Capacity optimization configuration method and system of optical storage overcharge station

Abstract

The invention discloses a capacity optimization configuration method and system for an optical storage overcharge station, and relates to the technical field of optical storage capacity configuration, and the method comprises the following steps: building an equivalent daily total cost target optimization function containing loss electric quantity penalty cost; generating an initial configuration scheme set based on a preset constraint condition set; obtaining the equivalent daily total cost by solving the target optimization function, carrying out iterative updating by using an improved fireworks algorithm until a convergence condition is met, obtaining a final configuration scheme, and obtaining an optimal capacity parameter and an operation strategy based on the final configuration scheme; realizing capacity configuration optimization based on the optimal capacity parameter and the operation strategy; according to the improved fireworks algorithm, Gaussian variation and Cauchy variation are dynamically switched on the basis of iteration times through step-by-step explosion, and meanwhile, a configuration scheme is updated by utilizing a preset selection strategy, so that improvement is realized. The method is used for solving the problems that an existing method is difficult to consider investment cost and power supply reliability and a solving algorithm is easy to fall into local optimum.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic and energy storage capacity configuration technology, and more specifically, to a method and system for optimizing the capacity configuration of photovoltaic and energy storage supercharging stations. Background Technology

[0002] Photovoltaic-storage supercharging stations, as a new type of infrastructure integrating photovoltaic power generation, energy storage systems and supercharging facilities, are widely used in the new energy vehicle industry.

[0003] Current methods for configuring the capacity of photovoltaic-storage-supercharging stations have the following shortcomings: First, existing configuration methods mostly aim only at minimizing equipment investment and operating costs, ignoring the risk of insufficient power supply due to transformer capacity limitations, and failing to quantify the potential economic losses caused by load gaps. This makes it difficult for configuration schemes to achieve an effective trade-off between reducing investment costs and ensuring service levels. Second, when dealing with complex nonlinear problems involving multiple variables, existing solution algorithms often use fixed search patterns for optimization strategies, lacking the adaptive ability to dynamically adjust mutation mechanisms based on iterative processes. This makes the algorithms prone to getting trapped in local optima during the optimization process, making it difficult to obtain accurate global optimal solutions.

[0004] Therefore, there is an urgent need to propose a method and system for optimizing the capacity configuration of photovoltaic-storage-supercharging stations that takes into account both quantitative power supply risks and adaptive optimization capabilities. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a capacity optimization configuration method and system for photovoltaic-storage supercharging stations. The present invention solves the problems of existing configuration methods being unable to balance investment costs and power supply reliability, as well as the problems of traditional solution algorithms being prone to getting trapped in local optima, by introducing a power loss penalty mechanism and using an improved fireworks algorithm that integrates step-by-step explosion and optimized adaptive mutation.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method and system for optimizing the capacity configuration of a photovoltaic-storage-supercharging station includes the following steps: establishing an objective optimization function for the total daily cost; generating an initial configuration scheme set based on a preset set of constraints; obtaining the total daily cost of the initial configuration scheme set by solving the objective optimization function and iteratively updating the initial configuration scheme set using an improved fireworks algorithm until the convergence condition is met to obtain the final configuration scheme; and obtaining the optimal capacity parameters and operating strategy based on the final configuration scheme; and optimizing the capacity configuration based on the optimal capacity parameters and operating strategy. The improved fireworks algorithm achieves this improvement by progressively exploding the configuration scheme and dynamically switching between Gaussian and Cauchy mutations based on the number of iterations, while simultaneously updating the configuration scheme using a preset selection strategy.

[0007] In a preferred embodiment, the step-by-step explosion includes: using the initial configuration scheme set as the fireworks population for improving the fireworks algorithm, and classifying the fireworks into levels according to a preset ratio based on the equivalent daily total cost of the configuration scheme corresponding to each fireworks.

[0008] In a preferred embodiment, the dynamic switching between Gaussian mutation and Cauchy mutation based on the number of iterations includes: screening fireworks to be mutated based on the total cost of the equivalent day, and performing adaptive mutation operations on the fireworks to be mutated, as shown in the following formula: , , in, For adaptive switching coefficients, This represents the current iteration number. The maximum number of iterations is preset. , The first The first firework was at the The values ​​after and before the mutation in each dimension; Follows a standard Gaussian distribution. It follows a standard Cauchy distribution. Let be the switching probability between Gaussian and Cauchy mutations.

[0009] In a preferred embodiment, updating the configuration scheme using a preset selection strategy includes: selecting and combining multiple elite fireworks according to a preset ratio based on the initial fireworks, progressive explosions, and sparks generated by adaptive mutation in the current iteration; selecting championship fireworks or elite fireworks from the remaining fireworks after removing the multiple elite fireworks based on the local optimum judgment criterion; and combining the multiple elite fireworks, championship fireworks, or elite fireworks according to a preset ratio to form the updated fireworks population, i.e., the initial configuration scheme set for the next iteration.

[0010] In a preferred embodiment, the step of selecting championship fireworks or elite fireworks based on the local optimum judgment criterion includes: sorting the remaining fireworks according to their corresponding equivalent daily total cost values; calculating the difference between the minimum equivalent daily total cost of the current iteration and the minimum equivalent daily total cost of the previous iteration; if the difference is less than a preset stagnation threshold, it is determined that the improved fireworks algorithm has fallen into a local optimum and the championship fireworks are selected using the tournament selection method; otherwise, the elite fireworks are obtained using the elite selection method; the tournament selection method includes dividing the remaining fireworks into three subsets based on the sorting results, selecting each subset using the tournament selection method, and combining them into championship fireworks.

[0011] In a preferred embodiment, the total cost includes the equivalent daily investment cost of energy storage and transformers, the grid purchase cost, and the penalty cost for lost electricity; the formula for the equivalent daily investment cost of energy storage and transformers is as follows: , , in, For the unit cost of the duty day, This represents the total initial investment cost. The formula for the capital recovery factor is as follows: The discount rate is... This refers to the service life; , , , These are energy storage capacity, rated energy storage power, and transformer capacity, respectively. This corresponds to the rated capacity or power. , These are the equivalent daily investment costs for energy storage and transformers, respectively. , , These are the equivalent daily costs per unit of energy storage capacity, per unit of power, and per unit of transformer capacity, respectively.

[0012] In a preferred embodiment, the power loss penalty cost includes: , , in, To incur penalties for lost electricity, The preset power loss penalty factor, Electricity price for charging service providers To optimize the cycle, For time step, To reduce power loss, for The grid-connected power at any given time.

[0013] In a preferred embodiment, generating the initial configuration scheme set includes: determining a feasible solution space based on a preset set of constraints; generating a numerical vector containing energy storage capacity, rated energy storage power, and transformer capacity within the feasible solution space using a random distribution algorithm; and using the numerical vector as a set of configuration schemes to form the initial configuration scheme set.

[0014] In a preferred embodiment, obtaining the equivalent daily total cost of the initial configuration scheme set by solving the objective optimization function includes: calculating the equivalent daily investment cost of energy storage and transformers based on the numerical vectors in the initial configuration scheme set; using the numerical vectors as constraints and minimizing the grid power purchase cost and the power loss penalty cost as the planning objective, and using a linear programming solver to solve for the grid power purchase cost and the power loss penalty cost; and adding the equivalent daily investment cost, grid power purchase cost, and power loss penalty cost to obtain the equivalent daily total cost.

[0015] A capacity optimization configuration system for a photovoltaic-storage-supercharging station includes the following modules: a function construction module for establishing an equivalent daily total cost objective optimization function; an initial generation module for generating an initial configuration scheme set based on a preset set of constraints; an iterative solution module for obtaining the equivalent daily total cost of the initial configuration scheme set by solving the objective optimization function and iteratively updating the initial configuration scheme set using an improved fireworks algorithm until the convergence condition is met to obtain the final configuration scheme, and obtaining the optimal capacity parameters and operating strategy based on the final configuration scheme; and a configuration optimization module for optimizing the capacity configuration based on the optimal capacity parameters and operating strategy. The improved fireworks algorithm achieves this improvement by progressively exploding the configuration scheme and dynamically switching between Gaussian and Cauchy mutations based on the number of iterations, while simultaneously updating the configuration scheme using a preset selection strategy.

[0016] The technical effects and advantages of the capacity optimization configuration method and system for photovoltaic-storage-supercharging stations of this invention are as follows: 1. This invention establishes an objective function for the total cost per day of equivalent value, uses transformer capacity as a decision variable and quantifies power supply risks, thereby achieving a dynamic trade-off between reducing infrastructure costs and ensuring power supply reliability, and avoiding investment waste or potential power supply safety hazards caused by improper selection of transformer capacity.

[0017] 2. This invention utilizes an improved fireworks algorithm to iteratively update the initial configuration scheme set, employing a step-by-step explosion strategy and a mechanism that dynamically switches between Gaussian mutation and Cauchy mutation based on the number of iterations. This solves the problem that traditional algorithms are prone to getting trapped in local optima due to population homogeneity when dealing with multivariate coupled problems. It balances the breadth of global exploration in the early stage of the algorithm with the accuracy of local development in the later stage, thereby improving the solution quality and convergence speed of the optimal configuration scheme. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the capacity optimization configuration method for a photovoltaic-storage-supercharging station provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the iterative solution process based on the improved fireworks algorithm and CPLEX solver provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of power balance scheduling for a typical day at a photovoltaic-storage-supercharging station, provided in an embodiment of the present invention.

[0021] Figure 4 A curve showing the change in the state of charge of an energy storage system on a typical day, provided for an embodiment of the present invention.

[0022] Figure 5 This is a schematic diagram of the capacity optimization configuration system module for the photovoltaic-storage-supercharging station provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1, Figure 1 The present invention provides a method for optimizing the capacity configuration of a photovoltaic-storage-supercharging station, comprising the following steps: S1, establish the equivalent daily total cost objective optimization function that includes the penalty cost for lost electricity, as follows: In this embodiment, the total cost includes the equivalent daily investment cost of energy storage and transformers, the grid purchase cost, and the penalty cost for lost electricity. The equivalent daily investment cost of the energy storage and transformer is calculated based on a capital recovery factor, the formula of which is as follows: , in, This is the capital recovery factor. The discount rate is... The service life of the equipment; The formula for the equivalent daily investment cost of the energy storage and transformer is as follows: , , in, For the unit cost of the duty day, The total initial investment cost is obtained based on market prices; , , , These are energy storage capacity, rated energy storage power, and transformer capacity, respectively. This corresponds to the rated capacity or power. , These are the equivalent daily investment costs for energy storage and transformers, respectively. , , These are the equivalent daily costs per unit of energy storage capacity, per unit of power, and per unit of transformer capacity, respectively.

[0025] The cost of purchasing electricity from the power grid is determined by the amount of electricity purchased and the electricity price in each time period, as shown in the following formula: , in, For the cost of purchasing electricity from the power grid, Time-of-use pricing; for The grid-connected power at any given time, when positive, indicates that power is being supplied to the substation; To optimize the cycle, For time step; In this embodiment, the penalty cost for lost power includes: , , , in, To incur penalties for lost electricity, The preset power loss penalty factor, The unit price for charging services represents the service revenue corresponding to each unit of charging electricity. To reduce power loss, specifically, the photovoltaic-storage-supercharging station... The power exceeding the transformer capacity at any given time is considered as power loss. The power loss penalty factor is preset based on the potential economic losses caused by user churn and service level decline, reflecting the operator's emphasis on service reliability; when When this occurs, it means that the operator treats the penalty cost of lost electricity as equivalent to the direct charging service fee revenue that should have been generated from that portion of electricity, thus regarding the lost electricity as an equivalent revenue loss; when This indicates that the operator has increased redundant investment in energy storage or transformer capacity during the planning stage to achieve an extremely low power shortage rate, thereby avoiding the extreme situation where users cannot charge their devices.

[0026] The objective function for optimizing the total cost of the equivalent day takes minimizing the total cost of the equivalent day as its optimization objective, and the formula is as follows: , The specific constraints of the total cost target optimization function for the equivalent day are as follows: The power balance constraint is expressed by the following formula: , in, For a moment The power demand for overcharging is determined by the vehicle's charging behavior; For a moment The output power of a photovoltaic system is affected by irradiance and temperature. , These represent the time intervals of the energy storage system. The charging and discharging power cannot both be positive at the same time; Transformer capacity constraint, the formula is as follows: , , in, This is a safety operating factor used to reflect the operating margin of the transformer, with a value ranging from 0.8 to 0.9. , These are the upper and lower limits of transformer capacity preset based on the site conditions of the photovoltaic-storage supercharging station, grid access permits, and estimated load levels. The boundary constraints for energy storage configuration are as follows: , , in, , These are the preset upper and lower limits of energy storage capacity. , These are the preset upper and lower limits of rated energy storage power; the upper limit of energy storage capacity is preset based on the site space limitations and investment budget of the photovoltaic-storage supercharging station, and is usually 1000kWh; the upper limit of rated energy storage power is preset based on the transformer capacity access limitations and the peak load shaving demand of the system, and is usually 600kW; while the lower limit of energy storage capacity and the lower limit of rated energy storage power are usually set to 0. The energy storage power constraint specifically means that the energy storage charging and discharging power shall not exceed the rated power, as shown in the following formula: , , SOC dynamic constraints, the formula is as follows: , in, Indicates that the energy storage system is in State of charge at time t, , These are charging efficiency and discharging efficiency, respectively. The formulas for SOC intraday balance and upper / lower limits are as follows: , , in, Used to ensure daily cycle balance; , These are preset upper and lower limits for the state of charge; for example, the upper limit could be 100% and the lower limit could be 10%. The mutual exclusion constraint between charging and discharging is expressed by the following formula: , , in, are binary variables and ,when This indicates that it is in charging mode. This indicates that the device is in discharge mode. Take the rated power of energy storage The value is to ensure that the energy storage system is at all times The charging and discharging power are not both positive at the same time; The nonnegativity constraint is defined by the following formula: ; In this step, by establishing an equivalent daily total cost target optimization function that includes investment costs and lost power penalty costs, and setting constraints, a deep synergy between the economic efficiency and power supply reliability of the photovoltaic-storage supercharging station throughout its entire life cycle is achieved. This avoids resource waste caused by oversized transformers or power supply safety hazards caused by undersized transformers, and provides a scientific and practical decision-making basis for subsequent optimization.

[0027] S2, Generate an initial configuration scheme set based on a preset set of constraints; In this embodiment, S2 includes: S201. Determine the feasible solution space based on the preset set of constraints. The specific steps are as follows: The range of values ​​is determined by the preset upper and lower limits of the energy storage configuration boundary constraints and transformer capacity constraints, and a closed three-dimensional region is defined in the multi-dimensional coordinate system. The parameter combination represented by any coordinate point in the three-dimensional region satisfies the basic physical constraints, thus forming the feasible solution space for algorithm optimization.

[0028] S202. Within the feasible solution space, a numerical vector containing energy storage capacity, rated energy storage power, and transformer capacity is generated using a random distribution algorithm. This numerical vector is then used as a set of configuration schemes to form an initial configuration scheme set, as follows: Set the initial configuration scheme set size to This refers to the initial size of the fireworks population; a uniformly distributed random algorithm is used to independently generate random values ​​following a uniform distribution within preset ranges for energy storage capacity, rated energy storage power, and transformer capacity, as shown in the following formula: , in, , indicating the first One configuration scheme, namely the initial fireworks; These correspond to the three decision dimensions: energy storage capacity, rated energy storage power, and transformer capacity, respectively. Indicates the first The configuration scheme is in the first Values ​​in each dimension; , They represent the first The decision variables for each dimension are set with upper and lower limits based on the boundary constraints of energy storage configuration and transformer capacity constraints; A random number between 0 and 1 that follows a uniform distribution; The three generated values ​​are arranged in a fixed order to form a three-dimensional numerical vector; this numerical vector serves as the digital carrier of the configuration scheme. These numerical vectors together form the initial configuration scheme set for subsequent iterative optimization.

[0029] This step determines the feasible solution space based on physical boundary constraints and initializes the population using a uniformly distributed random algorithm. This ensures that the initial configuration scheme set fully and uniformly covers the entire feasible solution space, guaranteeing the diversity of the initial fireworks population. This lays the data foundation for subsequent improvements to the fireworks algorithm's global search, fast convergence, and avoidance of getting trapped in local optima.

[0030] S3. The equivalent daily total cost of the initial configuration scheme set is obtained by solving the objective optimization function, and the initial configuration scheme set is iteratively updated using the improved fireworks algorithm until the convergence condition is met to obtain the final configuration scheme. Based on the final configuration scheme, the optimal capacity parameters and operating strategy are obtained, as follows: like Figure 2 As shown, Figure 2 The document illustrates the two-layer coupled solution process based on the improved fireworks algorithm and CPLEX solver provided in this embodiment. The process mainly includes a capacity configuration optimization loop on the left and a running strategy scheduling loop on the right, and achieves collaborative optimization of the two-layer model through the transfer of the total cost of the equivalent day.

[0031] In this embodiment, the process of obtaining the equivalent daily total cost of the initial configuration scheme set by solving the objective optimization function is as follows: For each configuration scheme in the initial configuration scheme set, i.e. each set of numerical vectors, extract the values ​​corresponding to the energy storage capacity, energy storage rated power and transformer capacity, substitute the values ​​into the formula of the equivalent daily investment cost of energy storage and transformer, and obtain the equivalent daily investment cost of energy storage and transformer. The extracted values ​​of energy storage capacity, rated energy storage power, and transformer capacity are taken as known constants and substituted into the constraints of the objective optimization function. Then, the inequality constraint boundary is instantiated and assigned values, and the assigned constraints are used as the physical boundary conditions for operation. Based on the physical boundary conditions of operation, a mixed-integer linear programming model is constructed with the objective function of minimizing the sum of the grid purchase cost and the penalty cost for lost electricity. Since the mixed-integer linear programming model contains binary variables describing the mutual exclusion logic of charging and discharging, a CPLEX solver specifically using the branch and bound method is adopted to construct a search tree within the feasible solution space enclosed by the above constraints. Through relaxation, bounding, and pruning operations on the nodes of the search tree, the optimal charging and discharging power of the energy storage system within the optimization cycle under the current physical boundary conditions is obtained. Substituting the optimal charging and discharging power under the current operating physical boundary conditions into the power balance constraint formula, the optimal grid-connected power curve can be obtained; based on the optimal grid-connected power curve and transformer capacity limit, the minimum grid power purchase cost and the minimum power loss penalty cost under the optimal charging and discharging power are calculated. The total cost per day for each configuration scheme in the initial configuration scheme set is obtained by adding the equivalent day investment cost, the minimum grid power purchase cost, and the minimum power loss penalty cost.

[0032] It should be noted that the improved fireworks algorithm achieves its improvement by detonating fireworks in stages and dynamically switching between Gaussian and Cauchy mutations based on the number of iterations, while updating the configuration scheme using a preset selection strategy. In this embodiment, the step-by-step explosion includes: using the initial configuration scheme set as the fireworks population for improving the fireworks algorithm, and classifying the fireworks into levels according to a preset ratio based on the equivalent daily total cost of each fireworks configuration scheme, as follows: Based on the total cost value of the equivalent day, the corresponding fireworks groups are sorted in ascending order. That is, the smaller the total cost value, the better the corresponding configuration scheme. Based on the sorting results, the fireworks are divided into superior, medium and inferior grades according to a preset ratio, which can be 2:15:3. The explosion rules for each level are dynamically adjusted according to the iteration process: for superior fireworks, the number of explosion sparks is linearly reduced from 0.7M to 0.6M (where M is the total number of sparks) to focus on local development near superior fireworks; for medium fireworks, the number of explosion sparks is linearly reduced from 0.5M to 1 to achieve the goal of global exploration in the early stage of the algorithm and gradual narrowing in the later stage; for inferior fireworks, the number of explosion sparks is set to 0, and they are directly eliminated to avoid invalid search. It should be noted that in the later stages of the standard fireworks algorithm, the population differences gradually decrease and the explosion radius tends to be consistent, leading to wasted computational resources and local optima. This invention dynamically adjusts the number of explosion sparks at different levels according to the iteration process, forming a large-scale and small-scale collaborative search, which takes into account both population diversity and accuracy.

[0033] In this embodiment, the dynamic switching between Gaussian mutation and Cauchy mutation based on the number of iterations includes: Based on the ranking of total cost on equivalent days, the top 10% of fireworks are selected as fireworks to be mutated, and an adaptive mutation operation is performed on these fireworks, as follows: The current adaptive switching coefficient is calculated based on the current iteration number, using the following formula: , in, For adaptive switching coefficients, This represents the current iteration number; The maximum number of iterations can be set to 200. For each firework to be mutated, randomly select one dimension from the dimensions of energy storage capacity, rated energy storage power, or transformer capacity. And based on the preset switching probability threshold The specific formula for determining the mutation type is as follows: , in, , The first The first firework was at the The values ​​after and before the mutation in each dimension; Follows a standard Gaussian distribution. It follows a standard Cauchy distribution; Let be the switching probability between Gaussian and Cauchy mutations. The larger the value, the faster the search speed, but the worse the accuracy. In order to fully explore the global map and obtain higher accuracy at the beginning of the algorithm, it can be set to 0.3.

[0034] It should be noted that the standard fireworks algorithm mainly uses Gaussian mutation, which results in low mutation efficiency and difficulty in escaping local optima. This invention improves the algorithm's ability to escape local optima in complex multivariate solution spaces by dynamically switching between Gaussian mutation and Cauchy mutation based on the iterative process, combining the local search capability of Gaussian mutation and the global search capability of Cauchy mutation.

[0035] In this embodiment, updating the configuration scheme using a preset selection strategy includes: Based on the initial fireworks, progressive explosions, and sparks generated by adaptive mutation in the current iteration, multiple elite fireworks are selected and combined according to a preset ratio, which can be 1:1:2. After removing the elite fireworks, the remaining fireworks are selected based on the local optimality judgment criterion to select either championship fireworks or elite fireworks.

[0036] In this embodiment, the step of selecting championship fireworks or elite fireworks based on the local optimal judgment criterion includes: The remaining fireworks are sorted in ascending order according to their corresponding total cost per day. Calculate the difference between the minimum equivalent daily total cost in the current iteration and the minimum equivalent daily total cost in the previous iteration; If the difference is less than a preset stagnation threshold, the improved fireworks algorithm is determined to be trapped in a local optimum, and a tournament selection method is used to select the championship fireworks; otherwise, an elite selection method is used to obtain elite fireworks. The preset stagnation threshold is set based on the order of magnitude of the total cost and the convergence accuracy requirement, and can be 10. -4 ; The tournament selection method includes dividing the remaining fireworks into three subsets based on the ranking results, and applying the tournament selection method to each subset. Specifically, the top 30% of fireworks in each of the three subsets are selected and combined in a 1:1:1 ratio to form the championship fireworks. The elite selection method selects the top 70% of fireworks as elite fireworks. The multi-elite fireworks, championship fireworks, or elite fireworks are grouped into an updated fireworks population according to a preset ratio, which can be 3:7, i.e., the initial configuration scheme set for the next iteration.

[0037] Based on the initial configuration scheme set for the next iteration, repeat the above steps of calculating the total cost of the equivalent day and iteratively updating using the improved fireworks algorithm until the preset convergence condition is met. The convergence condition can be set to reach the preset maximum number of iterations. Based on the converged set of configuration schemes, the configuration scheme with the lowest median daily total cost is selected as the final configuration scheme, which includes the optimal energy storage capacity, rated energy storage power, and transformer capacity. Based on the optimal energy storage capacity, rated energy storage power, and transformer capacity, the CPLEX solver is used again to obtain the final optimized operating strategy for the charging and discharging power of the energy storage system within the cycle.

[0038] This step transforms the capacity values ​​generated by the improved fireworks algorithm into fixed physical boundary conditions and uses the CPLEX solver for global optimization, ensuring the global optimality of the strategy under a specific capacity and achieving accurate calculation of the full lifecycle cost of each configuration scheme.

[0039] S4, optimize capacity configuration based on the optimal capacity parameters and operating strategy; The theoretically optimal capacity parameters obtained by solving are transformed into standard equipment specifications in engineering practice, and the optimal operation strategy is implanted into the energy management system to guide real-time scheduling, thereby achieving capacity configuration optimization. Specifically, based on the national standard transformer capacity sequence, the calculated optimal transformer capacity is matched with the nearest available transformer or rounded up to select the transformer model. The physical configuration of the battery module and energy storage converter is determined based on the optimal energy storage capacity and rated energy storage power. At the same time, the optimal operation strategy is written into the energy management system as a benchmark scheduling instruction to control the energy storage system to execute peak shaving and valley filling strategies, thereby realizing the coordinated implementation of the hardware configuration and software operation of the photovoltaic-storage supercharging station.

[0040] This step maps the theoretically optimal capacity parameters to equipment specifications that conform to industry standards and embeds the optimal operating strategy into the energy management system, thus achieving a closed-loop connection from planning and design to operation control, ensuring the feasibility of the configuration scheme and the stability of equipment operation.

[0041] Example 2: To verify the effectiveness of the proposed method for optimizing the capacity configuration of photovoltaic-storage supercharging stations, a typical daily operation scenario of a photovoltaic-storage supercharging station in an industrial park was selected for case analysis. Industrial parks typically gather a large number of enterprise commuter vehicles and logistics vehicles, and the charging demand exhibits a dual-peak characteristic with both morning peak and afternoon peak charging, resulting in strong load fluctuations.

[0042] (1) Photovoltaic power output data were obtained based on the measured irradiance data of a typical day in a certain region. The rated installed capacity of the photovoltaic system was set to 100kWp. The photovoltaic power output characteristics of different seasons are significantly different. Considering that the capacity configuration of the photovoltaic-storage supercharging station usually needs to meet the consumption demand under high photovoltaic penetration and the regulation capability under extreme operating conditions, this embodiment specifically selects the typical summer day with the highest photovoltaic power output as the typical operating scenario data, and combines the dual peak load characteristics of the industrial area for simulation solution.

[0043] (2) In this embodiment, a typical day is used as the time scale, and the simulation step size is set to That is, each hour is an optimization time period, in order to reflect both the main temporal changes in the charging load and photovoltaic output of the industrial area, while also taking into account computational efficiency and model solution accuracy; the main technical and economic parameters involved in the objective optimization function are set as follows: Time-of-use pricing: A typical industrial and commercial time-of-use pricing mechanism is adopted, with a peak electricity price of RMB 1.2 / kWh, a flat electricity price of RMB 0.8 / kWh, and an off-peak electricity price of RMB 0.4 / kWh; Equipment parameters: The energy storage system uses lithium-ion batteries with a charge / discharge efficiency of 95% and a lifespan of 10 years; the transformer has a lifespan of 15 years; the discount rate is 8%. Constraints: To meet the large-scale charging and peak shaving needs of this industrial zone, the energy storage capacity optimization range is set to [0, 6000] kWh, the energy storage rated power optimization range is [0, 1000] kW, and the transformer capacity optimization range is [400, 2500] kVA; Penalty settings: The transformer safety operation coefficient is 0.9, and the power loss penalty factor is 2.

[0044] (3) Solve the problem using the two-layer optimization model (improved fireworks algorithm + CPLEX) described in Embodiment 1 of this invention, and set up the traditional scheme with "no energy storage" as a control group; solve the mixed integer linear programming problem based on the MATLAB-YALMIP platform and call the CPLEX solver; the solution accuracy threshold is 10. -6 The simulation environment is a computer system with an Intel Core i7 processor and 16GB of memory.

[0045] This embodiment solves a mixed-integer linear programming model and obtains the optimal capacity configuration scheme for photovoltaics, energy storage, and transformers under a typical daily scenario in an industrial area based on the model solution results. With the objective of minimizing the total equivalent daily cost of the system, and considering the time-of-use pricing mechanism, energy storage operation constraints, and transformer capacity constraints, the results shown in Table 1 are obtained: Table 1

[0046] As can be seen from Table 1, with the energy storage configuration, the rated capacity of the transformer is optimized to 1902.3kW, which is about 31.5% lower than 2779.5kW without energy storage. At the same time, the energy storage system effectively smooths the load curve by peak shaving and valley filling, reducing the peak load of the system from 2501.6kW to 1712.1kW, thus improving the safety margin of the power grid operation.

[0047] In terms of economics, the introduction of energy storage reduced the electricity purchase cost from RMB 11,306.0 / day to RMB 4,747.6 / day, a decrease of approximately 58%. The equivalent daily total cost, considering equipment investment, also decreased from RMB 11,573.1 / day to RMB 9,166.9 / day, resulting in an overall improvement in economics of approximately 20.8%. Therefore, the photovoltaic-energy storage synergistic configuration proposed in this method can effectively reduce operating costs and optimize transformer utilization while ensuring power supply security, achieving a synergistic optimization of system economy and security.

[0048] Based on the optimal capacity parameters, the optimal operating strategy for a typical day was further derived. To clearly demonstrate the energy flow and state changes within the photovoltaic-storage supercharging station, the analysis was conducted from two dimensions: power balance and state of charge. like Figure 3 As shown, Figure 3 This is a schematic diagram of power balance scheduling for a typical day at a photovoltaic-storage supercharging station. Figure 3 As can be seen, the industrial area's load experiences significant peaks between 7:00–9:00 and 12:00–14:00. During these times, the energy storage system executes a discharge strategy to release electrical energy to support load demand. After peak shaving by energy storage, the actual grid-connected power obtained from the grid is controlled at a low level, effectively avoiding transformer overload. Meanwhile, during the low-price period at night (0:00–8:00), the energy storage system is fully charged. During the daytime period when photovoltaic output is high, the energy storage system prioritizes consuming surplus photovoltaic power. This "charging during off-peak hours and discharging during peak hours" strategy achieves a balance between peak shaving and economic benefits. like Figure 4 As shown, Figure 4 The graph shows the state of charge (SOC) of the energy storage system, illustrating the dynamic change of the remaining battery charge percentage over time. Figure 4 The SOC curve exhibits a "charge-discharge-balance" pattern: the SOC gradually rises to its upper limit at night, continues to decline during peak hours, and returns to its initial level in the evening, achieving a closed-loop energy balance. This operating characteristic indicates that the energy storage system plays a significant role in energy time-shifting during typical daily operation, not only smoothing out fluctuations in photovoltaic output but also effectively mitigating the impact of peak loads on power distribution equipment.

[0049] Example 3, Figure 5 A capacity optimization configuration system for photovoltaic-storage-supercharging stations includes a function construction module, an initial generation module, an iterative solution module, and a configuration optimization module. The function building module is used to create an equivalent daily total cost target optimization function that includes the cost of penalty for lost electricity. The initial generation module is used to generate an initial configuration scheme set based on a preset set of constraints. The iterative solution module is used to obtain the equivalent daily total cost of the initial configuration scheme set by solving the objective optimization function, and to iteratively update the initial configuration scheme set using the improved fireworks algorithm until the convergence condition is met to obtain the final configuration scheme. Based on the final configuration scheme, the optimal capacity parameters and operating strategy are obtained. The configuration optimization module is used to optimize the capacity configuration based on the optimal capacity parameters and operating strategy. The improved fireworks algorithm achieves its improvement by detonating fireworks in stages and dynamically switching between Gaussian and Cauchy mutations based on the number of iterations, while updating the configuration scheme using a preset selection strategy.

[0050] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0051] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0052] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0053] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0054] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0055] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the capacity configuration of a photovoltaic-storage-supercharging station, characterized in that, Includes the following steps: Establish an equivalent daily total cost objective optimization function that includes the penalty cost for lost electricity; An initial configuration scheme set is generated based on a preset set of constraints; The equivalent daily total cost of the initial configuration scheme set is obtained by solving the objective optimization function, and the initial configuration scheme set is iteratively updated using the improved fireworks algorithm until the convergence condition is met to obtain the final configuration scheme. The optimal capacity parameters and operating strategy are then obtained based on the final configuration scheme. Capacity configuration optimization is achieved based on the aforementioned optimal capacity parameters and operating strategies; The improved fireworks algorithm achieves its improvement by detonating fireworks in stages and dynamically switching between Gaussian and Cauchy mutations based on the number of iterations, while updating the configuration scheme using a preset selection strategy.

2. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 1, characterized in that, The cascading explosions include: The initial configuration scheme set is used as the fireworks population for improving the fireworks algorithm, and the fireworks are classified into levels according to a preset ratio based on the equivalent daily total cost of the configuration scheme corresponding to each fireworks.

3. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 2, characterized in that, The dynamic switching between Gaussian and Cauchy mutations based on the number of iterations includes: Fireworks to be mutated are selected based on the total cost of the equivalent duty day, and an adaptive mutation operation is performed on the fireworks to be mutated. The specific formula is as follows: ， ， in, For adaptive switching coefficients, This represents the current iteration number. The maximum number of iterations is preset. , The first The first firework was at the The values ​​after and before the mutation in each dimension; Follows a standard Gaussian distribution. It follows a standard Cauchy distribution. Let be the switching probability between Gaussian and Cauchy mutations.

4. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 3, characterized in that, The configuration update scheme using a preset selection strategy includes: Based on the initial fireworks, progressive explosions, and sparks generated by adaptive mutation in the current iteration, multiple elite fireworks are selected and combined according to a preset ratio. Among the remaining fireworks after eliminating the multi-elite fireworks, the championship fireworks or elite fireworks are selected based on the local optimality judgment criterion. The aforementioned multi-elite fireworks, championship fireworks, or elite fireworks are grouped according to a preset ratio to form an updated fireworks population, which is the initial configuration scheme set for the next iteration.

5. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 4, characterized in that, The selection of championship fireworks or elite fireworks based on the local optimum judgment criterion includes: The remaining fireworks are sorted according to their corresponding total cost per day; Calculate the difference between the minimum equivalent daily total cost in the current iteration and the minimum equivalent daily total cost in the previous iteration; If the difference is less than the preset stagnation threshold, the improved fireworks algorithm is determined to be trapped in a local optimum and the tournament selection method is used to select the championship fireworks; otherwise, the elite selection method is used to obtain the elite fireworks. The tournament selection method includes dividing the remaining fireworks into three subsets based on the ranking results, and then selecting and combining the subsets into championship fireworks using the tournament selection method.

6. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 1, characterized in that, The total cost includes the equivalent daily investment cost of energy storage and transformers, the grid purchase cost, and the penalty cost for lost electricity. The formula for the equivalent daily investment cost of energy storage and transformers is as follows: ， ， in, For the unit cost of the duty day, This represents the total initial investment cost. The formula for the capital recovery factor is as follows: The discount rate is... This refers to the service life; , , , These are energy storage capacity, rated energy storage power, and transformer capacity, respectively. This corresponds to the rated capacity or power. , These are the equivalent daily investment costs for energy storage and transformers, respectively. , , These are the equivalent daily costs per unit of energy storage capacity, per unit of power, and per unit of transformer capacity, respectively.

7. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 6, characterized in that, The formula for the penalty cost of lost electricity is as follows: ， ， in, To incur penalties for lost electricity, The preset power loss penalty factor, Electricity price for charging service providers To optimize the cycle, For time step, To reduce power loss, for The grid-connected power at any given time.

8. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 1, characterized in that, The generation of the initial configuration scheme set includes: The feasible solution space is determined based on a pre-set set of constraints. Within the feasible solution space, a random distribution algorithm is used to generate numerical vectors containing energy storage capacity, rated energy storage power, and transformer capacity. These numerical vectors are then used as a set of configuration schemes to form an initial configuration scheme set.

9. The capacity optimization configuration method for photovoltaic-storage-supercharging stations according to claim 8, characterized in that, The equivalent daily total cost obtained by solving the objective optimization function for the initial configuration scheme set includes: Calculate the equivalent daily investment cost of energy storage and transformers based on the numerical vectors in the initial configuration scheme set; Using the numerical vector as a constraint and minimizing the grid purchase cost and the loss penalty cost as the planning objective, a linear programming solver is used to solve for the grid purchase cost and the loss penalty cost. The total cost for the equivalent day is obtained by adding the investment cost, the electricity purchase cost from the grid, and the penalty cost for lost electricity.

10. A capacity optimization configuration system for a photovoltaic-storage-supercharging station, characterized in that, Includes the following modules: The function building module is used to create an equivalent daily total cost target optimization function that includes the cost of penalty for lost electricity. The initial generation module is used to generate an initial configuration scheme set based on a preset set of constraints. The iterative solution module is used to obtain the equivalent daily total cost of the initial configuration scheme set by solving the objective optimization function, and to iteratively update the initial configuration scheme set using the improved fireworks algorithm until the convergence condition is met to obtain the final configuration scheme. Based on the final configuration scheme, the optimal capacity parameters and operating strategy are obtained. The configuration optimization module is used to optimize the capacity configuration based on the optimal capacity parameters and operating strategy. The improved fireworks algorithm achieves its improvement by detonating fireworks in stages and dynamically switching between Gaussian and Cauchy mutations based on the number of iterations, while updating the configuration scheme using a preset selection strategy.

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