Electric vehicle charging and discharging space-time distribution optimization method considering distribution line vulnerability

Abstract

The invention provides an electric vehicle charging and discharging space-time distribution optimization method considering distribution line vulnerability. Establishing a comprehensive model covering electric vehicle charging and discharging equipment, a distribution line and an energy storage system, and analyzing a distribution line vulnerability index and an electric vehicle charging and discharging demand; and defining an objective function including power distribution network operation, electric vehicle charging and discharging and renewable energy utilization cost, and adding the analysis result to reduce the line overload risk. Data are collected, wind and light power generation uncertainty is processed by adopting a two-stage robust method, electric vehicle charging and discharging space-time distribution is analyzed in combination with traffic flow and user behavior data, and an uncertainty set is established to reduce prediction errors. And solving the model by using a column and constraint generation algorithm, and determining the charging and discharging priorities of the electric vehicles according to electric energy resources and line conditions. According to the electric vehicle charging and discharging space-time distribution optimization method considering the distribution line vulnerability, the utilization rate of renewable energy sources is increased, and efficient coordination of the electric vehicle and the power distribution network is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization of electric vehicle charging and discharging scheduling in distribution network operation management, and in particular to a method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines. Background Art

[0002] In the current process of coordinated development of power systems and electric vehicles, as the number of electric vehicles continues to increase, their charging and discharging behaviors have brought a series of severe challenges to the distribution network, and existing technologies have obvious defects in dealing with these problems.

[0003] Traditional distribution network planning and operation management models are mostly designed based on relatively stable load demands, which are difficult to adapt to the high randomness and uneven temporal and spatial distribution of electric vehicle charging loads. In some urban areas, especially old communities, the construction of distribution network infrastructure is relatively lagging and the line capacity is limited. When a large number of electric vehicles are charged in a centralized manner after get off work, such as between 18:00 and 22:00 on weekdays, local distribution lines will experience serious current overload, resulting in a sharp increase in line losses and a sharp drop in voltage, seriously affecting the quality and reliability of power supply. However, existing technologies lack effective means to accurately predict and effectively regulate such complex and changeable charging loads, and are unable to adjust the charging strategy of electric vehicles in a timely manner to avoid overloads.

[0004] In terms of renewable energy utilization, although wind and solar power generation technology has made great progress, due to the natural intermittent and volatile nature of wind and solar energy, their large-scale access to the distribution network has further aggravated the power balance problem of the power system. Existing technologies are insufficient in dealing with the uncertainty of wind and solar power generation and the coordination of electric vehicle charging and discharging. They can only consider renewable energy generation or electric vehicle charging and discharging in isolation, and fail to achieve the organic combination and coordinated optimization of the two. For example, near wind farms and photovoltaic power stations in some areas, although there is abundant renewable energy, due to the lack of reasonable scheduling strategies, when there is excess wind and solar power generation, the energy storage function of electric vehicles cannot be fully utilized for consumption, resulting in a large amount of wind and solar power abandonment; and when wind and solar power generation is insufficient and the demand for electric vehicle charging is large, it is impossible to effectively coordinate grid resources to ensure the stability of power supply.

[0005] In addition, for the vulnerability assessment and management of distribution lines, existing technologies usually only focus on the static parameters and historical fault data of the lines, and lack dynamic analysis of the impact of electric vehicle charging and discharging under real-time operation. This makes it impossible to accurately determine which lines are prone to failure during the charging and discharging of electric vehicles when formulating distribution network operation strategies, making it difficult to take targeted protection and optimization measures, increasing the safety risks of distribution network operation. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the purpose of the present invention is to solve the distribution line operation risks and energy utilization efficiency problems caused by the charging and discharging of electric vehicles by integrating electric vehicles, distribution lines and energy storage systems, using advanced mathematical models and algorithms, to enhance the ability of the distribution network to accept electric vehicles, and to promote the stable, economical and sustainable operation of the power system.

[0007] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0008] A method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines comprises the following steps:

[0009] S1. Establish a comprehensive model, which includes key components such as electric vehicle charging and discharging equipment, distribution lines, and energy storage systems. The model is used to analyze the vulnerability assessment indicators of distribution lines and the charging and discharging demand of electric vehicles;

[0010] S2. Optimize the definition of the objective function, define the objective function of minimizing the operating cost, which includes the distribution network operation cost, the electric vehicle charging and discharging cost, and the renewable energy utilization cost; at the same time, add the distribution line vulnerability index and the electric vehicle charging and discharging demand obtained in S1 to the objective function to avoid high-power charging and discharging operations on fragile lines and reduce the risk of line overload;

[0011] S3. Spatiotemporal characteristics analysis: According to the objective function defined in S2, the uncertainty of wind and solar power generation and the spatiotemporal distribution of electric vehicle charging and discharging are analyzed. Considering the difference between weekdays and holidays and the grid load characteristics in different time periods, a two-stage robust method is used to deal with the uncertainty of wind and solar power generation, and an uncertainty set with spatiotemporal characteristics is established to reduce the impact of prediction errors and ensure the feasibility and robustness of the optimization scheme under different time and space conditions.

[0012] S4. Optimization of charging and discharging scheduling. Using the results of S3 analysis, the two-stage robust model is solved through the column and constraint generation algorithm to obtain the optimal charging and discharging scheduling model. According to the status of electric energy resources and the vulnerability of distribution lines, an electric vehicle charging and discharging priority model is established, giving priority to charging and discharging on non-fragile lines and during periods with abundant electric energy resources.

[0013] Furthermore, in the aforementioned S1, the comprehensive model integrates the electric vehicle charging and discharging equipment, distribution lines, and energy storage systems based on the power system network analysis and equipment operation principle, and analyzes the distribution line vulnerability assessment index and electric vehicle charging and discharging demand analysis through the model;

[0014] For the distribution line section:

[0015] The circuit model based on the node voltage method is adopted to describe the network topology and electrical parameter relationship with the node admittance matrix; for a distribution network with n nodes, the node voltage equation is:

[0016]

[0017] Y ij is the node admittance matrix element; V j is the voltage at node j, I i is the current injected into node i;

[0018] The line flow distribution is obtained through flow calculation, and then the line flow overload rate λ is calculated ij :

[0019]

[0020] I ij is the actual current of line ij, The maximum current allowed;

[0021] Simultaneously analyze voltage deviation: ΔV ij =V i -V j ;

[0022] ΔV ij is the voltage deviation from node i to node j, V i is the voltage of node i;

[0023] When ij >1 when line or ΔV ij If it exceeds the allowable range, the line is determined to be a fragile line, and its degree of fragility and occurrence period are recorded;

[0024] For electric vehicle charging and discharging equipment, the electric vehicle charging and discharging equipment model is built around battery characteristics, using an equivalent circuit battery model, considering factors such as battery capacity, charging and discharging efficiency, and charging efficiency. The battery state of charge (SOC) change equation is:

[0025] While charging:

[0026]

[0027] When protecting against electric shock:

[0028]

[0029] Among them, E EV is the battery capacity; η c is the charging efficiency; η d is the discharge efficiency; p c (t), p d(t) are the charging and discharging power at time t; Δt is the time interval;

[0030] Combined with the travel patterns of electric vehicles, the number of uses and battery SOC status in different time periods are counted to analyze the charging and discharging needs of electric vehicles;

[0031] For the energy storage system model, considering the capacity and charging and discharging efficiency of the energy storage system, its energy dynamic change equation is:

[0032] E es (t+1)=E es (t)+η es P es (t)Δt;

[0033] Among them, η es is the charge and discharge efficiency; P es (t) is the charge and discharge power; and the charge and discharge power satisfies:

[0034]

[0035] is the capacity of the energy storage system; The upper limit of charge and discharge power; is the lower limit of charge and discharge power;

[0036] Based on the electric vehicle charging and discharging equipment model and travel pattern statistics, the average number of electric vehicles in use and the battery SOC distribution are analyzed in different time periods.

[0037] The S2 includes:

[0038] S21. Detailed cost composition, including distribution network operation costs, electric vehicle charging and discharging costs, and renewable energy utilization costs;

[0039] The operating cost of the distribution network includes line loss cost and transformer loss cost. Transformer loss is related to the load rate and reflects the energy loss cost of the distribution network during the power transmission process.

[0040] Line loss cost C l It is expressed as:

[0041]

[0042] ρ ij is the line ij resistivity;

[0043] The transformer loss cost is expressed as:

[0044]

[0045] P loadis the transformer load power; a, b, c are fitting coefficients;

[0046] The cost of charging and discharging electric vehicles is composed of charging electricity and battery loss costs;

[0047] The charging electricity fee is expressed as:

[0048]

[0049] C EV-ch represents the electricity cost of charging electric vehicles; c t is the electricity price at time t; P EV,t is the charging power of the electric vehicle at time t;

[0050] The battery loss cost is estimated based on the battery cycle life model and is expressed as:

[0051]

[0052] C b is the battery loss cost; k is the battery replacement cost coefficient, N c,i is the remaining cycle life of the battery of the i-th electric vehicle, reflecting the impact of electric vehicle charging and discharging on battery life and the corresponding cost; P i,t is the charging and discharging power of the i-th electric vehicle at time. When the vehicle is charging, P i,t is a positive value; when the vehicle is discharging, P i,t is a negative value;

[0053] The cost of renewable energy utilization takes into account the maintenance cost of power generation equipment and the cost of wind and solar power abandonment. The maintenance cost of power generation equipment is the actual cost incurred. The formula for calculating the cost of wind and solar power abandonment is:

[0054]

[0055] is the maximum power generation of wind power and photovoltaic power generation; P w,t , P pv,t is the actual consumption; The unit cost of wind and solar power abandonment;

[0056] S22. Objective function construction and constraint addition;

[0057] Based on the above costs, the optimization objective function is constructed:

[0058] C=C l +C trans +C EV,ch +C mian +C r-l ;

[0059] C mianTo consider the maintenance cost of power generation equipment;

[0060] Line fragility constraint, introducing line fragility index line flow overload rate λ ij , add constraints It represents the upper limit of the line flow overload rate, that is, the line vulnerability index line flow overload rate λ ij Less than the upper limit of the line power flow overload rate, ensuring that high-power charging and discharging operations are avoided on fragile lines during the optimization process, reducing the risk of line overload;

[0061] Electric vehicle charging and discharging demand constraints: according to the analysis results of electric vehicle charging and discharging demand, set upper and lower power limits to ensure that the basic charging and discharging needs of electric vehicles are met, while avoiding damage to the battery caused by excessive charging and discharging;

[0062] Energy and power constraints of energy storage systems;

[0063]

[0064] Under the above constraints, the minimum value of the objective function is solved to achieve economic optimization operation of the system.

[0065] The S3 includes:

[0066] S31. Uncertainty handling of wind and solar power generation; using a two-stage robust approach to deal with the randomness and volatility of wind and solar power generation;

[0067] In the first stage, historical wind and solar power generation data and meteorological information are collected, and the probability distribution function is used to describe the uncertainty characteristics of wind and solar power generation. Based on the uncertainty characteristics, the uncertainty set of wind and solar power generation is constructed:

[0068]

[0069] f(P w ,P pv ) represents the joint probability distribution function between wind and solar power generation, taking into account the possible correlation between the two;

[0070] P w Represents wind power, P w min It is the lower limit of wind power determined after considering various factors, P w max is the corresponding upper limit value;

[0071] P pv Represents photovoltaic power generation; and Indicates the lower and upper limits of photovoltaic power generation;

[0072] In the second stage, the feasibility of plans and strategies is evaluated under each generated wind and solar power generation scenario, and the decision variables are adjusted to deal with the most unfavorable scenario; for a given electric vehicle charging and discharging plan and energy storage system scheduling strategy, the system cost under different wind and solar power generation scenarios is calculated. Then solve the following robust optimization problem:

[0073]

[0074] The meaning of the above formula is:

[0075] The goal is to minimize C(x EV ,x es ), that is, find the optimal electric vehicle charging and discharging plan x EV and energy storage system dispatch strategy x es , making the system cost the lowest;

[0076] Constraints It is required that in all possible wind and solar power generation scenarios s, the cost C s It cannot exceed the robustness adjustment parameter Γ plus the system cost C(x EV ,x es ); ensure that the optimization scheme is sufficiently robust in the face of the randomness and volatility of wind and solar power generation; at the same time, other system operation constraints must be met to ensure the normal operation of the system;

[0077] By solving this problem, the optimal charging and discharging plan and scheduling strategy considering the uncertainty of wind and solar power generation are obtained to ensure that the system can operate stably under various wind and solar power generation conditions;

[0078] Where Γ is a robustness adjustment parameter used to balance system cost and robustness. When Γ is large, the optimization scheme focuses more on coping with the uncertainty of wind and solar power generation, which will lead to a slight increase in cost, but stronger robustness. When Γ is small, the optimization scheme focuses more on reducing system costs and has poor adaptability when facing large fluctuations in wind and solar power generation.

[0079] Indicates that under a specific wind and solar power generation scenario s, given an electric vehicle charging and discharging plan x EV and energy storage system dispatch strategy x es The system cost when the power grid is running; the cost includes the distribution network operation cost, electric vehicle charging and discharging cost, renewable energy utilization cost, etc.

[0080] It means that for all wind-solar power generation scenarios s, it means that in the optimization process, all possible wind-solar power generation scenarios need to be considered to ensure that the optimization scheme can meet the constraints under different wind-solar power generation conditions;

[0081] “Other system operation constraints” include distribution line constraints and electric vehicle charging and discharging constraints; the constraints are to ensure the safety, stability and reliability of the system during operation;

[0082] S32. Analysis of the spatiotemporal distribution of electric vehicle charging and discharging. In addition to considering the differences between weekdays and holidays and the grid load characteristics in different time periods, traffic flow data and user behavior data are combined to analyze the spatiotemporal distribution of electric vehicle charging and discharging. Traffic flow data is used to determine the vehicle traffic density and dwell time in different areas at different time periods, so as to more accurately predict the accessibility and charging needs of electric vehicles.

[0083] For user behavior data, the charging and discharging demand model of electric vehicles is further refined by analyzing information such as users' travel habits, charging preferences, and battery SOC management strategies. Based on the refined charging and discharging demand model of electric vehicles, an uncertainty set of charging and discharging demand of electric vehicles with spatiotemporal characteristics is established:

[0084]

[0085] in represents the charging and discharging power demand of electric vehicles in region r and time t, represents the corresponding battery SOC state distribution; t represents time, T is a time set, covering all considered time periods; r represents different geographical areas, R is a geographical area set;

[0086] Combining the uncertainty set of wind and solar power generation Ω and the uncertainty set of electric vehicle charging and discharging demand ΞEV, a comprehensive uncertainty set is established:

[0087]

[0088] Pw′,t is the wind power at time t, and its value depends on factors such as wind speed and wind turbine performance at that moment; Pp′v,t is the photovoltaic power at time t;

[0089] By analyzing the temporal and spatial variation patterns of each element in the set, a more comprehensive and accurate reference for time and space dimensions is provided for optimized scheduling, reducing the impact of prediction errors on the optimization scheme, and ensuring that the optimization scheme is feasible and robust in a complex temporal and spatial environment.

[0090] The S4 includes:

[0091] S41. Using the results of S3 analysis, the column and constraint generation algorithm is used to solve the two-stage robust model. In the CCG algorithm, the main problem is solved first. The main problem is a mixed integer linear programming problem. Its objective function is to minimize the operating cost. The constraints include distribution network operation constraints, electric vehicle charging and discharging constraints, energy storage system constraints, and wind and solar power generation uncertainty constraints.

[0092] Then, columns and constraints are generated by solving subproblems. The subproblem is to find a scenario that violates the constraint or an improved solution given the solution of the main problem. In the uncertainty scenario of wind and solar power generation, the subproblem is expressed as finding a wind and solar power generation power combination (Pw, Ppv) that worsens the objective function value, that is, solving:

[0093]

[0094] Among them, x is the solution of the current main problem. The optimal charging and discharging scheduling model is obtained by continuously iterating and solving the main problem and sub-problems until the sub-problem has no solution or meets the convergence condition;

[0095] S42. Establish an electric vehicle charging and discharging priority model and define the power resource abundance index. The power resource abundance index is determined based on factors such as the grid load rate and the matching degree between renewable energy generation power and load. The power resource abundance index γt is expressed as:

[0096]

[0097] Where Pr,t is the renewable energy power generation at time t; Ps,t is the remaining power supply capacity of the power grid; Pl,t is the load power of the power grid at time t; When it is believed that the electric energy resources are abundant; among them, It is the lower limit of the richness index of electric energy resources;

[0098] According to the distribution line vulnerability index λ ij When sorting lines, lines with low vulnerability have higher priority; the priority of electric vehicle charging and discharging is determined based on the abundance of electric energy resources and line priority. When the line is not fragile and the electric energy resources are abundant, the priority of electric vehicle charging and discharging is equal to 1; otherwise, a lower value is set according to the situation; in scheduling, electric vehicle charging and discharging are arranged according to priority, giving priority to ensuring the safe and stable operation of the power grid and the efficient use of electricity, thereby improving the overall performance of the system.

[0099] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0100] The present invention establishes a comprehensive model and comprehensively considers the interaction between electric vehicle charging and discharging equipment, distribution lines and energy storage systems. It can accurately analyze the vulnerability of distribution lines and the charging and discharging needs of electric vehicles, thereby realizing precise regulation of electric vehicle charging and discharging, avoiding high-power charging and discharging operations on fragile lines, and effectively reducing the risk of line overload. The two-stage robust method is used to deal with the uncertainty of wind and solar power generation, and combined with the analysis of spatiotemporal characteristics, the coordinated optimization of renewable energy and electric vehicle charging and discharging is achieved, the utilization efficiency of renewable energy is improved, and the phenomenon of wind and solar abandonment is reduced. At the same time, the electric vehicle charging and discharging priority model established based on the status of electric energy resources and the vulnerability of distribution lines can better coordinate power grid resources with electric vehicle charging needs, ensure the safe and stable operation of the power grid and the service quality of electric vehicles, fill the gaps in the existing technology in these aspects, and provide strong support for the sustainable development of power systems and electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 The logical block diagram of a method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the vulnerability of distribution lines;

[0102] Figure 2 A logical block diagram of the method for defining the optimization objective function in S2;

[0103] Figure 3 It is the logic block diagram of the spatiotemporal characteristic analysis method in S3;

[0104] Figure 4 This is the logic block diagram of the charging and discharging scheduling optimization method in S4. DETAILED DESCRIPTION

[0105] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0106] like Figure 1 As shown, a method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines comprises the following steps:

[0107] S1. Establish a comprehensive model, which includes key components such as electric vehicle charging and discharging equipment, distribution lines, and energy storage systems. The model is used to analyze the vulnerability assessment indicators of distribution lines and the charging and discharging demand of electric vehicles;

[0108] S2. Optimize the definition of the objective function, define the objective function of minimizing the operating cost, which includes the distribution network operation cost, the electric vehicle charging and discharging cost, and the renewable energy utilization cost; at the same time, add the distribution line vulnerability index and the electric vehicle charging and discharging demand obtained in S1 to the objective function to avoid high-power charging and discharging operations on fragile lines and reduce the risk of line overload;

[0109] S3. Spatiotemporal characteristic analysis: According to the objective function defined in S2, the uncertainty of wind and solar power generation and the spatiotemporal distribution of electric vehicle charging and discharging are analyzed. The differences between weekdays and holidays and the grid load characteristics in different time periods are considered. A two-stage robust method is used to handle the uncertainty of wind and solar power generation, and an uncertainty set with spatiotemporal characteristics is established to reduce the impact of prediction errors and ensure the feasibility and robustness of the optimization scheme under different time and space conditions. The spatiotemporal characteristic analysis provides a reference for the time dimension of the optimization scheduling, and the uncertainty processing ensures the feasibility and robustness of the optimization scheme under various circumstances.

[0110] S4. Optimization of charging and discharging scheduling. Using the results of S3 analysis, the two-stage robust model is solved through the column and constraint generation algorithm to obtain the optimal charging and discharging scheduling model. According to the status of electric energy resources and the vulnerability of distribution lines, an electric vehicle charging and discharging priority model is established, giving priority to charging and discharging on non-fragile lines and during periods with abundant electric energy resources.

[0111] In S1, the comprehensive model is based on the power system network analysis and equipment operation principle, and integrates the electric vehicle charging and discharging equipment, distribution lines, and energy storage systems. The model analyzes the distribution line vulnerability assessment index and the electric vehicle charging and discharging demand analysis;

[0112] For the distribution line section:

[0113] The circuit model based on the node voltage method is adopted to describe the network topology and electrical parameter relationship with the node admittance matrix; for a distribution network with n nodes, the node voltage equation is:

[0114]

[0115] Y ij is the node admittance matrix element; V j is the voltage at node j, I i is the current injected into node i;

[0116] The line flow distribution is obtained through flow calculation, and then the line flow overload rate λ is calculated ij :

[0117]

[0118] I ijis the actual current of line ij, The maximum current allowed;

[0119] Simultaneously analyze voltage deviation: ΔV ij =V i -V j ;

[0120] ΔV ij is the voltage deviation from node i to node j, V i is the voltage of node i;

[0121] When ij >1 when line or ΔV ij If it exceeds the allowable range, the line is determined to be a fragile line, and its fragility and occurrence period are recorded to provide a basis for line status for subsequent optimization and scheduling, so as to avoid arranging charging and discharging operations of electric vehicles that may cause overload on fragile lines;

[0122] For electric vehicle charging and discharging equipment, the electric vehicle charging and discharging equipment model is built around battery characteristics, using an equivalent circuit battery model, considering factors such as battery capacity, charging and discharging efficiency, and charging efficiency. The battery state of charge (SOC) change equation is:

[0123] While charging:

[0124]

[0125] When protecting against electric shock:

[0126]

[0127] Among them, E EV is the battery capacity; η c is the charging efficiency; η d is the discharge efficiency; p c (t), p d (t) are the charging and discharging power at time t; Δt is the time interval;

[0128] Combined with the travel patterns of electric vehicles, the number of uses and battery SOC status in different time periods are counted to analyze the charging and discharging needs of electric vehicles;

[0129] For the energy storage system model, considering the capacity and charging and discharging efficiency of the energy storage system, its energy dynamic change equation is:

[0130] E es (t+1)=E es (t)+η es P es (t)Δt;

[0131] Among them, η esis the charge and discharge efficiency; P es (t) is the charge and discharge power; and the charge and discharge power satisfies:

[0132]

[0133] is the capacity of the energy storage system; The upper limit of charge and discharge power; is the lower limit of charge and discharge power;

[0134] Based on the electric vehicle charging and discharging equipment model and travel pattern statistics, the average number of electric vehicles in use and the battery SOC distribution are analyzed in different time periods (such as weekday peak, off-peak, off-peak, holidays, etc.); for example, after the morning peak on weekdays, a large number of electric vehicles consume power and are in a low SOC state, and the charging demand in the area increases at this time; while in the night off period, some electric vehicles are parked for a long time and the battery SOC is high, which has a certain discharge potential; through these analyses, the charging and discharging power demand range of electric vehicles in different time periods is determined, which provides basic data for formulating reasonable charging and discharging scheduling strategies, realizes effective management of electric vehicle charging and discharging, and ensures stable operation of the power grid and the needs of electric vehicle users.

[0135] like Figure 2 As shown, the S2 includes:

[0136] S21. Detailed cost composition, including distribution network operation costs, electric vehicle charging and discharging costs, and renewable energy utilization costs;

[0137] The operating cost of the distribution network includes line loss cost and transformer loss cost. Transformer loss is related to the load rate and reflects the energy loss cost of the distribution network during the power transmission process.

[0138] Line loss cost C l It is expressed as:

[0139]

[0140] ρ ij is the line ij resistivity;

[0141] The transformer loss cost is expressed as:

[0142]

[0143] P load is the transformer load power; a, b, c are fitting coefficients;

[0144] The cost of charging and discharging electric vehicles is composed of charging electricity (c t is the electricity price at time t) and the battery loss cost;

[0145] The charging electricity fee is expressed as:

[0146]

[0147] The battery loss cost is estimated based on the battery cycle life model and is expressed as:

[0148]

[0149] k is the battery replacement cost coefficient, N c,i is the remaining cycle life of the battery of the i-th electric vehicle, reflecting the impact of electric vehicle charging and discharging on battery life and the corresponding cost;

[0150] The cost of renewable energy utilization takes into account the maintenance cost of power generation equipment and the cost of wind and solar power abandonment. The maintenance cost of power generation equipment is the actual cost incurred. The formula for calculating the cost of wind and solar power abandonment is:

[0151]

[0152] is the maximum power generation of wind power and photovoltaic power generation; P w,t , P pv,t is the actual consumption; The unit cost of wind and solar power abandonment;

[0153] S22. Add constraints, construct objective function and add constraints;

[0154] Based on the above costs, the optimization objective function is constructed:

[0155] C=C l +C trans +C EV,ch +C mian +C r-l ;

[0156] C mian To consider the maintenance cost of power generation equipment;

[0157] Line fragility constraint, introducing line fragility index line flow overload rate λ ij , add constraints It represents the upper limit of the line flow overload rate, that is, the line flow overload rate λ of the line vulnerability index ij Less than the upper limit of the line power flow overload rate, ensuring that high-power charging and discharging operations are avoided on fragile lines during the optimization process, reducing the risk of line overload;

[0158] Electric vehicle charging and discharging demand constraints: according to the analysis results of electric vehicle charging and discharging demand, set upper and lower power limits to ensure that the basic charging and discharging needs of electric vehicles are met, while avoiding damage to the battery caused by excessive charging and discharging;

[0159] Energy and power constraints of energy storage systems;

[0160]

[0161] Under the above constraints, the minimum value of the objective function is solved to achieve economic optimization operation of the system.

[0162] like Figure 3 As shown, Figure 3 As shown, the S3 includes:

[0163] S31. Uncertainty handling of wind and solar power generation; using a two-stage robust approach to deal with the randomness and volatility of wind and solar power generation;

[0164] In the first stage, historical wind and solar power generation data and meteorological information are collected, and the probability distribution function is used to describe the uncertainty characteristics of wind and solar power generation. Based on the uncertainty characteristics, the uncertainty set of wind and solar power generation is constructed:

[0165]

[0166] f(P w ,P pv ) represents the joint probability distribution function between wind and solar power generation, taking into account the possible correlation between the two. For example, in some areas, if sunny weather with strong wind speed is more common, then in this case, the probability of wind power and photovoltaic power generation being at a high level at the same time will be relatively high, which will be reflected in . When constructing the uncertainty set and performing optimization calculations, it is necessary to consider this correlation, so that the optimization scheme is more in line with the actual situation and improves the reliability and stability of the system operation;

[0167] P w Represents wind power, P w min The lower limit of wind power is determined after considering various factors (such as statistical analysis of historical wind speed data combined with the lower limit of weather forecasts, the power generation capacity of wind turbines at low wind speeds, etc.). w max is the corresponding upper limit value;

[0168] P pv Represents photovoltaic power generation; and Indicates the lower and upper limits of photovoltaic power generation;

[0169] By conducting a statistical analysis of the wind speed and light intensity data in the region over the past few years, combined with the current weather forecast, it was determined that in the coming period, wind power will fluctuate between 50MW and 150MW, and photovoltaic power will fluctuate between 30MW and 100MW;

[0170] In the second stage, the feasibility of plans and strategies is evaluated under each generated wind and solar power generation scenario, and the decision variables are adjusted to deal with the most unfavorable scenario; for a given electric vehicle charging and discharging plan and energy storage system scheduling strategy, the system cost under different wind and solar power generation scenarios is calculated. Then solve the following robust optimization problem:

[0171]

[0172] The meaning of the above formula is:

[0173] The goal is to minimize C(x EV ,x es ), that is, find the optimal electric vehicle charging and discharging plan x EV and energy storage system dispatch strategy x es , making the system cost the lowest;

[0174] Constraints It is required that in all possible wind and solar power generation scenarios s, the cost C s It cannot exceed the robustness adjustment parameter Γ plus the system cost C(x EV ,x es ); ensure that the optimization scheme is sufficiently robust in the face of the randomness and volatility of wind and solar power generation; at the same time, other system operation constraints must be met to ensure the normal operation of the system;

[0175] By solving this problem, the optimal charging and discharging plan and scheduling strategy considering the uncertainty of wind and solar power generation are obtained to ensure that the system can operate stably under various wind and solar power generation conditions;

[0176] Where Γ is a robustness adjustment parameter used to balance system cost and robustness. When Γ is large, the optimization scheme focuses more on coping with the uncertainty of wind and solar power generation, which may lead to a slight increase in system cost, but the system is more robust. When Γ is small, the optimization scheme focuses more on reducing system cost, but may have poor adaptability when facing large fluctuations in wind and solar power generation.

[0177] Indicates that under a specific wind and solar power generation scenario s, given an electric vehicle charging and discharging plan x EV and energy storage system dispatch strategy x esThe system cost when charging and discharging electric vehicles is low; the system cost here may include the distribution network operation cost, electric vehicle charging and discharging cost, renewable energy utilization cost, etc.; for example, when electric vehicles are charged in large quantities during peak electricity price periods and the wind and solar power generation is low, the system cost may be high;

[0178] It means that for all wind-solar power generation scenarios s, it means that in the optimization process, all possible wind-solar power generation scenarios need to be considered to ensure that the optimization scheme can meet the constraints under different wind-solar power generation conditions;

[0179] “Other system operation constraints”, which include distribution line constraints (such as line flow overload rate constraints, voltage deviation constraints, voltage fluctuation rate constraints, etc.) and electric vehicle charging and discharging constraints (such as charging and discharging power constraints, battery SOC constraints, etc.); these constraints are to ensure the safety, stability and reliability of the system during operation;

[0180] S32. Analysis of the spatiotemporal distribution of electric vehicle charging and discharging. In addition to considering the differences between weekdays and holidays and the characteristics of power grid loads in different time periods, traffic flow data and user behavior data are combined to analyze the spatiotemporal distribution of electric vehicle charging and discharging. Traffic flow data is used to determine the vehicle traffic density and dwell time in different areas at different time periods, so as to more accurately predict the accessibility and charging needs of electric vehicles. For example, near transportation hubs, vehicle dwell time is short, but charging demand is more concentrated; while in residential areas, vehicle dwell time is long, and charging demand is relatively dispersed but the total amount is large.

[0181] For user behavior data, the charging and discharging demand model of electric vehicles is further refined by analyzing information such as users' travel habits, charging preferences, and battery SOC management strategies. For example, some users may prefer to charge at work, while others prefer to charge at home. Some users may adjust the charging time according to fluctuations in electricity prices. Based on these analyses, an uncertainty set of electric vehicle charging and discharging demand with spatiotemporal characteristics is established:

[0182]

[0183] in represents the charging and discharging power demand of electric vehicles in region r and time t, represents the corresponding battery SOC state distribution; t represents time, T is a time set, covering all considered time periods; r represents different geographical areas, R is a geographical area set;

[0184] Combining the uncertainty set of wind and solar power generation Ω and the uncertainty set of electric vehicle charging and discharging demand ΞEV, a comprehensive uncertainty set is established:

[0185]

[0186] Pw′,t is the wind power at time t, and its value depends on factors such as wind speed and wind turbine performance at that moment; Pp′v,t is the photovoltaic power at time t;

[0187] By analyzing the temporal and spatial variation patterns of each element in the set, a more comprehensive and accurate reference for time and space dimensions is provided for optimized scheduling, reducing the impact of prediction errors on the optimization scheme, and ensuring that the optimization scheme is feasible and robust in a complex temporal and spatial environment.

[0188] like Figure 4 As shown, the S4 includes:

[0189] S41. Using the results of S3 analysis, the column and constraint generation (CCG) algorithm is used to solve the two-stage robust model. In the CCG algorithm, the main problem is solved first. The main problem is a mixed integer linear programming (MILP) problem, whose objective function is to minimize the operating cost. The constraints include distribution network operation constraints, electric vehicle charging and discharging constraints, energy storage system constraints, and wind and solar power generation uncertainty constraints.

[0190] Then, columns and constraints are generated by solving subproblems. The subproblem is to find a scenario that violates the constraint or an improved solution given the solution of the main problem. In the uncertainty scenario of wind and solar power generation, the subproblem is expressed as finding a wind and solar power generation power combination (Pw, Ppv) that worsens the objective function value, that is, solving:

[0191]

[0192] Among them, x is the solution of the current main problem. The optimal charging and discharging scheduling model is obtained by continuously iterating and solving the main problem and sub-problems until the sub-problem has no solution or meets the convergence condition;

[0193] S42. Establish an electric vehicle charging and discharging priority model and define the power resource abundance index. The power resource abundance index is determined based on factors such as the grid load rate and the matching degree between renewable energy generation power and load. The power resource abundance index γt is expressed as:

[0194]

[0195] Where Pr,t is the renewable energy power generation at time t; Ps,t is the remaining power supply capacity of the power grid; Pl,t is the load power of the power grid at time t; When it is believed that the electric energy resources are abundant; among them, It is the lower limit of the richness index of electric energy resources;

[0196] According to the distribution line vulnerability index λ ij When sorting lines, lines with low vulnerability have higher priority; the priority of electric vehicle charging and discharging is determined based on the abundance of electric energy resources and line priority. When the line is not fragile and the electric energy resources are abundant, the priority of electric vehicle charging and discharging is equal to 1; otherwise, a lower value is set according to the situation; in scheduling, electric vehicle charging and discharging are arranged according to priority, giving priority to ensuring the safe and stable operation of the power grid and the efficient use of electricity, thereby improving the overall performance of the system.

[0197] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines, characterized in that: The following steps are involved: S1. Establish a comprehensive model, which includes key components of electric vehicle charging and discharging equipment, distribution lines, and energy storage systems. The model is used to analyze the vulnerability assessment indicators of distribution lines and the charging and discharging demand of electric vehicles; S2. Optimize the definition of the objective function, define the objective function of minimizing the operating cost, which includes the distribution network operation cost, the electric vehicle charging and discharging cost, and the renewable energy utilization cost; at the same time, add the distribution line vulnerability index and the electric vehicle charging and discharging demand obtained in S1 to the objective function to avoid high-power charging and discharging operations on fragile lines and reduce the risk of line overload; S3. Spatiotemporal characteristics analysis: According to the objective function defined in S2, the uncertainty of wind and solar power generation and the spatiotemporal distribution of electric vehicle charging and discharging are analyzed. Considering the difference between weekdays and holidays and the grid load characteristics in different time periods, a two-stage robust method is used to deal with the uncertainty of wind and solar power generation, and an uncertainty set with spatiotemporal characteristics is established to reduce the impact of prediction errors and ensure the feasibility and robustness of the optimization scheme under different time and space conditions. S4. Optimization of charging and discharging scheduling. Using the results of S3 analysis, the two-stage robust model is solved through the column and constraint generation algorithm to obtain the optimal charging and discharging scheduling model. According to the status of electric energy resources and the vulnerability of distribution lines, an electric vehicle charging and discharging priority model is established, giving priority to charging and discharging on non-fragile lines and during periods with abundant electric energy resources.

2. The method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines according to claim 1, characterized in that: In S1, the comprehensive model is based on the power system network analysis and equipment operation principle, and integrates the electric vehicle charging and discharging equipment, distribution lines, and energy storage systems. The model analyzes the distribution line vulnerability assessment indicators and the electric vehicle charging and discharging demand analysis.

3. The method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines according to claim 2, characterized in that: The distribution line part adopts a circuit model based on the node voltage method, and uses the node admittance matrix to describe the network topology and electrical parameter relationship; for a distribution network with n nodes, the node voltage equation is: Y ij is the node admittance matrix element; V j is the voltage at node j, I i is the current injected into node i; The line flow distribution is obtained through flow calculation, and then the line flow overload rate λ is calculated ij : I ij is the actual current of line ij, The maximum current allowed; Simultaneously analyze voltage deviation: ΔV ij =V i -V j ; ΔV ij is the voltage deviation from node i to node j, V i is the voltage of node i; When ij >1 when line or ΔV ij If it exceeds the allowable range, the line is determined to be a fragile line, and its degree of fragility and occurrence period are recorded.

4. The method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines according to claim 2, characterized in that: The electric vehicle charging and discharging equipment and the electric vehicle charging and discharging equipment model are constructed around the battery characteristics, using an equivalent circuit battery model, taking into account the battery capacity, charging and discharging efficiency and charging efficiency factors, and the battery state of charge (SOC) change equation is: While charging: When protecting against electric shock: Among them, E EV is the battery capacity; η c is the charging efficiency; η d is the discharge efficiency; p c (t), p d (t) are the charging and discharging power at time t; Δt is the time interval; Based on the travel patterns of electric vehicles, the usage volume and battery SOC status in different time periods are counted to analyze the charging and discharging needs of electric vehicles.

5. The method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines according to claim 2, characterized in that: The energy storage system model takes into account the capacity of the energy storage system and the charging and discharging efficiency, and its energy dynamic change equation is: From es (t+1)=E es (t)+η es P es (t)Δt; Among them, η es is the charge and discharge efficiency; P es (t) is the charge and discharge power; and the charge and discharge power satisfies: is the capacity of the energy storage system; The upper limit of charge and discharge power; is the lower limit of charge and discharge power; Based on the electric vehicle charging and discharging equipment model and travel pattern statistics, the average number of electric vehicles in use and the battery SOC distribution are analyzed in different time periods.

6. The method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines according to claim 1, characterized in that: The S2 includes: S21. Detailed cost composition, including distribution network operation costs, electric vehicle charging and discharging costs, and renewable energy utilization costs; The operating cost of the distribution network includes line loss cost and transformer loss cost. Transformer loss is related to the load rate and reflects the energy loss cost of the distribution network during the power transmission process. Line loss cost C l It is expressed as: ρ ij is the line ij resistivity; The transformer loss cost is expressed as: P load is the transformer load power; a, b, c are fitting coefficients; The cost of charging and discharging electric vehicles is composed of charging electricity and battery loss costs; The charging electricity fee is expressed as: C EV-ch represents the electricity cost of charging electric vehicles; c t is the electricity price at time t; P EV,t is the charging power of the electric vehicle at time t; The battery loss cost is estimated based on the battery cycle life model and is expressed as: C b is the battery loss cost; k is the battery replacement cost coefficient, N c,i is the remaining cycle life of the battery of the i-th electric vehicle, reflecting the impact of electric vehicle charging and discharging on battery life and the corresponding cost; P i,t is the charging and discharging power of the i-th electric vehicle at time. When the vehicle is charging, P i,t is a positive value; when the vehicle is discharging, P i,t is a negative value; The cost of renewable energy utilization takes into account the maintenance cost of power generation equipment and the cost of wind and solar power abandonment. The maintenance cost of power generation equipment is the actual cost incurred, and the formula for calculating the cost of wind and solar power abandonment is: is the maximum power generation of wind power and photovoltaic power generation; P w,t , P pv,t is the actual consumption; The unit cost of wind and solar power abandonment; S22. Add constraints, construct objective function and add constraints; Combining the above costs, we construct the optimization objective function: C=C l +C trans +C EV,ch +C mian +C r-l ; C mian To consider the maintenance cost of power generation equipment; Line fragility constraint, introducing line fragility index line flow overload rate λ ij , add constraints It represents the upper limit of the line flow overload rate, that is, the line vulnerability index line flow overload rate λ ij Less than the upper limit of the line power flow overload rate, ensuring that high-power charging and discharging operations are avoided on fragile lines during the optimization process, reducing the risk of line overload; Electric vehicle charging and discharging demand constraints: according to the analysis results of electric vehicle charging and discharging demand, set upper and lower power limits to ensure that the basic charging and discharging needs of electric vehicles are met, while avoiding damage to the battery caused by excessive charging and discharging; Energy and power constraints of energy storage systems; Under the above constraints, the minimum value of the objective function is solved to achieve economic optimization operation of the system.

7. The method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines according to claim 1, characterized in that: The S3 includes: S31. Uncertainty handling of wind and solar power generation; using a two-stage robust approach to deal with the randomness and volatility of wind and solar power generation; In the first stage, historical wind and solar power generation data and meteorological information are collected, and the probability distribution function is used to describe the uncertainty characteristics of wind and solar power generation. Based on the uncertainty characteristics, the uncertainty set of wind and solar power generation is constructed: f(P w ,P pv ) represents the joint probability distribution function between wind and solar power generation, taking into account the possible correlation between the two; P w Represents wind power, P w min It is the lower limit of wind power determined after considering various factors, P w max is the corresponding upper limit value; P pv Represents photovoltaic power generation; and Indicates the lower and upper limits of photovoltaic power generation; In the second stage, the feasibility of plans and strategies is evaluated under each generated wind and solar power generation scenario, and the decision variables are adjusted to deal with the most unfavorable scenario; for a given electric vehicle charging and discharging plan and energy storage system scheduling strategy, the system cost under different wind and solar power generation scenarios is calculated. Then solve the following robust optimization problem: The meaning of the above formula is: The goal is to minimize C(x EV ,x es ), that is, find the optimal electric vehicle charging and discharging plan x EV and energy storage system dispatch strategy x es , making the system cost the lowest; Constraints It is required that in all possible wind and solar power generation scenarios s, the cost C s It cannot exceed the robustness adjustment parameter Γ plus the system cost C(x EV ,x es ); ensure that the optimization scheme is sufficiently robust in the face of the randomness and volatility of wind and solar power generation; at the same time, other system operation constraints must be met to ensure the normal operation of the system; By solving this problem, the optimal charging and discharging plan and scheduling strategy considering the uncertainty of wind and solar power generation are obtained to ensure that the system can operate stably under various wind and solar power generation conditions; Where Γ is a robustness adjustment parameter used to balance system cost and robustness. When Γ is large, the optimization scheme focuses more on coping with the uncertainty of wind and solar power generation, which will lead to a slight increase in cost, but stronger robustness. When Γ is small, the optimization scheme focuses more on reducing system costs and has poor adaptability when facing large fluctuations in wind and solar power generation. Indicates that under a specific wind and solar power generation scenario s, given an electric vehicle charging and discharging plan x EV and energy storage system dispatch strategy x es The system cost includes the distribution network operation cost, electric vehicle charging and discharging cost, and renewable energy utilization cost. For all wind-solar power generation scenarios s, it means that in the optimization process, all possible wind-solar power generation scenarios need to be considered to ensure that the optimization scheme can meet the constraints under different wind-solar power generation conditions; "Other system operation constraints" include distribution line constraints and electric vehicle charging and discharging constraints; the constraints are to ensure the safety, stability and reliability of the system during operation; S32. Analysis of the spatiotemporal distribution of electric vehicle charging and discharging. In addition to considering the differences between weekdays and holidays and the grid load characteristics in different time periods, traffic flow data and user behavior data are combined to analyze the spatiotemporal distribution of electric vehicle charging and discharging. Traffic flow data is used to determine the vehicle traffic density and dwell time in different areas at different time periods, so as to more accurately predict the accessibility and charging needs of electric vehicles. For user behavior data, the charging and discharging demand model of electric vehicles is further refined by analyzing the user's travel habits, charging preferences and battery SOC management strategy information; based on the refined charging and discharging demand model of electric vehicles, an uncertainty set of charging and discharging demand of electric vehicles with spatiotemporal characteristics is established: in represents the charging and discharging power demand of electric vehicles in region r and time t, represents the corresponding battery SOC state distribution; t represents time, T is a time set, covering all considered time periods; r represents different geographical areas, R is a geographical area set; Combining the uncertainty set of wind and solar power generation Ω and the uncertainty set of electric vehicle charging and discharging demand ΞEV, a comprehensive uncertainty set is established: Pw′,t is the wind power at time t, and its value depends on the wind speed and wind turbine performance factors at that moment; Pp′v,t is the photovoltaic power at time t; By analyzing the temporal and spatial variation patterns of each element in the set, a more comprehensive and accurate reference for time and space dimensions is provided for optimized scheduling, reducing the impact of prediction errors on the optimization scheme, and ensuring that the optimization scheme is feasible and robust in a complex temporal and spatial environment.

8. The method for optimizing the spatiotemporal distribution of electric vehicle charging and discharging taking into account the fragility of distribution lines according to claim 1, characterized in that: The S4 includes: S41. Using the results of S3 analysis, the column and constraint generation algorithm is used to solve the two-stage robust model. In the CCG algorithm, the main problem is first solved. The main problem is a mixed integer linear programming problem, whose objective function is to minimize the operating cost. The constraints include distribution network operation constraints, electric vehicle charging and discharging constraints, energy storage system constraints, and wind and solar power generation uncertainty constraints; Then, columns and constraints are generated by solving subproblems. The subproblem is to find a scenario that violates the constraint or an improved solution given the solution of the main problem. In the uncertainty scenario of wind and solar power generation, the subproblem is expressed as finding a wind and solar power generation power combination (Pw, Ppv) that worsens the objective function value, that is, solving: Among them, x is the solution of the current main problem. The optimal charging and discharging scheduling model is obtained by continuously iterating and solving the main problem and sub-problems until the sub-problem has no solution or meets the convergence condition; S42. Establish an electric vehicle charging and discharging priority model and define the power resource abundance index. The power resource abundance index is determined based on the grid load rate and the matching degree between renewable energy generation power and load. The power resource abundance index γt is expressed as: Where Pr,t is the renewable energy generation power at time t; Ps,t is the remaining power supply capacity of the power grid; Pl,t is the power load power of the power grid at time t; When it is believed that the electric energy resources are abundant; among them, It is the lower limit of the richness index of electric energy resources; According to the distribution line vulnerability index λ ij When sorting lines, lines with low vulnerability have higher priority; the priority of electric vehicle charging and discharging is determined based on the abundance of electric energy resources and line priority. When the line is not fragile and the electric energy resources are abundant, the priority of electric vehicle charging and discharging is equal to 1; otherwise, a lower value is set according to the situation; in scheduling, electric vehicle charging and discharging are arranged according to priority, giving priority to ensuring the safe and stable operation of the power grid and the efficient use of electricity, thereby improving the overall performance of the system.

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