High-speed service area micro-grid multi-zone-area optical storage system capacity optimization configuration method and system

Abstract

The invention provides a high-speed service area micro-grid multi-area optical storage system capacity optimization configuration method and system, and the method comprises the steps: obtaining the charging and discharging power of each energy storage subsystem, the power generation power of each photovoltaic subsystem and the total power utilization power in each distribution transformer area in each time period within a preset time period, and substituting the power into a pre-established optical storage system optimization scheduling model, solving the optimal scheduling model of the optical storage system by using an SCIP solver to obtain the power grid electricity purchase cost within a preset duration; and determining the equivalent power grid electricity purchase cost based on the cost, substituting the equivalent power grid electricity purchase cost into the optical storage capacity configuration model, and solving the optical storage capacity configuration model by using a particle swarm algorithm to obtain the optimal configuration capacity and the optimal configuration power of each energy storage subsystem in each distribution transformer district and the optimal configuration installed capacity of each photovoltaic subsystem. The technical scheme of the invention is suitable for a typical high-power supply and high-metering highway power consumer multi-distribution transformer district total 10kV bus micro-grid system, and the comprehensive cost of a light storage system is remarkably reduced.

Description

Technical Field

[0001] The present application relates to the field of capacity configuration of a high-speed service area photovoltaic storage system, and in particular to a method and system for optimizing the capacity configuration of a multi-station photovoltaic storage system in a microgrid of a high-speed service area. Background Art

[0002] As the degree of transportation electrification continues to increase, the transportation system provides a broad space for energy consumption, while the energy system guarantees the energy demand of the transportation system. At the same time, the rich natural resources such as scenery and light contained in my country's highways have not been effectively utilized for a long time. Therefore, promoting the green integration of transportation and energy, building a green energy system for highways, and achieving distributed self-consistency and self-sufficiency in road energy consumption are the inevitable choices for the transformation of the development model of the transportation industry.

[0003] The construction of photovoltaic power generation systems using idle resources such as roofs and slopes of stations such as service areas along the highway, and the configuration of a certain capacity of energy storage to smooth the fluctuations of photovoltaic output and improve the economy of the system, is one of the forms of self-consistent systems for highway energy use. Reasonable planning and operation plans for photovoltaic storage systems can maximize the effective consumption of clean energy along the highway, the self-consistency level of energy use of the road system, and the improvement of economic benefits. Therefore, it is of great research significance to coordinate and optimize the capacity of photovoltaic storage systems in highway areas. At present, scholars at home and abroad have conducted a lot of research on the optimal configuration of photovoltaic storage microgrid capacity under different application scenarios.

[0004] However, the existing literature mainly studies the photovoltaic storage system under a single distribution transformer area, and there are few studies on the optimal configuration of photovoltaic storage in highway microgrids. The proposed method is not completely applicable to the microgrid system with a total of 10kV busbars in multiple distribution transformer areas of typical high-supply and high-meter highway power users. In addition, the common optimization configuration method only selects typical power generation and consumption scenarios for design of relevant system operation strategies, which has certain limitations and cannot fully simulate the system power generation and consumption combination scenarios under various dates and meteorological types throughout the year, affecting the rationality and accuracy of the configuration results. Summary of the invention

[0005] The present application provides a method and system for optimizing the configuration of the capacity of a high-speed service area microgrid multi-station optical storage system, so as to at least solve the technical problem of reasonable configuration results and low accuracy in the prior art.

[0006] The first embodiment of the present application provides a method for optimizing the configuration of the capacity of a multi-station optical storage system in a high-speed service area microgrid, the method comprising:

[0007] Obtain the charging and discharging power of each energy storage subsystem, the generating power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each time period within the preset time length;

[0008] Substitute the charging and discharging power of each energy storage subsystem, the power generation power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each period within the preset time into the pre-established photovoltaic storage system optimization scheduling model, and use the SCIP solver to optimize and solve the photovoltaic storage system optimization scheduling model to obtain the power purchase cost of the power grid within the preset time;

[0009] The equivalent grid electricity purchase cost is determined based on the grid electricity purchase cost within the preset time period, and the equivalent grid electricity purchase cost is substituted into the pre-established photovoltaic storage capacity configuration model. The photovoltaic storage capacity configuration model is optimized and solved using the particle swarm algorithm to obtain the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem.

[0010] Preferably, the process of establishing the photovoltaic storage system optimization scheduling model includes:

[0011] The first objective function is constructed with the goal of minimizing the energy cost of the high-speed service area within a preset time;

[0012] Taking the power balance constraint of the photovoltaic storage system, the operation constraint of the energy storage subsystem, and the safe operation constraint of the photovoltaic storage system as constraint conditions, and combining the first objective function, an optimization scheduling model of the photovoltaic storage system is constructed;

[0013] Among them, the energy storage subsystem operation constraints include: physical constraints on the energy storage subsystem charge state, energy storage subsystem charging power constraints, energy storage subsystem discharge power constraints, energy storage subsystem charge and discharge state constraints, and energy storage subsystem battery charge state initial and terminal state consistency constraints within the scheduling period.

[0014] Furthermore, the calculation formula of the first objective function is as follows:

[0015]

[0016] Where F1 is the energy cost of the high-speed service area during the preset time, P grid,t is the power transmitted from the public connection point to the microgrid system in the service area during period t, c buy,t is the electricity price purchased from the power grid during period t, P gridre,t is the power returned to the grid by the microgrid in the service area from the public connection point during period t, c sell,t is the price of electricity sent back to the grid during period t, Δt is the time interval between two adjacent dispatches, c ε is the power penalty coefficient, ε p is the power penalty term, and T is the total number of time periods within the preset time length.

[0017] Furthermore, the calculation formula of the power balance constraint of the photovoltaic storage system is as follows:

[0018]

[0019] In the formula, is the discharge power of energy storage subsystem n in distribution transformer area i in time period t, is the charging power of energy storage subsystem n in distribution transformer area i in time period t, N tran N is the total number of 10kv bus distribution transformers in the service area. bess,i is the total number of energy storage subsystems installed under distribution transformer area i, is the power generation of PV subsystem j in distribution transformer area i in time period t, is the total power consumption of distribution transformer area i in time period t, N PV,i The total number of photovoltaic subsystems installed under distribution transformer area i;

[0020] The calculation formula of the physical constraint of the state of charge of the energy storage subsystem is as follows:

[0021]

[0022] The calculation formula of the energy storage subsystem charging power constraint is as follows:

[0023]

[0024] The calculation formula of the energy storage subsystem discharge power constraint is as follows:

[0025]

[0026] The calculation formula of the energy storage subsystem charge and discharge state constraint is as follows:

[0027]

[0028] The calculation formula for the consistency constraint between the initial and final state of the battery state of charge of the energy storage subsystem during the scheduling period is as follows:

[0029]

[0030] In the formula, is the rated capacity of energy storage subsystem n in distribution transformer area i, is the charge state of the energy storage subsystem n in the distribution transformer area i at time period t+1, is the charge state of the energy storage subsystem n in the distribution transformer area i in time period t, is the charging efficiency of energy storage subsystem n in distribution transformer area i, is the discharge efficiency of energy storage subsystem n in distribution transformer area i, is the maximum allowable charging power of energy storage system n in distribution transformer area i in time period t, is the rated power of the power conversion equipment of the energy storage subsystem n in the distribution transformer area i, is the maximum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the minimum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the maximum allowable discharge power of energy storage system n in distribution transformer area i in time period t, is the charging state of the energy storage subsystem n in the distribution transformer area i in time period t, is the discharge state of energy storage subsystem n in distribution transformer area i in time period t, is the charge state of the energy storage subsystem n in the distribution transformer area i in the first period of the dispatch cycle, is the charge state of the energy storage subsystem n in the distribution transformer area i in the Tth period within the dispatching cycle;

[0031] The calculation formula of the safe operation constraint of the photovoltaic storage system is as follows:

[0032]

[0033] S grid +S gridre ≤1

[0034] In the formula, is the maximum allowable transmission power of transformer i in the distribution transformer area, P gridmax is the maximum positive transmission power allowed at the public connection point between the microgrid and the grid in the service area, P gridremax is the maximum reverse transmission power allowed at the public connection point between the microgrid and the grid in the service area, P demandmax is the maximum grid power demand limit, S grid The power is in the positive transmission state at the common connection point, S gridre It is the power reverse transmission state at the common connection point.

[0035] Furthermore, the process of establishing the optical storage capacity configuration model includes:

[0036] The second objective function is constructed with the goal of minimizing the comprehensive economic cost of equal annual value;

[0037] Taking the energy storage capacity constraint, the photovoltaic installed capacity constraint and the energy storage subsystem charge and discharge rate constraint as constraint conditions, and combining the second objective function, a photovoltaic storage capacity configuration model is constructed.

[0038] Furthermore, the calculation formula of the second objective function is as follows:

[0039] F2=minC=C inv +C com +C ope

[0040] In the formula, F2 is the minimum value of the comprehensive economic cost of equal annual value, C is the comprehensive economic cost of equal annual value, and C invis the equivalent annual average investment cost of the photovoltaic storage system, C com is the average annual fixed operation and maintenance cost, C ope is the equivalent grid electricity purchasing cost.

[0041] Furthermore, the calculation formula of the energy storage capacity constraint is as follows:

[0042]

[0043] In the formula, Configure the capacity upper limit for the energy storage subsystem in distribution transformer area i;

[0044] The calculation formula of the photovoltaic installed capacity constraint is as follows:

[0045]

[0046] In the formula, Configure the installed power upper limit for the PV subsystem in distribution transformer area i. is the rated power of the jth photovoltaic system under the distribution transformer area i;

[0047] The calculation formula for the charge and discharge rate constraint of the energy storage subsystem is as follows:

[0048]

[0049] In the formula, θ min is the minimum value of the charge and discharge rate coefficient of the energy storage subsystem, θ max is the maximum value of the charge and discharge rate coefficient of the energy storage subsystem, It is the rated power of the nth energy storage system PCS in distribution transformer area i.

[0050] The second embodiment of the present application proposes a capacity optimization configuration system for a high-speed service area microgrid multi-station optical storage system, including:

[0051] An acquisition module is used to obtain the charging and discharging power of each energy storage subsystem, the generating power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each time period within a preset time period;

[0052] The first optimization module is used to substitute the charging and discharging power of each energy storage subsystem in each distribution transformer area in each period within the preset time, the power generation power of each photovoltaic subsystem, and the total power consumption into the pre-established photovoltaic storage system optimization scheduling model, and use the SCIP solver to optimize and solve the photovoltaic storage system optimization scheduling model to obtain the power purchase cost of the power grid within the preset time;

[0053] The second optimization module is used to determine the equivalent grid electricity purchase cost based on the grid electricity purchase cost within the preset time period, substitute the equivalent grid electricity purchase cost into the pre-established photovoltaic storage capacity configuration model, and use the particle swarm algorithm to optimize and solve the photovoltaic storage capacity configuration model to obtain the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem.

[0054] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0055] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present application.

[0056] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0057] The present application proposes a method and system for optimizing the configuration of the capacity of a multi-area photovoltaic storage system in a microgrid in a high-speed service area, wherein the method comprises: obtaining the charging and discharging power of each energy storage subsystem in each distribution transformer area in each time period within a preset time length, the power generation power of each photovoltaic subsystem, and the total power consumption; substituting the charging and discharging power of each energy storage subsystem in each distribution transformer area in each time period within the preset time length, the power generation power of each photovoltaic subsystem, and the total power consumption into a pre-established photovoltaic storage system optimization scheduling model, optimizing and solving the photovoltaic storage system optimization scheduling model using a SCIP solver to obtain the power purchase cost of the power grid within the preset time length; determining an equivalent power purchase cost of the power grid based on the power purchase cost of the power grid within the preset time length, substituting the equivalent power purchase cost of the power grid into a pre-established photovoltaic storage capacity configuration model, optimizing and solving the photovoltaic storage capacity configuration model using a particle swarm algorithm, and obtaining the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem. The technical solution proposed in this application is suitable for microgrid systems with a total of 10kV busbars in multiple distribution transformer areas of typical high-supply and high-meter highway power users, which significantly reduces the overall cost of the photovoltaic storage system.

[0058] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0060] Figure 1A flowchart of a method for optimizing capacity configuration of a high-speed service area microgrid multi-station optical storage system according to an embodiment of the present application;

[0061] Figure 2 A flowchart of optimizing the optical storage configuration capacity according to an embodiment of the present application;

[0062] Figure 3 A structural diagram of a typical optical storage system in a high-speed service area provided according to an embodiment of the present application;

[0063] Figure 4 It is an iterative optimization result curve provided according to one embodiment of the present application;

[0064] Figure 5 A curve of the total reverse grid-connected power consumption of the system in an annual manner under the change of energy storage capacity provided according to an embodiment of the present application;

[0065] Figure 6 An annual equivalent total cost curve for energy storage capacity changes provided according to an embodiment of the present application;

[0066] Figure 7 Schematic diagram of photovoltaic power generation, total power consumption and grid power supply curves in a typical day operation result provided according to an embodiment of the present application

[0067] Figure 8 A schematic diagram of a transformer outgoing line power curve in a distribution transformer area in a typical daily operation result provided according to an embodiment of the present application;

[0068] Fig. 9 A schematic diagram of the SOC curve of each energy storage system battery in a typical day operation result provided according to an embodiment of the present application;

[0069] Fig.10 A structural diagram of a capacity optimization configuration system for a high-speed service area microgrid multi-station photovoltaic storage system provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0070] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0071] The present application proposes a method and system for optimizing the configuration of the capacity of a multi-area photovoltaic storage system in a high-speed service area microgrid, wherein the method comprises: obtaining the charging and discharging power of each energy storage subsystem in each distribution transformer area in each time period within a preset time length, the power generation power of each photovoltaic subsystem, and the total power consumption; substituting the charging and discharging power of each energy storage subsystem in each distribution transformer area in each time period within the preset time length, the power generation power of each photovoltaic subsystem, and the total power consumption into a pre-established photovoltaic storage system optimization scheduling model, and optimizing and solving the photovoltaic storage system optimization scheduling model using a SCIP solver to obtain the power purchase cost of the power grid within the preset time length; determining an equivalent power purchase cost of the power grid based on the power purchase cost of the power grid within the preset time length, substituting the equivalent power purchase cost of the power grid into a pre-established photovoltaic storage capacity configuration model, and optimizing and solving the photovoltaic storage capacity configuration model using a particle swarm algorithm to obtain the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem. The technical solution proposed in this application is suitable for microgrid systems with a total of 10kV busbars in multiple distribution transformer areas of typical high-supply and high-meter highway power users, which significantly reduces the overall cost of the photovoltaic storage system.

[0072] The following describes the capacity optimization configuration method and system of the high-speed service area microgrid multi-station optical storage system in an embodiment of the present application with reference to the accompanying drawings.

[0073] Embodiment 1

[0074] Figure 1 A flowchart of a method for optimizing capacity configuration of a high-speed service area microgrid multi-station optical storage system according to an embodiment of the present application is provided. Figure 1 As shown, the method includes:

[0075] Step 1: Obtain the charging and discharging power of each energy storage subsystem, the generating power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each time period within the preset time.

[0076] Step 2: Substitute the charging and discharging power of each energy storage subsystem in each distribution transformer area in each period within the preset time, the power generation power of each photovoltaic subsystem, and the total power consumption into the pre-established photovoltaic storage system optimization scheduling model, and use the SCIP solver to optimize and solve the photovoltaic storage system optimization scheduling model to obtain the power grid purchase cost within the preset time.

[0077] It should be noted that the SCIP solver is used to solve the inner optimization model, namely the optimal scheduling model of the photovoltaic storage system, to realize the full-year optimal scheduling operation simulation of the service area microgrid system under a certain photovoltaic storage capacity combination and the calculation of the annual total electricity purchase cost.

[0078] In the embodiment of the present disclosure, the process of establishing the optimization scheduling model of the photovoltaic storage system includes:

[0079] The first objective function is constructed with the goal of minimizing the energy cost of the high-speed service area within a preset time;

[0080] Taking the power balance constraint of the photovoltaic storage system, the operation constraint of the energy storage subsystem, and the safe operation constraint of the photovoltaic storage system as constraint conditions, and combining the first objective function, an optimization scheduling model of the photovoltaic storage system is constructed;

[0081] Among them, the energy storage subsystem operation constraints include: physical constraints on the energy storage subsystem charge state, energy storage subsystem charging power constraints, energy storage subsystem discharge power constraints, energy storage subsystem charge and discharge state constraints, and energy storage subsystem battery charge state initial and terminal state consistency constraints within the scheduling period.

[0082] Furthermore, the calculation formula of the first objective function is as follows:

[0083]

[0084] Where F1 is the energy cost of the high-speed service area during the preset time, P grid,t is the power transmitted from the public connection point to the microgrid system in the service area during period t, c buy,t is the electricity price purchased from the power grid during period t, P gridre,t is the power returned to the grid by the microgrid in the service area from the public connection point during period t, c sell,t is the price of electricity sent back to the grid during period t, Δt is the time interval between two adjacent dispatches, c ε is the power penalty coefficient, ε p is the power penalty term, T is the total number of time periods within the preset time length, and it should be noted that c ε It is the penalty coefficient for the network power exceeding the set maximum demand value. It needs to be set separately. The larger the value, the greater the penalty for the network power exceeding the set maximum demand value, which is more unfavorable to the target optimization. p Indicates the specific power level at which the grid power exceeds the set maximum demand value.

[0085] Furthermore, the calculation formula of the power balance constraint of the photovoltaic storage system is as follows:

[0086]

[0087] In the formula, is the discharge power of energy storage subsystem n in distribution transformer area i in time period t, is the charging power of energy storage subsystem n in distribution transformer area i in time period t, N tran N is the total number of 10kv bus distribution transformers in the service area. bess,i is the total number of energy storage subsystems installed under distribution transformer area i, is the power generation of PV subsystem j in distribution transformer area i in time period t, is the total power consumption of distribution transformer area i in time period t, N PV,i The total number of photovoltaic subsystems installed under distribution transformer area i;

[0088] The calculation formula of the physical constraint of the state of charge of the energy storage subsystem is as follows:

[0089]

[0090] The calculation formula of the energy storage subsystem charging power constraint is as follows:

[0091]

[0092] The calculation formula of the energy storage subsystem discharge power constraint is as follows:

[0093]

[0094] The calculation formula of the energy storage subsystem charge and discharge state constraint is as follows:

[0095]

[0096] The calculation formula for the consistency constraint between the initial and final state of the battery state of charge of the energy storage subsystem during the scheduling period is as follows:

[0097]

[0098] In the formula, is the rated capacity of energy storage subsystem n in distribution transformer area i, is the charge state of the energy storage subsystem n in the distribution transformer area i at time period t+1, is the charge state of the energy storage subsystem n in the distribution transformer area i in time period t, is the charging efficiency of energy storage subsystem n in distribution transformer area i, is the discharge efficiency of energy storage subsystem n in distribution transformer area i, is the maximum allowable charging power of energy storage system n in distribution transformer area i in time period t, is the rated power of the power conversion equipment of the energy storage subsystem n in the distribution transformer area i, is the maximum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the minimum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the maximum allowable discharge power of energy storage system n in distribution transformer area i in time period t, is the charging state of the energy storage subsystem n in the distribution transformer area i in time period t, is the discharge state of energy storage subsystem n in distribution transformer area i in time period t, Both are integer variables of 0 and 1. is the charge state of the energy storage subsystem n in the distribution transformer area i in the first period of the dispatch cycle, is the charge state of the energy storage subsystem n in the distribution transformer area i in the Tth period within the dispatching cycle;

[0099] The calculation formula of the safe operation constraint of the photovoltaic storage system is as follows:

[0100]

[0101] S grid +S gridre ≤1

[0102] In the formula, is the maximum allowable transmission power of transformer i in the distribution transformer area, P gridmax is the maximum positive transmission power allowed at the public connection point between the microgrid and the grid in the service area, P gridremax is the maximum reverse transmission power allowed at the public connection point between the microgrid and the grid in the service area, P demandmax is the maximum grid power demand limit, S grid The power is in the positive transmission state at the common connection point, S gridre It is the power reverse transmission state at the common connection point.

[0103] It should be noted that the photovoltaic storage system optimization scheduling model is a mixed integer linear programming model, which has a unique optimal solution. The model can be efficiently solved based on a dedicated solver. The model can adapt to different photovoltaic power generation, electricity consumption, and time-of-use electricity price scenarios in the microgrid system in the service area to achieve economically optimal operation calculations.

[0104] Step 3: Determine the equivalent grid electricity purchase cost based on the grid electricity purchase cost within the preset time period, substitute the equivalent grid electricity purchase cost into the pre-established photovoltaic storage capacity configuration model, and use the particle swarm algorithm to optimize and solve the photovoltaic storage capacity configuration model to obtain the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem.

[0105] The process of establishing the optical storage capacity configuration model includes:

[0106] The second objective function is constructed with the goal of minimizing the comprehensive economic cost of equal annual value;

[0107] Taking the energy storage capacity constraint, the photovoltaic installed capacity constraint and the energy storage subsystem charge and discharge rate constraint as constraint conditions, and combining the second objective function, a photovoltaic storage capacity configuration model is constructed.

[0108] Furthermore, the calculation formula of the second objective function is as follows:

[0109] F2=minC=C inv +Ccom +C ope (11)

[0110] In the formula, F2 is the minimum value of the comprehensive economic cost of equal annual value, C is the comprehensive economic cost of equal annual value, and C inv is the equivalent annual average investment cost of the photovoltaic storage system, C com is the average annual fixed operation and maintenance cost, C ope is the equivalent grid electricity purchasing cost.

[0111] It should be noted that

[0112] In the formula, r CR is the capital recovery coefficient, γ is the discount rate, m is the planned operating life of the system, C E is the unit capacity price of the energy storage subsystem, C p is the unit power price of the energy storage subsystem, C pv is the price per installed power of the photovoltaic subsystem, α is the operation and maintenance cost rate of the energy storage subsystem, β is the operation and maintenance cost rate of the photovoltaic subsystem, N days The total number of days the system runs per year, that is, the total number of days within the preset duration.

[0113] Furthermore, the calculation formula of the energy storage capacity constraint is as follows:

[0114]

[0115] In the formula, Configure the capacity upper limit for the energy storage subsystem in distribution transformer area i;

[0116] The calculation formula of the photovoltaic installed capacity constraint is as follows:

[0117]

[0118] In the formula, Configure the installed power upper limit for the PV subsystem in distribution transformer area i. is the rated power of the jth photovoltaic system under the distribution transformer area i;

[0119] The calculation formula for the charge and discharge rate constraint of the energy storage subsystem is as follows:

[0120]

[0121] In the formula, θ min is the minimum value of the charge and discharge rate coefficient of the energy storage subsystem, θ max is the maximum value of the charge and discharge rate coefficient of the energy storage subsystem, It is the rated power of the nth energy storage system PCS in distribution transformer area i.

[0122] It should be noted that the particle swarm optimization (PSO) algorithm in the swarm intelligence optimization algorithm is applied to the optimal configuration of photovoltaic and energy storage capacity of the outer microgrid system, and the optimal calculation of photovoltaic and energy storage configuration capacity is realized by learning from the foraging behavior of birds. The specific process is as follows Figure 2 shown.

[0123] Specifically, the photovoltaic storage microgrid in two distribution transformers in a highway service area in the southwest region is taken as the analysis object. The system structure is as follows: Figure 3 The time-of-use electricity price is shown in Table 1:

[0124] Table 1

[0125]

[0126] Taking into account the symmetry of the layout of building facilities and equipment in service area I and II, as well as the available space and area for the installation of the photovoltaic storage system, the energy storage subsystem adopts the same capacity parameters, and two sets are symmetrically arranged in area I and area II; the photovoltaic subsystem adopts the same capacity parameters, and one set is symmetrically arranged in area I and area II.

[0127] The parameters of the optimization configuration example of the photovoltaic storage system are shown in Table 2. Among them, the charge and discharge ratio of each energy storage subsystem is 0.5, and the unilateral efficiency of charge and discharge is 0.95. Under the installation space and area restrictions, the maximum capacity of a single energy storage subsystem is set to 1000kWh, and the maximum installed power of a single photovoltaic system is set to 100kW. Set the reverse grid-connected electricity price csell.t to 0 to ensure that the system has no reverse grid-connected electricity under the economically optimal operation strategy, and make full use of the power generation capacity of the installed photovoltaic subsystem under the configured photovoltaic storage capacity combination. Through the coordinated optimization and scheduling of each energy storage subsystem, the internal energy consumption of the service area microgrid system is achieved to the greatest extent.

[0128] Table 2

[0129]

[0130] Based on the above data setting parameters and PV storage capacity configuration requirements, the number of PSO iterations is 50, the population size is 10, and the iteration process is as follows: Figure 4 shown.

[0131] At the 8th iteration, the equivalent annual total cost of the service area microgrid system reached the optimal value of 1,044,234 yuan, achieving rapid convergence and verifying the effectiveness of the algorithm. The optimal configuration capacity combination is obtained as follows: one photovoltaic subsystem is configured in service area I and II, with a rated installed power of 100kW; two energy storage subsystems are configured in service area I and II, with a single rated capacity of 82kWh and a rated power of 41kW. The results show that the rated power of the photovoltaic subsystem is configured as the upper limit of the installed power of the service area, so as to fully utilize the distributed photovoltaic power generation resources that can be developed in the service area. Because the installed capacity of the photovoltaic subsystem is limited by the maximum conditions, it is still necessary to purchase electricity from the power grid during the stage of insufficient photovoltaic power generation. By reasonably configuring the capacity of each energy storage subsystem and optimizing the operation and scheduling, the lowest energy cost under the time-of-use electricity purchase price can be fully realized.

[0132] It should be noted that for the reverse grid-connected power when the energy storage capacity is reduced, the installed rated power of each photovoltaic subsystem in service area I and II is kept unchanged at 100kW. On the basis of the optimal energy storage configuration capacity, the capacity of each energy storage subsystem is reduced and the rated power is set at 0.5 times the charge and discharge rate. The annual total reverse grid-connected power curve of the system under the change of energy storage capacity is obtained as follows: Figure 5 shown.

[0133] It can be seen that as the capacity of the energy storage subsystem decreases, it is still impossible to fully absorb the photovoltaic power under the strategy of maximizing photovoltaic absorption and operating economic optimization due to the limitation of energy storage capacity and power. The annual reverse grid-connected power gradually increases, which does not meet the requirements of photovoltaic self-generation and self-use in the service area microgrid system. The optimal energy storage capacity corresponding to the photovoltaic storage configuration scheme in this paper is the minimum energy storage configuration capacity to ensure full photovoltaic absorption throughout the year.

[0134] For the annual comprehensive cost when the energy storage capacity increases, the rated power of each photovoltaic subsystem in service area I and II is kept unchanged at 100kW, and the capacity of each energy storage subsystem is increased on the basis of the optimal energy storage configuration capacity. The equivalent total cost curve for the year under the change of energy storage capacity is obtained as follows: Figure 6 shown.

[0135] As the capacity of the energy storage subsystem increases, although the photovoltaic power generation can be fully absorbed within the service area microgrid, the increased investment cost of the energy storage subsystem is greater than the electricity purchase cost saved by optimizing the scheduling operation strategy during operation, resulting in an increase in the total economic cost and over-configuration of the energy storage subsystem capacity.

[0136] Through analysis, it is found that the capacities of the photovoltaic and energy storage subsystems of the service area microgrid are interconnected, and a coordinated optimization configuration is required to achieve the optimal system economy under a certain combination of the two.

[0137] It should be noted that the typical daily operation results of the annual optimization dispatch simulation under the optimal configuration capacity of the microgrid photovoltaic storage in the service area are selected, such as Figure 7 , Figure 8 and Fig. 9 shown.

[0138] Through the soc curves of a total of 4 energy storage systems in service areas I and II on a typical day, it can be seen that each energy storage system is fully charged during the valley price period before 7:00; during the parity period of 7:00-10:00, the load demand is greater than photovoltaic power generation, the energy storage subsystem is neither charged nor discharged, and the part of the load that exceeds photovoltaic power generation is provided by the power grid; during the peak price period of 10:00-12:00, each energy storage subsystem discharges to meet the load demand in the system and there is no reverse grid access at the public connection point; during the parity period of 12:00-15:00, the four energy storage subsystems are charged to the maximum extent under the power limit of the safe transmission of the two distribution transformers and the public connection point, and are discharged to the termination of soc before 21:00 during the parity period, completing a day's scheduling operation.

[0139] The high-speed service area microgrid optimization scheduling and operation strategy designed in this paper takes into account the capacity and operation limitations of the energy storage system and the safety limitations of the service area power supply and distribution system. It adapts to changes in power load, photovoltaic power generation, and time-of-use electricity prices. By generating the optimal charging and discharging plan of the energy storage system, it achieves the goal of full local photovoltaic absorption and optimal system operation economy. It can support the calculation of the annual optimal operating economic indicators of the system under a specific photovoltaic storage configuration capacity.

[0140] In summary, the present embodiment proposes a method for optimizing the capacity configuration of a multi-station photovoltaic storage system in a high-speed service area microgrid. Aiming at the capacity configuration problem of a typical multi-distribution transformer photovoltaic storage system in a high-supply and high-metering high-speed service area microgrid, a scheduling strategy is proposed to achieve the maximum local consumption of photovoltaic power generation and the economically optimal operation, so as to achieve the maximum self-consistency of system energy consumption under a specific photovoltaic storage configuration capacity. On this basis, a two-layer optimization model for photovoltaic storage configuration is established, an algorithm flow is designed, and a particle swarm algorithm and a SCIP mixed integer programming solver are used to implement the algorithm solution. The rationality of the model is verified by actual engineering cases, and has the following effects: (1) The scheduling strategy integrates photovoltaic power generation, equipment power consumption, time-of-use electricity prices, and energy storage systems and power supply and distribution system safety operation constraints, and is suitable for economically optimal control of system operation in different scenarios. (2) Based on the annual comprehensive cost generated by the simulation of the optimal scheduling of the inner layer typical year system, the optimization model uses a swarm intelligence optimization algorithm to comprehensively weigh the annual power purchase cost and the system investment and operation and maintenance cost, and effectively achieves the optimal coordinated configuration of photovoltaic and energy storage system capacity under the minimization of the annual comprehensive cost. The model comprehensively simulates different operating scenarios throughout the year, and the results are reasonable and generally applicable. (3) The designed model solving algorithm uses a dedicated solver to calculate the optimal solution for the inner mixed integer linear model, and applies a swarm intelligence search algorithm to the outer model to achieve the optimal configuration of photovoltaic storage. The algorithm can effectively calculate the optimal configuration capacity of photovoltaic storage in the high-speed service area scenario. It can also provide technical support for the configuration of photovoltaic storage capacity in microgrids in service areas along highways, and provide a certain reference for the implementation of the transportation-energy integration strategy in the planning of distributed microgrids in highway areas.

[0141] Embodiment 2

[0142] Fig.10 The structure diagram of a high-speed service area microgrid multi-station optical storage system capacity optimization configuration system provided according to an embodiment of the present application is as follows: Fig.10 As shown, the system comprises:

[0143] The acquisition module 100 is used to obtain the charging and discharging power of each energy storage subsystem, the generating power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each time period within a preset time length;

[0144] The first optimization module 200 is used to substitute the charging and discharging power of each energy storage subsystem, the power generation power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each period within the preset time into the pre-established photovoltaic storage system optimization scheduling model, and use the SCIP solver to optimize and solve the photovoltaic storage system optimization scheduling model to obtain the power purchase cost of the power grid within the preset time;

[0145] The second optimization module 300 is used to determine the equivalent grid electricity purchase cost based on the grid electricity purchase cost within the preset time period, substitute the equivalent grid electricity purchase cost into the pre-established photovoltaic storage capacity configuration model, and use the particle swarm algorithm to optimize and solve the photovoltaic storage capacity configuration model to obtain the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem.

[0146] The process of establishing the optimal scheduling model of the photovoltaic storage system includes:

[0147] The first objective function is constructed with the goal of minimizing the energy cost of the high-speed service area within a preset time;

[0148] Taking the power balance constraint of the photovoltaic storage system, the operation constraint of the energy storage subsystem, and the safe operation constraint of the photovoltaic storage system as constraint conditions, and combining the first objective function, an optimization scheduling model of the photovoltaic storage system is constructed;

[0149] Among them, the energy storage subsystem operation constraints include: physical constraints on the energy storage subsystem charge state, energy storage subsystem charging power constraints, energy storage subsystem discharge power constraints, energy storage subsystem charge and discharge state constraints, and energy storage subsystem battery charge state initial and terminal state consistency constraints within the scheduling period.

[0150] It should be noted that the calculation formula of the first objective function is as follows:

[0151]

[0152] Where F1 is the energy cost of the high-speed service area during the preset time, P grid,t is the power transmitted from the public connection point to the microgrid system in the service area during period t, c buy,t is the electricity price purchased from the power grid during period t, P gridre,t is the power returned to the grid by the microgrid in the service area from the public connection point during period t, c sell,t is the price of electricity sent back to the grid during period t, Δt is the time interval between two adjacent dispatches, c ε is the power penalty coefficient, ε p is the power penalty term, and T is the total number of time periods within the preset time length.

[0153] The calculation formula of the power balance constraint of the photovoltaic storage system is as follows:

[0154]

[0155] In the formula, is the discharge power of energy storage subsystem n in distribution transformer area i in time period t, is the charging power of energy storage subsystem n in distribution transformer area i in time period t, N tranN is the total number of 10kv bus distribution transformers in the service area. bess,i is the total number of energy storage subsystems installed under distribution transformer area i, is the power generation of PV subsystem j in distribution transformer area i in time period t, is the total power consumption of distribution transformer area i in time period t, N PV,i The total number of photovoltaic subsystems installed under distribution transformer area i;

[0156] The calculation formula of the physical constraint of the state of charge of the energy storage subsystem is as follows:

[0157]

[0158] The calculation formula of the energy storage subsystem charging power constraint is as follows:

[0159]

[0160] The calculation formula of the energy storage subsystem discharge power constraint is as follows:

[0161]

[0162] The calculation formula of the energy storage subsystem charge and discharge state constraint is as follows:

[0163]

[0164] The calculation formula for the consistency constraint between the initial and final state of the battery state of charge of the energy storage subsystem during the scheduling period is as follows:

[0165]

[0166] In the formula, is the rated capacity of energy storage subsystem n in distribution transformer area i, is the charge state of the energy storage subsystem n in the distribution transformer area i at time period t+1, is the charge state of the energy storage subsystem n in the distribution transformer area i in time period t, is the charging efficiency of energy storage subsystem n in distribution transformer area i, is the discharge efficiency of energy storage subsystem n in distribution transformer area i, is the maximum allowable charging power of energy storage system n in distribution transformer area i in time period t, is the rated power of the power conversion equipment of the energy storage subsystem n in the distribution transformer area i, is the maximum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the minimum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the maximum allowable discharge power of energy storage system n in distribution transformer area i in time period t, is the charging state of the energy storage subsystem n in the distribution transformer area i in time period t, is the discharge state of energy storage subsystem n in distribution transformer area i in time period t, is the charge state of the energy storage subsystem n in the distribution transformer area i in the first period of the dispatch cycle, is the charge state of the energy storage subsystem n in the distribution transformer area i in the Tth period within the dispatching cycle;

[0167] The calculation formula of the safe operation constraint of the photovoltaic storage system is as follows:

[0168]

[0169] S grid +S gridre ≤1

[0170] In the formula, is the maximum allowable transmission power of transformer i in the distribution transformer area, P gridmax is the maximum positive transmission power allowed at the public connection point between the microgrid and the grid in the service area, P gridremax is the maximum reverse transmission power allowed at the public connection point between the microgrid and the grid in the service area, P demandmax is the maximum grid power demand limit, S grid The power is in the positive transmission state at the common connection point, S gridre It is the power reverse transmission state at the common connection point.

[0171] The process of establishing the optical storage capacity configuration model includes:

[0172] The second objective function is constructed with the goal of minimizing the comprehensive economic cost of equal annual value;

[0173] Taking the energy storage capacity constraint, the photovoltaic installed capacity constraint and the energy storage subsystem charge and discharge rate constraint as constraint conditions, and combining the second objective function, a photovoltaic storage capacity configuration model is constructed.

[0174] It should be noted that the calculation formula of the second objective function is as follows:

[0175] F2=minC=C inv +C com +C ope

[0176] In the formula, F2 is the minimum value of the comprehensive economic cost of equal annual value, C is the comprehensive economic cost of equal annual value, and C inv is the equivalent annual average investment cost of the photovoltaic storage system, C com is the average annual fixed operation and maintenance cost, C ope is the equivalent grid electricity purchasing cost.

[0177] The calculation formula of the energy storage capacity constraint is as follows:

[0178]

[0179] In the formula, Configure the capacity upper limit for the energy storage subsystem in distribution transformer area i;

[0180] The calculation formula of the photovoltaic installed capacity constraint is as follows:

[0181]

[0182] In the formula, Configure the installed power upper limit for the PV subsystem in distribution transformer area i. is the rated power of the jth photovoltaic system under the distribution transformer area i;

[0183] The calculation formula for the charge and discharge rate constraint of the energy storage subsystem is as follows:

[0184]

[0185] In the formula, θ min is the minimum value of the charge and discharge rate coefficient of the energy storage subsystem, θ max is the maximum value of the charge and discharge rate coefficient of the energy storage subsystem, It is the rated power of the nth energy storage system PCS in distribution transformer area i.

[0186] In summary, the capacity optimization configuration system of the multi-area photovoltaic storage system in a high-speed service area microgrid proposed in this embodiment is suitable for microgrid systems with a total of 10kV busbars in multiple distribution transformer areas of typical high-supply and high-meter highway power users, which significantly reduces the comprehensive cost of the photovoltaic storage system.

[0187] Embodiment 3

[0188] In order to implement the above embodiments, the present disclosure further proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in Embodiment 1 is implemented.

[0189] Embodiment 4

[0190] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0191] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0192] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0193] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing the capacity configuration of a multi-station optical storage system in a high-speed service area microgrid, characterized in that: The method comprises: Obtain the charging and discharging power of each energy storage subsystem, the generating power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each time period within the preset time length; Substitute the charging and discharging power of each energy storage subsystem, the power generation power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each period within the preset time into the pre-established photovoltaic storage system optimization scheduling model, and use the SCIP solver to optimize and solve the photovoltaic storage system optimization scheduling model to obtain the power purchase cost of the power grid within the preset time; The equivalent grid electricity purchase cost is determined based on the grid electricity purchase cost within the preset time period, and the equivalent grid electricity purchase cost is substituted into the pre-established photovoltaic storage capacity configuration model. The photovoltaic storage capacity configuration model is optimized and solved using the particle swarm algorithm to obtain the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem.

2. The method according to claim 1, characterized in that The process of establishing the optimal scheduling model of the photovoltaic storage system includes: The first objective function is constructed with the goal of minimizing the energy cost of the high-speed service area within a preset time; Taking the power balance constraint of the photovoltaic storage system, the operation constraint of the energy storage subsystem, and the safe operation constraint of the photovoltaic storage system as constraint conditions, and combining the first objective function, an optimization scheduling model of the photovoltaic storage system is constructed; Among them, the energy storage subsystem operation constraints include: physical constraints on the energy storage subsystem charge state, energy storage subsystem charging power constraints, energy storage subsystem discharge power constraints, energy storage subsystem charge and discharge state constraints, and energy storage subsystem battery charge state initial and terminal state consistency constraints within the scheduling period.

3. The method according to claim 2, characterized in that The calculation formula of the first objective function is as follows: Where F1 is the energy cost of the high-speed service area during the preset time, P grid,t is the power transmitted from the public connection point to the microgrid system in the service area during period t, c buy,t is the electricity price purchased from the power grid during period t, P gridre,t is the power returned to the grid by the microgrid in the service area from the public connection point during period t, c sell,t is the price of electricity sent back to the grid during period t, Δt is the time interval between two adjacent dispatches, c ε is the power penalty coefficient, ε p is the power penalty term, and T is the total number of time periods within the preset time length.

4. The method according to claim 3, characterized in that The calculation formula of the power balance constraint of the photovoltaic storage system is as follows: In the formula, is the discharge power of energy storage subsystem n in distribution transformer area i in time period t, is the charging power of energy storage subsystem n in distribution transformer area i in time period t, N tran N is the total number of 10kv bus distribution transformers in the service area. bess,i is the total number of energy storage subsystems installed under distribution transformer area i, is the power generation of PV subsystem j in distribution transformer area i in time period t, is the total power consumption of distribution transformer area i in time period t, N PV,i The total number of photovoltaic subsystems installed under distribution transformer area i; The calculation formula of the physical constraint of the state of charge of the energy storage subsystem is as follows: The calculation formula of the energy storage subsystem charging power constraint is as follows: The calculation formula of the energy storage subsystem discharge power constraint is as follows: The calculation formula of the energy storage subsystem charge and discharge state constraint is as follows: The calculation formula for the consistency constraint between the initial and final state of the battery state of charge of the energy storage subsystem during the scheduling period is as follows: In the formula, is the rated capacity of energy storage subsystem n in distribution transformer area i, is the charge state of the energy storage subsystem n in the distribution transformer area i at time period t+1, is the charge state of the energy storage subsystem n in the distribution transformer area i in time period t, is the charging efficiency of energy storage subsystem n in distribution transformer area i, is the discharge efficiency of energy storage subsystem n in distribution transformer area i, is the maximum allowable charging power of energy storage system n in distribution transformer area i in time period t, is the rated power of the power conversion equipment of the energy storage subsystem n in the distribution transformer area i, is the maximum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the minimum allowable state of charge of energy storage subsystem n in distribution transformer area i, is the maximum allowable discharge power of energy storage system n in distribution transformer area i in time period t, is the charging state of the energy storage subsystem n in the distribution transformer area i in time period t, is the discharge state of energy storage subsystem n in distribution transformer area i in time period t, is the charge state of the energy storage subsystem n in the distribution transformer area i in the first period of the dispatch cycle, is the charge state of the energy storage subsystem n in the distribution transformer area i in the Tth period within the dispatching cycle; The calculation formula of the safe operation constraint of the photovoltaic storage system is as follows: S grid +S gridre ≤1 In the formula, is the maximum allowable transmission power of transformer i in the distribution transformer area, P gridmax is the maximum positive transmission power allowed at the public connection point between the microgrid and the grid in the service area, P gridremax is the maximum reverse transmission power allowed at the public connection point between the microgrid and the grid in the service area, P demandmax is the maximum grid power demand limit, S grid The power is in the positive transmission state at the common connection point, S gridre It is the power reverse transmission state at the common connection point.

5. The method according to claim 4, characterized in that The process of establishing the optical storage capacity configuration model includes: The second objective function is constructed with the goal of minimizing the comprehensive economic cost of equal annual value; Taking the energy storage capacity constraint, the photovoltaic installed capacity constraint and the energy storage subsystem charge and discharge rate constraint as constraint conditions, and combining the second objective function, a photovoltaic storage capacity configuration model is constructed.

6. The method according to claim 5, characterized in that The calculation formula of the second objective function is as follows: F2=minC=C inv +C com +C ope In the formula, F2 is the minimum value of the comprehensive economic cost of equal annual value, C is the comprehensive economic cost of equal annual value, and C inv is the equivalent annual average investment cost of the photovoltaic storage system, C com is the average annual fixed operation and maintenance cost, C ope is the equivalent grid electricity purchase cost.

7. The method according to claim 6, characterized in that The calculation formula of the energy storage capacity constraint is as follows: In the formula, Configure the capacity upper limit for the energy storage subsystem in distribution transformer area i; The calculation formula of the photovoltaic installed capacity constraint is as follows: In the formula, Configure the upper limit of installed power for the PV subsystem in distribution transformer area i. is the rated power of the jth photovoltaic system under the distribution transformer area i; The calculation formula for the charge and discharge rate constraint of the energy storage subsystem is as follows: In the formula, θ min is the minimum value of the charge and discharge rate coefficient of the energy storage subsystem, θ max is the maximum value of the charge and discharge rate coefficient of the energy storage subsystem, It is the rated power of the nth energy storage system PCS in distribution transformer area i.

8. A high-speed service area microgrid multi-station optical storage system capacity optimization configuration system, characterized in that: The system comprises: An acquisition module is used to obtain the charging and discharging power of each energy storage subsystem, the generating power of each photovoltaic subsystem, and the total power consumption in each distribution transformer area in each time period within a preset time period; The first optimization module is used to substitute the charging and discharging power of each energy storage subsystem in each distribution transformer area in each period within the preset time, the power generation power of each photovoltaic subsystem, and the total power consumption into the pre-established photovoltaic storage system optimization scheduling model, and use the SCIP solver to optimize and solve the photovoltaic storage system optimization scheduling model to obtain the power purchase cost of the power grid within the preset time; The second optimization module is used to determine the equivalent grid electricity purchase cost based on the grid electricity purchase cost within the preset time period, substitute the equivalent grid electricity purchase cost into the pre-established photovoltaic storage capacity configuration model, and use the particle swarm algorithm to optimize and solve the photovoltaic storage capacity configuration model to obtain the optimal configuration capacity and optimal configuration power of each energy storage subsystem in each distribution transformer area, and the optimal configuration installed capacity of each photovoltaic subsystem.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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