Multi-target port optical storage charging station energy optimization method

Abstract

The invention relates to a multi-target port optical storage charging station energy optimization method. The method is technically characterized in that a port optical storage charging station energy optimization economic cost target function and a stable operation target function are established; setting an energy scheduling constraint condition and an energy optimization strategy; solving the multi-target port optical storage charging station energy optimization model by adopting a multi-target whale migration algorithm; an external archiving mechanism considering double crowding degrees is adopted; and selecting a compromise solution in the Pareto solution set by adopting a superior and inferior solution distance method to obtain a scheduling scheme. According to the method, the multi-target operation influence of the optical storage charging station is considered, the energy optimization model is established, the multi-target whale migration algorithm and the external archiving mechanism considering the double crowding degree are provided, and reference can be provided for energy scheduling of the port optical storage charging station.

Description

Technical Field

[0001] The present invention belongs to the technical field of active power distribution network optimization operation, and in particular to a multi-objective port photovoltaic storage charging station energy optimization method. Background Art

[0002] As energy-intensive hubs, ports face an urgent need to restructure their energy systems. Traditional port energy systems primarily rely on utility power, resulting in high carbon emissions. At the same time, port areas generally offer open space and abundant rooftop resources, providing a natural advantage for the large-scale deployment of photovoltaic (PV) power systems. In recent years, the rapid adoption of electric port machinery, shore power facilities, and electric logistics vehicles has led to explosive growth in power demand for charging infrastructure, posing a significant challenge to the load regulation capabilities of port power grids. Against this backdrop, building integrated photovoltaic (PV)-storage-charging (SSC) energy systems has become an effective means of transforming port energy systems. However, the complex optimization problems associated with the coupling of multiple energy flows have yet to be effectively addressed. Current research primarily focuses on single-dimensional optimization and control, lacking a coordinated analysis of the intermittent nature of PV output, the cycling of energy storage systems, and the dynamic characteristics of charging loads at the energy scheduling level, resulting in limited system economics. These issues severely restrict the overall benefits of PV-storage-charging station systems, necessitating the development of intelligent optimization methods and energy optimization strategies with multi-dimensional coordination capabilities.

[0003] This paper addresses the problem that traditional optimization models, which often use single-objective weighting methods, struggle to effectively optimize the energy distribution of solar-powered charging stations. This paper proposes a multi-objective energy optimization method for port solar-powered charging stations. This method achieves Pareto optimality for port solar-powered charging stations based on the station's energy optimization strategy and a multi-objective collaborative optimization algorithm. This paper provides valuable insights into promoting green port development. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and improve the energy utilization of port photovoltaic storage charging stations. This invention considers the interaction between photovoltaic, energy storage, and electric vehicles in the photovoltaic storage charging station and proposes a multi-objective energy optimization method for port photovoltaic storage charging stations. The present invention solves the technical problem by adopting the following technical solutions: A multi-objective port solar energy storage charging station energy optimization method includes the following steps: Step 1: Establish the energy optimization economic cost objective function and stable operation objective function of the port solar storage charging station; Step 2: Set the energy scheduling constraints for the port solar-storage charging station, including power balance constraints, energy storage operation constraints, charging pile operation constraints, node voltage constraints, and branch current constraints. Step 3: Set the energy optimization strategy of the port solar-storage charging station and define the control priority of the internal resources of the solar-storage charging station; Step 4: Use the multi-objective whale migration algorithm to find a feasible solution to the multi-objective port solar storage charging station energy optimization model; Step 5: Use the external archiving mechanism that considers double crowding to optimize the Pareto solution set obtained by the multi-objective whale migration algorithm; Step 6: Repeat steps 4 and 5, and output the Pareto solution set after the number of iterations is met; Step 7: Use the superior-inferior solution distance method to select a compromise solution in the Pareto solution set to obtain the energy scheduling plan for the solar-storage charging station.

[0005] The advantages and positive effects of the present invention are: 1. This invention establishes a dual-objective function of economic cost and system stability, taking into account both operational economic benefits and grid security requirements, avoiding the drawbacks of optimizing a single economic objective, and achieving improved comprehensive benefits of the port solar-storage charging station. 2. This paper proposes a new multi-objective meta-heuristic optimization algorithm by establishing a multi-objective whale migration algorithm, which can provide a reference solution algorithm for multi-objective engineering optimization problems; 3. The present invention proposes an external archiving mechanism that takes dual congestion into consideration, which can comprehensively quantify the congestion level of the Pareto solution set in the search space and the congestion level in the target space, avoid falling into the local optimal solution, and improve the solution set effect of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The present invention will be further described below with reference to the accompanying drawings and embodiments; Figure 1 It is a flow chart of a multi-objective energy optimization method for a port photovoltaic storage charging station; Figure 2 It is a flowchart of the multi-objective whale migration algorithm; Figure 3 This is the improved IEEE33 node topology diagram of this embodiment; Figure 4 is the load variation curve of this embodiment; Figure 5 is the photovoltaic curve of this embodiment; Figure 6 is the charging station utilization rate curve of this embodiment; Figure 7 This is the energy distribution of the photovoltaic charging station in this embodiment. DETAILED DESCRIPTION

[0007] Figure 1The process of a multi-objective port photovoltaic charging station energy optimization method is as follows: start → establish the economic cost objective function and stable operation objective function of the port photovoltaic charging station energy optimization → set the constraint conditions for the energy scheduling of the port photovoltaic charging station → set the energy optimization strategy of the port photovoltaic charging station and limit the control priority of the internal resources of the photovoltaic charging station → use the multi-objective whale migration algorithm to solve the feasible solution of the multi-objective port photovoltaic charging station energy optimization model → use the external archiving mechanism considering double congestion to optimize the Pareto solution set obtained by the multi-objective whale migration algorithm → output the Pareto solution set after the number of iterations is met → use the superior and inferior solution distance method to select the compromise solution in the Pareto solution set to obtain the photovoltaic charging station energy scheduling plan → end; Figure 2 The process of a multi-objective whale migration algorithm is shown to be: start → population initialization → position update → non-dominated sorting → external archive overflow judgment → double congestion screening → iteration end condition judgment → non-dominated solution set output → end; The test system used in the embodiment is an improved IEEE33 node system, and the system topology and node load type are as follows: Figure 3 As shown, CIL represents industrial and commercial type load, and REL represents residential type load; Figure 4 Load curves for industrial and commercial type loads and residential type loads; Figure 5 The photovoltaic output curve of the photovoltaic storage charging station; Figure 6 is the charging station utilization curve of the solar-storage charging station; The capacity and location information of the photovoltaic charging station configured in this embodiment is shown in Table 1. The unit energy conversion and usage costs for photovoltaic equipment, energy storage equipment, unidirectional charging piles, and bidirectional charging piles are RMB 55 / KW / year, RMB 120 / KW / year, RMB 90 / KW / year, and RMB 160 / KW / year, respectively. The unit operating cycle is 1 hour. The time-of-use electricity price for 0-5 hours and 20-23 hours is RMB 0.4, the time-of-use electricity price for 6-9 hours and 16-19 hours is RMB 1.1, and the time-of-use electricity price for 10-15 hours is RMB 0.8. The penalty coefficients for network loss, abandoned solar energy, and net load fluctuation are 1.1, 1.0, and 2.5, respectively. The energy conversion efficiency of the energy storage is 0.95, the self-discharge rate of the energy storage equipment is 0.998, and the upper and lower limits of the energy storage state of charge are 0.9 and 0.1, respectively. The reverse discharge power constraint coefficient for the bidirectional charging pile is 1.5. The population size is set to 100, the external archive size is 100, and the number of population iterations is 500; Table 1. Capacity and location information of solar-storage charging stations

[0008] based on Figures 1-6The present invention provides a multi-objective port solar storage charging station energy optimization method, and the specific implementation method is as follows: Step 1: Establish the energy optimization economic cost objective function and stable operation objective function of the port solar storage charging station. The objective function is as follows: (1) (2) (3) (4) Where, is the economic cost objective function; To stabilize the operation of the objective function; Operation and maintenance costs of the solar-storage charging station; The cost of purchasing electricity from the distribution network; is the penalty cost of the distribution network; is the number of nodes in the distribution network; is the node voltage of node n; is the rated voltage of node n; Energy optimization cycle for solar storage charging stations; is the number of solar-storage charging stations; , , , The unit electricity conversion cost of photovoltaic equipment, energy storage equipment, one-way charging piles, and two-way charging piles; t is the unit operation cycle; For the n The output power of the photovoltaic equipment in each photovoltaic charging station; and For the n The charging and discharging power of the energy storage equipment in each solar-storage charging station; is the charging power of the one-way charging pile in the nth solar-storage charging station; and For the n The charging and discharging power of the bidirectional charging piles in each solar-storage charging station; Indicates time-of-use electricity price; The purchased power of the source distribution network; is the number of source distribution network branches; For branch m resistance; and Outflow branch m Active power and reactive power; For branch m The terminal voltage; , and is the penalty coefficient for network loss, abandoned optical energy and net load fluctuation; is the abandoned power of the photovoltaic equipment in the nth photovoltaic storage charging station; is the net load fluctuation of the distribution network, that is, the difference between the net load in period t and the net load in period t-1; Step 2: Set the energy scheduling constraints for the port solar-storage charging station, including power balance constraints, energy storage operation constraints, charging pile operation constraints, node voltage constraints, and branch current constraints. The constraint expressions are as follows: Power balance constraints: (5) (6) Where, For nodes no Incoming photovoltaic power; For nodes no The inflowing energy storage discharge power; For nodes no The discharge power of the incoming bidirectional charging pile; For nodes no Load power; For nodes no Outgoing energy storage charging power; For nodes no Outgoing one-way charging pile charging power; For nodes no Outgoing bidirectional charging pile charging power; For nodes no voltage; For nodes no The number of adjacent nodes; is the voltage of the adjacent node; , , and For nodes no and nodes m Conductance, susceptance and voltage phase angle difference of the middle branch; For nodes no Inflowing reactive power; is the reactive power flowing out of node no; Energy storage operation constraints: (7) (8) (9) Where, For the nThe state of charge of the energy stored in each solar-powered charging station; The efficiency of electric energy conversion for energy storage; is the self-discharge rate of the energy storage device; and The upper and lower limits of the state of charge of the energy storage; Bidirectional charging pile operation constraints: (10) (11) (12) Where, Indicates the adjusted power of the bidirectional charging pile in the nth solar-storage charging station, that is, the power adjusted up or down based on the original charging power; is the reverse discharge power constraint coefficient of the bidirectional charging pile; represents the utilization rate of the bidirectional charging pile in the nth solar-storage charging station; The construction power of a single bidirectional charging pile. The energy for reverse charging in a bidirectional charging pile mainly comes from the remaining capacity of the battery in the electric vehicle. Different electric vehicles have different support effects on the power grid due to different access times and access states. The present invention regards the electric vehicles in the photovoltaic charging station as a whole and constrains the reverse discharge of the electric vehicles by using the utilization rate and the reverse discharge power constraint coefficient. At the same time, to ensure the usage needs of electric vehicle users, the total charging power of the bidirectional charging pile remains unchanged, that is, the reverse charging power of the bidirectional charging pile is consistent with the supplementary charging power. Node voltage constraints: (13) Where, and For nodes no The lower and upper voltage limits; Branch current constraints: (14) Where, For branch nm The upper limit of current; Step 3: Set the energy optimization strategy for the port solar-storage charging station to limit the control priority of the internal resources of the solar-storage charging station. The specific strategy is as follows: During the operation of the photovoltaic storage charging station, the photovoltaic output reaches a peak during the day and is zero at night. This periodicity causes large fluctuations in the net load. Therefore, the present invention preferentially uses energy storage to adjust the photovoltaic output power, and then uses bidirectional charging piles for reverse charging to optimize the operation of the port distribution network. The energy storage charging threshold is used to calculate the peak value of the photovoltaic output power. and energy storage discharge threshold As an optimization variable to control the charge and discharge state of energy storage; when When and When the energy storage enters the discharge state, the energy storage enters the non-working state at other times. During the charging period of the energy storage, the charging power is proportionally distributed according to the photovoltaic power size in different periods. During the discharge period of the energy storage, the discharge power is proportionally distributed according to the net load power in different periods minus a reference value. This reference value is the minimum net load power value in adjacent periods of the discharge period. The control method of the bidirectional charging pile charging and discharging state is the same as that of the energy storage. Step 4: Use the multi-objective whale migration algorithm to find a feasible solution to the multi-objective port solar storage charging station energy optimization model. The algorithm formula of the multi-objective whale migration algorithm is as follows: (1) Initialization of the Good Electric Set and Logistic Chaotic Map Population Considering the complexity of the target space of multi-objective optimization, the good point set and logistic chaos mapping are used to initialize the population. The ergodicity and randomness of the two strategies are used to improve the diversity of the multi-objective whale migration algorithm at the beginning of the search. (15) (16) (17) (18) (19) (20) Where, The population of the multi-target whale migration algorithm; is the i-th individual; is the value of the i-th individual in the j-th dimension; is the number of variable dimensions; is the population size, which is an even number; is the lower limit of the variable dimension; is the upper limit of the variable dimension; is the mapping space of the good point set; is the mapping space of Logistic chaotic mapping; is the mapping value of the best point; is the remainder function; The smallest prime number that satisfies certain conditions; Represents a function for finding prime numbers; Represents the iterative variable in the Logistic chaotic map; (2) Bootstrapping phase The guidance phase is mainly based on the leadership individual and the best individual The position of is preliminarily explored in the search space; (twenty one) (twenty two) Where, is the newly generated i-th individual; Central individual for external archiving; Represents 1 row generated by the Logistic Chaotic Map Random vector of columns; is the individual with the closest Euclidean distance to individual Mi in the target space; Mbest is a random individual in the external archive A; Mlead is the population After the non-dominated sorting, the non-dominated individuals are screened out Will be saved in external archive middle; For population Middle-level leadership individuals The number of , that is, the number of non-dominated individuals; For external archive The number of individuals in For external archive The i-th individual in .

[0009] (3) Exploration stage In the exploration phase, the algorithm is based on the leader individual Develop the search space and introduce the Levy flight strategy to improve the diversity of the algorithm; (twenty three) (twenty four) (25) (26) Where, For population Middle-level leadership individuals the central individual; For population The i-th leader individual in ; represents the Lévy flight function; , , For the control parameters, , , ; represents the Gamma function; (4) Non-dominated sorting mechanism Since different objectives in multi-objective optimization may conflict with each other, the present invention screens out the Pareto optimal solution set by distinguishing the dominance relationship of decomposition (i.e., a solution is not worse than other solutions in all objectives and is better than other solutions in at least one objective). It also balances the convergence and diversity of solutions to avoid the optimization process being biased towards a single objective. (27) Where, represents the u-th optimization objective function; and Represents two solutions in the search space (i.e., two individuals in the population), if and If the above formula is satisfied, then Dominate , expressed as ; Step 5: Use the external archiving mechanism that takes into account the double congestion to optimize the Pareto solution set obtained by the multi-objective whale migration algorithm; external archiving The non-dominated solution set saved in the guides the subsequent search direction. There is a scale limit , so it is necessary to delete the external archive after it overflows. The calculation formula and deletion strategy of double congestion are as follows: (44) (45) (46) (47) (48) (49) Where, For external archive Medium individuals The degree of crowding in the search space; Indicates external archive In the mth dimension, it is larger than the individual The minimum dimension value of ; Indicates external archive In the mth dimension, it is smaller than the individual The maximum dimension value of ; Indicates that in the variable dimension m, external archive The maximum dimension value in ; Indicates that in the variable dimension m, external archive The minimum dimension value present in ; For external archive Medium individuals The degree of crowding in the target space; To optimize the number of targets; Indicates external archive In the case of the jth objective function, the jth objective function is greater than the individual The minimum objective function value of Indicates external archive In the case of j-th objective function, the individual The maximum objective function value of Indicates external archive Objective function The maximum value of Indicates external archive Objective function The minimum value of For external archive Medium individuals Double crowding; and is the weight factor; is the current iteration number; is the maximum number of iterations; It is a nonlinear decreasing factor used to adjust the weight strategy. In the early stage of iteration, the double crowding tends to the search space, aiming to enhance the ergodicity of the algorithm; in the later stage of iteration, the double crowding tends to the target space, aiming to enhance the uniformity of the Pareto solution set. Step 6: Repeat steps 4 and 5, and output the Pareto solution set after the number of iterations is met; Step 7: Use the superior-inferior solution distance method to select a compromise solution in the Pareto solution set to obtain the energy scheduling plan for the solar-storage charging station.

[0010] Based on the above information and steps, the energy optimization results of the four photovoltaic charging stations obtained in this embodiment are as follows: Figure 7 As shown in the figure, Load represents the load power. Indicates the power of a one-way charging pile, Indicates the charging power of the bidirectional charging pile, PV indicates the photovoltaic power, Indicates the charging and discharging power of energy storage, Indicates the adjusted power of the bidirectional charging pile, Indicates the abandoned power of photovoltaic power; The spatiotemporal distribution of energy from the four PV-storage charging station systems shows that the charging power of the energy storage varies with the fluctuations in PV power. When the load increases at night, the energy storage releases stored energy to smooth out load fluctuations. The bidirectional charging piles replenish the distribution network with energy through reverse charging during periods of low PV power, and supplement the energy used for reverse charging during periods of high PV power, thus achieving the effect of load shifting. The energy flow results from the four PV-storage charging stations show very little PV power curtailment, demonstrating that the optimization method can fully utilize PV. In the above embodiments, the superior-inferior solution distance method and the non-dominated sorting method are existing technologies and are well known to those skilled in the art. It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention is not limited to the embodiments described in the specific implementation methods. Any other implementation methods derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A multi-objective port solar storage charging station energy optimization method, characterized in that: The following steps are involved: Step 1: Establish the energy optimization economic cost objective function and stable operation objective function of the port solar storage charging station. The objective function is as follows: (1) (2) (3) (4) Where, is the economic cost objective function; To stabilize the operation of the objective function; Operation and maintenance costs of the solar-storage charging station; The cost of purchasing electricity from the distribution network; is the penalty cost of the distribution network; is the number of nodes in the distribution network; is the node voltage of node n; is the rated voltage of node n; Energy optimization cycle for solar storage charging stations; is the number of solar-storage charging stations; , , , The unit electricity conversion cost of photovoltaic equipment, energy storage equipment, one-way charging piles, and two-way charging piles; t is the unit operation cycle; For the n The output power of the photovoltaic equipment in each photovoltaic charging station; and For the n The charging and discharging power of the energy storage equipment in each solar-storage charging station; is the charging power of the one-way charging pile in the nth solar-storage charging station; and For the n The charging and discharging power of the bidirectional charging piles in each solar-storage charging station; Indicates time-of-use electricity price; The purchased power of the source distribution network; is the number of source distribution network branches; For branch m resistance; and Outflow branch m Active power and reactive power; For branch m The terminal voltage; , and is the penalty coefficient for network loss, abandoned optical energy and net load fluctuation; is the abandoned power of the photovoltaic equipment in the nth photovoltaic storage charging station; is the net load fluctuation of the distribution network, that is, the difference between the net load in period t and the net load in period t-1; Step 2: Set the energy scheduling constraints for the port solar-storage charging station, including power balance constraints, energy storage operation constraints, charging pile operation constraints, node voltage constraints, and branch current constraints. The constraint expressions are as follows: Power balance constraints: (5) (6) Where, For nodes no Incoming photovoltaic power; For nodes no The inflowing energy storage discharge power; For nodes no The discharge power of the incoming bidirectional charging pile; For nodes no Load power; For nodes no Outgoing energy storage charging power; For nodes no Outgoing one-way charging pile charging power; For nodes no Outgoing bidirectional charging pile charging power; For nodes no voltage; For nodes no The number of adjacent nodes; is the voltage of the adjacent node; , , and For nodes no and nodes m Conductance, susceptance and voltage phase angle difference of the middle branch; For nodes no Inflowing reactive power; is the reactive power flowing out of node no; Energy storage operation constraints: (7) (8) (9) Where, For the n The state of charge of the energy stored in each solar-powered charging station; The efficiency of electric energy conversion for energy storage; is the self-discharge rate of the energy storage device; and The upper and lower limits of the state of charge of the energy storage; Bidirectional charging pile operation constraints: (10) (11) (12) Where, Indicates the adjusted power of the bidirectional charging pile in the nth solar-storage charging station, that is, the power adjusted up or down based on the original charging power; is the reverse discharge power constraint coefficient of the bidirectional charging pile; represents the utilization rate of the bidirectional charging pile in the nth solar-storage charging station; The construction power of a single bidirectional charging pile; Node voltage constraints: (13) Where, and For nodes no The lower and upper voltage limits; Branch current constraints: (14) Where, For branch nm The upper limit of current; Step 3: Set the energy optimization strategy for the port solar-storage charging station to limit the control priority of the internal resources of the solar-storage charging station. The specific strategy is as follows: In the operation of the photovoltaic storage charging station, energy storage is used to adjust the photovoltaic output power first, and then the bidirectional charging pile is used for reverse charging to optimize the operation of the port distribution network; the energy storage charging threshold is used to adjust the photovoltaic output power. and energy storage discharge threshold As an optimization variable to control the charge and discharge state of energy storage; when When and When , the energy storage enters the discharge state; The rest of the time, the energy storage enters an inactive state. During the energy storage charging period, the charging power is allocated proportionally according to the photovoltaic power size in different periods. During the energy storage discharge period, the discharge power is allocated proportionally according to the net load power in different periods minus a reference value. This reference value is the minimum net load power value in adjacent periods of the discharge period. The control method for the charging and discharging state of the bidirectional charging pile is the same as that for the energy storage. Step 4: Use the multi-objective whale migration algorithm to find a feasible solution to the multi-objective port solar storage charging station energy optimization model. The algorithm formula of the multi-objective whale migration algorithm is as follows: (1) Initialization of the Jiadian set and the Logistic Chaotic Map population (15) (16) (17) (18) (19) (20) Where, The population of the multi-target whale migration algorithm; is the i-th individual; is the value of the i-th individual in the j-th dimension; is the number of variable dimensions; is the population size, which is an even number; is the lower limit of the variable dimension; is the upper limit of the variable dimension; is the mapping space of the good point set; is the mapping space of Logistic chaotic mapping; is the mapping value of the best point; is the remainder function; The smallest prime number that satisfies certain conditions; Represents a function for finding prime numbers; Represents the iterative variable in the Logistic chaotic map; (2) Bootstrapping phase The guidance phase is mainly based on the leadership individual and the best individual The position of is preliminarily explored in the search space; (21) (22) Where, is the newly generated i-th individual; Central individual for external archiving; Represents 1 row generated by the Logistic Chaotic Map Random vector of columns; is the individual with the closest Euclidean distance to individual Mi in the target space; Mbest is a random individual in the external archive A; Mlead is the population After the non-dominated sorting, the non-dominated individuals are screened out Will be saved in external archive middle; For population Middle-level leadership individuals The number of , that is, the number of non-dominated individuals; For external archive The number of individuals in For external archive The i-th individual in ; (3) Exploration stage In the exploration phase, the algorithm is based on the leader individual Develop the search space and introduce the Levy flight strategy to improve the diversity of the algorithm; (23) (24) (25) (26) Where, For population Middle-level leadership individuals the central individual; For population The i-th leader individual in ; represents the Lévy flight function; , , is the control parameter, , , ; represents the Gamma function; (4) Non-dominated sorting mechanism Since different objectives in multi-objective optimization may conflict with each other, the Pareto optimal solution set is screened out by distinguishing the dominance relationship of the decomposed regions; (27) Where, represents the u-th optimization objective function; and Represents two solutions in the search space (i.e., two individuals in the population), if and If the above formula is satisfied, then Dominate , expressed as ; Step 5: Use the external archiving mechanism that takes into account the double congestion to optimize the Pareto solution set obtained by the multi-objective whale migration algorithm; external archiving The non-dominated solution set saved in the guides the subsequent search direction. There is a scale limit , so it is necessary to delete the external archive after it overflows. The calculation formula and deletion strategy of double congestion are as follows: (44) (45) (46) (47) (48) (49) Where, For external archive Medium individuals The degree of crowding in the search space; Indicates external archive In the mth dimension, it is larger than the individual The minimum dimension value of ; Indicates external archive In the mth dimension, it is smaller than the individual The maximum dimension value of ; Indicates that in the variable dimension m, external archive The maximum dimension value present in ; Indicates that in the variable dimension m, external archive The minimum dimension value present in ; For external archive Medium individuals The degree of crowding in the target space; To optimize the number of targets; Indicates external archive In the case of the jth objective function, the jth objective function is greater than the individual The minimum objective function value of Indicates external archive In the case of j-th objective function, the individual The maximum objective function value of Indicates external archive Objective function The maximum value of Indicates external archive Objective function The minimum value of For external archive Medium individuals Double crowding; and is the weight factor; is the current iteration number; is the maximum number of iterations; It is a nonlinear decreasing factor used to adjust the weight strategy. In the early stage of iteration, the double congestion tends to search the space, aiming to enhance the ergodicity of the algorithm. In the later stages of iteration, the double crowding tends to the target space, aiming to enhance the uniformity of the Pareto solution set; Step 6: Repeat steps 4 and 5, and output the Pareto solution set after the number of iterations is met; Step 7: Use the superior-inferior solution distance method to select a compromise solution in the Pareto solution set to obtain the energy scheduling plan for the solar-storage charging station.

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