Optimal configuration method of integrated photovoltaic energy storage and charging power station considering energy efficient utilization and load emergency power supply

Abstract

This invention provides an optimized configuration method for integrated photovoltaic-storage-charging power stations that considers both efficient energy utilization and emergency load power supply. First, an electric vehicle load model is established, comprehensively considering factors such as the start time of electric vehicle charging, the required driving distance for charging, and the charging duration. Then, a centralized integrated configuration of photovoltaic, energy storage, and charging stations is implemented, taking into account both emergency and normal operating conditions. This model aims to optimize the overall construction cost, overall operating cost, and loss-of-load cost of the integrated photovoltaic-storage-charging power station. Finally, by solving the optimization model, the number, location, and energy storage capacity of the integrated photovoltaic-storage-charging power stations are determined. The calculation results demonstrate that this invention achieves integrated configuration of photovoltaic, energy storage, and charging, comprehensively considering both normal and emergency operating conditions. While ensuring power supply to critical loads during fault conditions, it also achieves efficient photovoltaic utilization under normal operating conditions, improving system reliability and economy.

Description

Technical Field

[0001] This invention belongs to the field of emergency power supply technology, specifically relating to an optimized configuration method for an integrated photovoltaic-storage-charging power station that considers both efficient energy utilization and emergency load power supply. Background Technology

[0002] In recent years, photovoltaics, energy storage, and electric vehicles and their charging stations have rapidly developed in power distribution networks as important components of new energy development. Accelerating research on integrated photovoltaic-energy storage-charging technologies has made the configuration strategies for these technologies a key research focus. Therefore, to meet higher and more comprehensive planning requirements, the planning and configuration of photovoltaic-energy storage-charging systems is crucial for the stable and economical operation of the power system.

[0003] Currently, photovoltaic (PV), energy storage, and charging (ESC) planning is mostly decentralized. Most studies consider factors such as system energy efficiency, operating costs, and grid stability when planning the site selection and capacity of PV, energy storage, and charging stations. However, decentralized deployments involve high capital investment in construction and operation and maintenance, and the dispatching system is complex and difficult to manage. In contrast, integrated PV-ESC-charging strategies integrate these resources, addressing these issues. Currently, integrated PV-ESC-charging strategies are generally based on the normal operating conditions of the distribution network, considering the construction and operation and maintenance costs of integrated power plants to improve system operational economy. However, in the face of emergencies such as disasters, this strategy neglects the potential of each resource to support critical loads during fault periods, resulting in significant economic losses from load shedding in the distribution network. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides an optimized configuration method for an integrated photovoltaic, energy storage, and charging power station that considers efficient energy utilization and emergency load power supply. This method integrates photovoltaic, energy storage, and charging station configurations, comprehensively considering both emergency and normal operating conditions. An optimized configuration model for the integrated photovoltaic, energy storage, and charging power station is constructed with the goal of achieving the optimal balance between overall construction cost, overall operating cost, and load failure cost. This fully ensures reliable power supply to critical loads under fault conditions and further reduces the system's operating costs.

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

[0006] A method for optimizing the configuration of integrated photovoltaic-storage-charging power stations that considers energy-efficient utilization and emergency power supply is characterized by the following steps:

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

[0008] S2: A configuration model for an integrated photovoltaic, energy storage, and charging power station was constructed with the goal of optimizing the overall construction cost, overall operating cost, and off-load cost.

[0009] S3: The objective function of the configuration model of the photovoltaic-storage-charging integrated power station is solved using the Cplex commercial solver, thereby realizing the site selection and capacity determination of the photovoltaic-storage-charging integrated power station.

[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] Using the user's trip end time as the electric vehicle's starting charging time, the probability density function of the electric vehicle's starting charging time is obtained through fitting as follows:

[0013]

[0014] In the formula, f s (t) represents the probability density function at the start of charging of the electric vehicle; t s The charging time at the beginning; σ s The standard deviation (μ) represents the degree of deviation between the actual departure and arrival times and the expected values. s The expected value of the function;

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

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

[0017]

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

[0019] (3) Analyze the charging time of electric vehicles

[0020] Electric vehicle charging time T c This can be expressed using factors such as charging efficiency, state of charge, and charging power.

[0021]

[0022] In the formula, S n This represents the initial state of charge (S) of the electric vehicle. s E represents the desired state of charge for electric vehicles. s For the battery capacity of electric vehicles; η ev,ch For electric vehicle charging efficiency; P ev,ch The charging power for electric vehicles;

[0023] 4) Predict electric vehicle charging load based on Monte Carlo simulation.

[0024] 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 charging end time of the electric vehicle; iteratively superimpose the charging load of a single electric vehicle to obtain the charging load of all electric vehicles in a day;

[0025]

[0026] In the formula: For the m-th simulation at time t, N represents the charging load of all electric vehicles. ev P represents the number of electric vehicles. ev,t,n Let represent the charging power of the nth electric vehicle at time t.

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

[0028]

[0029] In the formula: M is the total number of simulations; P EV,t The total charging load of electric vehicles in the transformer area at time t after the simulation ends.

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

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

[0032] In the formula: F CON F represents the total construction cost. RUN F represents the overall operating cost. EC This represents the cost of load loss.

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

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

[0035] In the formula: C land C. Land use costs for integrated photovoltaic, energy storage, and charging power stations;pis C. Construction costs for integrated photovoltaic, energy storage, and charging power station infrastructure; pile C. Cost of purchasing charging stations; ess Cost of configuring the energy storage component of the photovoltaic-energy storage charging station;

[0036]

[0037] In the formula: Land costs for individual photovoltaic and energy storage charging stations, photovoltaic systems, and energy storage systems, respectively; This is a binary decision variable representing whether node i should build a photovoltaic-storage-charging station; 0 indicates no construction, and 1 indicates construction. Since it's an integrated configuration, the charging station, photovoltaic system, and energy storage share the same decision variable; N node This refers to the number of distribution network nodes. These represent the infrastructure construction costs for a single photovoltaic-energy storage charging station, photovoltaic system, and energy storage system, respectively. The number of charging piles is represented by the number of charging piles installed at the photovoltaic-storage charging station at node i. This refers to the unit price of the charging pile. and These are the capacity cost factor and rated power cost factor for energy storage configurations, respectively. and These are the rated capacity and rated power of the energy storage configured at node i, respectively; τ is the planning period conversion factor, which is related to time and discount rate; r is the discount rate; and n is the equipment planning period.

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

[0039]

[0040] In the formula: To reduce the operation and maintenance costs of integrated photovoltaic, energy storage, and charging power stations; C is the operation and maintenance cost of the energy storage component; loss For line loss cost; C grid Cost of purchasing electricity from the main grid;

[0041] The specific calculation formula is as follows:

[0042]

[0043] In the formula: t0 is the start time of operation; Δt is the duration of operation; a is the proportional coefficient of labor cost for charging station operation and maintenance; P EV,t P represents the total charging load of electric vehicles at time t. grid,t c represents the power exchanged with the power grid at time t; ess,t P represents the unit operation and maintenance cost of energy storage at time t. dch,t and P ch,tc represents the charging and discharging power of the energy storage section of the photovoltaic-energy storage charging station at time t, respectively; loss,t I represents the unit network loss price at time t; ij r is the square of the branch current between node i and node j; ij c is the resistance of the branch between node i and node j; grid,t Let P be the purchase and sale price of electricity from the grid at time t; grid,t Let t be the power exchanged between the distribution network and the main network.

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

[0045]

[0046] Where: n ec The average number of typhoon disasters per year for the regional power distribution network; t0 is the start time of the typhoon disaster; T ec Duration of typhoon disaster; c ec Value of load loss; Importance weights determined based on load importance; μ i,t p represents the percentage of load loss at node i at time t; load,i,t Let be 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 power flow constraints, as shown in the following equation:

[0049]

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

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

[0052]

[0053] Where: μ i p represents the percentage of load loss at node i; load,i q load,i These represent the active and reactive loads at node i in the distribution network; p i q i P injects active and reactive power into distribution network node i, respectively; EV For the charging load of electric vehicles; P grid Q grid These represent the active and reactive power exchanged with the upstream power grid, respectively; P ch P dch These represent the charging and discharging power of the energy storage section of the integrated photovoltaic-energy storage-charging power station; P pv Q pv These represent the active and reactive power of the photovoltaic power generation portion of the integrated photovoltaic-storage-charging power station; P wind Q wind These represent the active and reactive power of the wind turbine at time t; Ω N For the regional distribution network node set;

[0054] Safe operation constraints:

[0055]

[0056] In the formula, I ij.max I ij.min V represents the upper and lower limits of the branch current between node i and node j; j.max V j.min Let be 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 upstream power grid, the following constraints on the upper and lower limits of the interactive power must be met:

[0058]

[0059] In the formula, P grid,t Q grid,t These represent the active and reactive power exchanged with the upstream power grid, respectively; P grid,max P grid,min These represent the maximum and minimum values ​​of the interactive active power allowed to pass through the interconnection branch between the distribution network and the upstream power grid, respectively; Q grid,max Q grid,min These are the maximum and minimum values ​​of reactive power exchange allowed to pass through the interconnection branch between the distribution network and the upper-level power grid, respectively.

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

[0061]

[0062] In the formula, T represents 24 hours; P EV,t Let t be the total charging load of electric vehicles in the transformer area at time t; Let T represent the number of charging piles distributed at node i, indicating the number of charging piles installed at the photovoltaic-storage charging station. d For safety reasons, the maximum daily operating time for charging stations is P p The operating power of the charging piles, that is, the sum of the maximum operating capacities of all charging piles, shall 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;

[0063] For new energy sources connected to the distribution network, this includes not only wind power plants configured within the distribution network area itself, but also the photovoltaic power generation portion of integrated photovoltaic-storage-charging power stations, all of which need to meet the output limit constraints:

[0064]

[0065] In the formula, The photovoltaic power generated by the photovoltaic system installed at node j in time period t is The power generation of the wind turbine power station installed at node j in time period t is subject to upper and lower power limits. The binary decision variable indicates whether node i should build a photovoltaic-storage-charging station; 0 indicates that the photovoltaic-storage-charging station should not be built, and 1 indicates that the photovoltaic-storage-charging station should be built.

[0066] For the energy storage component, the constraints include upper limits on rated power and capacity, charging and discharging power, state of charge, and energy storage balance, as follows:

[0067]

[0068] In the formula, The rated power for energy storage at point j; The upper limit of the configurable rated power of energy storage; Let j be the capacity of the energy storage at point j; This represents the maximum configurable energy storage capacity. The binary decision variable indicates whether node i should build a photovoltaic-storage-charging station. 0 indicates that the photovoltaic-storage-charging station should not be built, and 1 indicates that the photovoltaic-storage-charging station should be built. Since it is an integrated configuration, the charging station and energy storage share the same decision variable. and Ω represents the charging and discharging power of the energy storage section of the photovoltaic-energy storage charging station at point j. Its output is constrained by the charging and discharging indicator and the upper and lower power limits. N For the set of nodes in the regional distribution network; S max and S minThese are the upper and lower limits of the state of charge, respectively. The energy storage capacity of the photovoltaic-storage charging station at node j at time t0 is the energy value of the energy storage component. The initial state of charge of the energy storage section of the photovoltaic-energy storage charging station at node j at time t0; Let η be the remaining energy stored in the photovoltaic-storage charging station at node j at time t, which is constrained by the upper and lower limits of the state of charge and the rated capacity; ch and η dch These represent the charging and discharging efficiencies of energy storage in photovoltaic-energy storage charging stations.

[0069] The beneficial effects of this invention are:

[0070] 1. The photovoltaic-storage-charging integrated power station optimization configuration strategy proposed in this invention takes into account both emergency and normal operating conditions, and centralizes and integrates photovoltaic, energy storage and charging station configuration.

[0071] 2. Taking into full account the uncertainty of charging load, and with the goal of optimizing the system's economic cost, an optimized configuration model for the integrated photovoltaic-storage-charging power station was constructed. Ultimately, the economic cost was optimized, and the power supply to important loads under fault conditions was guaranteed, thereby improving the system's operational reliability and economy. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 This is a flowchart of the optimized configuration strategy for integrated photovoltaic, energy storage, and charging power stations that takes into account both efficient energy utilization and emergency power supply for loads.

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

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

[0076] Figure 4 This is a schematic diagram of the site selection results for Scheme 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 This is a diagram showing the cost of load loss under four different scenarios provided in the embodiments of the present invention;

[0079] Figure 7 This is a comprehensive annualized construction cost diagram for four schemes provided in the embodiments of the present invention;

[0080] Figure 8 This is a comprehensive annualized operating cost diagram for four schemes provided in the embodiments of the present invention; Detailed Implementation

[0081] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0082] This invention provides an optimized configuration method for an integrated photovoltaic-storage-charging power station that considers energy-efficient utilization and emergency power supply. The method includes the following steps: Figure 1 As shown:

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

[0084] S2: A configuration model for an integrated photovoltaic, energy storage, and charging power station was constructed with the goal of optimizing the overall construction cost, overall operating cost, and off-load cost.

[0085] S3: The objective function of the configuration model of the photovoltaic-storage-charging integrated power station is solved using the Cplex commercial solver, thereby realizing the site selection and capacity determination of the photovoltaic-storage-charging integrated power station.

[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] Using the user's trip end time as the electric vehicle's starting charging time, the probability density function of the electric vehicle's starting charging time is obtained through fitting as follows:

[0090]

[0091] Among them, f s (t) represents the probability density function at the start of charging of the electric vehicle; t s The charging time at the beginning; σ s The standard deviation (μ) represents the degree of deviation between the actual departure and arrival times and the expected values. s The expected value of the function;

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

[0093] The probability density function for the daily driving distance of an electric vehicle can be calculated as follows:

[0094]

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

[0096] (3) Analyze the charging time of electric vehicles

[0097] Calculations show that the charging time T for an electric vehicle is... c This can be expressed using factors such as charging efficiency, state of charge, and charging power.

[0098]

[0099] Among them, S n This represents the initial state of charge (S) of the electric vehicle. s E represents the desired state of charge for electric vehicles. s For the battery capacity of electric vehicles; η ev,ch For electric vehicle charging efficiency; P ev,ch The 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 initial state of charge of a single electric vehicle, calculate the charging time of the electric vehicle, and then obtain the charging end time of the electric vehicle; 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, For the m-th simulation at time t, N represents the charging load of all electric vehicles. ev P represents the number of electric vehicles. ev,t,n Let represent the charging power of the nth electric vehicle at time t;

[0104] The charging loads obtained from each simulation are summed up, and the total charging load is obtained at the end of the simulation. The average electric vehicle charging load of each simulation is calculated as the charging load curve.

[0105]

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

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

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

[0109] Among them, F CON F represents the total construction cost. RUN F represents the overall operating cost. EC This represents the cost of load loss.

[0110] It should be noted that:

[0111] The comprehensive construction cost refers to the total expenses incurred by various construction activities during the construction period, including land use costs, infrastructure construction costs, charging pile purchase costs, and energy storage configuration costs. The objective function is as follows:

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

[0113] Among them, C land C. Land use costs for integrated photovoltaic, energy storage, and charging power stations; pis C. Construction costs for integrated photovoltaic, energy storage, and charging power station infrastructure; pile C. Cost of purchasing charging stations; ess Cost of configuring the energy storage component of the photovoltaic-energy storage charging station;

[0114]

[0115] in, Land costs for individual photovoltaic and energy storage charging stations, photovoltaic systems, and energy storage systems, respectively; This is a binary decision variable representing whether node i should build a photovoltaic-storage-charging station; 0 indicates no construction, and 1 indicates construction. Since it's an integrated configuration, the charging station, photovoltaic system, and energy storage share the same decision variable; N node This refers to the number of distribution network nodes. These represent the infrastructure construction costs for a single photovoltaic-energy storage charging station, photovoltaic system, and energy storage system, respectively. The number of charging piles is represented by the number of charging piles installed at the photovoltaic-storage charging station at node i. This refers to the unit price of the charging pile. and These are the capacity cost factor and rated power cost factor for energy storage configurations, respectively. and Here, represents the rated capacity and rated power of the energy storage configured at node i, respectively; τ is the planning life conversion factor, which is related to time and the discount rate, r is the discount rate, and n is the equipment planning life. Since the photovoltaic-energy storage charging station is an integrated configuration, both photovoltaic and energy storage can be built inside the charging station, so the land cost only needs to be calculated for the charging station portion.

[0116] It should be noted that:

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

[0118]

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

[0120] The specific calculation formula is as follows:

[0121]

[0122] In the formula: t0 is the start time of operation; Δt is the duration of operation; a is the proportional coefficient of labor cost for charging station operation and maintenance; P EV,t P represents the total charging load of electric vehicles at time t. grid,t c represents the power exchanged with the power grid at time t; ess,t P represents the unit operation and maintenance cost of energy storage at time t. dch,t and P ch,t c represents the charging and discharging power of the energy storage section of the photovoltaic-energy storage charging station at time t, respectively; loss,t I represents the unit network loss price at time t; ij r is the square of the branch current between node i and node j; ij c is the resistance of the branch between node i and node j; grid,t Let P be the purchase and sale price of electricity from the grid at time t; grid,t Let t be the power exchanged between the distribution network and the main network.

[0123] It should be noted that:

[0124] Offload cost refers to the economic loss caused by insufficient power supply to meet load demands. The objective of this study is to minimize offload cost, and the objective function is as follows:

[0125]

[0126] Where, n ec The average number of typhoon disasters per year for the regional power distribution network; t0 is the start time of the typhoon disaster; T ec Duration of typhoon disaster; c ec Value of load loss; Importance weights determined based on load importance; μ i,t p represents the percentage of load loss at node i at time t; load,i,t Let be the active power demand of the load at node i at time t.

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

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

[0129]

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

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

[0132]

[0133] Where, μ i p represents the percentage of load loss at node i; load,i q load,i These represent the active and reactive loads at node i in the distribution network; p i qi P injects active and reactive power into distribution network node i, respectively; EV For the charging load of electric vehicles; P grid Q grid These represent the active and reactive power exchanged with the upstream power grid, respectively; P ch P dch These represent the charging and discharging power of the energy storage section of the integrated photovoltaic-energy storage-charging power station; P pv Q pv These represent the active and reactive power of the photovoltaic power generation portion of the integrated photovoltaic-storage-charging power station; P wind Q wind These represent the active and reactive power of the wind turbine at time t; Ω N For the regional distribution network node set;

[0134] Safe operation constraints:

[0135]

[0136] Among them, I ij.max I ij.min V represents the upper and lower limits of the branch current between node i and node j; j.max V j.min Let be 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 upstream power grid, the following constraints on the upper and lower limits of the interactive power must be met:

[0138]

[0139] Among them, P grid,t Q grid,t These represent the active and reactive power exchanged with the upstream power grid, respectively; P grid,max P grid,min These represent the maximum and minimum values ​​of the interactive active power allowed to pass through the interconnection branch between the distribution network and the upstream power grid, respectively; Q grid,max Q grid,min These are the maximum and minimum values ​​of reactive power exchange allowed to pass through the interconnection branch between the distribution network and the upper-level power grid, respectively.

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

[0141]

[0142] Where T represents 24 hours; P EV,t Let t be the total charging load of electric vehicles in the transformer area at time t; Let T represent the number of charging piles distributed at node i, indicating the number of charging piles installed at the photovoltaic-storage charging station. dFor safety reasons, the maximum daily operating time for charging stations is P p The operating power of the charging piles, that is, the sum of the maximum operating capacities of all charging piles, shall 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;

[0143] For new energy sources connected to the distribution network, this includes not only wind power plants configured within the distribution network area itself, but also the photovoltaic power generation portion of integrated photovoltaic-storage-charging power stations, all of which need to meet the output limit constraints:

[0144]

[0145] in, The photovoltaic power generated by the photovoltaic system installed at node j in time period t is The power generation of the wind turbine power station installed at node j in time period t is subject to upper and lower power limits. The binary decision variable indicates whether node i should build a photovoltaic-storage-charging station. 0 indicates that the photovoltaic-storage-charging station should not be built, and 1 indicates that the photovoltaic-storage-charging station should be built. Since it is an integrated configuration of photovoltaic, energy storage and charging, the charging station and photovoltaic share the same decision variable, while the wind turbine is installed on a fixed node.

[0146] For the energy storage component, the constraints include upper limits on rated power and capacity, charging and discharging power, state of charge, and energy storage balance, as follows:

[0147]

[0148] in, The rated power for energy storage at point j; The upper limit of the configurable rated power of energy storage; Let j be the capacity of the energy storage at point j; This represents the maximum configurable energy storage capacity. The binary decision variable indicates whether node i should build a photovoltaic-storage-charging station. 0 indicates that the photovoltaic-storage-charging station should not be built, and 1 indicates that the photovoltaic-storage-charging station should be built. Since it is an integrated configuration, the charging station and energy storage share the same decision variable. and Ω represents the charging and discharging power of the energy storage section of the photovoltaic-energy storage charging station at point j. Its output is constrained by the charging and discharging indicator and the upper and lower power limits. N For the set of nodes in the regional distribution network; S max and S min These are the upper and lower limits of the state of charge, respectively. The energy storage capacity of the photovoltaic-storage charging station at node j at time t0 is the energy value of the energy storage component. The initial state of charge of the energy storage section of the photovoltaic-energy storage charging station at node j at time t0; Let η be the remaining energy stored in the photovoltaic-storage charging station at node j at time t, which is constrained by the upper and lower limits of the state of charge and the rated capacity; ch and η dch These represent the charging and discharging efficiencies of energy storage in photovoltaic-energy storage charging stations.

[0149] The objective function of the configuration model of the photovoltaic-storage-charging integrated power station is solved using the Cplex commercial solver. The optimized configuration strategy of the photovoltaic-storage-charging integrated power station, which takes into account energy efficiency and emergency power supply, is obtained. Based on this, the site selection and capacity determination of the photovoltaic-storage-charging integrated power station can be realized.

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

[0151] I. Example Description

[0152] To verify the effectiveness and superiority of the method involved in this invention, this embodiment uses an improved IEEE 33-node distribution network as an example for calculation analysis, and performs site selection and capacity determination for an integrated photovoltaic-storage-charging power station according to the method involved in this invention. The rated voltage of the IEEE 33-node distribution network is 12.66kV, the rated power is 1MVA, and the important loads are nodes 8, 13, 24, 25, and 30. Wind turbines are installed in nodes 4, 13, 26, and 31. This invention assumes that the number of EVs managed by the substation charging station is 1000. The fault type is set as typhoon, the fault time period is 14:00-19:00, and the fault lines are 13-16 and 31-33. During the fault period, electric vehicles stop charging, and wind turbines and photovoltaics stop outputting power. Other parameters are shown in Table 1:

[0153] Table 1. Parameter Settings for the Case Study

[0154]

[0155]

[0156] II. Case Comparison and Analysis

[0157] The following four schemes were set up for comparative analysis:

[0158] Option 1: Blank control option, which does not consider emergency conditions or configure a charging station site selection and capacity model with facilities such as photovoltaics and energy storage.

[0159] Option 2: A distributed configuration of photovoltaic and energy storage facilities, but without considering emergency conditions, for the location and capacity of charging stations.

[0160] Option 3: A charging station site selection and capacity determination model that integrates distributed photovoltaic and energy storage facilities, taking into account both emergency and normal operating conditions.

[0161] Option 4: Taking into account both emergency and normal operating conditions, and integrating photovoltaic, energy storage and charging stations into a unified configuration, namely the site selection and capacity determination model for an integrated photovoltaic-energy storage-charging station.

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

[0163] Table 2. Site selection and gradation results for different schemes

[0164]

[0165]

[0166] The topology diagrams of the site selection results for the four schemes are as follows: Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown. Option 1 has charging stations located at nodes 12, 13, and 31, without photovoltaic (PV) or energy storage configurations. Option 2 has charging stations at nodes 12, 13, and 31, with PV at nodes 2 and 21, and energy storage configured at node 2, with a total energy storage configuration cost of 254,700 yuan. Option 3 has charging stations at nodes 5, 13, and 26, with PV at nodes 2 and 21. Due to consideration of emergency conditions, energy storage was added at nodes 2 and 25, with a total energy storage configuration cost of 398,100 yuan. Option 4, being an integrated configuration, has PV, energy storage, and charging stations all located at nodes 2, 21, and 25. The energy storage capacity is slightly lower than in Option 3, at 394,200 yuan. (And combined with...) Figure 6 The configuration of energy storage at critical load nodes 8, 13, 24, 25, and 30, compared to the solution without taking any measures, enabled the restoration of power supply, verifying the correctness of the proposed solution in realizing the model of supporting power supply to critical load points in emergency situations.

[0167] (2) Load loss analysis

[0168] This invention highlights the importance of load by setting a loss-of-load cost. Figure 6 This is a comparison chart of load shedding under four different scenarios. Figure 6It is evident that Schemes 1 and 2 do not consider emergency conditions, resulting in extremely high loss-of-load costs. Scheme 3 considers emergency conditions and utilizes energy storage to support the power supply of critical loads, reducing the loss-of-load cost of critical loads by 99.4% and the total loss-of-load cost by 91.3% compared to Scheme 2. The present invention adds the constraint of integrating photovoltaic, energy storage, and charging stations into a single configuration, thus limiting the location of energy storage. Therefore, the loss-of-load cost is slightly increased by 0.76% compared to Scheme 3. However, energy storage still plays a crucial role in supporting critical loads during emergencies, reducing the loss-of-load cost of critical loads by 99.4% and the total loss-of-load cost by 91.2% compared to Scheme 2. This demonstrates that the present invention also ensures the power supply of critical loads under fault conditions, improving power supply reliability.

[0169] (3) Economic cost analysis

[0170] Table 3. Economic Costs of Different Schemes (Ten Thousand Yuan)

[0171] Option 1 86.37 150.16 3754.48 3991.01 Option 2 147.49 67.44 3680.61 3895.54 Option 3 173.02 62.30 321.97 557.29 Option 4 141.07 58.41 324.43 523.91

[0172] Based on Table 3 Figure 7 , Figure 8 It can be seen that, compared with the blank control group Scheme 1, Scheme 2 is equipped with photovoltaic and energy storage. Although it increases the construction cost, the power generation of photovoltaic and the peak shaving and valley filling effect of energy storage greatly reduce the cost of electricity purchase, resulting in a 55.1% reduction in comprehensive operating cost and a 2.5% reduction in total cost.

[0173] Compared to Scheme 2, Scheme 3 incorporates considerations for emergency conditions, resulting in an increased energy storage capacity. Although this increases the overall construction cost by 17.3%, the energy storage's ability to support critical loads during fault conditions reduces the cost of load loss by 91.3%, significantly improving power supply reliability. Compared to Scheme 2, Scheme 3 reduces the total cost by 85.7%, greatly lowering economic costs. Scheme 4, which also considers emergency conditions, reduces the cost of load loss by 91.2% and the total cost by 86.6% compared to Scheme 2. These results validate the necessity of considering emergency conditions.

[0174] Compared to the distributed configuration scheme 3, the integrated configuration scheme 4 has several advantages, such as: Figure 7 As shown, due to the saving of land area and the sharing of some basic equipment, the overall construction cost of Scheme 4 is reduced by 18.5%; on the other hand, as Figure 8As shown, the overall operating cost of Scheme 4 is reduced by 6.2% compared to Scheme 3. This is because the integrated configuration of Scheme 4 reduces line losses and electricity purchase costs. Regarding load failure costs, Scheme 4 increases slightly by 0.76% compared to Scheme 3, but it still ensures power supply to critical loads under fault conditions. Overall, the total cost of Scheme 4 is reduced by 5.99% compared to Scheme 3, i.e., 333,800 yuan. This demonstrates that, compared to a distributed configuration strategy, the strategy of this invention can further reduce the overall system cost while ensuring system power supply reliability, thus exhibiting better economic efficiency.

[0175] The above description is merely illustrative of the embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, any modifications, equivalent substitutions, improvements, etc., made without creative effort within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of integrated photovoltaic-storage-charging power stations that considers efficient energy utilization and emergency power supply, characterized in that: Includes the following steps: S1: Construct an electric vehicle charging load model; S2: A configuration model for an integrated photovoltaic, energy storage, and charging power station was constructed with the goal of optimizing the overall construction cost, overall operating cost, and off-load cost. The integrated photovoltaic, energy storage, and charging power station configuration model includes the objective function and corresponding constraints. The objective function of the integrated photovoltaic, energy storage, and charging power station configuration model is as follows: ； In the formula: Indicates the total construction cost. Indicates the overall operating cost. Indicates the cost of load depletion; The objective function for loss of load cost is as follows: ； In the formula: The average number of typhoon disasters affecting the regional power distribution network per year; This marks the start of the typhoon disaster. Duration of typhoon disaster; Value of load loss; Importance weights are determined based on the importance of the load; For nodes Load at Percentage of load loss at any given time; for At any given moment The active power demand of the load; S3: The objective function of the configuration model of the photovoltaic-storage-charging integrated power station is solved using the Cplex commercial solver, thereby realizing the site selection and capacity determination of the photovoltaic-storage-charging integrated power station.

2. The method for optimizing the configuration of an integrated photovoltaic-storage-charging power station considering energy-efficient utilization and emergency power supply according to claim 1, characterized in that, The construction of the electric vehicle charging load model includes the following steps: (1) Analyze the starting charging time of electric vehicles Using the user's trip end time as the electric vehicle's starting charging time, the probability density function of the electric vehicle's starting charging time is obtained through fitting as follows: ； In the formula, Represents the probability density function of the moment when an electric vehicle begins charging; Charging time at the starting point; The standard deviation represents the degree of deviation between the actual departure and arrival times and the expected values. The expected value of the function; (2) Analyze the driving distance of electric vehicles The probability density function of the daily driving distance of an electric vehicle is: ； In the formula, This represents the probability density function of the daily driving distance of an electric vehicle. This refers to the driving distance of the electric vehicle. This represents the expected distance traveled. The standard deviation represents the degree of deviation between the actual distance traveled and the expected value. (3) Analyze the charging time of electric vehicles Electric vehicle charging time Expressed using charging efficiency, state of charge, and charging power: ； In the formula, This represents the initial state of charge of an electric vehicle. The desired state of charge for electric vehicles; For the battery capacity of electric vehicles; For electric vehicle charging efficiency; Power for charging electric vehicles; (4) Predicting electric vehicle charging load based on Monte Carlo simulation method Initialize the parameters of the number of electric private cars and electric taxis, electric vehicle battery capacity, charging power and simulation number; 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 charging end time of the electric vehicle; iteratively superimpose the charging load of a single electric vehicle to obtain the charging load of all electric vehicles in a day; ； In the formula: For the first Secondary simulation All electric vehicle charging loads at all times; For the number of electric vehicles; Indicates the first electric vehicles The charging power at any given moment; The charging load obtained from each simulation is summed up, and the total charging load is obtained at the end of the simulation. The average electric vehicle charging load of each simulation is calculated as the charging load curve. ； In the formula: M is the total number of simulations; for Electric vehicle charging load at all times.

3. The method for optimizing the configuration of an integrated photovoltaic-storage-charging power station considering efficient energy utilization and emergency power supply according to claim 2, characterized in that, The overall construction cost objective function is as follows: ； In the formula: Land costs for integrated photovoltaic, energy storage, and charging power stations; Costs for the construction of infrastructure for integrated photovoltaic, energy storage, and charging power stations; Cost of purchasing charging stations; Cost of configuring the energy storage component of the photovoltaic-energy storage charging station; ； In the formula: , , Land costs for individual photovoltaic and energy storage charging stations, photovoltaic systems, and energy storage systems, respectively; Binary decision variables, representing nodes Whether to build a photovoltaic-storage charging station is determined by a variable: 0 indicates no construction and 1 indicates construction. Since it is an integrated configuration, the charging station, photovoltaic system, and energy storage system share the same decision variable. This refers to the number of distribution network nodes. , , These represent the infrastructure construction costs for a single photovoltaic-energy storage charging station, photovoltaic system, and energy storage system, respectively. The number of charging piles is distributed, representing the nodes. The number of charging piles installed at the photovoltaic and energy storage charging station. This refers to the unit price of the charging pile. and These are the capacity cost factor and rated power cost factor for energy storage configurations, respectively. and Configured on the nodes respectively The rated capacity and rated power of the energy storage at the location; This is the discount factor for the planning period, which is related to both time and the discount rate, where r is the discount rate. Y The planned service life of the equipment.

4. The method for optimizing the configuration of an integrated photovoltaic-storage-charging power station considering efficient energy utilization and emergency power supply according to claim 3, characterized in that, The overall operating cost objective function is as follows: ； In the formula: To reduce the operation and maintenance costs of integrated photovoltaic, energy storage, and charging power stations; For the operation and maintenance costs of the energy storage component; Cost of line loss; Cost of purchasing electricity from the main grid; The specific calculation formula is as follows: ； In the formula: Duration of operation; 'a' represents the ratio of labor costs for charging station operation and maintenance; for It constantly exchanges power with the power grid; for The unit operation and maintenance cost of energy storage at all times; and The energy storage section of the photovoltaic-storage charging station is respectively located in The charging and discharging power at any given time; for Price per unit of network loss per moment; For nodes and nodes The square of the branch current; For nodes and nodes The resistance of the branches between; for The purchase and sale price of electricity from the power grid is always at hand.

5. The method for optimizing the configuration of an integrated photovoltaic-storage-charging power station considering energy-efficient utilization and emergency power supply according to claim 4, characterized in that, The constraints are as follows: First, the operation of the distribution network must meet power flow constraints, as shown in the following equation: ； In the formula: , They are nodes The active and reactive power injected; For nodes voltage, For nodes voltage, , They are nodes Next node The outflow of active and reactive power; , They are nodes Next node The outflow of active and reactive power; For nodes and nodes Branch current between; , They are nodes and nodes The resistance and reactance of the branches between them; , They are nodes Conductivity and susceptance to ground; In addition, all power flows in the distribution network must satisfy power balance constraints, as follows: ； In the formula: For nodes Percentage of load loss; , Distribution network nodes Active and reactive loads; , Distribution network nodes Injecting active and reactive power; The charging load for electric vehicles; , These are the active and reactive power exchanged with the upper-level power grid, respectively. , These represent the charging and discharging power of the energy storage section of the integrated photovoltaic-energy storage-charging power station. , These refer to the active and reactive power of the photovoltaic power generation component in the integrated photovoltaic-storage-charging power station. , They are respectively The active and reactive power of the wind turbine at all times; For the regional distribution network node set; Safe operation constraints: ； In the formula, , For nodes and nodes The upper and lower limits of the branch current between; For nodes and nodes The square of the branch current; For nodes The square of the voltage; , For nodes The upper and lower limits of the node voltage; For the power purchase and sale variables between the distribution network and the upstream power grid, the upper and lower limits of the interactive power must be satisfied, as follows: ； In the formula, , These are the maximum and minimum values ​​of the interactive active power allowed to pass through the connection branch between the distribution network and the upper-level power grid, respectively. , These are the maximum and minimum values ​​of reactive power exchange allowed to pass through the interconnection branch between the distribution network and the upper-level power grid, respectively. For integrated photovoltaic, energy storage, and charging power stations, the constraints on charging station demand and quantity are as follows: ； In the formula, T represents 24 hours; The number distribution of charging piles, representing nodes. The number of charging piles installed at the photovoltaic and energy storage charging station. The maximum daily operating time for charging stations is set for safety reasons. The operating power of the charging piles, that is, the sum of the maximum operating capacities of all charging piles, shall not be less than the total demand of electric vehicle users in the target area. and These are the lower and upper limits of the number of charging piles that a single charging station can accommodate, respectively. For new energy sources connected to the distribution network, this includes not only wind power plants configured within the distribution network area itself, but also the photovoltaic power generation portion of integrated photovoltaic-storage-charging power stations, all of which need to meet the output limit constraints: ； In the formula, For nodes The assembled photovoltaic in the first Power generation during the period For nodes The assembled wind turbine power station in the first Power generation during the period Binary decision variables, representing nodes Whether to build a photovoltaic-storage charging station: 0 indicates no photovoltaic-storage charging station will be built, and 1 indicates that a photovoltaic-storage charging station will be built. For the energy storage component, the constraints are as follows: ； In the formula, For configuration in Rated power of point-to-point energy storage; The upper limit of the configurable rated power of energy storage; For configuration in The capacity of energy storage; This represents the maximum configurable energy storage capacity. Binary decision variables, representing nodes Whether to build a photovoltaic-storage charging station is indicated by 0 (no construction) and 1 (construction). Since it is an integrated configuration, the charging station and energy storage share the same decision variable. and They are respectively The charging and discharging power of the energy storage section of the photovoltaic-energy storage charging station at the point of operation is constrained by the charging and discharging indicator and the upper and lower power limits. For the regional distribution network node set; and These are the upper and lower limits of the state of charge, respectively. for Time Node The amount of electricity stored in the energy storage section of the photovoltaic-storage charging station; for Time Node The initial state of charge of the energy storage section of the photovoltaic-storage charging station; for Time Node The remaining electricity in the energy storage section of the photovoltaic-storage charging station is constrained by the upper and lower limits of the state of charge and the rated capacity. and These represent the charging and discharging efficiencies of energy storage in photovoltaic-energy storage charging stations.

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