H. Zhao, Q. Wu, et al., “Dynamic Programming - Based Battery Scheduling Algorithm for Maximizing Self - Consumption of Solar Energy”, IEEE Transactions on Sustainable Energy, 2022.

[0007] [4] R. Sioshansi, “Modeling the Impacts of Electricity Tariffs on Energy Storage Operation”, Energy Economics, 2019.

[0008] [5] C. Jiang, Y. Gong, et al., “A PSO - Based Energy Storage Scheduling Model for Renewable Integration”, IEEE Transactions on Power Systems, 2021.

[0009] [6] A. K. Basu, S. Chowdhury, et al., “Optimal Battery Scheduling Using Genetic Algorithm”, IEEE Transactions on Smart Grid, 2020.

[0010] [7] Y. Du, Z. Wang, et al., “Reinforcement Learning - Based Energy Storage Dispatch for Smart Grids”, IEEE Transactions on Smart Grid, 2022.

[0011] [8]X.Chen,L.Xu,et al.,“LSTM-Based Battery Energy Management forMicrogrids”,Energy,2021。 Summary of the Invention

[0012] In order to overcome the deficiencies of the prior art, the present invention provides an energy storage charging and discharging optimization method based on a multi-time scale ant colony optimization algorithm, which realizes finding the optimal charging and discharging strategy and optimizing the charging and discharging strategy through the ant colony optimization algorithm and the multi-time scale optimization strategy, so as to dynamically adjust the charging and discharging plan, thereby maximizing the economic benefits of the energy storage station.

[0013] In order to achieve the above invention purpose, the present invention adopts the following technical solutions:

[0014] An energy storage charging and discharging optimization method based on a multi-time scale ant colony optimization algorithm, comprising the following steps:

[0015] S101. Based on the energy storage station revenue objective function, the energy storage station charging and discharging efficiency constraint, the energy storage station capacity constraint, and the energy storage state of the energy storage station, construct an energy storage station cross-market collaboration model;

[0016] S102. Optimize the energy storage station cross-market collaboration model based on the ant colony optimization algorithm to find the optimal charging and discharging strategy;

[0017] S103. Dynamically adjust the charging and discharging plan for the optimal charging and discharging strategy based on the multi-time scale optimization strategy.

[0018] Furthermore, the expression of the energy storage station revenue objective function is as follows:

[0019]

[0020] Among them, T is the total number of optimization periods, is the electricity price in the spot market at time t, is the charging and discharging power of the energy storage station participating in the spot market at time t (a positive value indicates discharging, and a negative value indicates charging), is the frequency regulation cost in the ancillary service market at time t, is the frequency regulation power of the energy storage station participating in the ancillary service market at time t, is the energy storage cost of the energy storage station at time t.

[0021] Furthermore, the expression of the energy storage station charging and discharging efficiency constraint is as follows:

[0022]

[0023] Among them, and are the actual charging and discharging powers of the energy storage station at time period t, η charge and η discharge are the charging efficiency and discharging efficiency of the energy storage station respectively, and are the input and output powers of the energy storage station at time period t respectively.

[0024] Furthermore, the expression of the energy storage station capacity constraint is as follows:

[0025] SOC min ≤SOC t ≤SOC max

[0026] Among them, SOC t is the energy storage state of the energy storage station at time period t, SOC min and SOC max are the minimum and maximum energy storage capacities of the energy storage station respectively.

[0027] Furthermore, the expression of the energy storage state of the energy storage station is as follows:

[0028]

[0029] Among them, Δt is the time period length, E max is the maximum energy storage capacity of the energy storage station, SOC t is the energy storage state of the energy storage station at time period t, is the actual charging efficiency of the energy storage station at time period t, is the actual discharging power of the energy storage station at time period t, SOC t+1 is the energy storage state of the energy storage station at time period t + 1.

[0030] Furthermore, optimizing the cross-market coordination model of the energy storage station based on the ant colony optimization algorithm to find the optimal charging and discharging strategy includes the following steps:

[0031] Each ant represents a charging and discharging strategy, and the parameters of the ant colony algorithm are initialized. The parameters of the ant colony algorithm include the number of ants, pheromone concentration, and heuristic factor;

[0032] During the search process, the pheromone concentration is updated according to the value of the objective function. The expression of the pheromone concentration update is as follows:

[0033] τ ij (t + 1)=(1 - ρ)·τ ij (t)+Δτ ij

[0034] Among them, τ ij(t) is the pheromone concentration of path ij at time t, ρ is the pheromone evaporation coefficient, and Δτ ij is the increment of pheromone left by the ant on path ij;

[0035] Select a path according to the pheromone concentration and heuristic information. The expression of the path selection probability is as follows:

[0036]

[0037] Among them, η ij is the heuristic information of path ij, which is usually related to the objective function value. α and β are the weight factors of pheromone and heuristic information respectively, and P ij is the path selection probability, τ ij is the pheromone concentration of path ij, τ ik is the pheromone concentration of path ik, η ik is the heuristic information of path ik.

[0038] Furthermore, the multi-time scale optimization strategy includes three-day strategy optimization, two-day strategy optimization, and one-day strategy optimization.

[0039] Furthermore, dynamically adjusting the charging and discharging plan for the optimal charging and discharging strategy based on the multi-time scale optimization strategy includes:

[0040] When performing three-day strategy optimization, generate a three-day charging and discharging plan according to the three-day electricity price prediction. The expression is as follows:

[0041]

[0042] Among them, T 3d is the total number of time periods in three days, is the electricity price in the spot market at time t, is the charging and discharging power of the energy storage station participating in the spot market at time t (a positive value indicates discharging, and a negative value indicates charging), is the frequency regulation cost in the auxiliary service market at time t, is the frequency regulation power of the energy storage station participating in the auxiliary service market at time t, is the energy storage cost of the energy storage station at time t;

[0043] When performing two-day strategy optimization, generate a two-day charging and discharging plan according to the two-day electricity price prediction;

[0044] When performing one-day strategy optimization, generate a one-day charging and discharging plan according to the one-day electricity price prediction and load prediction.

[0045] Advantages of the present application: Multi-time scale dynamic optimization is achieved. Traditional optimization methods only perform single-day optimization and cannot effectively cope with short-term fluctuations and long-term trends of market electricity prices. By adopting a multi-time scale optimization strategy, the charge and discharge strategy of the energy storage system can be flexibly adjusted according to the fluctuations of market electricity prices and changes in power demand, ensuring that the energy storage system can achieve the optimal benefit under various market conditions.

[0046] Cross-market coordination is achieved. Traditional optimization methods mostly focus on a single market and lack a cross-market coordination mechanism. To address this issue, the present application proposes a multi-market decision optimization method that allows the energy storage system to dynamically select the most suitable market to participate in according to the forecast of spot market electricity prices and the compensation fees in the ancillary service market, maximizing the overall benefit.

[0047] Global optimization is achieved. Traditional methods lack flexibility and global optimization capabilities, while the present application uses the ant colony optimization algorithm to avoid local optima, thereby improving the accuracy of the strategy.

[0048] Efficiency and capacity constraints are achieved. By dynamically updating the charge and discharge efficiency formula and the energy storage state of the energy storage station, the feasibility of the strategy is ensured. Brief Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a schematic diagram of the steps of an energy storage charge and discharge optimization method based on a multi-time scale ant colony optimization algorithm of the present invention. Detailed Embodiments

[0051] The embodiments of the present invention will be described in detail below with reference to the drawings.

[0052] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0053] Embodiment 1

[0054] An energy storage charging and discharging optimization method based on an ant colony optimization algorithm with multiple time scales includes the following steps:

[0055] S101. Based on the revenue objective function of the energy storage station, the charging and discharging efficiency constraint of the energy storage station, the capacity constraint of the energy storage station, and the energy storage state of the energy storage station, construct a cross-market collaborative model for the energy storage station;

[0056] Construct a cross-market collaborative model for the energy storage station through the revenue objective function of the energy storage station, the charging and discharging efficiency constraint of the energy storage station, the capacity constraint of the energy storage station, and the energy storage state of the energy storage station, and combine the revenue of the spot market and the ancillary service market to achieve cross-market collaborative optimization. The goal of the energy storage station is to maximize its total revenue by participating in the spot market and the ancillary service market. The expression of the revenue objective function of the energy storage station is as follows:

[0057]

[0058] where T is the total number of optimization periods, is the electricity price in the spot market at time t, is the charging and discharging power of the energy storage station participating in the spot market at time t (a positive value indicates discharging, and a negative value indicates charging), is the frequency regulation cost in the ancillary service market at time t, is the frequency regulation power of the energy storage station participating in the ancillary service market at time t, is the energy storage cost of the energy storage station at time t.

[0059] It should be noted that since traditional optimization methods mostly focus on a single market and lack a cross-market coordination mechanism. To address this issue, this application uses a multi-market decision optimization method, allowing the energy storage system to dynamically select the most suitable market to participate in according to the spot market electricity price prediction and the compensation cost in the ancillary service market, maximizing the overall revenue.

[0060] The charge-discharge efficiency of the energy storage station affects its actual revenue. Therefore, it is necessary to consider the constraint of the charge-discharge efficiency. The expression of the charge-discharge efficiency constraint of the energy storage station is as follows:

[0061]

[0062] Among them, and are the actual charging and discharging powers of the energy storage station at time t, respectively. η charge and η discharge are the charging efficiency and discharging efficiency of the energy storage station, respectively. and are the input and output powers of the energy storage station at time t, respectively.

[0063] The capacity constraint of the energy storage station ensures that its state of charge (SOC) is within a reasonable range at any time. The expression of the capacity constraint of the energy storage station is as follows:

[0064] SOC min ≤SOC t ≤SOC max

[0065] Among them, SOC t is the state of charge of the energy storage station at time t, SOC min and SOC max are the minimum and maximum energy storage capacities of the energy storage station, respectively.

[0066] The expression of the state of charge of the energy storage station is as follows:

[0067]

[0068] Among them, Δt is the time interval length, E max is the maximum energy storage capacity of the energy storage station, SOC t is the state of charge of the energy storage station at time t, is the actual charging efficiency of the energy storage station at time t, is the actual discharging power of the energy storage station at time t, SOC t+1 is the state of charge of the energy storage station at time t + 1.

[0069] S102. Optimize the cross-market collaborative model of the energy storage station based on the ant colony optimization algorithm to find the optimal charge-discharge strategy;

[0070] To optimize the participation strategy of energy storage stations in multiple markets, an ant colony optimization algorithm is introduced. The ant colony optimization algorithm simulates the foraging behavior of ants to find the optimal charging and discharging strategy. It should be noted that the ant colony optimization algorithm can avoid local optima and improve the accuracy of the optimal charging and discharging strategy.

[0071] Optimizing the cross-market coordination model of energy storage stations based on the ant colony optimization algorithm to find the optimal charging and discharging strategy includes the following steps:

[0072] Each ant represents a charging and discharging strategy, and the parameters of the ant colony algorithm are initialized. The parameters of the ant colony algorithm include the number of ants, pheromone concentration, and heuristic factor;

[0073] During the search process, ants update the pheromone concentration according to the value of the objective function. The expression for updating the pheromone concentration is as follows:

[0074] τ ij (t + 1)=(1 - ρ)·τ ij (t)+Δτ ij

[0075] Where τ ij (t) is the pheromone concentration of path ij at time t, ρ is the pheromone evaporation coefficient, and Δτ ij is the pheromone increment left by the ant on path ij;

[0076] Ants select paths according to the pheromone concentration and heuristic information. The expression for the path selection probability is as follows:

[0077]

[0078] Where η ij is the heuristic information of path ij, usually related to the value of the objective function. α and β are the weight factors of pheromone and heuristic information respectively, and P ij is the path selection probability, τ ij is the pheromone concentration of path ij, τ ik is the pheromone concentration of path ik, and η ik is the heuristic information of path ik.

[0079] S103. Dynamically adjust the charging and discharging plan based on the optimal charging and discharging strategy according to the multi-time scale optimization strategy;

[0080] Optimized based on the multi-time scale optimization strategy, the energy storage station can flexibly adjust the charge and discharge plan according to the prediction results of three days, two days or one day. Through the multi-time scale optimization strategy, the energy storage station can flexibly adjust the charge and discharge plan according to the prediction results of different time scales, so as to better adapt to market changes and improve economic benefits. It should be noted that since the traditional optimization method only performs single-day optimization, it cannot effectively cope with the short-term fluctuations and long-term trends of market electricity prices. By adopting the multi-time scale optimization strategy, this application can flexibly adjust the charge and discharge strategy of the energy storage system according to the fluctuations of market electricity prices and changes in power demand, ensuring that the energy storage system can achieve the optimal benefit under various market conditions.

[0081] Based on the multi-time scale optimization strategy, the optimal charge and discharge strategy is dynamically adjusted to the charge and discharge plan, which can flexibly adjust the charge and discharge plan. The multi-time scale optimization strategy includes three-day strategy optimization, two-day strategy optimization and one-day strategy optimization.

[0082] Dynamically adjusting the charge and discharge plan based on the multi-time scale optimization strategy for the optimal charge and discharge strategy includes:

[0083] When performing three-day strategy optimization, a three-day charge and discharge plan is generated according to the three-day electricity price prediction, and its expression is as follows:

[0084]

[0085] where, T 3d is the total number of time periods in three days, is the electricity price in the spot market at time period t, is the charge and discharge power of the energy storage station participating in the spot market at time period t (a positive value indicates discharge, and a negative value indicates charge), is the frequency modulation cost in the auxiliary service market at time period t, is the frequency modulation power of the energy storage station participating in the auxiliary service market at time period t, is the energy storage cost of the energy storage station at time period t;

[0086] When performing two-day strategy optimization, a two-day charge and discharge plan is generated according to the two-day electricity price prediction, and its expression is as follows:

[0087]

[0088] where, T 2d is the total number of time periods in three days, is the electricity price in the spot market at time period t, is the charge and discharge power of the energy storage station participating in the spot market at time period t (a positive value indicates discharge, and a negative value indicates charge), is the frequency modulation cost in the auxiliary service market at time period t, is the frequency regulation power of the energy storage station participating in the ancillary service market at time t, is the energy storage cost of the energy storage station at time t.

[0089] When performing one-day strategy optimization, a one-day charge and discharge plan is generated based on the one-day electricity price forecast and load forecast, and its expression is as follows:

[0090]

[0091] where T 1d is the total number of time periods in three days, is the electricity price in the spot market at time t, is the charge and discharge power of the energy storage station participating in the spot market at time t (a positive value indicates discharge, and a negative value indicates charge), is the frequency regulation cost in the ancillary service market at time t, is the frequency regulation power of the energy storage station participating in the ancillary service market at time t, is the energy storage cost of the energy storage station at time t.

[0092] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0093] The terms "first", "second", "third", etc. in the specification of this application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of this application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0094] The above is the case. The above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing energy storage charging and discharging based on an ant colony optimization algorithm with multiple time scales, characterized in that, It includes the following steps: S101. Based on the revenue objective function of the energy storage station, the charge and discharge efficiency constraint of the energy storage station, the capacity constraint of the energy storage station, and the energy storage state of the energy storage station, construct an energy storage station cross-market collaboration model; S102. Optimize the energy storage station cross-market collaboration model based on the ant colony optimization algorithm to find the optimal charge and discharge strategy; S103. Dynamically adjust the charge and discharge plan based on the optimal charge and discharge strategy with a multi-time scale optimization strategy.

2. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 1, characterized in that The expression of the revenue objective function of the energy storage station is as follows: where T is the total number of optimization periods, is the electricity price in the spot market at time period t, is the charge and discharge power of the energy storage station participating in the spot market at time period t (a positive value indicates discharging, and a negative value indicates charging), is the frequency regulation cost in the ancillary service market at time period t, is the frequency regulation power of the energy storage station participating in the ancillary service market at time period t, is the energy storage cost of the energy storage station at time period t.

3. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 1, characterized in that The expression of the charge and discharge efficiency constraint of the energy storage station is as follows: Wherein, and are the actual charging and discharging powers of the energy storage station at time t, respectively, η charge and η discharge are the charging efficiency and discharging efficiency of the energy storage station, respectively, and are the input and output powers of the energy storage station at time t, respectively.

4. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 1, characterized in that The expression of the charge and discharge efficiency constraint of the energy storage station is as follows: Among them, and are the actual charging and discharging powers of the energy storage station at time t, respectively. η charge and η discharge are the charging efficiency and discharging efficiency of the energy storage station, respectively. and are the input and output powers of the energy storage station at time t, respectively.

5. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 1, wherein The expression of the capacity constraint of the energy storage station is as follows: SOC min ≤SOC t ≤SOC max Among them, SOC t is the energy storage state of the energy storage station at time t, SOC min and SOC max are the minimum and maximum energy storage capacities of the energy storage station, respectively.

6. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 1, characterized in that, The expression of the energy storage state of the energy storage station is as follows: where Δt is the time period length, E max is the maximum energy storage capacity of the energy storage station, and SOC t is the energy storage state of the energy storage station at time period t, is the actual charging efficiency of the energy storage station at time period t, is the actual discharge power of the energy storage station at time period t, and SOC t+1 is the energy storage state of the energy storage station at time period t + 1.

7. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 1, characterized in that The step of optimizing the energy storage station cross-market collaboration model based on the ant colony optimization algorithm to find the optimal charge and discharge strategy includes the following steps: Let each ant represent a charge and discharge strategy, and initialize the parameters of the ant colony algorithm. The parameters of the ant colony algorithm include the number of ants, the pheromone concentration, and the heuristic factor; During the search process, the pheromone concentration is updated according to the value of the objective function; Select a path according to the pheromone concentration and the heuristic information.

8. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 7, wherein During the search process, the pheromone concentration is updated according to the value of the objective function. The expression of the pheromone concentration update is as follows: τ ij (t + 1) = (1 - ρ)·τ ij (t) + Δτ ij Among them, τ ij (t) is the pheromone concentration of path ij at time t, ρ is the pheromone evaporation coefficient, and Δτ ij is the pheromone increment left by the ant on path ij.

9. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 7, characterized in that The expression of the path selection probability for selecting a path according to the pheromone concentration and the heuristic information is as follows: Among them, η ij is the heuristic information of path ij, which is usually related to the objective function value. α and β are the weight factors of pheromone and heuristic information respectively. P ij is the path selection probability, τ ij is the pheromone concentration of path ij, τ ik is the pheromone concentration of path ik, and η ik is the heuristic information of path ik.

10. The energy storage charge and discharge optimization method based on the multi-time scale ant colony optimization algorithm according to claim 1, wherein Dynamically adjusting the charge and discharge plan based on the optimal charge and discharge strategy with a multi-time scale optimization strategy includes: When performing a three-day strategy optimization, generate a three-day charge and discharge plan according to the three-day electricity price forecast. Its expression is as follows: Among them, T 3d is the total number of three-day periods, is the electricity price in the spot market at time period t, is the charging and discharging power of the energy storage station participating in the spot market at time period t (a positive value indicates discharging, and a negative value indicates charging), is the frequency regulation cost of the ancillary service market at time period t, is the frequency regulation power of the energy storage station participating in the ancillary service market at time period t, is the energy storage cost of the energy storage station at time period t; When performing a two-day strategy optimization, generate a two-day charge and discharge plan according to the two-day electricity price forecast; When performing a one-day strategy optimization, generate a one-day charge and discharge plan according to the one-day electricity price forecast and the load forecast.

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