Method for improving photovoltaic consumption capability of power distribution network by considering space-time characteristics of rural electric vehicles

Abstract

The invention discloses a power distribution network photovoltaic consumption capability improving method considering time-space characteristics of rural electric vehicles, and belongs to the technical field of power distribution networks. The method comprises the following steps: constructing a rural electric vehicle charging and discharging behavior model based on a rural road condition, a meteorological condition and a temperature; determining a rural electric vehicle charging load curve and regulation potential based on the rural electric vehicle trip chain and the rural electric vehicle charging and discharging behavior model; the actual charging and discharging power range of the electric vehicle can be determined based on the charging load curve and the regulation potential of the rural electric vehicle, constraint conditions are constructed based on the actual charging and discharging power range of the electric vehicle, and a power distribution network optimization scheduling model which comprises a target function and the constraint conditions and faces the improvement of the photovoltaic consumption capability is constructed; and solving the power distribution network optimization scheduling model by using a grey wolf algorithm. According to the invention, the relation among the economy, the safety and the photovoltaic absorption capability can be effectively balanced.

Description

A method for improving the photovoltaic absorption capacity of power distribution networks considering the spatiotemporal characteristics of rural electric vehicles Technical Field

[0001] This invention belongs to the field of power distribution network technology, and in particular relates to a method for improving the photovoltaic absorption capacity of power distribution networks that takes into account the spatiotemporal characteristics of rural electric vehicles. Background Technology

[0002] With the accelerated construction of new power systems, a high proportion of distributed photovoltaic (PV) power and new loads such as electric vehicles are increasingly being connected to rural power distribution networks, leading to operational risks such as voltage exceeding limits, transformer overload, and power flow backflow. Therefore, accurately characterizing the spatiotemporal characteristics of rural electric vehicles and effectively improving the distributed PV absorption capacity through interactive operation with the rural power distribution network is of great significance.

[0003] Regarding the analysis of electric vehicle (EV) operating characteristics and charging / discharging behavior, Gu Rui et al. from Shanghai University of Electric Power analyzed the EV travel characteristics in rural tourism scenarios and established a multi-timescale charging load prediction model for rural tourism electric buses to predict the daily charging load curve of scenic area buses. Xu Yang et al. from Chongqing University of Posts and Telecommunications considered the influence of slope parameters in mountainous cities, simulated EV travel and charging activities, and predicted the spatiotemporal distribution of charging load. Niu Mutong et al. from Southwest Jiaotong University proposed a multi-timescale EV load prediction model considering seasonal characteristics to achieve comprehensive short-term, medium-term, and long-term multi-timescale EV load prediction. Zhu Yongsheng et al. from Zhongyuan University of Technology considered the suddenness and subjectivity of EV users' charging behavior under sudden events and reconstructed the EV travel chain. Most of the above studies focus on the EV travel characteristics under urban power grids, but rural users' charging behavior is highly spatiotemporally random, and typical charging scenarios differ significantly from urban power distribution networks. Therefore, travel chain modeling methods for urban transportation are difficult to directly apply to rural scenarios.

[0004] In terms of distributed photovoltaic (PV) grid integration capacity, current research mainly focuses on dynamic simulation methods and mathematical optimization methods. Xue Lei et al. from China Agricultural University used scenario simulation to generate various typical PV integration schemes and selected the scheme with the highest overall benefit. Liu Dunnan et al. from North China Electric Power University analyzed the influencing factors on the grid's capacity to accept distributed PV and constructed an evaluation index for distributed PV integration capacity. Liang Zhifeng et al. from the National Power Dispatch and Control Center evaluated the distributed PV integration capacity of the distribution network using a data-driven method. Most of these studies analyze the influencing factors of distributed PV grid integration on the distribution network, with relatively little research considering the impact of the spatiotemporal regulation capabilities of rural electric vehicles on improving the distributed PV integration capacity of rural distribution networks.

[0005] In summary, existing methods for travel chain modeling and improving distributed photovoltaic absorption capacity are ill-suited to the operational characteristics of rural power distribution networks. Summary of the Invention

[0006] This invention proposes a method for improving the photovoltaic absorption capacity of power distribution networks that takes into account the spatiotemporal characteristics of electric vehicles in rural areas. It is used to solve the technical problem of how to accurately characterize the temporal charging and discharging power characteristics, spatial transfer characteristics and regulation potential of electric vehicles in typical rural scenarios.

[0007] The first aspect of this invention proposes a method for improving the photovoltaic absorption capacity of a distribution network considering the spatiotemporal characteristics of rural electric vehicles. The method includes: Step S1: Constructing a rural electric vehicle travel chain structure, instantiating the rural electric vehicle travel chain based on the type of rural electric vehicle travel activities, spatial transfer probability, travel time, and dwell time; constructing a rural electric vehicle charging and discharging behavior model based on rural road conditions, meteorological conditions, and temperature; determining the rural electric vehicle charging load curve and regulation potential based on the rural electric vehicle travel chain and the rural electric vehicle charging and discharging behavior model; Step S2: Determining the actual charging and discharging power range of electric vehicles based on the rural electric vehicle charging load curve and regulation potential, constructing constraints based on the actual charging and discharging power range of electric vehicles, and constructing a distribution network optimization scheduling model for improving photovoltaic absorption capacity, including an objective function and constraints; wherein the constraints include power flow constraints, branch current constraints, energy storage device constraints, distribution transformer load rate constraints, controllable power supply constraints, and actual charging and discharging constraints of electric vehicles; Step S3: Solving the distribution network optimization scheduling model using the Grey Wolf algorithm.

[0008] Preferably, in step S1, constructing a rural electric vehicle travel chain structure, and instantiating the rural electric vehicle travel chain based on the type of rural electric vehicle travel activity, spatial transfer probability, travel time, and stay duration, includes: constructing a rural electric vehicle travel chain structure, wherein the rural electric vehicle travel chain links different travel purposes of rural electric vehicle users based on time sequence, representing the sequence of various travel activities. The rural electric vehicle travel chain includes a time layer and a spatial layer. The time layer represents the time change from the start to the end of various travel activities, including several driving time periods, stay time periods, arrival time points, and departure time points. The spatial layer corresponds to the time layer, and the spatial layer represents the spatial location corresponding to the driving time periods, stay time periods, arrival time points, and departure time points in the time layer; and instantiating the rural electric vehicle travel chain based on the type of rural electric vehicle travel activity, spatial transfer probability, travel time, and stay duration.

[0009] Preferably, in step S1, constructing a rural electric vehicle charging and discharging behavior model based on rural road conditions, weather conditions, and temperature includes: In the formula, and Electric vehicles The charging and discharging power at any given time; and Upper and lower limits for electric vehicle charging power; and These are the upper and lower limits of the discharge power of electric vehicles; and Electric vehicles Time and The energy of a moment; and For the charging and discharging efficiency of electric vehicles; For time intervals; The speed of the electric vehicle; for Operating speed under normal weather conditions. Normal weather refers to weather conditions without rain, snowfall, road icing, sandstorms, or dense fog, and with ambient temperatures between 10 and 25°C. Weather correction factor for normal weather. ; This is a weather correction factor. ; The ambient temperature is The power consumption when the air conditioner is turned on; For speed is Power consumption per unit distance per hour; For temperature The minimum effective capacity of an electric vehicle; For temperature The maximum effective capacity of electric vehicles at that time.

[0010] Preferably, the objective function is: in, Let be the objective function. This refers to the amount of photovoltaic power absorbed. Due to voltage vulnerability, The operating cost is denoted by n, where n is the photovoltaic number. Where is the total number of photovoltaic cells, and T is the total number of time periods. for Moment Photovoltaics active power, This is a weighting factor for the average voltage vulnerability. for Average voltage vulnerability index value at any given time. This is a weighting factor for the vulnerability of voltage balance. for Voltage balance index value at any time The total number of nodes. Number the nodes. After normalization Time Node Overall voltage vulnerability value, Before normalization Time Node Overall voltage vulnerability value, Before normalization Minimum vulnerability value of all nodes at any given time. Before normalization The maximum vulnerability value of all nodes at any given time. for Time Node voltage, For nodes Rated voltage, This is the maximum voltage offset. For nodes connected to adjustable power supplies, For nodes connected to energy storage, For the connected electric vehicle nodes, The cost of operation and maintenance per unit of adjustable power supply. The number of nodes connected to an adjustable power source. for Nodes that are always connected to an adjustable power source The actual power generation capacity of the adjustable power source The operation and maintenance cost per unit of energy storage charging and discharging power. This represents the total number of nodes connected to energy storage. for Nodes that are constantly connected to energy storage Energy storage charging power, for Nodes that are constantly connected to energy storage The discharge power of the stored energy Cost of responding to charging and discharging power demand per unit of electric vehicle This represents the number of electric vehicle nodes that can be connected. for Electric vehicle nodes that are always connected The regulating power, Cost per unit of network loss power For the active power loss of the distribution network, For all branches of the distribution network, For nodes , The set of conductances, for Time Node voltage, for Time Node voltage, for The phase angle difference between the beginning and end nodes of the branch at any given time.

[0011] Preferably, the constraints include: This represents the minimum node voltage. This represents the maximum value of the node voltage; branch road The minimum value of the current. branch road Current value, branch road The maximum value of the current; Let be the active power at the beginning of the line at time t. Let t be the reactive power at the beginning of the circuit. This is the heavy load factor. The rated capacity of the distribution transformer in the power distribution network; Adjustable power supply at time t Lower limit of power generation capacity Adjustable power supply at time t Power generation capacity Adjustable power supply at time t Maximum power generation capacity; Energy storage device at time t The upper limit of charging power, For energy storage devices Charging status, for Real-time energy storage devices The charging power, Energy storage device at time t The upper limit of discharge power, For energy storage devices Discharge state, Energy storage device at time t+1 The state of charge, Energy storage device at time t The state of charge, Energy storage device at time t Charging efficiency, For time intervals, Energy storage device at time t The discharge efficiency, for Real-time energy storage devices The discharge power, Energy storage device at time t The lower limit of the state of charge. Energy storage device at time t The upper limit of the state of charge, Energy storage device at time t State of charge at the start of scheduling Energy storage device at time t The state of charge at the end of the scheduling process.

[0012] Preferably, in step S3, the Grey Wolf algorithm is used to solve the distribution network optimization scheduling model, including: Step S31: Constructing the solution vector of the Grey Wolf algorithm, i.e., the control variables, with the following expression: Where T1 is the total number of optimized scheduling periods; , , , , These are, respectively, the adjustable power supply power column vector, the energy storage charging power column vector, the energy storage discharging power column vector, the electric vehicle charging power column vector, and the electric vehicle discharging power column vector; Step S32: Obtain initial data, which includes the rural power distribution network topology, load factor, and electric vehicle parameters; instantiate constraints based on the initial data; initialize the population size. The maximum number of iterations H is determined; the current number of iterations h=1 is initialized; the position of each gray wolf in the gray wolf population is initialized using sin chaos, and the position corresponds to the solution vector: in, and The first Only Gray Wolf, the first Only gray wolves are The location of 4-dimensional space; The number of gray wolves in the population; Step S33: If the current iteration number is greater than the maximum iteration number H, proceed to step S37; otherwise, proceed to step S34; Step S34: Calculate the fitness value of each gray wolf in the population based on the position of each gray wolf in the population, and the fitness function is the objective function F; Select the three gray wolves with the smallest fitness values ​​and satisfying the constraints from the gray wolf population as leader wolves; Compare the fitness value of the leader wolf with the smallest fitness value and satisfying the constraints with the fitness value corresponding to the global optimal solution. If the fitness value of the leader wolf is less than the fitness value corresponding to the global optimal solution, then update the global optimal solution to the control variable of the leader wolf; otherwise, the global optimal solution remains unchanged; Step S35: Determine the value of the dynamic weight factor: The positions of each gray wolf in the gray wolf population at iteration number h+1 are calculated based on the dynamic weighting factor: in, and These represent the current iteration and the next iteration, respectively. A gray wolf The location of 4-dimensional space; These are the weighting coefficients. For the first In the nth iteration The global optimal solution of dimension; Indicates a uniform distribution; A random number between [0,1] that follows a normal distribution; This is a warning value; Set the threshold; Step S36: Assign the current iteration number to h+1, and proceed to step S33; Step S37: Use the fitness value corresponding to the global optimal solution as the optimal result of the optimization model.

[0013] The second aspect of this invention proposes a distribution network photovoltaic absorption capacity enhancement device considering the spatiotemporal characteristics of rural electric vehicles. The device includes: an initialization module configured to construct a rural electric vehicle travel chain structure, instantiating the rural electric vehicle travel chain based on rural electric vehicle travel activity types, spatial transfer probabilities, travel time, and dwell time; constructing a rural electric vehicle charging and discharging behavior model based on rural road conditions, meteorological conditions, and temperature; determining the rural electric vehicle charging load curve and regulation potential based on the rural electric vehicle travel chain and the rural electric vehicle charging and discharging behavior model; a model construction module configured to determine the actual charging and discharging power range of electric vehicles based on the rural electric vehicle charging load curve and regulation potential, constructing constraints based on the actual charging and discharging power range of electric vehicles, and constructing a distribution network optimization scheduling model for photovoltaic absorption capacity enhancement, including an objective function and constraints; wherein the constraints include power flow constraints, branch current constraints, energy storage device constraints, distribution transformer load rate constraints, controllable power supply constraints, and actual charging and discharging constraints of electric vehicles; and a calculation module configured to solve the distribution network optimization scheduling model using the Grey Wolf algorithm.

[0014] To address the issue that existing methods for constructing travel chains and improving distributed photovoltaic (PV) absorption capacity are not applicable to rural scenarios, this paper first establishes a rural electric vehicle (EV) travel chain and charging / discharging behavior model, considering factors such as typical rural functional areas, road conditions, and weather conditions, to obtain typical EV charging load curves and control potential. Second, with the objective functions of maximizing PV absorption capacity, minimizing distribution network voltage vulnerability, and minimizing operating costs, and with constraints including power flow constraints, branch current constraints, energy storage device constraints, distribution transformer load rate constraints, controllable power supply constraints, and EV control potential, a distribution network optimization scheduling model for improving PV absorption capacity is constructed. Finally, an improved Grey Wolf algorithm is used to solve the distribution network optimization scheduling model.

[0015] The present invention has the following technical effects: (1) The present invention analyzes the influence of various factors on the travel behavior and charging and discharging behavior of electric vehicles in rural areas, and constructs a travel chain and charging and discharging behavior model of electric vehicles in typical rural scenarios.

[0016] (2) The distribution network optimization scheduling model proposed in this invention for improving photovoltaic absorption capacity comprehensively considers multiple objective functions such as distributed photovoltaic absorption capacity, voltage vulnerability, and operating cost, and can effectively balance the relationship between economy, security and photovoltaic absorption capacity.

[0017] (3) The improved gray wolf algorithm proposed in this invention has a faster convergence speed and more iterative data, and can solve the distribution network optimization scheduling model for improving photovoltaic absorption capacity more quickly and accurately. Attached Figure Description

[0018] Figure 1 is a flowchart illustrating the method for improving the photovoltaic absorption capacity of power distribution networks that takes into account the spatiotemporal characteristics of rural electric vehicles provided by the present invention.

[0019] Figure 2 is a schematic diagram of the spatiotemporal changes corresponding to the travel chain of the present invention.

[0020] Figure 3 is a schematic diagram of the simple chain and complex chain structure models of the rural electric vehicle of the present invention.

[0021] Figure 4 is a schematic diagram illustrating the impact of road conditions, weather conditions, and temperature on charging demand according to the present invention.

[0022] Figure 5 is a flowchart illustrating the improved gray wolf optimization algorithm of this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0024] As shown in Figure 1, a method for improving the photovoltaic absorption capacity of a distribution network considering the spatiotemporal characteristics of rural electric vehicles includes: Step S1: Constructing a rural electric vehicle travel chain structure, instantiating the rural electric vehicle travel chain based on the type of rural electric vehicle travel activities, spatial transfer probability, travel time, and stay duration; constructing a rural electric vehicle charging and discharging behavior model based on rural road conditions, meteorological conditions, and temperature; determining the rural electric vehicle charging load curve and regulation potential based on the rural electric vehicle travel chain and the rural electric vehicle charging and discharging behavior model; Step S2: Determining the actual charging and discharging power range of electric vehicles based on the rural electric vehicle charging load curve and regulation potential, constructing constraints based on the actual charging and discharging power range of electric vehicles, and constructing a distribution network optimization scheduling model for improving photovoltaic absorption capacity, including an objective function and constraints; wherein, the constraints include power flow constraints, branch current constraints, energy storage device constraints, distribution transformer load rate constraints, controllable power supply constraints, and actual charging and discharging constraints of electric vehicles; Step S3: Solving the distribution network optimization scheduling model using the Grey Wolf algorithm.

[0025] Compared to urban areas, rural residents' travel is characterized by fixed routes, short distances, and strong time regularity. However, the coverage of public charging infrastructure is low, and charging locations are highly concentrated in homes or village-level charging stations. These characteristics make traditional travel chain modeling methods designed for urban traffic difficult to apply directly to rural scenarios. Therefore, it is necessary to construct an electric vehicle travel chain model adapted to rural characteristics to provide fundamental data support for charging load forecasting and improving the photovoltaic absorption capacity of the power distribution network.

[0026] In step S1, a rural electric vehicle travel chain structure is constructed. This is done by instantiating the rural electric vehicle travel chain based on the types of rural electric vehicle travel activities, spatial transfer probabilities, travel time, and dwell time. This includes: constructing the rural electric vehicle travel chain structure, where the rural electric vehicle travel chain links different travel purposes of rural electric vehicle users based on time sequence, representing a sequence of various travel activities. The rural electric vehicle travel chain includes a time layer and a spatial layer. The time layer represents the time changes from the start to the end of various travel activities, including several travel time periods, dwell time periods, arrival time points, and departure time points. The spatial layer corresponds to the time layer, representing the spatial location corresponding to the travel time periods, dwell time periods, arrival time points, and departure time points in the time layer; and instantiating the rural electric vehicle travel chain based on the types of rural electric vehicle travel activities, spatial transfer probabilities, travel time, and dwell time.

[0027] The spatial transition probability is the first in the time layer. The probability of moving from the starting position to the destination position within a time period. The time period refers to either the travel time or the stop time. In the formula, the transition probability Matrix elements For the first The journey time starts from the starting position. to destination The transition probability is given by z, where z is the total number of spatial locations and T is the total number of time periods. The index number is the starting position. This is the index number of the destination location.

[0028] Travel time follows a normal distribution.

[0029] Travel activities include going home, market trading, farming, visiting relatives, and tourism.

[0030] In this invention, the rural electric vehicle travel chain links different travel purposes of electric vehicle users in a specific temporal sequence, representing a sequence of various travel activities that can effectively depict users' daily travel patterns. The spatiotemporal relationship corresponding to the travel chain is shown in Figure 2, where the time layer represents the time change from start to finish. Indicates the time periods of travel and stay. The arrival and departure times are indicated by the spatial layer, which represents the spatial changes during the journey. This indicates the distance traveled between nodes.

[0031] (1) Types of Rural Electric Vehicle Travel Activities This invention describes the daily travel behavior characteristics of vehicles based on the rural electric vehicle travel chain. According to the travel chain structure and regional functions, and considering factors such as the pace of life, production and living activities of rural residents, the functional areas of rural areas can be divided into five typical areas: residential areas, village-level living and production areas, township central areas, county towns and external scenic areas and other areas, as shown in Table 1.

[0032] Table 1. Rural Electric Vehicle Charging Scenarios Classification

[0033] Rural residents' travel activities are mainly based on simple chains, and the purpose of travel can be divided into 5 categories: home (H), market trading (MT), farming activities (FA), visiting relatives (VR), and leisure tourism (LT). Considering the travel chain structure with the longest number of travel destinations, as shown in Figure 3.

[0034] (2) Spatial transition probability: The travel chain of rural electric vehicles can be viewed as a Markov chain. At any given moment, the destination of a rural electric vehicle is only related to its previous destination and is independent of its destination at other times, exhibiting no aftereffect. According to Markov chain theory, each travel destination is considered a state, denoted as... From position Convert to position The transition probability, then One-step spatial transition probability of a time period It can be written in matrix form as shown below.

[0035] In the formula, For the first The time period journey starts from the starting position to destination The transition probability is given by z, where z is the total number of spatial locations and T is the total number of time periods. The index number is the starting position. This is the index number of the destination location.

[0036] (3) Travel Time: The initial travel time of rural electric vehicle users is approximately normally distributed, and its density function is... for: In the formula, This is the initial departure time; This represents the average starting departure time. This represents the initial travel time variance.

[0037] (4) Dwell Time: The dwell time of vehicles with different purposes in the rural electric vehicle travel chain exhibits different probability distribution characteristics. Statistical data shows that the dwell time of MT and FA type travel activities approximately follows a normal distribution, while the dwell time of VR, H, and LT type travel activities approximately follows an exponential distribution. The fitting results of the mean and standard deviation for each type of travel activity are shown in Table 2.

[0038] In the rural electric vehicle travel chain, the dwell time of vehicles with different purposes exhibits different probability distribution characteristics. Statistical data shows that the time spent on MT and FA type travel activities approximately follows a normal distribution. For VR, H, and LT type travel activities, which follow an exponential distribution, the probability density function of their dwell time is shown below. The fitting results of the mean and standard deviation for each type of travel activity are shown in Table 2.

[0039] Table 2. Fitting results of the probability distribution of dwell time for rural electric vehicle travel activities

[0040] In step S1, a rural electric vehicle charging and discharging behavior model is constructed based on rural road conditions, weather conditions, and temperature, including: In the formula, and Electric vehicles The charging and discharging power at any given time; and Upper and lower limits for electric vehicle charging power; and These are the upper and lower limits of the discharge power of electric vehicles; and Electric vehicles Time and The energy of a moment; and For the charging and discharging efficiency of electric vehicles; For time intervals; The speed of the electric vehicle; for Operating speed under normal weather conditions. Normal weather refers to weather conditions without rain, snowfall, road icing, sandstorms, or dense fog, and with ambient temperatures between 10 and 25°C. Weather correction factor for normal weather. ; This is a weather correction factor. ; The ambient temperature is The power consumption when the air conditioner is turned on; For speed is Power consumption per unit distance per hour; For temperature The minimum effective capacity of an electric vehicle; For temperature The maximum effective capacity of electric vehicles at that time.

[0041] In this invention, the mileage and travel time of rural electric vehicle users during a task can be obtained using real-time driving speed. However, external factors can interfere with driving speed, thus affecting the travel time. The impact of rural road conditions, weather conditions, and temperature on charging demand is shown in Figure 4.

[0042] (1) Impact of Rural Road Conditions on Electric Vehicle Energy Consumption Different road grades in rural areas affect the driving speed of EVs, thus affecting the energy consumption of EVs. This paper only uses average speed as a reference. Road types can be divided into four grades: Level 1, Level 2, Level 3, and Level 4. The average speed of EVs on each level of road is shown in Table 3.

[0043] Table 3 Average speeds on roads of all levels

[0044] The power consumption E per unit mileage under different road grades is shown in the formula.

[0045] In the formula: This refers to the power consumption per unit mileage of an electric vehicle when traveling on roads of grade m. for The operating speed during a given time period.

[0046] (2) The impact of rural meteorological conditions on the energy consumption of electric vehicles. In rural areas, roads are of lower grade and have poorer road conditions, making them susceptible to weather factors. Rain, snow, ice, dense fog, and strong winds and dust can reduce visibility and road surface adhesion coefficient, thereby limiting the driving speed of electric vehicles and changing their energy consumption.

[0047] In the formula: for Operating speed under normal weather conditions during the specified time period; This is a weather correction factor. .

[0048] Table 4 Weather correction parameters for different seasons and weather conditions

[0049] (3) The impact of temperature on the energy consumption of electric vehicles In rural areas, the temperature difference between day and night is large and low temperatures are frequent in winter. The ambient temperature has a significant impact on the performance of electric vehicle batteries and the energy consumption of vehicle air conditioning.

[0050] ① The effect of temperature on the relative electric capacity of electric vehicles.

[0051] The effective capacity of a battery decreases non-linearly under low-temperature conditions, and its relative capacity can be expressed as follows: In the formula: For temperature Effective capacity at that time; The rated capacity of the electric vehicle battery; This is the optimal operating temperature for electric vehicle batteries (approximately 25°C). , All are environmental sensitivity coefficients.

[0052] ② The impact of temperature on the power consumption of vehicle air conditioning.

[0053] Changes in ambient temperature can affect the start and stop of air conditioning, and different temperatures will affect its operation. The air conditioner usage rate was analyzed, and the relevant data were fitted to obtain the normal distribution function of the air conditioner start probability. for: In the formula: , These are the mean and standard deviation of a normal distribution, respectively, for heating start-up. , ; Cooling start , .

[0054] The calculation model for unit power consumption under the influence of temperature is shown in the equation.

[0055] In the formula: The ambient temperature is The power consumption when the air conditioner is turned on; , These are the cooling and heating capacities of the electric vehicle's air conditioning system, respectively, with values ​​of 1.2kW and 1.5kW. S represents the actual speed of the electric vehicle; S represents the driving distance of the electric vehicle. and These are the upper limit of the heating temperature and the lower limit of the cooling temperature for air conditioners.

[0056] ③ Charging Strategies for Rural Electric Vehicle Users: In rural areas, electric vehicle users' charging behavior is highly flexible and subjective, but their choices are often influenced by factors such as region type, travel motivation, and dwell time. Generally, owners tend to opportunistically charge at home or frequently stopped locations (such as farmland, rural markets, and tourist attractions). Assuming that electric vehicle users reach a point with available charging and discharging facilities during their journey, under the guidance of V2G technology, electric vehicles can provide bidirectional charging and discharging capabilities, but the maximum charging and discharging power limitations and energy storage capacity limitations of electric vehicles need to be considered.

[0057] In the formula: and Electric vehicles The charging and discharging power at any given time; and Upper and lower limits for electric vehicle charging power; and These are the upper and lower limits of the discharge power of electric vehicles; and Electric vehicles Time and The energy of a moment; and For the charging and discharging efficiency of electric vehicles; and These represent the maximum and minimum capacities of electric vehicles, respectively.

[0058] In one embodiment of the present invention, the regulation potential is obtained after obtaining the rural electric vehicle charging load curve. Based on the rural electric vehicle load curve and the regulation potential, the actual charging and discharging range of the rural electric vehicle can be determined. In subsequent steps, the actual charging and discharging of the rural electric vehicle is used as a decision variable, and constraints are determined based on the actual charging and discharging range.

[0059] Furthermore, the objective function is: in, Let be the objective function. This refers to the amount of photovoltaic power absorbed. Due to voltage vulnerability, The operating cost is denoted by n, where n is the photovoltaic number. Where is the total number of photovoltaic cells, and T is the total number of time periods. for Moment Photovoltaics active power, This is a weighting factor for the average voltage vulnerability. for Average voltage vulnerability index value at any given time. This is a weighting factor for the vulnerability of voltage balance. for Voltage balance index value at any time The total number of nodes. Number the nodes. After normalization Time Node Overall voltage vulnerability value, Before normalization Time Node Overall voltage vulnerability value, Before normalization Minimum vulnerability value of all nodes at any given time. Before normalization The maximum vulnerability value of all nodes at any given time. for Time Node voltage, For nodes Rated voltage, This is the maximum voltage offset. For nodes connected to adjustable power supplies, For nodes connected to energy storage, For the connected electric vehicle nodes, The cost of operation and maintenance per unit of adjustable power supply. The number of nodes connected to an adjustable power source. for Nodes that are always connected to an adjustable power source The actual power generation capacity of the adjustable power source The operation and maintenance cost per unit of energy storage charging and discharging power. This represents the total number of nodes connected to energy storage. for Nodes that are constantly connected to energy storage Energy storage charging power, for Nodes that are constantly connected to energy storage The discharge power of the stored energy Cost of responding to charging and discharging power demand per unit of electric vehicle This represents the number of electric vehicle nodes that can be connected. for Electric vehicle nodes that are always connected The regulating power, Cost per unit of network loss power For the active power loss of the distribution network, For all branches of the distribution network, For nodes , The set of conductances, for Time Node voltage, for Time Node voltage, for The phase angle difference between the beginning and end nodes of the branch at any given time.

[0060] In this invention, the optimal scheduling of the distribution network aimed at improving photovoltaic (PV) absorption capacity is a multi-objective optimization problem. Reasonable control of operating equipment reduces the impact of distributed generation (DG) access on the safe operation of the distribution network. While ensuring the economic efficiency of distribution network operation, it also increases the absorption capacity of DG. Therefore, the objective functions are maximizing PV absorption capacity, minimizing active power losses, and minimizing operating costs. An optimal scheduling model for the distribution network is established using constraints such as power flow constraints, node voltage constraints, and branch current constraints.

[0061] The factors to be considered in the objective function include: (1) Maximizing photovoltaic absorption capacity. In order to absorb as much DG as possible in the distribution network through equipment regulation, photovoltaic absorption capacity is used as one of the objective functions.

[0062] In the formula: This refers to the amount of photovoltaic power absorbed. for Moment Photovoltaics The active power; This represents the total number of photovoltaic units.

[0063] (2) Minimum Voltage Vulnerability: To represent the degree of voltage improvement achieved by equipment regulation in the distribution network, a comprehensive voltage vulnerability index is proposed. This comprehensive index measures the risk resistance capability by analyzing the degree of voltage deviation in the distribution network. The lower the vulnerability, the more stable the voltage, and the better the risk resistance capability.

[0064] In the formula: for Time Node The voltage; For nodes The rated voltage; This is the maximum voltage offset; and Before normalization The maximum and minimum vulnerability values ​​of all nodes at any given time; The total number of nodes; for Average voltage vulnerability index value at any given time; for The voltage balance index value at any time mainly reflects the correlation between nodes, that is, the voltage collapse of any node will cause the collapse of other nodes. and These are the weighting coefficients for average voltage vulnerability and equilibrium vulnerability, respectively.

[0065] (3) Minimum operating cost consists of four parts: adjustable power supply operation and maintenance cost, energy storage charging and discharging cost, electric vehicle demand response cost, and network loss cost.

[0066] In the formula: The unit adjustable power supply power operation and maintenance cost; Operation and maintenance cost per unit energy storage charging and discharging power; Unit electric vehicle charging / discharging power demand response cost; Cost per unit of network loss power; for Time Node The actual power generation capacity of the adjustable power source; and for Time Node The discharge power and charging power of energy storage; for Time Node The power regulation of electric vehicles; Active power loss in the distribution network; For all branches of the network; For nodes , The set of electrical conductances; , for Time Node , The voltage; for Time branch start and end nodes , The phase angle difference.

[0067] Taking into account photovoltaic (PV) absorption capacity, grid voltage vulnerability, and operating costs, the multi-objective optimization function of the distribution network optimization scheduling model aimed at improving PV absorption capacity is: .

[0068] Furthermore, the constraints include: This represents the minimum node voltage. This represents the maximum value of the node voltage; branch road The minimum value of the current. branch road Current value, branch road The maximum value of the current; Let be the active power at the beginning of the line at time t. Let t be the reactive power at the beginning of the circuit. This is the heavy load factor. The rated capacity of the distribution transformer in the power distribution network; Adjustable power supply at time t Lower limit of power generation capacity Adjustable power supply at time t Power generation capacity Adjustable power supply at time t Maximum power generation capacity; Energy storage device at time t The upper limit of charging power, For energy storage devices Charging status, for Real-time energy storage devices The charging power, Energy storage device at time t The upper limit of discharge power, For energy storage devices Discharge state, Energy storage device at time t+1 The state of charge, Energy storage device at time t The state of charge, Energy storage device at time t Charging efficiency, For time intervals, Energy storage device at time t The discharge efficiency, for Real-time energy storage devices The discharge power, Energy storage device at time t The lower limit of the state of charge. Energy storage device at time t The upper limit of the state of charge, Energy storage device at time t State of charge at the start of scheduling Energy storage device at time t The state of charge at the end of the scheduling process.

[0069] In this invention, the constraints include power flow constraints of distribution network branches, node voltage constraints, branch current constraints, distribution transformer load rate constraints, adjustable power supply constraints, and energy storage device constraints.

[0070] (1) Power flow constraints of distribution network branches.

[0071] The Distflow branch model is used to describe the multi-period power flow constraint model of the distribution network.

[0072] (2) Node voltage constraints.

[0073] (3) Branch current constraint.

[0074] (4) Load rate constraint of distribution transformer.

[0075] In the formula: The rated capacity of the distribution transformer in the power distribution network; This is the overload factor.

[0076] (5) Adjustable power supply constraint.

[0077] (6) Constraints on energy storage equipment.

[0078] In step S3, the Grey Wolf algorithm is used to solve the distribution network optimization scheduling model, including: Step S31: Constructing the solution vector of the Grey Wolf algorithm, i.e., the control variables, with the following expression: Where T1 is the total number of optimized scheduling periods; , , , , These are, respectively, the adjustable power supply power column vector, the energy storage charging power column vector, the energy storage discharging power column vector, the electric vehicle charging power column vector, and the electric vehicle discharging power column vector; Step S32: Obtain initial data, which includes the rural power distribution network topology, load factor, and electric vehicle parameters; instantiate constraints based on the initial data; initialize the population size. The maximum number of iterations H is determined; the current number of iterations h=1 is initialized; the position of each gray wolf in the gray wolf population is initialized using sin chaos, and the position corresponds to the solution vector: in, and The first Only Gray Wolf, the first Only gray wolves are The location of 4-dimensional space; The number of gray wolves in the population; Step S33: If the current iteration number is greater than the maximum iteration number H, proceed to step S37; otherwise, proceed to step S34; Step S34: Calculate the fitness value of each gray wolf in the population based on the position of each gray wolf in the population, and the fitness function is the objective function F; Select the three gray wolves with the smallest fitness values ​​and satisfying the constraints from the gray wolf population as leader wolves; Compare the fitness value of the leader wolf with the smallest fitness value and satisfying the constraints with the fitness value corresponding to the global optimal solution. If the fitness value of the leader wolf is less than the fitness value corresponding to the global optimal solution, then update the global optimal solution to the control variable of the leader wolf; otherwise, the global optimal solution remains unchanged; Step S35: Determine the value of the dynamic weight factor: The positions of each gray wolf in the gray wolf population at iteration number h+1 are calculated based on the dynamic weighting factor: in, and These represent the current iteration and the next iteration, respectively. A gray wolf The location of 4-dimensional space; These are the weighting coefficients. For the first In the nth iteration The global optimal solution of dimension; Indicates a uniform distribution; A random number between [0,1] that follows a normal distribution; This is a warning value; Set the threshold; Step S36: Assign the current iteration number to h+1, and proceed to step S33; Step S37: Use the fitness value corresponding to the global optimal solution as the optimal result of the optimization model.

[0079] In this invention, the actual charging and discharging range constraints of electric vehicles are determined based on electric vehicle parameters, and power flow constraints, node voltage constraints, branch current constraints, and distribution transformer load rate constraints are determined based on the rural power distribution network topology and load factor.

[0080] The Gray Wolf Algorithm optimizes its algorithm by simulating the social hierarchy and hunting behavior of gray wolves within a pack, learning their actions in tracking, surrounding, and attacking prey. When simulating prey tracking behavior, the Gray Wolf Algorithm assumes the search range is... Dimension, prey and the first The positions of the gray wolves are as follows: and The process by which a gray wolf discovers its prey and forms an encirclement can be represented as follows: In the formula: It is the swing factor; The convergence factor; and A random number between [0, 1]; The control parameter is linearly reduced from 2 to 0; The distance between the wolf and its prey; and Iterations The location of the prey afterwards was the same as the first One gray wolf position.

[0081] (1) Control variables: The distribution network optimization scheduling model for improving the distributed photovoltaic absorption capacity uses electric vehicle charging and discharging power, adjustable power supply power value, and energy storage charging and discharging power value as control variables. The expression is as follows: In the formula: T1 is the total number of optimized scheduling periods; The overall matrix formed by encoding the control variables; , , , , These are, respectively, the adjustable power supply column vector, the energy storage charging power column vector, the energy storage discharging power column vector, the electric vehicle charging power column vector, and the electric vehicle discharging power column vector.

[0082] (2) The position of the gray wolf population can be represented by a matrix. In the formula: This indicates the number of gray wolves in the population.

[0083] Based on the above formula, the fitness matrix of the gray wolf population It can be represented as: (3) Gray Wolf Population Position Update Method ① Sin Chaotic Initialization of the Population To solve the problems of poor diversity and uneven distribution of the initial population in the gray wolf optimization algorithm, a Sin chaotic model is introduced to uniformly initialize the population, thereby enhancing its optimization efficiency and speed. The Sin chaotic initialization of the gray wolf population is shown in the following formula: In the formula: and The first Only Gray Wolf, the first Only gray wolves are The location in 3D space.

[0084] ② Gray Wolf Position Update Method: In the basic Gray Wolf algorithm, the control parameter decreases linearly from 2 to 0, which cannot adapt well to complex search spaces. Therefore, a dynamic weight factor is introduced to adjust the change of the control parameter and accelerate the convergence speed of the algorithm. The calculation formula is shown below: In the formula: This represents the current iteration number; This represents the maximum number of iterations. and These represent the current iteration and the next iteration, respectively. A gray wolf The location of 4-dimensional space; For the first In the nth iteration The global optimal solution of dimension; Indicates a uniform distribution; A random number between [0,1] that follows a normal distribution; This is a warning value; The threshold value is used.

[0085] This invention models the spatiotemporal characteristics and charging / discharging behavior of electric vehicles in rural areas. It constructs a model of the travel chain and charging / discharging behavior of rural electric vehicles, considering factors such as functional areas, road conditions, weather conditions, and temperature in typical rural scenarios, thus accurately depicting the spatiotemporal characteristics of rural electric vehicles.

[0086] This invention presents a distribution network optimization scheduling method aimed at improving the distributed photovoltaic (PV) absorption capacity. First, from the perspective of grid operation safety, a voltage vulnerability index is proposed to measure the grid's resilience. Second, with the objective functions of maximizing distributed PV absorption capacity, minimizing the voltage vulnerability index, and minimizing operating costs, an optimal distribution network scheduling model is constructed using constraints such as distribution network branch power flow constraints, node voltage constraints, branch current constraints, distribution transformer load rate constraints, controllable power supply constraints, energy storage device constraints, and electric vehicle regulation potential. Finally, an improved Grey Wolf algorithm is used to solve the model. This method can maximize the PV absorption capacity of rural distribution networks while ensuring grid security and economy.

[0087] The apparatus provided for carrying out the present invention will be described below. The specific implementation process and technical effects are as described above and will not be repeated below.

[0088] Optionally, embodiments of the present invention provide a distribution network photovoltaic absorption capacity enhancement device considering the spatiotemporal characteristics of rural electric vehicles. The device includes: an initialization module configured to construct a rural electric vehicle travel chain structure, instantiating the rural electric vehicle travel chain based on the rural electric vehicle travel activity type, spatial transfer probability, travel time, and stay duration; constructing a rural electric vehicle charging and discharging behavior model based on rural road conditions, meteorological conditions, and temperature; determining the rural electric vehicle charging load curve and regulation potential based on the rural electric vehicle travel chain and the rural electric vehicle charging and discharging behavior model; a model construction module configured to determine the actual charging and discharging power range of electric vehicles based on the rural electric vehicle charging load curve and regulation potential, constructing constraints based on the actual charging and discharging power range of electric vehicles, and constructing a distribution network optimization scheduling model for photovoltaic absorption capacity enhancement, including an objective function and constraints; wherein the constraints include power flow constraints, branch current constraints, energy storage device constraints, distribution transformer load rate constraints, controllable power supply constraints, and actual charging and discharging constraints of electric vehicles; and a calculation module configured to solve the distribution network optimization scheduling model using the Grey Wolf algorithm.

[0089] The above system is used to execute the methods provided in the foregoing embodiments, and its implementation principle and technical effects are similar, so they will not be described again here.

[0090] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0091] The modules described above can be connected or communicate with each other via wired or wireless connections. Wired connections may include metal cables, optical fibers, hybrid cables, or any combination thereof. Wireless connections may include connections via LAN, WAN, Bluetooth, ZigBee, or NFC, or any combination thereof. Two or more modules can be combined into a single module, and any module can be divided into two or more units. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here.

[0092] It should be noted that these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Furthermore, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Additionally, these modules can be integrated together to form a System-on-a-Chip (SOC).

[0093] The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0094] The present invention also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, is used to perform the above-described method embodiments.

[0095] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0096] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0097] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0098] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for improving the photovoltaic absorption capacity of a distribution network considering the spatiotemporal characteristics of rural electric vehicles, characterized in that, The method includes: Step S1: Constructing a rural electric vehicle travel chain structure, instantiating the rural electric vehicle travel chain based on rural electric vehicle travel activity types, spatial transfer probabilities, travel time, and dwell time; constructing a rural electric vehicle charging and discharging behavior model based on rural road conditions, weather conditions, and temperature; determining the rural electric vehicle charging load curve and regulation potential based on the rural electric vehicle travel chain and the rural electric vehicle charging and discharging behavior model; Step S2: Determining the actual charging and discharging power range of electric vehicles based on the rural electric vehicle charging load curve and regulation potential, constructing constraints based on the actual charging and discharging power range of electric vehicles, and constructing a distribution network optimization scheduling model for improving photovoltaic absorption capacity, including an objective function and constraints; wherein, the constraints include power flow constraints, branch current constraints, energy storage device constraints, distribution transformer load rate constraints, controllable power supply constraints, and actual charging and discharging constraints of electric vehicles; Step S3: Solving the distribution network optimization scheduling model using the Grey Wolf algorithm.

2. The method as described in claim 1, characterized in that, In step S1, a rural electric vehicle travel chain structure is constructed. This is done by instantiating the rural electric vehicle travel chain based on the types of rural electric vehicle travel activities, spatial transfer probabilities, travel time, and dwell time. This includes: constructing the rural electric vehicle travel chain structure, where the rural electric vehicle travel chain links different travel purposes of rural electric vehicle users based on time sequence, representing a sequence of various travel activities. The rural electric vehicle travel chain includes a time layer and a spatial layer. The time layer represents the time changes from the start to the end of various travel activities, including several travel time periods, dwell time periods, arrival time points, and departure time points. The spatial layer corresponds to the time layer, representing the spatial location corresponding to the travel time periods, dwell time periods, arrival time points, and departure time points in the time layer; and instantiating the rural electric vehicle travel chain based on the types of rural electric vehicle travel activities, spatial transfer probabilities, travel time, and dwell time.

3. The method as described in claim 1, characterized in that, In step S1, a rural electric vehicle charging and discharging behavior model is constructed based on rural road conditions, weather conditions, and temperature, including: In the formula, and Electric vehicles The charging and discharging power at any given time; and Upper and lower limits for electric vehicle charging power; and These are the upper and lower limits of the discharge power of electric vehicles; and Electric vehicles Time and The energy of a moment; and For the charging and discharging efficiency of electric vehicles; For time intervals; The speed of the electric vehicle; for Operating speed under normal weather conditions. Normal weather refers to weather conditions without rain, snowfall, road icing, sandstorms, or dense fog, and with ambient temperatures between 10 and 25°C. Weather correction factor for normal weather. ； This is a weather correction factor. ； The ambient temperature is The power consumption when the air conditioner is turned on; For speed is Power consumption per unit distance per hour; For temperature The minimum effective capacity of an electric vehicle; For temperature The maximum effective capacity of electric vehicles at that time.

4. The method as described in claim 1, characterized in that, The objective function is: in, Let be the objective function. This refers to the amount of photovoltaic power absorbed. Due to voltage vulnerability, The operating cost is denoted by n, where n is the photovoltaic number. Where is the total number of photovoltaic cells, and T is the total number of time periods. for Moment Photovoltaics active power, This is a weighting factor for the average voltage vulnerability. for Average voltage vulnerability index value at any given time. This is a weighting factor for the vulnerability of voltage balance. for Voltage balance index value at any time The total number of nodes. Number the nodes. After normalization Time Node Overall voltage vulnerability value, Before normalization Time Node Overall voltage vulnerability value, Before normalization Minimum vulnerability value of all nodes at any given time. Before normalization The maximum vulnerability value of all nodes at any given time. for Time Node voltage, For nodes Rated voltage, This is the maximum voltage offset. For nodes connected to adjustable power supplies, For nodes connected to energy storage, For the connected electric vehicle nodes, The cost of operation and maintenance per unit of adjustable power supply. The number of nodes connected to an adjustable power source. for Nodes that are always connected to an adjustable power source The actual power generation capacity of the adjustable power source The operation and maintenance cost per unit of energy storage charging and discharging power. This represents the total number of nodes connected to energy storage. for Nodes that are constantly connected to energy storage Energy storage charging power, for Nodes that are constantly connected to energy storage The discharge power of the stored energy Cost of responding to charging and discharging power demand per unit of electric vehicle This represents the number of electric vehicle nodes that can be connected. for Electric vehicle nodes that are always connected The regulating power, Cost per unit of network loss power For the active power loss of the distribution network, For all branches of the distribution network, For nodes 、 The set of conductances, for Time Node voltage, for Time Node voltage, for The phase angle difference between the beginning and end nodes of the branch at any given time.

5. The method as described in claim 1, characterized in that, The constraints include: This represents the minimum node voltage. This represents the maximum value of the node voltage; branch road The minimum value of the current. branch road Current value, branch road The maximum value of the current; Let be the active power at the beginning of the line at time t. Let t be the reactive power at the beginning of the circuit. This is the heavy load factor. The rated capacity of the distribution transformer in the power distribution network; Adjustable power supply at time t Lower limit of power generation capacity Adjustable power supply at time t Power generation capacity Adjustable power supply at time t Maximum power generation capacity; Energy storage device at time t The upper limit of charging power, For energy storage devices Charging status, for Real-time energy storage devices The charging power, Energy storage device at time t The upper limit of discharge power, For energy storage devices Discharge state, Energy storage device at time t+1 The state of charge, Energy storage device at time t The state of charge, Energy storage device at time t Charging efficiency, For time intervals, Energy storage device at time t The discharge efficiency, for Real-time energy storage devices The discharge power, Energy storage device at time t The lower limit of the state of charge. Energy storage device at time t The upper limit of the state of charge, Energy storage device at time t State of charge at the start of scheduling Energy storage device at time t The state of charge at the end of the scheduling process.

6. The method as described in claim 5, characterized in that, In step S3, the Grey Wolf algorithm is used to solve the distribution network optimization scheduling model, including: Step S31: Constructing the solution vector of the Grey Wolf algorithm, i.e., the control variables, with the following expression: Where T1 is the total number of optimized scheduling periods; 、 、 、 、 These are, respectively, the adjustable power supply power column vector, the energy storage charging power column vector, the energy storage discharging power column vector, the electric vehicle charging power column vector, and the electric vehicle discharging power column vector; Step S32: Obtain initial data, which includes the rural power distribution network topology, load factor, and electric vehicle parameters; instantiate constraints based on the initial data; initialize the population size. The maximum number of iterations H is determined; the current number of iterations h=1 is initialized; the position of each gray wolf in the gray wolf population is initialized using sin chaos, and the position corresponds to the solution vector: in, and The first Only Gray Wolf, the first Only gray wolves are The location of 4-dimensional space; The number of gray wolves in the population; Step S33: If the current iteration number is greater than the maximum iteration number H, proceed to step S37; otherwise, proceed to step S34; Step S34: Calculate the fitness value of each gray wolf in the population based on the position of each gray wolf in the population, and the fitness function is the objective function F; Select the three gray wolves with the smallest fitness values ​​and satisfying the constraints from the gray wolf population as leader wolves; Compare the fitness value of the leader wolf with the smallest fitness value and satisfying the constraints with the fitness value corresponding to the global optimal solution. If the fitness value of the leader wolf is less than the fitness value corresponding to the global optimal solution, then update the global optimal solution to the control variable of the leader wolf; otherwise, the global optimal solution remains unchanged; Step S35: Determine the value of the dynamic weight factor: The positions of each gray wolf in the gray wolf population at iteration number h+1 are calculated based on the dynamic weighting factor: in, and These represent the current iteration and the next iteration, respectively. A gray wolf The location of 4-dimensional space; These are the weighting coefficients. For the first In the nth iteration The global optimal solution of dimension; Indicates a uniform distribution; A random number between [0,1] that follows a normal distribution; This is a warning value; Set the threshold; Step S36: Assign the current iteration number to h+1, and proceed to step S33; Step S37: Use the fitness value corresponding to the global optimal solution as the optimal result of the optimization model.

7. A device for enhancing the photovoltaic absorption capacity of a power distribution network, considering the spatiotemporal characteristics of rural electric vehicles, characterized in that, The device includes: Initialization Module: Configured to construct the rural electric vehicle travel chain structure, instantiating the rural electric vehicle travel chain based on rural electric vehicle travel activity types, spatial transfer probabilities, travel time, and dwell time; constructing a rural electric vehicle charging and discharging behavior model based on rural road conditions, weather conditions, and temperature; determining the rural electric vehicle charging load curve and control potential based on the rural electric vehicle travel chain and the rural electric vehicle charging and discharging behavior model; Model Construction Module: Configured to determine the actual charging and discharging power range of electric vehicles based on the rural electric vehicle charging load curve and control potential, constructing constraints based on the actual charging and discharging power range of electric vehicles, and constructing a distribution network optimization scheduling model for improving photovoltaic absorption capacity, including an objective function and constraints; the constraints include power flow constraints, branch current constraints, energy storage device constraints, distribution transformer load rate constraints, controllable power supply constraints, and actual charging and discharging constraints of electric vehicles; Calculation Module: Configured to solve the distribution network optimization scheduling model using the Grey Wolf algorithm.

8. An electronic device, characterized in that, The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method as described in any one of claims 1-6.

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