Optical storage and charging-containing power distribution network optimal scheduling method and device considering flexible interconnection

Abstract

The invention belongs to the technical field of power distribution network optimization scheduling, and particularly relates to a flexible interconnection-considered optical storage and charging-containing power distribution network optimization scheduling method and device, and the method comprises the steps: building a rapid charging station charging load model, an energy storage charging and discharging model, and an SOP interconnection device power interaction model; constructing a power distribution network economic operation optimization objective function considering the power distribution network electricity purchase cost, the energy storage operation cost and the SOP interaction cost; constructing a power distribution network active power loss optimization objective function taking the SOP access point as a control variable; establishing operation constraint conditions of the power distribution network, the energy storage and the SOP; a double-layer optimization algorithm is constructed, an upper-layer algorithm realizes optimization of an SOP access point, a lower-layer algorithm realizes control of energy storage charging and discharging and SOP power interaction, an optimization result is output by solving the double-layer optimization algorithm, and optimal scheduling of the power distribution network containing the light storage and charging considering flexible interconnection is completed. The overall performance of the power distribution network system is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization and dispatching, and specifically relates to a distribution network optimization and dispatching method and device including photovoltaic storage and charging taking into account flexible interconnection. Background Art

[0002] With the adjustment of energy structure, the consumption system with clean energy as the core has become an unstoppable development trend. As one of the new energy technologies, the photovoltaic storage and charging system has high potential and practical application value, and can provide an effective solution for energy transformation. In order to cope with the challenges of the gradual depletion of traditional energy and the increasing environmental problems, the transformation to new energy substitution is being accelerated. The photovoltaic storage and charging integrated charging station uses photovoltaic power, battery power and public grid power to realize the charging function of electric vehicles. On the one hand, it is conducive to improving the utilization rate of renewable clean energy and electric vehicles, thereby promoting the promotion of green and low-carbon travel; on the other hand, it is conducive to reducing the dependence of charging stations on the public grid and reducing the load impact caused by the access of large-scale electric vehicles.

[0003] For the optimization of distribution networks with photovoltaic storage and charging, in existing studies, the paper "The effect of price-responsive loads uncertainty on the risk-constrained optimal operation of asmart micro-grid" comprehensively considers the interaction between the public power grid, renewable energy system, energy storage system and electric vehicle charging load, and formulates a mixed integer energy scheduling strategy with the lowest overall operating cost. The paper "Research on photovoltaic storage and charging smart charging station and its energy scheduling optimization" designs the system architecture of photovoltaic storage and charging integrated charging station, and establishes an energy scheduling optimization model considering time-of-use electricity prices. The paper "Adaptive and robust day-ahead optimization scheduling of urban intelligent photovoltaic storage charging tower" takes the charging and discharging cost of energy storage system, the electricity purchase cost of charging station and the charging cost of electric vehicles as the objective function, and proposes an adaptive and robust multi-layer scheduling optimization strategy including day-ahead, backup and real-time energy regulation. The paper "Research on real-time energy management strategy of photovoltaic electric vehicle charging station" establishes an analytical prediction model for real-time electricity prices, and based on this, studies the energy management strategy of photovoltaic storage and charging integrated charging station. The document "Research on Multi-Objective Optimal Operation of Integrated Photovoltaic Storage and Charging Systems Based on Demand Response" established a load demand response model, and based on this, formulated multi-objective energy scheduling strategies for isolated operation and grid-connected operation of the integrated photovoltaic storage and charging systems.

[0004] The document "Research on Optimal Operation of Electric Vehicle Charging Stations Based on Demand Response" takes community charging stations as the research object and establishes an optimization model with the goal of minimizing the peak-valley difference of load on the basis of meeting the charging needs of electric vehicle users. The optimized peak-valley electricity price and the corresponding user responsiveness are solved through the demand response model. The document "Multi-objective Optimization Dispatch Method for Electric Vehicle Photovoltaic Charging Station" comprehensively analyzes the operating characteristics of electric vehicle charging, photovoltaic power generation and energy storage systems, and proposes a dual-objective optimization model that considers electricity purchase costs and battery cycle power. The document "Research on Coordinated Optimization Control Strategy of Source, Storage and Load in Microgrid Based on Automatic Demand Response" proposes an optimization control model for microgrids containing electric vehicles based on a multi-agent method under the automatic demand response mode, targeting microgrids with distributed power sources and energy storage systems as the power supply side and conventional loads and electric vehicle loads as the user side.

[0005] In summary, the existing schemes have not realized the research on the optimal dispatching method of the distribution network after the PV storage and charging are connected through flexible interconnection. Therefore, it is necessary to study the modeling method of the distribution network containing PV storage and charging based on SOP (smart soft switch, Soft Open Point) flexible interconnection, and propose an optimal dispatching method for the distribution network containing PV storage and charging that can effectively adapt to the SOP flexible interconnection. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the present invention provides a distribution network optimization scheduling method and device with photovoltaic storage and charging that takes into account flexible interconnection, which achieves cost reduction, efficiency improvement, reliability enhancement and efficient absorption of renewable energy, thereby optimizing the overall performance of the distribution network system.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is to consider the flexible interconnection of the distribution network with photovoltaic storage and charging optimization scheduling method, including the following steps: S1. For fast charging stations, energy storage and intelligent soft switch SOPs, a fast charging station charging load model, an energy storage charging and discharging model and a SOP interconnected device power interaction model are established respectively; S2. Taking the distribution network operation cost as the optimization target, construct the distribution network economic operation optimization objective function considering the distribution network power purchase cost, energy storage operation cost and SOP interaction cost; S3, taking the active network loss of the distribution network as the optimization target, constructing the distribution network active network loss optimization objective function with the SOP access point as the control variable; S4. Establish operating constraints for distribution network, energy storage and SOP, including distribution network operating constraints, energy storage capacity and charging and discharging constraints, SOP operating constraints and line congestion management constraints; S5. Construct a two-layer optimization algorithm. The upper-layer algorithm realizes the optimization of SOP access points, and the lower-layer algorithm realizes the control of energy storage charging and discharging and SOP power interaction. By solving the two-layer optimization algorithm and outputting the optimization results, the optimal scheduling of the distribution network containing photovoltaic storage and charging considering flexible interconnection is completed.

[0008] As a preferred solution of the present invention, in S1, the fast charging station contains multiple charging piles, so the charging load in the fast charging station is formed by the charging of the charging piles. Therefore, the charging load model of the fast charging station is expressed as: ; In the formula, Charging load for fast charging stations; is a binary variable, indicating the usage status of the charging pile. Indicates that the charging pile is being used. Indicates that the charging pile is not in use; is the single pile power value of the fast charging pile; The number of the charging pile in the fast charging station; The number of charging posts in the fast charging station; The energy storage definition charging and discharging model is expressed as: ; In the formula, is the charging and discharging power of energy storage; is the charging power of the energy storage; is the discharge power of energy storage; is a binary variable, indicating the charging state of the energy storage, Indicates that it is charging. Indicates not charging; is a binary variable, indicating the discharge state of the energy storage, Indicates that it is discharging. Indicates no discharge; The power interaction model of SOP interconnected devices is expressed as: ; In the formula, The power exchange value for the fast charging station through SOP; is the forward power exchange value; is the negative power exchange value; is a binary variable, indicating the state of SOP forward power exchange, Indicates that the SOP is exchanging power in the forward direction. It indicates that no forward power exchange occurs in the SOP; is a binary variable, indicating the state of negative power exchange of SOP, Indicates that the SOP is exchanging negative power. Indicates that no negative power exchange occurs in the SOP.

[0009] As a preferred solution of the present invention, in S2, the distribution network economic operation optimization objective function is expressed as: ; ; ; ; In the formula, The operating cost of the distribution network; The cost of purchasing electricity from the distribution network; The operating cost of energy storage; is the control cost of SOP; t is the time segment; T is the total number of time segments; is the time-of-use electricity price of the distribution network during period t; is the amount of electricity purchased by the distribution network during period t; is the unit dispatch cost of energy storage; is the charge and discharge amount of energy storage during period t; Unit scheduling costs for SOP; is the interactive power of SOP.

[0010] As a preferred solution of the present invention, in S3, the distribution network active network loss optimization objective function is expressed as: ; ; In the formula, To optimize the objective function of SOP access nodes; is the active network loss of the distribution network; i and j are the node numbers of the distribution network, i≠j, ij is a branch in the distribution network with node i as the starting point and node j as the end point; is the number of nodes in the distribution network; is the number of branches in the distribution network; is a binary variable, When SOP accesses node i, When SOP accesses node j; for The current amplitude flowing through branch ij during the time period; is the resistance value of branch ij; The unit scheduling period.

[0011] As a preferred solution of the present invention, in S4, the distribution network operation constraint is expressed as: ; ; ; ; ; ; In the formula, is the square of the voltage amplitude at node i during period t; is the square of the voltage amplitude at node j during period t; and are the upper and lower limits of the voltage amplitude of the distribution network nodes respectively; is the square of the current amplitude flowing through branch ij during period t; is the upper limit of the square of the current amplitude flowing through branch ij; is the active power flowing through branch ij during period t; is the number of photovoltaic power generation; is the number of photovoltaic power generation; is a binary variable, indicating whether photovoltaic power generation is installed at node j. ,otherwise ; is the power value of photovoltaic power generation during period t; is the interactive power value of SOP in period t; is the active load of access node j during period t; is the number of the fast charging station; is the number of fast charging stations; is a binary variable, representing a fast charging station Is it connected to node j? If yes, ,otherwise ; is the value of charging load during period t; is the charging and discharging power value of energy storage during period t; is the reactive power flowing through branch ij during period t; is the reactance value of branch ij; is the reactive load connected to node j during period t; h is the node number of the distribution network, j≠h, represents branch jh; is the active power flowing through branch jh during period t; is the reactive power flowing through branch jh during period t.

[0012] As a preferred embodiment of the present invention, in S4, the energy storage capacity and charge and discharge constraints are expressed as: ; ; ; ; ; ; In the formula, is the charging and discharging power of the energy storage during period t; It is the period t ; It is the period t ; is the charging power of the energy storage during period t; is the discharge power of the energy storage during period t; , They are the remaining power of energy storage in period t and period t-1 respectively; The charging and discharging efficiency of energy storage; and They are the upper and lower limits of the remaining energy storage capacity respectively; and are the upper and lower limits of the energy storage charging power respectively; and are the upper and lower limits of the energy storage discharge power respectively.

[0013] As a preferred embodiment of the present invention, in the S4, the SOP operation constraint is expressed as: ; ; ; ; In the formula, is the power exchange value of the fast charging station through SOP during period t; is the forward power exchange value during period t; is the negative power exchange value in period t; For the period t ; For the period t ; and are the maximum and minimum values ​​of the SOP forward transmission power respectively; and are the maximum and minimum values ​​of the negative transmission power of SOP respectively.

[0014] As a preferred solution of the present invention, in S4, the line congestion management constraint is expressed as: ; In the formula, is the blocking capacity of the forward active power of branch ij.

[0015] As a preferred solution of the present invention, in the above S5, the upper layer algorithm calculates the access position of the SOP, and its objective function is: , the control variable is the access position of SOP; the objective function of the lower layer algorithm is , the control variables are the charging and discharging state of energy storage and the power exchange of SOP; the solution process of the two-layer optimization algorithm is: S51, initialize the access position of the distribution network S0P, initialize the energy storage charging and discharging state and the power exchange state of the S0P, and initialize the number of iterations k; S52, based on matlab and Gurobi, solve , obtain the charging and discharging scheduling results of energy storage and the power transmission state of SOP, which is and ; S53, based on matlab and Gurobi, solve , obtain the access position of SOP, which is the access node; S54, Judgment Is it less than or equal to the convergence coefficient? , if yes, output the optimization result, otherwise k=k+1, return to S52 to continue iteration; where, is the iteration error, which represents the error between the kth and k-1th calculation results, expressed as: ; In the formula, is the power exchange value of the fast charging station through SOP during period t-1; is the charging and discharging power of energy storage during period t-1; , are the period t and the period t-1 respectively. .

[0016] The distribution network optimization dispatching device with flexible interconnection including photovoltaic storage and charging includes: a memory having a computer processing program stored thereon; The processor is used to execute the computer processing program in the memory to implement the above-mentioned distribution network optimization scheduling method including photovoltaic storage and charging considering flexible interconnection.

[0017] The beneficial effects of the present invention are: The present invention achieves multi-faceted optimization of distribution network operation by introducing intelligent soft switching (SOP) technology; by establishing mathematical models of fast charging stations, energy storage and SOP, an optimization objective function with the goal of minimizing the distribution network operation cost is constructed, and the active network loss of the distribution network is taken as the optimization object, which further reduces the energy consumption of the power grid; by considering the distribution network operation constraints, energy storage capacity and charging and discharging constraints, and SOP transmission capacity constraints, the safe and stable operation of the distribution network system is ensured.

[0018] The present invention proposes a two-layer optimization algorithm, the upper layer optimizes the SOP access point, and the lower layer controls the energy storage charging and discharging and SOP power interaction, which further improves the dispatching flexibility and economy of the distribution network; it not only improves the distribution network's ability to absorb renewable energy and reduces dependence on traditional power grids, but also realizes the flexible interconnection of the distribution network by optimizing the coordinated operation of energy storage and SOP, improves the reliability and power quality of the power grid, and ultimately achieves the purpose of reducing operating costs, improving the efficiency of the distribution network system, and enhancing the distribution network's adaptability to uncertain factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow diagram of the method of the present invention; Figure 2 It is a schematic diagram of the architecture of the double-layer optimization algorithm of the present invention; Figure 3 It is a schematic diagram of the flow of the double-layer optimization algorithm of the present invention; Figure 4 It is a topological diagram of the distribution network containing photovoltaic storage and charging during the verification process of the present invention; Figure 5 It is a schematic diagram of the simulation results of energy storage 1 output during the verification process of the present invention; Figure 6 It is a schematic diagram of the simulation results of energy storage 2 output during the verification process of the present invention; Figure 7 It is a schematic diagram of the SOP1 power simulation result during the verification process of the present invention; Figure 8 It is a schematic diagram of the SOP2 power simulation results during the verification process of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present invention are further described below in conjunction with the accompanying drawings: Example 1 like Figure 1 As shown, the optimal dispatching method of the distribution network including photovoltaic storage and charging with flexible interconnection is included in the following steps: S1. For fast charging stations, energy storage and intelligent soft switch SOPs, a fast charging station charging load model, an energy storage charging and discharging model and a SOP interconnected device power interaction model are established respectively; S2. Taking the distribution network operation cost as the optimization target, construct the distribution network economic operation optimization objective function considering the distribution network power purchase cost, energy storage operation cost and SOP interaction cost; S3, taking the active network loss of the distribution network as the optimization target, constructing the distribution network active network loss optimization objective function with the SOP access point as the control variable; S4. Establish operating constraints for distribution network, energy storage and SOP, including distribution network operating constraints, energy storage capacity and charging and discharging constraints, SOP operating constraints and line congestion management constraints; S5. Construct a two-layer optimization algorithm. The upper-layer algorithm realizes the optimization of SOP access points, and the lower-layer algorithm realizes the control of energy storage charging and discharging and SOP power interaction. By solving the two-layer optimization algorithm and outputting the optimization results, the optimal scheduling of the distribution network containing photovoltaic storage and charging considering flexible interconnection is completed.

[0021] In S1, the fast charging station contains multiple charging piles, so the charging load in the fast charging station is aggregated by the charging piles. Therefore, the charging load model of the fast charging station is expressed as: ; In the formula, Charging load for fast charging stations; is a binary variable, indicating the usage status of the charging pile. Indicates that the charging pile is being used. Indicates that the charging pile is not in use; is the single pile power value of the fast charging pile; The number of the charging pile in the fast charging station; The number of charging posts in the fast charging station; The energy storage definition charging and discharging model is expressed as: ; In the formula, is the charging and discharging power of energy storage; is the charging power of the energy storage; is the discharge power of energy storage; is a binary variable, indicating the charging state of the energy storage, Indicates that it is charging. Indicates not charging; is a binary variable, indicating the discharge state of the energy storage, Indicates that it is discharging. Indicates no discharge; The power interaction model of SOP interconnected devices is expressed as: ; In the formula, The power exchange value for the fast charging station through SOP; is the forward power exchange value; is the negative power exchange value; is a binary variable, indicating the state of SOP forward power exchange, Indicates that the SOP is exchanging power in the forward direction. It indicates that no forward power exchange occurs in the SOP; is a binary variable, indicating the state of negative power exchange of SOP, Indicates that the SOP is exchanging negative power. Indicates that no negative power exchange occurs in the SOP.

[0022] In S2, the distribution network economic operation optimization objective function is expressed as: ; ; ; ; In the formula, The operating cost of the distribution network; The cost of purchasing electricity from the distribution network; The operating cost of energy storage; is the control cost of SOP; t is the time segment; T is the total number of time segments, usually 24, representing one day; is the time-of-use electricity price of the distribution network during period t; is the amount of electricity purchased by the distribution network during period t; is the unit dispatch cost of energy storage; is the charge and discharge amount of energy storage during period t; Unit scheduling costs for SOP; is the interactive power of SOP.

[0023] In S3, the distribution network active power loss optimization objective function is expressed as: ; ; In the formula, To optimize the objective function of SOP access nodes; is the active network loss of the distribution network; i and j are the node numbers of the distribution network, i≠j, ij is a branch in the distribution network with node i as the starting point and node j as the end point; is the number of nodes in the distribution network; is the number of branches in the distribution network; is a binary variable, When SOP accesses node i, When SOP accesses node j; for The current amplitude flowing through branch ij during the time period; is the resistance value of branch ij; The unit scheduling period.

[0024] In S4, the distribution network operation constraints are expressed as: ; ; ; ; ; ; In the formula, is the square of the voltage amplitude at node i during period t; is the square of the voltage amplitude at node j during period t; and are the upper and lower limits of the voltage amplitude of the distribution network nodes respectively; is the square of the current amplitude flowing through branch ij during period t; is the upper limit of the square of the current amplitude flowing through branch ij; is the active power flowing through branch ij during period t; is the number of photovoltaic power generation; is the number of photovoltaic power generation; is a binary variable, indicating whether photovoltaic power generation is installed at node j. ,otherwise ; is the power value of photovoltaic power generation during period t; is the interactive power value of SOP in period t; is the active load of access node j during period t; is the number of the fast charging station; is the number of fast charging stations; is a binary variable, representing a fast charging station Is it connected to node j? If yes, ,otherwise ; is the value of charging load during period t; is the charging and discharging power value of energy storage during period t; is the reactive power flowing through branch ij during period t; is the reactance value of branch ij; is the reactive load connected to node j during period t; h is the node number of the distribution network, j≠h, represents branch jh; is the active power flowing through branch jh during period t; is the reactive power flowing through branch jh during period t.

[0025] In S4, the energy storage capacity and charge and discharge constraints are expressed as: ; ; ; ; ; ; In the formula, is the charging and discharging power of the energy storage during period t; It is the period t ; It is the period t ; is the charging power of the energy storage during period t; is the discharge power of the energy storage during period t; , They are the remaining power of energy storage in period t and period t-1 respectively; The charging and discharging efficiency of energy storage; and They are the upper and lower limits of the remaining energy storage capacity respectively; and are the upper and lower limits of the energy storage charging power respectively; and are the upper and lower limits of the energy storage discharge power respectively.

[0026] In S4, the SOP operation constraints are expressed as: ; ; ; ; In the formula, is the power exchange value of the fast charging station through SOP during period t; is the forward power exchange value during period t; is the negative power exchange value in period t; For the period t ; For the period t ; and are the maximum and minimum values ​​of the SOP forward transmission power respectively; and are the maximum and minimum values ​​of the negative transmission power of SOP respectively.

[0027] In S4, the line congestion management constraint is expressed as: ; In the formula, is the blocking capacity of the forward active power of branch ij.

[0028] like Figure 2 The figure shows a typical two-layer optimization algorithm architecture. In the upper-layer architecture, in order to reduce the active network loss of the distribution network based on the scheduling results of the lower-layer algorithm, the upper-layer algorithm adjusts the interconnected nodes of the SOP to adjust the power flow distribution of the distribution network, thereby reducing the active network loss of the distribution network. In the lower-layer architecture, in order to reduce the operating cost of the distribution network based on the planning results of the upper-layer algorithm, the lower-layer algorithm minimizes the operating cost of the distribution network by adjusting the exchange power of the energy storage and SOP.

[0029] In S5, the specific two-layer optimization algorithm process is as follows Figure 3 The upper algorithm calculates the access position of SOP, and its objective function is , the control variable is the access position of SOP; the objective function of the lower layer algorithm is , the control variables are the charging and discharging state of energy storage and the power exchange of SOP; the solution process of the two-layer optimization algorithm is: S51, initialize the access position of the distribution network S0P, initialize the energy storage charging and discharging state and the power exchange state of the S0P, and initialize the number of iterations k; S52, based on matlab and Gurobi, solve , obtain the charging and discharging scheduling results of energy storage and the power transmission state of SOP, which is and ; S53, based on matlab and Gurobi, solve , obtain the access position of SOP, which is the access node; S54, Judgment Is it less than or equal to the convergence coefficient? , if yes, output the optimization result, otherwise k=k+1, return to S52 to continue iteration; where, is the iteration error, which represents the error between the kth and k-1th calculation results, expressed as: ; In the formula, is the power exchange value of the fast charging station through SOP during period t-1; is the charging and discharging power of energy storage during period t-1; , are the period t and the period t-1 respectively. .

[0030] The verification process is: On the matlab software, build based on Figure 4 The topology of the distribution network with solar energy storage and charging shown in the figure includes energy storage 1 and energy storage 2. The charging and discharging states of the energy storage are as follows: Figure 5 and Figure 6 As shown in Table 3, the charge and discharge states of the energy storage do not differ greatly. The power simulation results for SOP1 and SOP2 (the calculated access points are shown in Table 3) are shown in Table 3. Figure 7 and Figure 8 As shown, there is a difference between the method proposed by the present invention and the method that does not optimize the SOP access position. Since the access position of the SOP changes, the power exchange changes. Table 1 shows the operating cost of the distribution network. The method proposed by the present invention reduces the operating cost of the distribution network. Table 2 shows the active network loss of the distribution network. The method proposed by the present invention reduces the active network loss of the distribution network. Table 3 shows the calculation results of the flexible interconnection nodes of the SOP. The flexible interconnection nodes of the SOP are effectively adjusted using the method proposed by the present invention.

[0031] Table 1 Simulation results of distribution network operation cost method Distribution network operation cost (yuan) Proposed method 56638.19 SOP access point not optimized 57183.38 SOP not installed 58647.69 Table 2 Distribution network loss simulation results method Active power loss of distribution network (kWh) Proposed method 2297.00 SOP access point not optimized 2401.33 SOP not installed 2488.88 Table 3 Calculation results of SOP flexible interconnection nodes method SOP1 Flexible Interconnect Node SOP2 Flexible Interconnect Node Proposed method 16 12 SOP access point not optimized 31 8 Example 2 The distribution network optimization dispatching device with flexible interconnection including photovoltaic storage and charging includes: a memory having a computer processing program stored thereon; The processor is used to execute the computer processing program in the memory to implement the distribution network optimization scheduling method containing photovoltaic storage and charging considering flexible interconnection as described in Example 1.

Claims

1. A distribution network optimization dispatching method with photovoltaic storage and charging considering flexible interconnection, characterized by The following steps are involved: S1. For fast charging stations, energy storage and intelligent soft switch SOPs, a fast charging station charging load model, an energy storage charging and discharging model and a SOP interconnected device power interaction model are established respectively; S2. Taking the distribution network operation cost as the optimization target, construct the distribution network economic operation optimization objective function considering the distribution network power purchase cost, energy storage operation cost and SOP interaction cost; S3, taking the active network loss of the distribution network as the optimization target, constructing the distribution network active network loss optimization objective function with the SOP access point as the control variable; S4. Establish operating constraints for distribution network, energy storage and SOP, including distribution network operating constraints, energy storage capacity and charging and discharging constraints, SOP operating constraints and line congestion management constraints; S5. Construct a two-layer optimization algorithm. The upper-layer algorithm realizes the optimization of SOP access points, and the lower-layer algorithm realizes the control of energy storage charging and discharging and SOP power interaction. By solving the two-layer optimization algorithm and outputting the optimization results, the optimal scheduling of the distribution network containing photovoltaic storage and charging considering flexible interconnection is completed.

2. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 1 is characterized in that: In S1, the fast charging station contains multiple charging piles, so the charging load in the fast charging station is formed by the charging of the charging piles. Therefore, the charging load model of the fast charging station is expressed as: ； In the formula, Charging load for fast charging stations; is a binary variable, indicating the usage status of the charging pile. Indicates that the charging pile is being used. Indicates that the charging pile is not in use; is the single pile power value of the fast charging pile; The number of the charging pile in the fast charging station; The number of charging posts in the fast charging station; The energy storage definition charging and discharging model is expressed as: ； In the formula, is the charging and discharging power of energy storage; The charging power of the energy storage; is the discharge power of energy storage; is a binary variable, indicating the charging state of the energy storage, Indicates that it is charging. Indicates not charging; is a binary variable, indicating the discharge state of the energy storage, Indicates that it is discharging. Indicates no discharge; The power interaction model of SOP interconnected devices is expressed as: ； In the formula, The power exchange value for the fast charging station through SOP; is the forward power exchange value; is the negative power exchange value; is a binary variable, indicating the state of SOP forward power exchange, Indicates that the SOP is exchanging power in the forward direction. It indicates that no forward power exchange occurs in the SOP; is a binary variable, indicating the state of negative power exchange of SOP, Indicates that the SOP is exchanging negative power. Indicates that no negative power exchange occurs in the SOP.

3. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 2 is characterized in that: In S2, the distribution network economic operation optimization objective function is expressed as: ； ； ； ； In the formula, The operating cost of the distribution network; The cost of purchasing electricity from the distribution network; The operating cost of energy storage; Cost of SOP regulation; t is the time segment; T is the total number of time segments; is the time-of-use electricity price of the distribution network during period t; is the amount of electricity purchased by the distribution network during period t; is the unit dispatch cost of energy storage; is the charge and discharge amount of energy storage during period t; Unit scheduling costs for SOP; is the interactive power of SOP.

4. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 3 is characterized in that: In S3, the distribution network active power loss optimization objective function is expressed as: ； ； In the formula, To optimize the objective function of SOP access nodes; is the active network loss of the distribution network; i and j are the node numbers of the distribution network, i≠j, ij is a branch in the distribution network with node i as the starting point and node j as the end point; is the number of nodes in the distribution network; is the number of branches in the distribution network; is a binary variable, When SOP accesses node i, When SOP accesses node j; for The current amplitude flowing through branch ij during the time period; is the resistance value of branch ij; The unit scheduling period.

5. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 4 is characterized in that: In S4, the distribution network operation constraints are expressed as: ； ； ； ； ； ； In the formula, is the square of the voltage amplitude at node i during period t; is the square of the voltage amplitude at node j during period t; and are the upper and lower limits of the voltage amplitude of the distribution network nodes respectively; is the square of the current amplitude flowing through branch ij during period t; is the upper limit of the square of the current amplitude flowing through branch ij; is the active power flowing through branch ij during period t; is the number of photovoltaic power generation; is the number of photovoltaic power generation; is a binary variable, indicating whether photovoltaic power generation is installed at node j. ,otherwise ; is the power value of photovoltaic power generation during period t; is the interactive power value of SOP in period t; is the active load of access node j during period t; is the number of the fast charging station; is the number of fast charging stations; is a binary variable, representing a fast charging station Is it connected to node j? If yes, ,otherwise ; is the value of charging load during period t; is the charging and discharging power value of energy storage during period t; is the reactive power flowing through branch ij during period t; is the reactance value of branch ij; is the reactive load connected to node j during period t; h is the node number of the distribution network, j≠h, represents branch jh; is the active power flowing through branch jh during period t; is the reactive power flowing through branch jh during period t.

6. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 5 is characterized in that: In S4, the energy storage capacity and charge and discharge constraints are expressed as: ； ； ； ； ； ； In the formula, is the charging and discharging power of the energy storage during period t; It is the period t ; It is the period t ; is the charging power of the energy storage during period t; is the discharge power of the energy storage during period t; , They are the remaining power of energy storage in period t and period t-1 respectively; The charging and discharging efficiency of energy storage; and They are the upper and lower limits of the remaining energy storage capacity respectively; and are the upper and lower limits of the energy storage charging power respectively; and are the upper and lower limits of the energy storage discharge power respectively.

7. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 6 is characterized in that: In the above S4, the SOP operation constraints are expressed as: ； ； ； ； In the formula, is the power exchange value of the fast charging station through SOP during period t; is the forward power exchange value during period t; is the negative power exchange value in period t; For the period t ; For the period t ; and are the maximum and minimum values ​​of the SOP forward transmission power respectively; and are the maximum and minimum values ​​of the negative transmission power of SOP respectively.

8. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 7 is characterized in that: In S4, the line congestion management constraint is expressed as: ； In the formula, is the blocking capacity of the forward active power of branch ij.

9. The method for optimizing the dispatching of a distribution network including photovoltaic storage and charging with consideration of flexible interconnection according to claim 8 is characterized in that: In S5, the upper layer algorithm calculates the access position of SOP, and its objective function is , the control variable is the access position of SOP; The objective function of the lower layer algorithm is , the control variables are the charging and discharging state of energy storage and the power exchange of SOP; the solution process of the two-layer optimization algorithm is: S51, initialize the access position of the distribution network S0P, initialize the energy storage charging and discharging state and the power exchange state of the S0P, and initialize the number of iterations k; S52, based on matlab and Gurobi, solve , obtain the charging and discharging scheduling results of energy storage and the power transmission state of SOP, which is and ; S53, based on matlab and Gurobi, solve , obtain the access position of SOP, which is the access node; S54, Judgment Is it less than or equal to the convergence coefficient? , if yes, output the optimization result, otherwise k=k+1, return to S52 to continue iteration; where, is the iteration error, which represents the error between the kth and k-1th calculation results, expressed as: ； In the formula, is the power exchange value of the fast charging station through SOP during period t-1; is the charging and discharging power of energy storage during period t-1; , are the period t and the period t-1 respectively. .

10. A distribution network optimization dispatching device including photovoltaic storage and charging considering flexible interconnection, characterized in that: include: a memory having a computer processing program stored thereon; A processor is used to execute a computer processing program in a memory to implement the distribution network optimization scheduling method including photovoltaic storage and charging with consideration of flexible interconnection as described in any one of claims 1 to 9.

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