Optical storage system optimal configuration method combined with electricity clearing price of electricity market

Abstract

The invention provides an optical storage system optimal configuration method combined with power market clearing electricity price, comprising the following steps: determining the current type of an accessed energy storage system in an optical storage system, the current type of the energy storage system being a single energy storage system or a double energy storage system; according to the current type of the energy storage system and in combination with a preset constraint condition, an optimization objective function with the station daily income as the maximum is correspondingly constructed; wherein the optimization target is a first optimization target function or a second optimization target function; and acquiring actual operation parameters of the optical storage system, solving an optimal solution for the correspondingly constructed optimization objective function by adopting an improved myxobacteria optimization algorithm, and further configuring the optical storage system according to the solved optimal solution. According to the invention, the capacity configuration and control strategy optimization configuration of the single-double energy storage system can be realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a method for optimizing the configuration of a photovoltaic storage system in combination with electricity market clearing prices. Background Art

[0002] The proposed "30·60" dual carbon goals provide guidance for market-oriented reforms and innovations in the power industry. Achieving green and low-carbon economic development requires fully leveraging policy tools such as carbon emissions trading, ensuring effective integration and coordination between the power and carbon markets. As the global energy transition toward a low-carbon economy accelerates, the installed capacity of new energy sources, represented by photovoltaics, continues to rise. According to data from China's National Energy Administration, by the end of December 2024, China's installed renewable energy capacity will reach 1.889 billion kilowatts, with solar power generation capacity reaching approximately 890 million kilowatts, accounting for 26.6% of the country's total installed power generation capacity and 47.1% of the country's total installed renewable energy capacity. However, the volatility and intermittency of photovoltaic power generation have led to significant curtailment after large-scale grid connection. At the same time, the time-of-use electricity pricing mechanism fostered by power market reforms has created conditions for energy storage systems to participate in power dispatch. However, how to achieve the multi-objective synergy between the economic efficiency of photovoltaic and storage systems, reduce curtailment, increase carbon emissions, extend equipment life, and regulate grid supply and demand through optimized dispatch has become a core challenge hindering the efficient integration of new energy.

[0003] In recent years, research on solar-to-storage systems has primarily focused on optimizing capacity configuration and control strategies. However, these studies often combine relevant parameters such as storage lifespan and electricity costs to optimize capacity configuration or control strategies for single energy storage systems based on economic considerations. This approach fails to optimize capacity configuration and control strategies for dual energy storage systems.

[0004] Therefore, in order to solve the above technical problems, it is necessary to provide a new method for optimizing the configuration of photovoltaic storage systems, which can realize the optimal configuration of the capacity and control strategies of single and double energy storage systems. Summary of the Invention

[0005] The technical problem to be solved by the embodiments of the present invention is to provide a method for optimizing the configuration of a photovoltaic energy storage system in combination with the electricity market clearing price, which can realize the capacity configuration and control strategy optimization configuration of single and double energy storage systems.

[0006] To solve the above technical problems, an embodiment of the present invention provides a method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price, the method comprising the following steps:

[0007] S1. Determine the current type of the energy storage system connected to the solar-storage system, wherein the current type of the energy storage system is a single energy storage system or a dual energy storage system;

[0008] S2. Based on the current type of the energy storage system and in combination with preset constraints, construct an optimization objective function that maximizes the daily revenue of the station; wherein the optimization objective is the first optimization objective function or the second optimization objective function;

[0009] S3. Acquire actual operating parameters of the solar-to-storage system, and use the improved slime mold optimization algorithm to find an optimal solution for the corresponding optimization objective function, and further configure the solar-to-storage system based on the found optimal solution.

[0010] Wherein, the step S2 specifically includes:

[0011] If the current type of the energy storage system is a single energy storage system, with the system balance constraint, photovoltaic output constraint, and energy storage operation constraint as constraints, a first optimization objective function is constructed with the maximum daily revenue of the station as the corresponding constraint;

[0012] If the current type of the energy storage system is a dual energy storage system, with the system balance constraint, the photovoltaic output constraint and the energy storage operation constraint as constraints, a second optimization objective function is constructed with the station daily revenue as the maximum.

[0013] Among them, the expression of the system balance constraint is: and in, is the actual grid-connected power of the optical storage system; The bidding power of each time period for the day-ahead energy market bidding of the solar energy storage system, and and are the positive and negative unbalanced powers in period t respectively; b is the charge and discharge power of the single energy storage system or the dual energy storage system during period t; t is the charge and discharge logic variable of the single energy storage system or the dual energy storage system during period t, b=1 means discharging, b=0 means charging; is the discharge power of the single energy storage system or the dual energy storage system during period t; is the charging power of the single energy storage system or the dual energy storage system during period t; Bidding for output in each time period in the day-ahead energy market for the solar-to-storage system; is the discharge power of the single energy storage system or the dual energy storage system in the day-ahead energy market during period t; is the charging power of the single energy storage system or the dual energy storage system in the day-ahead energy market during period t;

[0014] The expression of the photovoltaic output constraint is: in, Forecast output of photovoltaic power station in each period of the day before; ct is a logical variable representing the unbalanced power state. When the actual photovoltaic output is greater than the photovoltaic decision output value, c t Take 1, otherwise take 0; M1 and M2 are preset positive numbers;

[0015] The expression of the energy storage operation constraint is: in, and are the maximum charge and discharge power of a single energy storage system or a dual energy storage system respectively; E t is the remaining power of the single energy storage system or the dual energy storage system in period t. The remaining power of the single energy storage system or the dual energy storage system in period t depends on the energy storage power in period t-1 and the power change in period t. t―1 is the remaining power of the single energy storage system or the dual energy storage system in the t-1 period; E bat The energy storage capacity of a single energy storage system or a dual energy storage system; SOC min The SOC is the minimum value of the single energy storage system or the dual energy storage system in actual operation; SOC max is the maximum SOC value in actual operation of a single energy storage system or a dual energy storage system; η c is the charging coefficient, which is a constant; η dc is the magnification factor, which is a constant.

[0016] The expressions of the first optimization objective function and the second optimization objective function are both maxW=W RD ―W pen ;in,

[0017] W pen is the output deviation penalty cost of the solar-storage system, and Among them, α is the deviation penalty coefficient, which is a constant;

[0018] W RD is the total daily income of the solar storage system, and W RD =W RDA +W ESD ―W ec ―W pvc Among them, W RDA is the day-ahead energy market revenue of the solar energy storage system; W ESD is the unbalanced electricity revenue of the single energy storage system or the dual energy storage system on the same day; W ec is the life depreciation daily cost of a single energy storage system or a dual energy storage system; W pvc is the life depreciation daily cost of the solar storage system; wherein, is the day-ahead energy market price during period t;

[0019] According to the difference between single and double energy storage systems, W ec There are two calculation methods:

[0020] If W ec is the daily depreciation cost of a single energy storage system, then and C IS is the equipment purchase cost of a single energy storage system; L BS is the battery life of a single energy storage system in years; C MS is the average annual maintenance cost of equipment for a single energy storage system; F BS is the battery life of a single energy storage system, and is the number of battery cycle life obtained from the nth charge and discharge depth on the day; if W ec is the daily cost of life depreciation of the dual energy storage system, then and are the life depreciation daily costs of the two single energy storage systems in the dual energy storage system, and L BD is the battery life of the i-th single energy storage system in the dual energy storage system, and F DS is the battery life of the i-th single energy storage system in the dual energy storage system, and The number of battery cycle life C obtained by the nth charge and discharge depth of the i-th single energy storage system in the dual energy storage system on the same day ID is the equipment purchase cost of the i-th single energy storage system in the dual energy storage system; C MD is the average annual maintenance cost of the equipment of the i-th single energy storage system in the dual energy storage system.

[0021] Wherein, in step S3, the improved slime mold optimization algorithm is constructed based on a preset slime mold optimization algorithm, which interferes with the generation of updated individual positions through reverse learning, quasi-reverse learning and quasi-reflective learning; wherein,

[0022] When initializing the population of the slime mold optimization algorithm, reverse learning is used to increase the coverage of the initial population;

[0023] In the middle stage of the slime mold optimization algorithm, quasi-reverse learning is used to avoid the local optimal solution when most individuals move toward the optimal position;

[0024] In the later stage of the slime mold optimization algorithm, a reflective solution is generated through quasi-reflective learning to effectively accelerate convergence.

[0025] The actual operating parameters of the photovoltaic storage system include transaction electricity price, penalty coefficient, meteorological data, photovoltaic storage parameters and photovoltaic output data.

[0026] The implementation of the embodiments of the present invention has the following beneficial effects:

[0027] The present invention constructs an optimization objective function that maximizes the daily revenue of the station according to the current type of the energy storage system, namely a single energy storage system or a dual energy storage system, and adopts an improved slime mold optimization algorithm to find the optimal solution to configure the photovoltaic storage system. It can realize the capacity configuration and control strategy optimization configuration of the single and dual energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, other drawings obtained based on these drawings still fall within the scope of the present invention.

[0029] Figure 1 A flowchart of a method for optimizing configuration of a photovoltaic storage system in combination with electricity market clearing prices provided by an embodiment of the present invention;

[0030] Figure 2 A topological diagram of a photovoltaic storage system in a photovoltaic storage system optimization configuration method combined with power market clearing electricity prices provided by an embodiment of the present invention;

[0031] Figure 3 A comparison chart of power consumption changes between a single energy storage system and a dual energy storage system in a method for optimizing configuration of a photovoltaic storage system in combination with power market clearing prices provided by an embodiment of the present invention;

[0032] Figure 4 A flowchart of an improved slime mold optimization algorithm in a method for optimizing configuration of a photovoltaic storage system combined with electricity market clearing prices provided by an embodiment of the present invention;

[0033] Figure 5 This is a comparison diagram of individual positions after reverse learning, quasi-reverse learning, and quasi-reflective learning when an improved slime mold optimization algorithm is used in a photovoltaic storage system optimization configuration method combined with power market clearing electricity prices provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0035] like Figure 1FIG. 1 is a method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price, according to an embodiment of the present invention. The method includes the following steps:

[0036] Step S1: Determine the current type of the energy storage system connected to the solar energy storage system, wherein the current type of the energy storage system is a single energy storage system or a dual energy storage system;

[0037] Step S2: Based on the current type of the energy storage system and in combination with preset constraints, construct an optimization objective function that maximizes the daily revenue of the station; wherein the optimization objective is the first optimization objective function or the second optimization objective function;

[0038] Step S3: obtaining actual operating parameters of the solar energy storage system, and using the improved slime mold optimization algorithm to find the optimal solution for the corresponding optimization objective function, and further configuring the solar energy storage system according to the found optimal solution.

[0039] The specific process is as follows: in step S1, Figure 2 As shown in the figure, the photovoltaic storage system mainly consists of an energy storage system, a photovoltaic system, a collection line, a converter, a public grid, and an energy management system (EMS); the solid line indicates the direction of current flow, and the dashed line indicates the EMS control line. The photovoltaic system transmits electricity to the collection line through a unidirectional connection, while the energy storage system and the public grid achieve energy exchange through a bidirectional connection. During the energy storage charging process, grid power is input into the collection line through an AC / DC converter and then stored in the energy storage battery through a DC / DC converter. During discharge, the energy storage power is injected into the collection line through a DC / DC converter and then fed back to the grid through a DC / AC converter. The EMS monitors the energy storage SOC, photovoltaic output, and line switch status in real time, generates an optimal control strategy based on dynamic constraints, and achieves the system's economic operation goals by issuing switching commands.

[0040] For PV sites, when daytime PV output exceeds typical load, appropriate energy storage can be deployed to prevent curtailment. When PV output reaches high load levels, it can be directly connected to the grid for sales, with energy storage also participating in the sales to compensate for actual PV output falling short of the day-ahead market bid. During nighttime peak loads, energy storage can be used to sell power to the grid, shaving peak loads and filling valleys. During low load periods, energy storage can purchase power from the grid to compensate for daytime loads.

[0041] In this case, deploying an energy storage system can not only compensate for insufficient PV production capacity during actual operation, but also alleviate to a certain extent issues such as PV curtailment and grid-side peak-shaving and valley-filling. Therefore, achieving carbon emission reduction benefits primarily depends on the utilization of PV production capacity, requiring the selection of an appropriate energy storage system while also ensuring its service life. In one example, the energy storage system primarily operates during the curtailment period from 11:00 to 13:00 and during the high-price market period from 18:00 to 20:00. During other periods, it only requires appropriate energy exchange from the grid to compensate for the shortfall in PV output during day-ahead market pre-sales and real-time market sales.

[0042] Due to the high cost of energy storage, in order to reduce the demand for energy storage capacity, such as Figure 3 As shown in the figure, depending on the energy storage method, the subsequent change rules can be divided into two situations:

[0043] (1) Single energy storage system: discharge at appropriate power from 18:00 to 20:00, and ensure that the SOC returns to the initial value at 22:00 to reduce the energy storage capacity demand.

[0044] (2) Dual energy storage system: Discharge at appropriate power between 18:00 and 20:00 to ensure that the SOC of the large energy storage returns to its initial value. Since the small energy storage only compensates for unbalanced power, it is necessary to purchase electricity after 22:00 to return the SOC to its initial value to ensure a compensation margin for the next day.

[0045] It can be seen from this that before constructing the optimization objective function, it is necessary to determine whether the current type of the energy storage system connected to the solar-storage system is a single energy storage system or a dual energy storage system.

[0046] In step S2, in order to suppress the photovoltaic curtailment rate and ensure the basic clearing income of new energy sites, the energy storage life attenuation factor is introduced into the optimization objective function to reduce the equipment loss cost and consider the carbon emission reduction benefit.

[0047] At this time, if the current type of the energy storage system is a single energy storage system, with the system balance constraint, photovoltaic output constraint, and energy storage operation constraint as the constraint conditions, the first optimization objective function with the station daily revenue as the maximum is constructed; or, if the current type of the energy storage system is a dual energy storage system, with the system balance constraint, photovoltaic output constraint, and energy storage operation constraint as the constraint conditions, the second optimization objective function with the station daily revenue as the maximum is constructed.

[0048] In one example, the expression for the system equilibrium constraint is and in, is the actual grid power of the optical storage system; The day-ahead energy market bidding power of the solar storage system in each period, and Bidding for output in each time period in the day-ahead energy market for the solar-storage system; is the discharge power of the single energy storage system or the dual energy storage system in the day-ahead energy market during period t; is the charging power of the single energy storage system or the dual energy storage system in the day-ahead energy market during period t; is the charge and discharge power of the single energy storage system or the dual energy storage system during period t; and are the positive and negative unbalanced powers in period t respectively; is the discharge power of the single energy storage system or the dual energy storage system during period t; b is the charging power of the single energy storage system or the dual energy storage system during period t; t is the charge and discharge logic variable of the single energy storage system or the dual energy storage system in period t, b=1 means discharging, and b=0 means charging.

[0049] The expression of photovoltaic output constraint is: in, Forecast output of photovoltaic power station in each period of the day before; c t is a logical variable representing the unbalanced power state. When the actual photovoltaic output is greater than the photovoltaic decision output value, c t Take 1, otherwise take 0; M1 and M2 are preset positive numbers.

[0050] The expression of energy storage operation constraint is: in, and are the maximum charge and discharge power of a single energy storage system or a dual energy storage system respectively; E t is the remaining power of the single energy storage system or the dual energy storage system in period t. The remaining power of the single energy storage system or the dual energy storage system in period t depends on the energy storage power in period t-1 and the power change in period t. t―1 is the remaining power of the single energy storage system or the dual energy storage system in the t-1 period; E bat The energy storage capacity of a single energy storage system or a dual energy storage system; SOC min The SOC is the minimum value of the single energy storage system or the dual energy storage system in actual operation; SOC max is the maximum SOC value in actual operation of a single energy storage system or a dual energy storage system; η c is the charging coefficient, which is a constant; η dc is the magnification factor, which is a constant.

[0051] The expression of the optimization objective function is maxW=W RD ―W pen ; That is, the expressions of the first optimization objective function and the second optimization objective function are the same, except that the parameter definitions and values ​​are different.

[0052] At this time, W RD is the total daily revenue of the solar storage system, and WRD =W RDA +W ESD ―W ec ―W pvc Among them, W RDA is the day-ahead energy market revenue of the solar-storage system; W ESD is the unbalanced electricity revenue of the single energy storage system or the dual energy storage system on the same day; W ec is the life depreciation daily cost of a single energy storage system or a dual energy storage system; W pvc is the daily cost of the life-cycle depreciation of the solar energy storage system.

[0053] in, is the day-ahead energy market price during period t;

[0054] Among them, if W ec is the daily depreciation cost of a single energy storage system, then L BS is the battery life of a single energy storage system in years, and F BS is the battery life of a single energy storage system, and C is the number of battery cycle life obtained from the nth charge and discharge depth on the day; IS is the equipment purchase cost of a single energy storage system; C MS The average annual maintenance cost of equipment for a single energy storage system;

[0055] Among them, if W ec is the daily cost of life depreciation of the dual energy storage system, then and are the life depreciation daily costs of the two single energy storage systems in the dual energy storage system, and L BD is the battery life of the i-th single energy storage system in the dual energy storage system, and F DS is the battery life of the i-th single energy storage system in the dual energy storage system, and The number of battery cycle life C obtained by the nth charge and discharge depth of the i-th single energy storage system in the dual energy storage system on the same day ID is the equipment purchase cost of the i-th single energy storage system in the dual energy storage system; C MD is the average annual maintenance cost of the equipment of the i-th single energy storage system in the dual energy storage system;

[0056] At this time, W pen is the output deviation penalty cost of the solar-storage system, and Among them, α is the deviation penalty coefficient, which is a constant.

[0057] In step S3, first, the actual operating parameters of the photovoltaic storage system are obtained, including transaction electricity price, penalty coefficient, meteorological data, photovoltaic storage parameters and photovoltaic output data.

[0058] Secondly, if Figure 4 As shown in the figure, an improved slime mold optimization algorithm is used to find the optimal solution for the corresponding optimization objective function. The improved slime mold optimization algorithm is based on the analysis and improvement of the operation mechanism of the traditional slime mold optimization algorithm. Specifically, reverse learning is used when initializing the population to increase the coverage of the initial population; in the middle of the operation, the weight of the optimal solution increases, and most individuals will move toward the optimal position. At this time, quasi-reverse learning can be used to avoid falling into the local optimal solution; in the later stage of the operation, quasi-reflective learning is used to generate reflective solutions, which can effectively accelerate convergence.

[0059] In addition, combined with the solution process, the population coverage is increased in the early and middle stages and the convergence is accelerated in the later stages. Therefore, the Sigmoid nonlinear convergence factor is used to control the reverse learning, quasi-reverse learning, and quasi-reflective learning processes.

[0060] It should be noted that using the traditional slime mold optimization algorithm to find the optimal solution to the optimization objective function is a conventional technical means in this field, and the specific content will not be repeated here.

[0061] In one example, the parameter setting and population initialization in the improved slime mold optimization algorithm include: using the energy storage charging and discharging power of one point every 15 minutes as the target position for the solution, initializing the population size to 500, the maximum number of iterations to 1000, the dimension to 96, the upper and lower search boundaries for each dimensional position, and the optimal position fitness being the highest daily energy storage benefit; among them, reverse learning is used when initializing the population to increase the coverage of the initial population.

[0062] In the improved slime mold optimization algorithm, the position update formula is expressed by the following function to update the individual position;

[0063]

[0064] Where t is the number of iterations; Si is the convergence factor; T is the maximum number of iterations; is the new position generated based on the original position; is the inverse solution of the i-th dimension; is the original solution of the i-th dimension; is the quasi-inverse solution of the i-th dimension;

[0065] lb i With ub i are the upper and lower boundaries of the i-th dimension; is the reflection solution of the i-th dimension.

[0066] It should be noted that Opposition-Based Learning (OBL) expands the search space by generating the reverse solution of the current solution to increase the probability of finding a better solution. Its core idea is: if the current solution is far away from the optimal solution, its reverse solution may be closer to the optimal area. Quasi-Opposition-Based Learning (QOBL) is improved based on OBL, and further refines the search range by generating quasi-reverse solutions in the area between the median of the upper and lower bounds and the reverse point. Quasi-reflection-based learning (QRBL) is an improved algorithm based on OBL and QOBL. At this time, The actual location of Figure 5 shown.

[0067] Finally, the photovoltaic storage system is optimized according to the optimal solution.

[0068] In this embodiment of the present invention, in order to effectively verify the overall performance of the QR3O-SMA (Improved Slime Mold Optimization Algorithm, Quasi-Reflection Triple Opposition SMA) algorithm, the widely used and authoritative CEC2017 test function set was selected, and the QR3O-SMA algorithm was compared with the traditional SMA (Slime Mold Optimization Algorithm) algorithm and the particle swarm optimization algorithm (PSO) in a multi-dimensional function extreme value optimization test.

[0069] To ensure fairness and objectivity, the algorithm was run 50 times independently and repeatedly using the same hardware and software configuration. The specific operating environment included Windows 11, an Intel(R) Core(TM) i5-12490F (3.0GHz) CPU, 16GB of RAM, and an RTX 3050 graphics card. All algorithm codes were implemented in Matlab R2022b. The same population size of 30 was used throughout the experiment, and the maximum number of iterations was 500.

[0070] The CEC2017 test set contains 30 functions, which can be divided into four categories based on their characteristics: unimodal functions (F1-F3), simple multimodal functions (F4-F10), hybrid functions (F11-F20), and combined functions (F21-F30). These functions cover complex optimization problems ranging from low-dimensional to high-dimensional (10D / 30D / 50D / 100D). Since photovoltaic data acquisition accuracy is 5 minutes per data point, and real-time optimization typically takes 15 minutes per stage, the model calculation dimension used in this paper is 96. This requires the algorithm to have high-dimensional search capabilities. Below, we select four categories of functions: F1, F4, F11, and F21 (all functions are actually tested, but for space reasons and fairness, the first function of each type is selected for presentation in this article). These functions are tested and compared across 100 dimensions, as shown in Tables 1 and 2.

[0071] Table 1

[0072]

[0073]

[0074] Table 2

[0075]

[0076] The bolded values ​​indicate the optimal solutions for the three functions. Analysis shows that the QR3O-SMA algorithm outperforms the other two algorithms in most function solving cases. In the 100-dimensional case, it achieves the most optimal solutions among the three algorithms, with only a few suboptimal solutions. Overall, the optimal solution capability is stronger than the other two algorithms, effectively addressing the SMA algorithm's tendency to fall into local optimal solutions, unstable optimization capabilities, and low optimization accuracy when solving complex global optimization functions.

[0077] In summary, the present invention establishes an optimization objective function for a photovoltaic storage system that combines time-of-use electricity prices. This function fully considers the combined cost of photovoltaic and energy storage batteries, considers typical photovoltaic output under different weather conditions, and proposes operating modes for single and dual energy storage groups based on the lifespan of lithium iron phosphate batteries. Based on the algorithmic operation mechanism, an optimized slime mold algorithm is proposed that combines reverse, quasi-reverse, and quasi-reflective algorithms with Sigmoid nonlinear convergence. The optimization objective function is solved, and simulations are conducted on day-ahead prediction and real-time control of new energy sites under three typical operating conditions. The following conclusions are drawn:

[0078] (1) The present invention can quickly and effectively solve the objective function, reduce the comprehensive cost of the photovoltaic storage system, and obtain the control strategy for the highest income of the new energy station under the premise of realizing peak shaving and valley filling on the grid side. The optimal energy storage capacity and the maximum charge and discharge power of the energy storage are obtained through subsequent calculations, which can be used as the basis for the purchase of photovoltaic storage.

[0079] (2) Through case analysis, it can be concluded that switching between large and small energy storage is more economical under three typical operating conditions compared with single energy storage. Small energy storage can be appropriately configured for day-ahead compensation according to the actual site operation conditions to achieve the optimal configuration.

[0080] The implementation of the embodiments of the present invention has the following beneficial effects:

[0081] The present invention constructs an optimization objective function that maximizes the daily revenue of the station according to the current type of the energy storage system, namely a single energy storage system or a dual energy storage system, and adopts an improved slime mold optimization algorithm to find the optimal solution to configure the photovoltaic storage system. It can realize the capacity configuration and control strategy optimization configuration of the single and dual energy storage systems.

[0082] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0083] The above disclosure is only a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price, characterized in that: The method comprises the following steps: S1. Determine the current type of the energy storage system connected to the solar-storage system, wherein the current type of the energy storage system is a single energy storage system or a dual energy storage system; S2. Based on the current type of the energy storage system and in combination with preset constraints, construct an optimization objective function that maximizes the daily revenue of the station; wherein the optimization objective is the first optimization objective function or the second optimization objective function; S3. Acquire actual operating parameters of the solar-to-storage system, and use the improved slime mold optimization algorithm to find an optimal solution for the corresponding optimization objective function, and further configure the solar-to-storage system based on the found optimal solution.

2. The method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price according to claim 1, characterized in that: The step S2 specifically includes: If the current type of the energy storage system is a single energy storage system, with the system balance constraint, photovoltaic output constraint, and energy storage operation constraint as constraints, a first optimization objective function is constructed with the maximum daily revenue of the station as the corresponding constraint; If the current type of the energy storage system is a dual energy storage system, with the system balance constraint, the photovoltaic output constraint and the energy storage operation constraint as constraints, a second optimization objective function is constructed with the station daily revenue as the maximum.

3. The method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price according to claim 2, characterized in that: The expression of the system equilibrium constraint is and in, is the actual grid-connected power of the optical storage system; Bidding power for each time period in the day-ahead energy market for the solar energy storage system; and are the positive and negative unbalanced powers in period t respectively; b is the charge and discharge power of the single energy storage system or the dual energy storage system during period t; t is the charge and discharge logic variable of the single energy storage system or the dual energy storage system during period t, b=1 means discharging, b=0 means charging; is the discharge power of the single energy storage system or the dual energy storage system during period t; is the charging power of the single energy storage system or the dual energy storage system during period t; in, Bidding for output in each time period in the day-ahead energy market for the solar-to-storage system; is the discharge power of the single energy storage system or the dual energy storage system in the day-ahead energy market during period t; is the charging power of the single energy storage system or the dual energy storage system in the day-ahead energy market during period t; The expression of the photovoltaic output constraint is: in, Forecast output of photovoltaic power station in each period of the day before; c t is a logical variable representing the unbalanced power state. When the actual photovoltaic output is greater than the photovoltaic decision output value, c t Take 1, otherwise take 0; M1 and M2 are preset positive numbers; The expression of the energy storage operation constraint is: in, and are the maximum charge and discharge power of a single energy storage system or a dual energy storage system respectively; E t is the remaining power of the single energy storage system or the dual energy storage system in period t. The remaining power of the single energy storage system or the dual energy storage system in period t depends on the energy storage power in period t-1 and the power change in period t. t―1 is the remaining power of the single energy storage system or the dual energy storage system in the t-1 period; η c is the charging coefficient, which is a constant; η dc is the amplification factor, which is a constant; SOC min The SOC is the minimum value of the single energy storage system or the dual energy storage system in actual operation; SOC max is the maximum SOC value in actual operation of a single energy storage system or a dual energy storage system; E bat The energy storage capacity of a single energy storage system or a dual energy storage system.

4. The method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price according to claim 3, characterized in that: The expressions of the first optimization objective function and the second optimization objective function are both maxW=W RD ―W pen ;in, W pen is the output deviation penalty cost of the solar-storage system, and Among them, α is the deviation penalty coefficient, which is a constant; W RD is the total daily income of the solar storage system, and W RD =W RDA +W ESD ―W ec ―W pvc Among them, W RDA is the day-ahead energy market revenue of the solar energy storage system; W ESD is the unbalanced electricity revenue of the single energy storage system or the dual energy storage system on the same day; W ec is the life depreciation daily cost of a single energy storage system or a dual energy storage system; W pvc is the life depreciation daily cost of the solar storage system; wherein, is the day-ahead energy market price during period t; According to the difference between single and double energy storage systems, W ec There are two calculation methods: If W ec is the daily depreciation cost of a single energy storage system, then and C IS is the equipment purchase cost of a single energy storage system; L BS is the battery life of a single energy storage system in years; C MS is the average annual maintenance cost of equipment for a single energy storage system; F BS is the battery life of a single energy storage system, and is the number of battery cycle life obtained from the nth charge and discharge depth on the day; if W ec is the daily cost of life depreciation of the dual energy storage system, then and are the life depreciation daily costs of the two single energy storage systems in the dual energy storage system, and L BD is the battery life of the i-th single energy storage system in the dual energy storage system, and F DS is the battery life of the i-th single energy storage system in the dual energy storage system, and The number of battery cycle life C obtained by the nth charge and discharge depth of the i-th single energy storage system in the dual energy storage system on the same day ID is the equipment purchase cost of the i-th single energy storage system in the dual energy storage system; C MD is the average annual maintenance cost of the equipment of the i-th single energy storage system in the dual energy storage system.

5. The method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price according to claim 4, characterized in that: In step S3, the improved slime mold optimization algorithm is constructed based on a preset slime mold optimization algorithm, which interferes with the generation of updated individual positions through reverse learning, quasi-reverse learning, and quasi-reflective learning; wherein, When initializing the population of the slime mold optimization algorithm, reverse learning is used to increase the coverage of the initial population; In the middle stage of the slime mold optimization algorithm, quasi-reverse learning is used to avoid the local optimal solution when most individuals move toward the optimal position; In the later stage of the slime mold optimization algorithm, a reflective solution is generated through quasi-reflective learning to effectively accelerate convergence.

6. The method for optimizing the configuration of a photovoltaic storage system in combination with the electricity market clearing price according to claim 5, characterized in that: The actual operating parameters of the photovoltaic storage system include transaction electricity price, penalty coefficient, meteorological data, photovoltaic storage parameters and photovoltaic output data.

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