Optical storage and charging integrated power station optimal configuration method considering energy efficient utilization and load emergency power supply

Abstract

The invention provides an optical storage and charging integrated power station optimal configuration method considering efficient energy utilization and load emergency power supply, which comprises the following steps: firstly, comprehensively considering indexes such as charging start time, driving distance required by charging and charging duration of an electric vehicle, and establishing an electric vehicle load model; then, centralized and integrated configuration is carried out on photovoltaic, energy storage and charging stations, emergency working conditions and normal working conditions of the photovoltaic, energy storage and charging stations are comprehensively considered, and a photovoltaic, energy storage and charging integrated power station configuration model with optimal comprehensive construction cost, comprehensive operation cost and loss-of-load cost as targets is constructed; and finally, by solving the optimization model, determining the number, the position and the energy storage configuration capacity of the light storage and charging integrated power stations. Example results show that integrated configuration of light storage and charging is achieved, normal and emergency operation working conditions of the system are comprehensively considered, important load power supply under the fault working condition is guaranteed, meanwhile, photovoltaic efficient utilization under the normal working condition is achieved, and the operation reliability and economical efficiency of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency power supply, and specifically relates to an optimization configuration method of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and load emergency power supply. Background Art

[0002] In recent years, photovoltaics, energy storage, electric vehicles and their charging stations have been developed rapidly in the distribution network as an important part of the development of new energy. Accelerating the research on technologies such as integrated configuration of photovoltaic, storage and charging, the configuration strategy of photovoltaic, storage and charging has also become a research focus. Therefore, in order to meet higher and more comprehensive overall planning requirements, how to plan and configure photovoltaic, storage and charging is crucial to the stable and economic operation of the power system.

[0003] At present, photovoltaic, energy storage and charging planning is mostly decentralized. Most studies consider factors such as system energy utilization efficiency, operating costs and grid stability to plan the site selection and capacity of photovoltaic, energy storage and charging stations. However, decentralized configuration requires a high capital investment in construction and operation and maintenance costs, and the dispatching system is complex and difficult to manage. In contrast, the photovoltaic, energy storage and charging integrated configuration strategy integrates the above resources and can solve the above problems. At present, the photovoltaic, energy storage and charging integrated configuration strategy is generally based on the normal operating conditions of the distribution network, and considers the construction cost and operation and maintenance cost of the integrated power station to improve the operation economy of the system. However, in the face of emergencies such as emergency disasters, such strategies ignore the potential of various resources to support the power supply of important loads during failures, so the distribution network will suffer high economic losses due to load loss. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the present invention provides an optimization configuration method for a photovoltaic, energy storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads. Photovoltaic, energy storage and charging stations are centrally integrated and configured, and emergency and normal operating conditions are comprehensively considered. An optimization configuration model for a photovoltaic, energy storage and charging integrated power station is constructed with the optimal goal including comprehensive construction cost, comprehensive operating cost and load loss cost, which fully ensures the reliable power supply of important loads under fault conditions and further reduces the operating cost of the system.

[0005] The present invention adopts the following technical solution:

[0006] The method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads is characterized by comprising the following steps:

[0007] S1: Construct electric vehicle charging load model;

[0008] S2: A photovoltaic storage and charging integrated power station configuration model was constructed with the goal of optimizing the comprehensive construction cost, comprehensive operation cost and load loss cost;

[0009] S3: The Cplex commercial solver is used to solve the objective function of the configuration model of the integrated photovoltaic, storage and charging power station, and the site selection and capacity determination of the integrated photovoltaic, storage and charging power station are achieved based on this.

[0010] Furthermore, the construction of the electric vehicle charging load model includes the following steps:

[0011] (1) Analyze the starting charging time of electric vehicles

[0012] Taking the end time of the user's trip as the start time of charging the electric vehicle, the probability density function of the start time of charging the electric vehicle is obtained by fitting as follows:

[0013]

[0014] In the formula, f s (t) represents the probability density function of the time when the electric vehicle starts charging; t s is the charging time at the start; s is the standard deviation, which indicates the degree of deviation between the actual departure and arrival time and the expected value; μ s is the expected value of the function;

[0015] (2) Analysis of the driving distance of electric vehicles

[0016] The probability density function of the daily driving distance of electric vehicles is:

[0017]

[0018] In the formula, f d (x) represents the probability density function of the daily driving distance of electric vehicles; x is the driving distance of electric vehicles; μ is the expected value of the driving distance; σ is the standard deviation, which represents the degree of deviation between the actual driving distance and the expected value;

[0019] (3) Analysis of charging time of electric vehicles

[0020] The time required to charge an electric vehicle is T c It can be expressed by factors such as charging efficiency, state of charge and charging power:

[0021]

[0022] In the formula, S n S is the initial charge state of the electric vehicle; s E is the expected state of charge of the electric vehicle; s is the battery capacity of the electric vehicle; η ev,ch is the charging efficiency of electric vehicles; P ev,ch Charging power for electric vehicles;

[0023] 4) Predict the charging load of electric vehicles based on the Monte Carlo simulation method.

[0024] Initialize the number of electric private cars and electric taxis, electric vehicle battery capacity, charging power, simulation times and other parameters; randomly select the starting charging time and starting state of charge of a single electric vehicle, calculate the charging time of the electric vehicle, and then obtain the end time of the electric vehicle charging; iteratively superimpose the charging load of a single electric vehicle to obtain the charging load of all electric vehicles in a day;

[0025]

[0026] Where: N is the charging load of all electric vehicles at time t in the mth simulation; ev is the number of electric vehicles; P ev,t,n represents the charging power of the nth electric car at time t.

[0027] The charging load obtained from each simulation is accumulated, and the total charging load is obtained at the end of the simulation. The average value of the electric vehicle charging load for each simulation is calculated as the charging load curve;

[0028]

[0029] Where: M is the total number of simulations; P EV,t is the total charging load of electric vehicles in the substation at time t after the simulation ends.

[0030] Furthermore, the photovoltaic storage and charging integrated power station configuration model includes a photovoltaic storage and charging integrated power station configuration model objective function and corresponding constraints. The photovoltaic storage and charging integrated power station configuration model objective function is as follows:

[0031] minF=F CON +F RUN +F EC (6)

[0032] Where: F CON represents the comprehensive construction cost, F RUN represents the comprehensive operating cost, F EC Represents the loss of load cost.

[0033] Furthermore, the comprehensive construction cost objective function is as follows:

[0034] f CON =C land +C pis +C pile +C ess (7)

[0035] Where: C land The land cost for the integrated photovoltaic storage and charging power station; Cpis The cost of infrastructure construction for integrated photovoltaic, storage and charging power stations; C pile Cost of purchasing charging pile; C ess Costs for configuring the energy storage portion of the solar-storage charging station;

[0036]

[0037] Where: The land cost of a single photovoltaic charging station, photovoltaic power generation, and energy storage; is a binary decision variable, indicating whether node i builds a photovoltaic charging station with energy storage. 0 means no photovoltaic charging station with energy storage is built, and 1 means a photovoltaic charging station with energy storage is built. Since it is an integrated configuration, the charging station, photovoltaics, and energy storage share the same decision variable. node is the number of distribution network nodes; They represent the infrastructure construction costs of a single photovoltaic charging station, photovoltaics, and energy storage, respectively; is the number distribution of charging piles, indicating the number of charging piles installed at the photovoltaic charging station at node i; The purchase price of the charging pile; and are the capacity cost coefficient and rated power cost coefficient of energy storage configuration respectively; and are the rated capacity and rated power of the energy storage configured at node i respectively; τ is the planned life conversion coefficient, which is related to time and discount rate, r is the discount rate, and n is the planned life of the equipment.

[0038] Furthermore, the comprehensive operating cost objective function is as follows:

[0039]

[0040] Where: Reduce the operation and maintenance costs of integrated photovoltaic, storage and charging power stations; C is the operation and maintenance cost of the energy storage part; loss is the line loss cost; C grid Cost of purchasing electricity from the main grid;

[0041] The specific calculation formula is as follows:

[0042]

[0043] Where: t0 is the start time of operation; Δt is the duration of operation; a is the labor cost ratio coefficient of charging station operation and maintenance; P EV,t is the total charging load of electric vehicles at time t; P grid,t is the power exchanged with the grid at time t; c ess,t P is the unit operation and maintenance cost of energy storage at time t; dch,t and P ch,tare the charging and discharging powers of the energy storage part of the photovoltaic charging station at time t; c loss,t is the unit network loss price at time t; I ij is the square of the branch current between node i and node j; r ij is the resistance of the branch between node i and node j; c grid,t P is the electricity price from the power grid at time t; grid,t is the exchange power between the distribution network and the main grid at time t.

[0044] Furthermore, the load loss cost objective function is as follows:

[0045]

[0046] Where: n ec is the annual average number of typhoon disasters in the regional distribution network; t0 is the time when the typhoon disaster starts; T ec is the duration of the typhoon disaster; c ec is the load loss value; is the importance weight determined according to the load importance; μ i,t is the load loss percentage at node i at time t; p load,i,t is the active power demand of the load at node i at time t.

[0047] Furthermore, the constraints are as follows:

[0048] First, the operation of the distribution network must meet the power flow constraints, as shown in the following formula:

[0049]

[0050] Where: p j ,q j are the active and reactive injected power of node j respectively; V j is the voltage at node j, V i is the voltage at node i, P jk , Q jk are the active and reactive power flowing from node j to the next node k; P ij , Q ij are the active and reactive power flowing from node i to node j respectively; I ij is the branch current between node i and node j; r ij 、x ij are the resistance and reactance of the branch between node i and node j respectively; g j , b j are the conductance and susceptance of node j to ground respectively;

[0051] In addition, each power flow in the distribution network must satisfy the power balance constraints as follows:

[0052]

[0053] Where: μ i is the load loss percentage of node i; p load,i ,q load,i are the active and reactive loads of distribution network node i respectively; p i ,q i are the active and reactive power injected into the distribution network node i respectively; P EV is the charging load of electric vehicles; P grid , Q grid are the interactive active and reactive power of the upper power grid respectively; P ch , P dch are the charging and discharging power of the energy storage part of the photovoltaic storage and charging integrated power station; P pv , Q pv They are respectively the active and reactive power of the photovoltaic power generation part of the integrated photovoltaic storage and charging power station; P wind , Q wind are respectively the active and reactive power of the wind turbine at time t; Ω N is the set of regional distribution network nodes;

[0054] Safe operation constraints:

[0055]

[0056] In the formula, I ij.max ,I ij.min is the upper and lower limits of the branch current between node i and node j; V j.max 、V j.min are the upper and lower limits of the node voltage at node j.

[0057] For the power purchase and sale variables between the distribution network and the upper power grid, the constraints of the upper and lower limits of interactive power need to be met as follows:

[0058]

[0059] Where P grid,t , Q grid,t are the interactive active and reactive power of the upper power grid respectively; P grid,max , P grid,min are the maximum and minimum values ​​of the interactive active power allowed to pass through the distribution network and the upper-level power grid connection branch; Q grid,max , Q grid,min They are respectively the maximum and minimum values ​​of reactive interaction power allowed to pass through the connecting branch between the distribution network and the upper-level power grid;

[0060] For the integrated photovoltaic, storage and charging power station, including the demand constraints and quantity constraints of charging stations, they are as follows:

[0061]

[0062] Where, T is 24 hours; P EV,t is the total charging load of electric vehicles in the area at time t; is the number distribution of charging piles, indicating the number of charging piles installed at the photovoltaic charging station at node i; T d is the maximum daily working time of the charging pile for safety reasons, P p The working power of the charging piles, that is, the sum of the maximum operating capacity of all charging piles should not be less than the total demand of electric vehicle users in the target area; The lower and upper limits of the number of charging piles that a single charging station can accommodate respectively;

[0063] The new energy sources connected to the distribution network include not only the wind power plants configured in the distribution network itself, but also the photovoltaic power generation part of the integrated photovoltaic storage and charging power station, all of which need to meet the output limit constraints:

[0064]

[0065] In the formula, The power generated by the photovoltaic installed at node j in period t, The power generated by the wind turbine power plant installed at node j in period t is subject to the upper and lower power limits. is a binary decision variable, indicating whether node i builds a solar-energy storage charging station. 0 means no solar-energy storage charging station is built, and 1 means a solar-energy storage charging station is built.

[0066] For the energy storage part, the constraints include energy storage rated power and capacity upper limit constraints, charge and discharge power constraints, charge state constraints, and energy storage power balance constraints, as follows:

[0067]

[0068] In the formula, is the rated power of the energy storage configured at point j; It is a configurable upper limit of energy storage rated power; is the capacity of energy storage configured at location j; is the upper limit of configurable energy storage capacity; is a binary decision variable, indicating whether node i builds a photovoltaic charging station with energy storage. 0 means no photovoltaic charging station with energy storage is built, and 1 means a photovoltaic charging station with energy storage is built. Due to the integrated configuration, the charging station and energy storage share the same decision variable. and are the charging and discharging power of the energy storage part of the photovoltaic charging station at point j, and its output is subject to the constraints of the charging and discharging mark and the upper and lower limits of power, Ω N is the node set of regional distribution network; S max and S minare the upper and lower limits of the state of charge, is the power value of the energy storage part of the photovoltaic charging station at node j at time t0; is the initial charge state of the energy storage part of the photovoltaic charging station at node j at time t0; is the remaining power of the energy storage part of the photovoltaic charging station at node j at time t, which is subject to the upper and lower limits of the state of charge and the rated capacity; η ch and η dch They are respectively the charging and discharging efficiency of energy storage in the photovoltaic charging station.

[0069] Beneficial effects of the present invention:

[0070] 1. The optimized configuration strategy of the integrated photovoltaic, energy storage and charging power station proposed in the present invention takes into account the efficient utilization of energy and emergency power supply of loads, comprehensively considers emergency conditions and normal conditions, and centralizes and integrates photovoltaic, energy storage and charging stations.

[0071] 2. Under the premise of fully considering the uncertainty of charging load, with the goal of optimizing the economic cost of the system, an optimization configuration model of an integrated photovoltaic storage and charging power station was constructed, which ultimately achieved the optimization of economic cost and ensured the power supply of important loads under fault conditions, thereby improving the reliability and economy of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0073] Figure 1 It is a flow chart of the optimal configuration strategy of the photovoltaic storage and charging integrated power station taking into account the efficient utilization of energy and emergency power supply of loads;

[0074] Figure 2 This is a schematic diagram of the site selection results for Plan 1;

[0075] Figure 3 This is a schematic diagram of the site selection results for Option 2;

[0076] Figure 4 This is a schematic diagram of the site selection results for Plan 3;

[0077] Figure 5 This is a schematic diagram of the site selection result of Scheme 4, i.e., the present invention;

[0078] Figure 6 It is a load loss cost diagram under four schemes provided for the embodiment of the present invention;

[0079] Figure 7 It is a comprehensive annualized construction cost diagram under four schemes provided for the embodiment of the present invention;

[0080] Figure 8 It is a comprehensive annualized operating cost diagram under four schemes provided for the embodiment of the present invention; DETAILED DESCRIPTION

[0081] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention;

[0082] The present invention provides a method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply of loads. The method comprises the following steps: Figure 1 As shown:

[0083] S1: Construct electric vehicle charging load model;

[0084] S2: A photovoltaic storage and charging integrated power station configuration model was constructed with the goal of optimizing the comprehensive construction cost, comprehensive operation cost and load loss cost;

[0085] S3: The Cplex commercial solver is used to solve the objective function of the configuration model of the integrated photovoltaic, storage and charging power station, and the site selection and capacity determination of the integrated photovoltaic, storage and charging power station are achieved based on this.

[0086] It should be noted that:

[0087] Constructing an electric vehicle charging load model includes the following steps:

[0088] (1) Analyze the starting charging time of electric vehicles

[0089] Taking the end time of the user's trip as the start time of charging the electric vehicle, the probability density function of the start time of charging the electric vehicle is obtained by fitting as follows:

[0090]

[0091] Among them, f s (t) represents the probability density function of the time when the electric vehicle starts charging; t s is the charging time at the start; s is the standard deviation, which indicates the degree of deviation between the actual departure and arrival time and the expected value; μ s is the expected value of the function;

[0092] (2) Analysis of the driving distance of electric vehicles

[0093] The probability density function of the daily driving distance of electric vehicles can be calculated as:

[0094]

[0095] Among them, f d (x) represents the probability density function of the daily driving distance of electric vehicles; x is the driving distance of electric vehicles; μ is the expected value of the driving distance; σ is the standard deviation, which represents the degree of deviation between the actual driving distance and the expected value;

[0096] (3) Analysis of charging time of electric vehicles

[0097] Calculate the time required to charge an electric vehicle. c It can be expressed by factors such as charging efficiency, state of charge and charging power:

[0098]

[0099] Among them, S n S is the initial charge state of the electric vehicle; s E is the expected state of charge of the electric vehicle; s is the battery capacity of the electric vehicle; η ev,ch is the charging efficiency of electric vehicles; P ev,ch Charging power for electric vehicles;

[0100] (4) Predicting electric vehicle charging load based on Monte Carlo simulation method

[0101] Initialize parameters such as the number of electric private cars and electric taxis, electric vehicle battery capacity, charging power and number of simulations; randomly select the starting charging time and starting state of charge of a single electric vehicle, calculate the charging time of the electric vehicle, and then obtain the end time of the electric vehicle charging; iteratively superimpose the charging load of a single electric vehicle to obtain the charging load of all electric vehicles in a day.

[0102]

[0103] in, N is the charging load of all electric vehicles at time t in the mth simulation; ev is the number of electric vehicles; P ev,t,n represents the charging power of the nth electric vehicle at time t;

[0104] The charging load obtained from each simulation is accumulated, and the total charging load is obtained at the end of the simulation. The average value of the electric vehicle charging load for each simulation is calculated as the charging load curve.

[0105]

[0106] Where M is the total number of simulations; P EV,t is the total charging load of electric vehicles in the substation at time t after the simulation ends.

[0107] In order to further implement the above technical solution, the photovoltaic storage and charging integrated power station configuration model in S2 includes the photovoltaic storage and charging integrated power station configuration model objective function and corresponding constraints. The photovoltaic storage and charging integrated power station configuration model objective function is as follows:

[0108] minF=F CON +F RUN +F EC (6)

[0109] Among them, F CON represents the comprehensive construction cost, F RUN represents the comprehensive operating cost, F EC Represents the loss of load cost.

[0110] It should be noted that:

[0111] The comprehensive construction cost refers to the comprehensive cost of various construction contents during the cycle, including land cost, infrastructure construction cost, charging pile purchase cost and energy storage configuration cost. The objective function is as follows:

[0112] f CON =C land +C pis +C pile +C ess (7)

[0113] Among them, C land The land cost for the integrated photovoltaic storage and charging power station; C pis The cost of infrastructure construction for integrated photovoltaic, storage and charging power stations; C pile Cost of purchasing charging pile; C ess Costs for configuring the energy storage portion of the solar-storage charging station;

[0114]

[0115] in, The land cost of a single photovoltaic charging station, photovoltaic power generation, and energy storage; is a binary decision variable, indicating whether node i builds a photovoltaic charging station with energy storage. 0 means no photovoltaic charging station with energy storage is built, and 1 means a photovoltaic charging station with energy storage is built. Since it is an integrated configuration, the charging station, photovoltaics, and energy storage share the same decision variable. node is the number of distribution network nodes; They represent the infrastructure construction costs of a single photovoltaic charging station, photovoltaics, and energy storage, respectively; is the number distribution of charging piles, indicating the number of charging piles installed at the photovoltaic charging station at node i; The purchase price of the charging pile; and are the capacity cost coefficient and rated power cost coefficient of energy storage configuration respectively; and are the rated capacity and rated power of the energy storage configured at node i respectively; τ is the planned life conversion coefficient, which is related to time and discount rate, r is the discount rate, and n is the planned life of the equipment. Since the photovoltaic and energy storage charging station is an integrated configuration, photovoltaic and energy storage can be built inside the charging station, so the land cost here only needs to calculate the charging station part.

[0116] It should be noted that:

[0117] The comprehensive operating cost includes the operation and maintenance cost of the integrated photovoltaic power station, the energy storage operation and maintenance cost, the line loss cost and the power purchase cost. The objective function is as follows:

[0118]

[0119] in, Reduce the operation and maintenance costs of integrated photovoltaic, storage and charging power stations; C is the operation and maintenance cost of the energy storage part; loss is the line loss cost; C grid Cost of purchasing electricity from the main grid;

[0120] The specific calculation formula is as follows:

[0121]

[0122] Where: t0 is the start time of operation; Δt is the duration of operation; a is the labor cost ratio coefficient of charging station operation and maintenance; P EV,t is the total charging load of electric vehicles at time t; P grid,t is the power exchanged with the grid at time t; c ess,t P is the unit operation and maintenance cost of energy storage at time t; dch,t and P ch,t are the charging and discharging powers of the energy storage part of the photovoltaic charging station at time t; c loss,t is the unit network loss price at time t; I ij is the square of the branch current between node i and node j; r ij is the resistance of the branch between node i and node j; c grid,t P is the electricity price from the power grid at time t; grid,t is the exchange power between the distribution network and the main grid at time t.

[0123] It should be noted that:

[0124] The load loss cost refers to the economic loss caused by insufficient power supply and failure to meet the load. The goal is to minimize the load loss cost. The objective function is as follows:

[0125]

[0126] Among them, n ec is the annual average number of typhoon disasters in the regional distribution network; t0 is the time when the typhoon disaster starts; T ec is the duration of the typhoon disaster; c ec is the load loss value; is the importance weight determined according to the load importance; μ i,t is the load loss percentage at node i at time t; p load,i,t is the active power demand of the load at node i at time t.

[0127] In order to further implement the above technical solution, the constraints of S2 are as follows:

[0128] First, the operation of the distribution network must meet the power flow constraints, as shown in the following formula:

[0129]

[0130] Among them, p j ,q j are the active and reactive injected power of node j respectively; V j is the voltage at node j, V i is the voltage at node i, P jk , Q jk are the active and reactive power flowing from node j to the next node k; P ij , Q ij are the active and reactive power flowing from node i to node j respectively; I ij is the branch current between node i and node j; r ij 、x ij are the resistance and reactance of the branch between node i and node j respectively; g j , b j are the conductance and susceptance of node j to ground respectively;

[0131] In addition, each power flow in the distribution network must satisfy the power balance constraints as follows:

[0132]

[0133] Among them, μ i is the load loss percentage of node i; p load,i ,q load,i are the active and reactive loads of distribution network node i respectively; p i ,qi are the active and reactive power injected into the distribution network node i respectively; P EV is the charging load of electric vehicles; P grid , Q grid are the interactive active and reactive power of the upper power grid respectively; P ch , P dch are the charging and discharging power of the energy storage part of the photovoltaic storage and charging integrated power station; P pv , Q pv They are respectively the active and reactive power of the photovoltaic power generation part of the integrated photovoltaic storage and charging power station; P wind , Q wind are respectively the active and reactive power of the wind turbine at time t; Ω N is the set of regional distribution network nodes;

[0134] Safe operation constraints:

[0135]

[0136] Among them, I ij.max ,I ij.min is the upper and lower limits of the branch current between node i and node j; V j.max 、V j.min are the upper and lower limits of the node voltage at node j.

[0137] For the power purchase and sale variables between the distribution network and the upper power grid, the constraints of the upper and lower limits of interactive power need to be met as follows:

[0138]

[0139] Among them, P grid,t , Q grid,t are the interactive active and reactive power of the upper power grid respectively; P grid,max , P grid,min are the maximum and minimum values ​​of the interactive active power allowed to pass through the distribution network and the upper-level power grid connection branch; Q grid,max , Q grid,min They are respectively the maximum and minimum values ​​of reactive interaction power allowed to pass through the connecting branch between the distribution network and the upper-level power grid;

[0140] For the integrated photovoltaic, storage and charging power station, including the demand constraints and quantity constraints of charging stations, they are as follows:

[0141]

[0142] Where, T is 24 hours; P EV,t is the total charging load of electric vehicles in the area at time t; is the number distribution of charging piles, indicating the number of charging piles installed at the photovoltaic charging station at node i; T dis the maximum daily working time of the charging pile for safety reasons, P p The working power of the charging piles, that is, the sum of the maximum operating capacity of all charging piles should not be less than the total demand of electric vehicle users in the target area; The lower and upper limits of the number of charging piles that a single charging station can accommodate respectively;

[0143] The new energy sources connected to the distribution network include not only the wind power plants configured in the distribution network itself, but also the photovoltaic power generation part of the integrated photovoltaic storage and charging power station, all of which need to meet the output limit constraints:

[0144]

[0145] in, The power generated by the photovoltaic installed at node j in period t, The power generated by the wind turbine power plant installed at node j in period t is subject to the upper and lower power limits. It is a binary decision variable, indicating whether a photovoltaic-storage charging station is built at node i. 0 means no photovoltaic-storage charging station is built, and 1 means a photovoltaic-storage charging station is built. Since it is an integrated photovoltaic-storage-charging configuration, the charging station and photovoltaic share the same decision variable, while the wind turbine is installed at a fixed node.

[0146] For the energy storage part, the constraints include energy storage rated power and capacity upper limit constraints, charge and discharge power constraints, charge state constraints, and energy storage power balance constraints, as follows:

[0147]

[0148] in, is the rated power of the energy storage configured at point j; It is a configurable upper limit of energy storage rated power; is the capacity of energy storage configured at location j; is the upper limit of configurable energy storage capacity; is a binary decision variable, indicating whether node i builds a photovoltaic charging station with energy storage. 0 means no photovoltaic charging station with energy storage is built, and 1 means a photovoltaic charging station with energy storage is built. Due to the integrated configuration, the charging station and energy storage share the same decision variable. and are the charging and discharging power of the energy storage part of the photovoltaic charging station at point j, and its output is subject to the constraints of the charging and discharging mark and the upper and lower limits of power, Ω N is the node set of regional distribution network; S max and S min are the upper and lower limits of the state of charge, is the power value of the energy storage part of the photovoltaic charging station at node j at time t0; is the initial charge state of the energy storage part of the photovoltaic charging station at node j at time t0; is the remaining power of the energy storage part of the photovoltaic charging station at node j at time t, which is subject to the upper and lower limits of the state of charge and the rated capacity; η ch and η dch They are respectively the charging and discharging efficiency of energy storage in the photovoltaic charging station.

[0149] The Cplex commercial solver is used to solve the objective function of the photovoltaic, storage and charging integrated power station configuration model, and the optimal configuration strategy of the photovoltaic, storage and charging integrated power station taking into account the efficient utilization of energy and emergency power supply of loads is obtained. Based on this, the site selection and capacity determination of the photovoltaic, storage and charging integrated power station are realized.

[0150] In order to enable those skilled in the art to better understand the present invention, the example analysis includes the following components:

[0151] 1. Case Description

[0152] In order to verify the effectiveness and superiority of the method involved in the present invention, this embodiment uses the improved IEEE33-node distribution network as an example for case analysis, and performs site selection and capacity determination of the integrated photovoltaic storage and charging power station according to the method involved in the present invention. The rated voltage of the IEEE33-node distribution network is 12.66kV, the rated power is 1MVA, the important loads are nodes 8, 13, 24, 25, and 30, and wind turbines are installed in nodes 4, 13, 26, and 31. The present invention assumes that the number of EVs under the jurisdiction of the charging station in the substation is 1,000. Set the fault type to typhoon, the fault time period to 14:00-19:00, and the fault lines to 13-16 and 31-33. During the fault period, electric vehicles stop charging, and wind turbines and photovoltaics stop outputting power. The remaining parameters are shown in Table 1:

[0153] Table 1 Example parameter settings

[0154]

[0155]

[0156] 2. Case Comparative Analysis

[0157] Set up the following four schemes for comparative analysis:

[0158] Plan 1: A blank control plan that does not take emergency conditions into consideration and does not configure a charging station site selection and sizing model for photovoltaic, energy storage and other facilities.

[0159] Option 2: Decentralized configuration of photovoltaic, energy storage and other facilities, but without considering the charging station site selection and sizing model for emergency conditions.

[0160] Option 3: Distributed configuration of photovoltaic, energy storage and other facilities, and a charging station site selection and sizing model that comprehensively considers emergency conditions and normal conditions

[0161] Option 4: Comprehensively consider emergency conditions and normal conditions, and integrate photovoltaics, energy storage and charging stations, that is, the site selection and sizing model of the integrated photovoltaic, energy storage and charging station.

[0162] (1) Verify the site selection and capacity determination results

[0163] Table 2 Site selection and capacity determination results of different schemes

[0164]

[0165]

[0166] The topological diagrams of the site selection results of the four schemes are as follows: Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown. In Scheme 1, the charging stations are located at nodes 12, 13, and 31, and photovoltaic and energy storage are not configured; in Scheme 2, the charging stations are located at nodes 12, 13, and 31, photovoltaics are located at nodes 2 and 21, and energy storage is configured at node 2, with a total energy storage configuration cost of 254,700 yuan; in Scheme 3, the charging stations are located at nodes 5, 13, and 26, photovoltaics are located at nodes 2 and 21, and energy storage configuration is added to take emergency conditions into consideration, and energy storage is configured at nodes 2 and 25, with a total energy storage configuration cost of 398,100 yuan; Scheme 4 is an integrated configuration, so photovoltaics, energy storage, and charging stations are all located at nodes 2, 21, and 25, and the energy storage configuration capacity is slightly lower than that of Scheme 3, at 394,200 yuan. Combined with Figure 6 Compared with the scheme without taking any measures, the important load nodes 8, 13, 24, 25, and 30 can be supported and restored to power supply after using energy storage, which verifies the correctness of the proposed scheme in supporting the power supply model of important load points under emergency conditions.

[0167] (2) Load loss analysis

[0168] The present invention reflects the importance of load by setting the loss of load cost. Figure 6 This is a comparison chart of load loss under four groups of schemes. Figure 6It can be seen that scheme 1 and scheme 2 do not consider emergency conditions, and the load loss cost is extremely high; scheme 3 considers emergency conditions and uses energy storage to support the power supply of important loads. Compared with scheme 2, the load loss cost of important loads is reduced by 99.4%, and the total load loss cost is reduced by 91.3%; the scheme of the present invention adds the constraint condition of centralized integrated configuration of photovoltaic, energy storage and charging stations, so there are some restrictions on the location of energy storage, so the load loss cost is slightly increased by 0.76% compared with scheme 3, but energy storage also plays a role in supporting important loads in the emergency process. Compared with scheme 2, the load loss cost of important loads is reduced by 99.4%, and the total load loss cost is reduced by 91.2%. It shows that the scheme of the present invention also plays a role in ensuring the power supply of important loads under fault conditions and improving the reliability of power supply.

[0169] (3) Economic cost analysis

[0170] Table 3 Economic costs of different schemes (10,000 yuan)

[0171] plan Comprehensive construction cost Comprehensive operating costs Lost load cost Total Cost Solution 1 86.37 150.16 3754.48 3991.01 Solution 2 147.49 67.44 3680.61 3895.54 Solution 3 173.02 62.30 321.97 557.29 Solution 4 141.07 58.41 324.43 523.91

[0172] Combined with Table 3 Figure 7 , Figure 8 It can be seen that compared with the blank control group Scheme 1, Scheme 2 is equipped with photovoltaics and energy storage. Although it increases the construction cost, the photovoltaic power generation and the peak-shaving and valley-filling effect of energy storage charging and discharging greatly reduce the cost of purchasing electricity, reducing the comprehensive operating cost by 55.1% and the total cost by 2.5%.

[0173] Compared with Scheme 2, Scheme 3 takes emergency conditions into consideration, which increases the configuration capacity of energy storage. Although this increases the overall construction cost by 17.3%, energy storage supports the power supply of important loads under fault conditions, reducing the load loss cost by 91.3%, and greatly improving the power supply reliability. Compared with Scheme 2, Scheme 3 reduces the total cost by 85.7%, greatly reducing the economic cost. Scheme 4, which also takes emergency conditions into consideration, reduces the load loss cost by 91.2% and the total cost by 86.6% compared with Scheme 2. The above results verify the necessity of considering emergency conditions.

[0174] The integrated configuration scheme 4 is compared with the decentralized configuration scheme 3. Figure 7 As shown in Figure 4, due to the saving of land area and sharing of some basic equipment, the comprehensive construction cost of Scheme 4 was reduced by 18.5%. Figure 8As shown in the figure, the comprehensive operating cost of Scheme 4 is reduced by 6.2% compared with Scheme 3, because the integrated configuration of Scheme 4 reduces line loss and electricity purchase cost; in terms of load loss cost, Scheme 4 slightly increases by 0.76% compared with Scheme 3, but it also guarantees the power supply of important loads under fault conditions; in general, the total cost of Scheme 4 is reduced by 5.99% compared with Scheme 3, that is, 333,800 yuan. This shows that compared with the decentralized configuration strategy, the strategy of the present invention can further reduce the comprehensive cost of the system while ensuring the reliability of system power supply, and has better economy.

[0175] The above is only for explaining the implementation mode of the present invention and is not intended to limit the present invention. For those skilled in the art, any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention without creative work should be included in the protection scope of the present invention.

Claims

1. An optimization configuration method for an integrated photovoltaic storage and charging power station taking into account efficient energy utilization and load emergency power supply, characterized in that: The following steps are involved: S1: Construct electric vehicle charging load model; S2: A photovoltaic storage and charging integrated power station configuration model was constructed with the goal of optimizing the comprehensive construction cost, comprehensive operation cost and load loss cost; S3: The Cplex commercial solver is used to solve the objective function of the configuration model of the integrated photovoltaic, storage and charging power station, and the site selection and capacity determination of the integrated photovoltaic, storage and charging power station are achieved based on this.

2. The method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads according to claim 1 is characterized in that: The construction of the electric vehicle charging load model comprises the following steps: (1) Analyze the starting charging time of electric vehicles Taking the end time of the user's trip as the start time of charging the electric vehicle, the probability density function of the start time of charging the electric vehicle is obtained by fitting as follows: In the formula, f s (t) represents the probability density function of the time when the electric vehicle starts charging; t s is the charging time at the start; s is the standard deviation, which indicates the degree of deviation between the actual departure and arrival time and the expected value; μ s is the expected value of the function; (2) Analysis of the driving distance of electric vehicles The probability density function of the daily driving distance of electric vehicles is: In the formula, f d (x) represents the probability density function of the daily driving distance of electric vehicles; x is the driving distance of electric vehicles; μ is the expected value of the driving distance; σ is the standard deviation, which represents the degree of deviation between the actual driving distance and the expected value; (3) Analysis of charging time of electric vehicles The time required to charge an electric vehicle is T c It can be expressed by charging efficiency, state of charge and charging power: In the formula, S n S is the initial charge state of the electric vehicle; s E is the expected state of charge of the electric vehicle; s is the battery capacity of the electric vehicle; η ev,ch is the charging efficiency of electric vehicles; P ev,ch Charging power for electric vehicles; (4) Predicting electric vehicle charging load based on Monte Carlo simulation method Initialize the number of electric private cars and electric taxis, electric vehicle battery capacity, charging power, simulation times and other parameters; randomly select the starting charging time and starting state of charge of a single electric vehicle, calculate the charging time of the electric vehicle, and then obtain the end time of the electric vehicle charging; iteratively superimpose the charging load of a single electric vehicle to obtain the charging load of all electric vehicles in a day; Where: N is the charging load of all electric vehicles at time t in the mth simulation; ev is the number of electric vehicles; P ev,t,n represents the charging power of the nth electric vehicle at time t; The charging load obtained from each simulation is accumulated, and the total charging load is obtained at the end of the simulation. The average value of the electric vehicle charging load for each simulation is calculated as the charging load curve; Where: M is the total number of simulations; P EV,t is the total charging load of electric vehicles in the substation at time t after the simulation ends.

3. The method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads according to claim 1 is characterized in that: The photovoltaic storage and charging integrated power station configuration model includes the photovoltaic storage and charging integrated power station configuration model objective function and corresponding constraints. The photovoltaic storage and charging integrated power station configuration model objective function is as follows: minF=F CON +F RUN +F EC (6) Where: F CON represents the comprehensive construction cost, F RUN represents the comprehensive operating cost, F EC Represents the loss of load cost.

4. The method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads according to claim 3 is characterized in that: The comprehensive construction cost objective function is as follows: minF CON =C land +C pis +C pile +C ess (7) Where: C land The land cost for the integrated photovoltaic storage and charging power station; C pis The cost of infrastructure construction for integrated photovoltaic, storage and charging power stations; C pile Cost of purchasing charging pile; C ess Costs for configuring the energy storage portion of the solar-storage charging station; Where: The land cost of a single photovoltaic charging station, photovoltaic power generation, and energy storage; is a binary decision variable, indicating whether node i builds a photovoltaic charging station with energy storage. 0 means no photovoltaic charging station with energy storage is built, and 1 means a photovoltaic charging station with energy storage is built. Since it is an integrated configuration, the charging station, photovoltaics, and energy storage share the same decision variable. node is the number of distribution network nodes; They represent the infrastructure construction costs of a single photovoltaic charging station, photovoltaics, and energy storage, respectively; is the number distribution of charging piles, indicating the number of charging piles installed at the photovoltaic charging station at node i; The purchase price of the charging pile; and are the capacity cost coefficient and rated power cost coefficient of energy storage configuration respectively; and are the rated capacity and rated power of the energy storage configured at node i respectively; τ is the planned life conversion coefficient, which is related to time and discount rate, r is the discount rate, and n is the planned life of the equipment.

5. The method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads according to claim 3 is characterized in that: The comprehensive operating cost objective function is as follows: Where: Reduce the operation and maintenance costs of integrated photovoltaic, storage and charging power stations; C is the operation and maintenance cost of the energy storage part; loss is the line loss cost; C grid Cost of purchasing electricity from the main grid; The specific calculation formula is as follows: Where: t0 is the start time of operation; Δt is the duration of operation; a is the labor cost ratio coefficient of charging station operation and maintenance; P EV,t is the total charging load of electric vehicles at time t; P grid,t is the power exchanged with the grid at time t; c ess,t P is the unit operation and maintenance cost of energy storage at time t; dch,t and P ch,t are the charging and discharging powers of the energy storage part of the photovoltaic charging station at time t; c loss,t is the unit network loss price at time t; I ij is the square of the branch current between node i and node j; r ij is the resistance of the branch between node i and node j; c grid,t P is the electricity price from the power grid at time t; grid,t is the exchange power between the distribution network and the main grid at time t.

6. The method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads according to claim 3 is characterized in that: The load loss cost objective function is as follows: Where: n ec is the annual average number of typhoon disasters in the regional distribution network; t0 is the time when the typhoon disaster starts; T ec The duration of the typhoon disaster; c ec is the load loss value; is the importance weight determined according to the load importance; μ i,t is the load loss percentage at node i at time t; p load,i,t is the active power demand of the load at node i at time t.

7. The method for optimizing the configuration of a photovoltaic storage and charging integrated power station taking into account efficient energy utilization and emergency power supply for loads according to claim 3 is characterized in that: The constraints are as follows: First, the operation of the distribution network must meet the power flow constraints, as shown in the following formula: Where: p j ,q j are the active and reactive injected power of node j respectively; V j is the voltage at node j, V i is the voltage at node i, P jk , Q jk are the active and reactive power flowing from node j to the next node k; P ij , Q ij are the active and reactive power flowing from node i to node j respectively; I ij is the branch current between node i and node j; r ij 、x ij are the resistance and reactance of the branch between node i and node j respectively; g j 、b j are the conductance and susceptance of node j to ground respectively; In addition, each power flow in the distribution network must satisfy the power balance constraints as follows: Where: μ i is the load loss percentage of node i; p load,i ,q load,i are the active and reactive loads of distribution network node i respectively; p i ,q i are the active and reactive power injected into the distribution network node i respectively; P EV is the charging load of electric vehicles; P grid , Q grid are the interactive active and reactive power of the upper power grid respectively; P ch , P dch are the charging and discharging power of the energy storage part of the photovoltaic storage and charging integrated power station; P pv , Q pv They are respectively the active and reactive power of the photovoltaic power generation part of the integrated photovoltaic storage and charging power station; P wind , Q wind are the active and reactive power of the wind turbine at time t; Ω N is the set of regional distribution network nodes; Safe operation constraints: In the formula, I ij.max ,I ij.min are the upper and lower limits of the branch current between node i and node j; V j.max 、V j.min are the upper and lower limits of the node voltage of node j; For the power purchase and sale variables between the distribution network and the upper power grid, the constraints of the upper and lower limits of interactive power need to be met, as follows: Where P grid,t , Q grid,t are the interactive active and reactive power of the upper power grid respectively; P grid,max , P grid,min are the maximum and minimum values ​​of the interactive active power allowed to pass through the distribution network and the upper-level power grid connection branch; Q grid,max , Q grid,min They are respectively the maximum and minimum values ​​of reactive interaction power allowed to pass through the connecting branch between the distribution network and the upper-level power grid; For the integrated photovoltaic, storage and charging power station, including the demand constraints and quantity constraints of charging stations, they are as follows: Where, T is 24 hours; P EV,t is the total charging load of electric vehicles in the area at time t; is the number distribution of charging piles, indicating the number of charging piles installed at the photovoltaic charging station at node i; T d is the maximum daily working time of the charging pile for safety reasons, P p The working power of the charging piles, that is, the sum of the maximum operating capacity of all charging piles should not be less than the total demand of electric vehicle users in the target area; and The lower and upper limits of the number of charging piles that a single charging station can accommodate respectively; The new energy sources connected to the distribution network include not only the wind power plants configured in the distribution network itself, but also the photovoltaic power generation part of the integrated photovoltaic storage and charging power station, all of which need to meet the output limit constraints: In the formula, The power generated by the photovoltaic installed at node j in period t, The power generated by the wind turbine power station installed at node j in period t, is a binary decision variable, indicating whether node i builds a solar-energy storage charging station. 0 means no solar-energy storage charging station is built, and 1 means a solar-energy storage charging station is built. For the energy storage part, the constraints include the following: In the formula, is the rated power of the energy storage configured at point j; It is a configurable upper limit of energy storage rated power; is the capacity of energy storage configured at location j; is the upper limit of configurable energy storage capacity; is a binary decision variable, indicating whether node i builds a photovoltaic charging station with energy storage. 0 means no photovoltaic charging station with energy storage is built, and 1 means a photovoltaic charging station with energy storage is built. Due to the integrated configuration, the charging station and energy storage share the same decision variable. and are the charging and discharging power of the energy storage part of the photovoltaic charging station at point j, and its output is subject to the constraints of the charging and discharging mark and the upper and lower limits of power, Ω N is the node set of regional distribution network; S max and S min are the upper and lower limits of the state of charge, is the power value of the energy storage part of the photovoltaic charging station at node j at time t0; is the initial charge state of the energy storage part of the photovoltaic charging station at node j at time t0; is the remaining power of the energy storage part of the photovoltaic charging station at node j at time t, which is subject to the upper and lower limits of the state of charge and the rated capacity; η ch and η dch They are respectively the charging and discharging efficiency of energy storage in the photovoltaic charging station.

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