Power distribution network source website collaborative planning method and system considering V2G mode

Abstract

The invention relates to the technical field of power distribution network source website collaborative planning, and particularly discloses a power distribution network source website collaborative planning method and system considering a V2G mode. The method comprises the following steps: acquiring an existing topological structure of a power distribution network, traffic road network structure data, electric vehicle number space-time distribution data, photovoltaic resource distribution data and a historical daily load power curve set; predicting to obtain a node load power set, a V2G charging and discharging station maximum schedulable power set and a PVG station maximum output power set in various typical day scenes in the future; establishing an upper-layer planning model by taking the minimum total economic cost of the system as a target, and establishing a lower-layer planning model by taking the minimum annual comprehensive operation cost as a target; and solving the bilayer planning model by adopting a nested iterative algorithm, and outputting a power distribution network source website collaborative planning scheme. According to the method, the economical efficiency and reliability of collaborative planning of the power distribution network, the PVG station and the V2G charging and discharging station are improved, the resource utilization efficiency is remarkably improved, and the total economic cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative planning of distribution network source - site, and in particular, to a method and system for collaborative planning of distribution network source - site considering the V2G mode. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] V2G (Vehicle - to - Grid), that is, vehicle - to - grid, V2G technology is a technology that enables electric vehicles (EVs) not only to charge from the grid but also to discharge power back to the grid.

[0004] With the development of the new power system and the promotion of energy transformation, the distribution network is gradually evolving towards the direction of high - proportion renewable energy access, high power electronics, and high - flexible interactivity. The explosive growth of electric vehicles and the large - scale grid connection of photovoltaic generation (PVG) stations bring new challenges to the planning and operation of the distribution network: on the one hand, the maturity of V2G (Vehicle - to - Grid) technology makes electric vehicles transform from a single load to a dual role of "adjustable load + mobile energy storage", changing the load structure and characteristics of the distribution network; on the other hand, the volatility and intermittency of PVG station output and the spatio - temporal randomness of electric vehicle charging demand exacerbate the peak - valley difference and voltage fluctuation problems of the distribution network, leading to the complexity of grid power flow.

[0005] Traditional planning methods usually plan PVG stations, distribution networks, and V2G charging stations independently, that is, separately optimize the PVG site selection and capacity determination, distribution network expansion, and V2G charging and discharging station layout, and optimize their respective objective functions. Although such methods simplify the computational complexity, they split the spatio - temporal complementary characteristics of distributed PVG stations and V2G charging and discharging stations, ignore the matching potential of charging and discharging loads and photovoltaic output in the time dimension, and the possibility of grid dynamic reconfiguration in the space dimension. In the case where the charging and discharging behavior of electric vehicles has a great impact on the voltage, power flow, and system stability of the distribution network, it will lead to waste of grid resources or improper system scheduling.

[0006] In recent years, some studies have attempted to conduct collaborative planning of V2G charging and discharging stations and distributed power sources to improve the planning and operation efficiency of the power grid. However, most of the existing joint planning methods are based on a determined distribution network. There are few literatures that simultaneously conduct research on the expansion planning of the distribution network, and they do not involve solving by incorporating PVG station output, V2G charging and discharging behavior, and distribution network topology optimization into a unified model.

[0007] In addition, when jointly planning PVG stations, distribution networks, and V2G charging and discharging stations, it is necessary to establish a unified optimization model and consider a large number of decision variables, such as the location of charging stations and the capacity of charging and discharging power, the installation location and rated power of photovoltaic power sources, the distribution network line upgrade plan, and the network reconstruction strategy. At the same time, it is also necessary to handle the non-linearity and dynamic changes of constraint conditions, which leads to an explosive growth in the problem dimension in combination, and traditional optimization algorithms face double challenges of computational efficiency and solution accuracy. Summary of the Invention

[0008] To solve the above problems, the present invention proposes a collaborative planning method and system for distribution network source and site considering the V2G mode, establishing a collaborative planning model for the expansion of distribution networks, photovoltaic power generation (PVG), and V2G charging and discharging stations, and improving the economy and reliability of distribution network planning schemes.

[0009] In some embodiments, the following technical solutions are adopted: A collaborative planning method for distribution network source and site considering the V2G mode, comprising: Obtain the existing topological structure of the distribution network, traffic road network structure data, spatio-temporal distribution data of the number of electric vehicles, photovoltaic resource distribution data, and historical daily load power curve sets; Confirm the preliminary spatial location constraints of each facility in the collaborative planning of the source and site; cluster the historical daily load power curves into typical daily load power curves, and then predict the node load power sets, the maximum schedulable power sets of V2G charging and discharging stations, and the maximum output power sets of PVG stations under various future typical daily scenarios; Establish an upper-layer planning model with the goal of minimizing the total economic cost of the system, and establish a lower-layer planning model with the goal of minimizing the annual comprehensive operating cost; Use the nested iteration algorithm to solve the two-layer planning model. Through the upper-layer planning model, decide the types of new / upgraded lines, the capacity and location of substations, and the site selection and capacity determination schemes of V2G charging and discharging stations and PVG stations, and output a preliminary planning scheme that meets the load growth demand and has the lowest investment cost, and transfer the distribution network topology and equipment information to the lower-layer model; solve the annual optimal operation results through the lower-layer planning model, and synchronously generate the over-limit branch and node marker sets and penalty costs and feedback them to the upper-layer planning model; Iteratively calculate until the investment-operation total cost fluctuates stably and the safety index meets the standard, and output the collaborative planning scheme of the distribution network source and site.

[0010] In other embodiments, the following technical solutions are adopted: A collaborative planning system for distribution network source and site considering the V2G mode, comprising: A data acquisition module, which is used to acquire the existing topological structure of the distribution network, traffic road network structure data, spatio-temporal distribution data of the number of electric vehicles, photovoltaic resource distribution data, and historical daily load power curve sets; A scenario generation module, which is used to confirm the preliminary spatial location constraints of each facility in the collaborative planning of the source website; cluster the historical daily load power curves into typical daily load power curves, and then predict the node load power sets, the maximum schedulable power sets of V2G charging and discharging stations, and the maximum output power sets of PVG stations under various future typical daily scenarios; A model construction module, which is used to establish an upper-layer planning model with the goal of minimizing the total economic cost of the system and a lower-layer planning model with the goal of minimizing the annual comprehensive operation cost; A model optimization and solution module, which is used to solve the bi-level programming model by using a nested iterative algorithm. Through the upper-layer planning model, it decides the types of new construction / upgrading of lines, the capacity and location of distribution transformers, and the site selection and capacity determination schemes of V2G charging and discharging stations and PVG stations, outputs a preliminary planning scheme that meets the load growth requirements and has the lowest investment cost, and transfers the distribution network topology and equipment information to the lower-layer model; through the lower-layer planning model, it solves the annual optimal operation results, and synchronously generates the over-limit branch and node marking sets and the penalty cost feedback to the upper-layer planning model; Iteratively calculate until the investment-operation total cost fluctuates stably and the safety index reaches the standard, and output the collaborative planning scheme of the distribution network source website.

[0011] Compared with the prior art, the beneficial effects of the present invention are: (1) Through the data collection of multiple systems (intelligent vehicle networking, geographic information system, energy management platform), and combined with the improved K-means clustering algorithm and Monte Carlo algorithm, the present invention accurately simulates and predicts the future load changes and the scheduling capabilities of V2G charging and discharging stations and photovoltaic stations, provides accurate basic data for the optimization planning of the distribution network, and ensures the rationality of the planning scheme; Based on the V2G mode, through the bi-level collaborative planning model, the present invention realizes the improvement of the economy and reliability of the collaborative planning of the distribution network, PVG stations and V2G charging and discharging stations, significantly improves the resource utilization efficiency, and reduces the total economic cost.

[0012] (2) The present invention constructs a bi-level collaborative planning model. By separating the planning layer and the operation layer, it effectively optimizes the resource allocation. The upper-layer planning layer ensures the minimization of the total economic cost, including the annual construction investment cost and the annual comprehensive operation cost; the lower-layer operation layer calculates the optimal state of the distribution network in actual operation through multi-scenario optimization, reduces the over-limit of node branches and resource waste, and realizes the balance between the economy and reliability of the distribution network.

[0013] Adopt the "decision transmission - state feedback - solution correction" strategy. Through the feedback mechanism, adjust the planning solution in real time, discover and solve the over - limit problems in a timely manner, make the planning and operation process more flexible, and perform rapid correction according to the actual situation to ensure the robustness and feasibility of the planning solution.

[0014] (3) The present invention uses an adaptive hybrid particle swarm optimization algorithm to solve the upper - layer planning model. During the upper - layer planning process, flexibly adjust the hybrid particle search strategy, and dynamically adjust the search range according to the severity of the over - limit branch and node parameters transmitted from the lower layer, which ensures the high efficiency of the algorithm and improves the convergence speed, and better solves complex decision - making problems.

[0015] Adopt a parallel particle swarm optimization algorithm to solve the lower - layer planning model. Through the combination of scenario parallel optimization and the particle swarm optimization algorithm, the computing efficiency can be effectively improved. The main computing node in the parallel computing framework distributes the parallel solution tasks of multiple typical - day scenarios to different computing sub - nodes, greatly accelerating the computing efficiency, and independently optimizing each scenario to finally obtain the optimal solution of the global operation cost.

[0016] (4) The present invention is based on the V2G implementation mode of the micro - grid. In the micro - grid mode of the lower - layer operation layer, the efficient operation of the V2G charging and discharging station is realized. Combining centralized and decentralized scheduling and control methods, the lower - layer operation layer includes two parts: a centralized scheduling layer and a decentralized execution layer. The centralized scheduling layer is the intelligent vehicle - to - grid system, which is responsible for the global economic optimization, while the decentralized execution layer is the V2G charging and discharging station, which is responsible for flexible adjustment according to the charging and discharging demands of electric vehicles, improving the operation efficiency and safety of the system. Brief Description of the Drawings

[0017] Figure 1 It is the architecture diagram of the double - layer collaborative planning model in the embodiment of the present invention; Figure 2 It is the schematic diagram of the process of the collaborative planning method for the power grid - source - load - station considering the V2G mode in the embodiment of the present invention. Detailed Embodiment

[0018] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0019] Embodiment 1 In one or more embodiments, a collaborative planning method for the power grid - source - load - station considering the V2G mode is disclosed. Combining Figure 2 , it specifically includes the following processes: S101: Obtain the existing topological structure of the distribution network, traffic road network structure data, spatio-temporal distribution data of the number of electric vehicles, PV resource distribution data, and historical daily load power curve set.

[0020] Specifically, in this embodiment, data is collected from the intelligent vehicle networking system, geographic information system, and energy management platform.

[0021] Among them, the existing topological structure of the distribution network includes: detailed equipment parameters such as the geographical coordinates and capacity of the substation nodes, line impedance parameters, and main transformer capacity. Initially determine the set of distribution network lines and substation node sets that can be newly built or upgraded.

[0022] The traffic road network structure data includes: coupling the actual geographical locations in the traffic road network with the topological structure of the distribution network, and initially determining the set of candidate nodes where electric vehicle V2G charging and discharging stations can be installed.

[0023] The spatio-temporal distribution data of the number of electric vehicles includes: charging period preferences, driving trajectories, and battery capacity characteristics, and generating the maximum schedulable power set of V2G charging and discharging under various typical daily scenarios through the Monte Carlo algorithm.

[0024] The PV resource distribution data includes: simulating and generating the maximum output power set of PVG under typical daily scenarios in the distribution network area through the Monte Carlo algorithm.

[0025] The historical daily load power curve set is the collected distribution network nodes i Historical daily load power curve set .

[0026] S102: Confirm the preliminary spatial location constraints of each facility in the collaborative planning of the source website; cluster the historical daily load power curves into typical daily load power curves, and then predict the node load power set, the maximum schedulable power set of V2G charging and discharging stations, and the maximum output power set of PVG stations under various future typical daily scenarios.

[0027] In this embodiment, analyze the mapping relationship between the topological structure of the distribution network and the traffic network, and combine with the urban planning scheme to confirm the preliminary geographical locations of each facility in the collaborative planning of the source website and the location constraints of the mapped distribution network nodes, and obtain a series of alternative sets, including: Θ Line,NU The alternative set of branches for line new construction or upgrade, ij For system substation nodes; Ω TR , Ω EVS , Ω PVG Are the alternative sets of new substation areas, electric vehicle V2G charging and discharging stations, and PVG station nodes respectively.

[0028] In this embodiment, the historical daily load curves are clustered into a set of typical daily load power curves by an improved K-means clustering algorithm, and the node load power sets under various future typical daily scenarios are obtained by the recursive method. The Monte Carlo algorithm and the improved K-means clustering algorithm are used to generate the maximum schedulable power sets of V2G charging and discharging power stations and the maximum output power sets of PVG power stations under various future typical daily scenarios.

[0029] In the lower operation layer part of the model, different typical daily scenarios correspond to different power flow calculation results, which can more accurately reflect the actual situation of power grid operation and increase the reliability of the model.

[0030] In this embodiment, the daily load curve sets of the distribution network nodes i are obtained. The interpolation method is used to correct the missing load data of each node. The typical daily load curves of each node are clustered respectively by the improved K-Means clustering algorithm, and finally the daily load curves and their probabilities under future typical daily scenarios are obtained by the recursive algorithm. Specifically: S1021: In the initial stage, a curve is randomly selected from the original samples as the initial clustering center curve. Secondly, two threshold parameters T 1 and T 2 are set, T 1 < T 2. The curves with a distance less than T 1 are the curves within the cluster, the curves with a distance in the range of T 1 to T 2 are the suspicious curves, and the curves outside the T 2 circle are the curves outside the cluster. A new cluster center curve is selected from the data marked as curves outside the cluster. Repeat the iteration until all the curves outside the cluster are correctly classified. After deleting the smaller clusters, the number of clusters is the initial K value, and then K initial clustering center curves are selected.

[0031] In this embodiment, by controlling the magnitudes of the threshold parameters T 1 and T 2, the granularity of the clustering clusters can be controlled, so that the number of clustering clusters is automatically determined by the data distribution without manual presetting; it alleviates the sensitivity of the K-Means clustering algorithm to the initial value and can avoid the problem that the clustering result falls into the local optimum affected by the initial K value.

[0032] S1023: After knowing the clustering number K value and the initial clustering center curves, the K-means clustering method is adopted: calculate the shortest Euclidean space distance between each object and each initial clustering center curve, update the center curves of all objects in each cluster, and obtain K corresponding clustering center curves. Repeat the iteration until the clustering center curves no longer change or reach the maximum number of iterations. ​

[0033] Cluster the nodes of the distribution network by the improved K-means clustering method i of s typical daily scenario load curves and their occurrence probabilities .

[0034] S1024: Typical daily load recursion model. Assume that the overall load growth rate and the scenario occurrence probability correction factor in the future planning year relative to the historical year are known. The simple daily load recursion model formula is: ; ; In the formula, , are the load curves and their probabilities under the typical daily scenario i of the distribution network nodes s respectively; and are the predicted values of the future typical daily load curve set and its probability set of node s under scenario i respectively.

[0035] Then normalize the probabilities to ensure that the occurrence probabilities satisfy the following formula: .

[0036] In this embodiment, combined with the future growth of the number of electric vehicles at each node in the area where the distribution network is located, the Monte Carlo algorithm is used to generate the maximum dispatchable power set of V2G charging and discharging under various future typical daily scenarios. Specifically: Obtain the number of electric vehicles at the distribution network node i in the planning year, denoted as . The charging duration and start charging time of each vehicle are usually within certain fixed time periods and start at a random moment of a day. Assume that the start charging time of each day follows a uniform distribution within the time range ( , ), , are the earliest and latest times to start charging the electric vehicle respectively. Assume that the charging and discharging power of each vehicle is , , both are fixed values. Assume that the charging and discharging durations are both fixed values, denoted as , respectively. Use Monte Carlo simulation to obtain the charging and discharging power of each node, and superimpose the corresponding charging and discharging power on the nodes of the planned charging and discharging station according to the charging service range.

[0037] The process of using the Monte Carlo algorithm to generate the maximum schedulable power set for V2G charging and discharging under various typical daily scenarios in the future is as follows: (1) Obtain the number of electric vehicles at the distribution network nodes i within the planned year .

[0038] (2) For each electric vehicle at node i , generate its charging start time according to the uniform distribution rule in ( , ), obtain the charging power and the charging duration , and generate the charging power of a single electric vehicle. For all electric vehicles within the service range of the same node i , add up the charging power of each vehicle to obtain the total schedulable charging power of this node in a day. Traverse all nodes of the distribution network to obtain the total schedulable charging power of each node in a day.

[0039] (3) For each electric vehicle at node i , after charging is completed, start generating its discharging start time according to the uniform distribution rule during the time other than charging, obtain the discharging power and the discharging duration , and generate the discharging power of a single electric vehicle. For all electric vehicles within the service range of the same node i , add up the discharging power of each vehicle to obtain the total schedulable discharging power of this node in a day. Traverse all nodes of the distribution network to obtain the total schedulable discharging power of each node in a day.

[0040] (4) Repeat M times the steps (2) and (3) to obtain M charging scenarios and discharging scenarios.

[0041] (5) Use the improved K-means clustering algorithm mentioned above, set the K value to S , and cluster the charging and discharging powers of M scenarios into the maximum schedulable power sets for electric vehicle charging and discharging hour by hour under S typical days at node i , which are respectively denoted as , .

[0042] In this embodiment, to deeply explore the PVG output characteristics of this area, the Monte Carlo algorithm is used to generate the maximum output power set of the PVG station under various typical daily scenarios in the future, specifically: Assume that considering factors such as temperature, component efficiency, and the installation angle of photovoltaic modules, only the influence of solar irradiance on the output of PVG is considered. Since the urban distribution network area is small, the solar irradiance at each node in this area of the distribution network is t the same at the same moment. Consequently, the output per unit capacity of PVG is the same. The specific steps to simulate the solar irradiance and photovoltaic power generation output for one day (24 hours) are as follows: (1) The solar irradiance under different scenarios conforms to a normal distribution, and the mean and variance of the normal distribution parameters are estimated from historical data or meteorological models. The typical day is divided into 24 hours ( t t = 1, 2, …, 24), and the expected irradiance t at each moment and the standard deviation of the irradiance are fitted from the historical data

[0043] parameters. t (2) Use Monte Carlo simulation to generate irradiance. Use the Monte Carlo method to generate a random irradiance value at each hour

[0044] . where is a random variable generated from the standard normal distribution.

[0045] (3) Repeat step (2) M N times to generate M different solar irradiance scenarios.

[0046] (4) Calculate the output power of the PVG station. For each simulated time period t the output power of the PVG station is calculated from the irradiance , and the formula is:[[]] . where is the efficiency of the photovoltaic module; is the rated power of the PVG station; is the standard irradiance; is the randomly generated irradiance at the t-th hour.

[0047] (5) Adopt the improved K-means clustering algorithm mentioned above, set the K value to S , and cluster the output power of the PVG station in M scenarios into the maximum dispatchable power set of the nodes S per hour under i K typical days, denoted as .

[0048] In this embodiment, the maximum schedulable power of V2G charging and discharging and the maximum schedulable output power of the PVG station under various typical daily scenarios in the future obtained by the Monte Carlo algorithm are only for the description of the generation method; the specific V2G charging and discharging station curves and the output curves of the PVG station need to be regenerated under different scenarios according to the selected site location and capacity specified by the upper-layer planning results, and then the optimal operation strategy of the operation layer is solved.

[0049] S103: Establish an upper-layer planning model with the goal of minimizing the total economic cost of the system.

[0050] Combined with Figure 1 As shown in the two-layer collaborative planning model, the objective function of the upper-layer planning model is to minimize the total economic cost, and the formula is: min F = C inv + C ope ; In the formula, the annual construction investment cost includes the annualized sum of the investment costs of new / upgraded lines, new substations, PVG stations, and V2G charging and discharging stations; the annual operation cost C ope The calculation will be calculated in the lower-layer operation layer and then fed back to the upper-layer planning layer.

[0051] Among them, the annual construction investment cost C inv is annualized through the full-life cycle investment annual cost conversion coefficient, including the new construction of lines , line upgrades , new substations , new V2G charging and discharging stations and new PVG stations The annualized investment cost, and the specific formula is: ; In the formula, is the alternative set of branches for new construction or upgrade of lines, ij is the system substation node; Ω TR , Ω EVS , Ω PVG are the alternative sets of nodes for new substations, electric vehicle V2G charging and discharging stations, and PVG stations respectively; K Line,N , K Line,U , K TR , K EVS ,K PVG They are respectively the sets of alternative types for new line construction and upgrade, new substation area construction, new V2G power station construction, and new PVG station construction, k which are optional types; R Line , R TR , R EVS , R PVG They are respectively the annual cost conversion factors for the whole life cycle investment of lines, substations, V2G power stations, and PVG stations, r where \(i\) is the discount rate.

[0052] , , , , They are respectively the 0-1 decision variables for new line construction, upgraded line construction, new substation area construction, new V2G power station construction, and new PVG station construction, where 0 means not newly built and 1 means newly built; , They are respectively the lengths of the newly built and upgraded lines, , , They are respectively the i rated capacities of the equipment of the types of newly built substations, newly built V2G power stations, and newly built PVG stations in the nodes, k which are continuous decision variables greater than 0; , , They are respectively the k numbers of the equipment of the types of newly built substations, newly built V2G power stations, and newly built PVG stations on the i nodes, which are integer variables greater than 0; These are all decision variables of the upper-level planning layer.

[0053] , , , , They are respectively k the unit investment and operation and maintenance cost coefficients for new line construction and upgrade, new substation area construction, new V2G power station construction, and new PVG station construction of the

[0054] The constraint conditions of the upper-layer planning layer include the constraints on the new construction and upgrade of line types (constraints on the new construction and upgrade of line types considering conventional load growth and feedback on new current and voltage over-limit); the constraints on the new construction type and capacity of the substation area (constraints on the new construction and upgrade of the substation area type considering conventional load growth and feedback on load over-limit); the constraints on the new construction of V2G charging and discharging stations (constraints on the site selection, type, quantity, and service capacity of the newly built V2G charging and discharging stations); the constraints on the new construction of PVG stations (constraints on the site selection and type, installed capacity, and quantity of PVG stations).

[0055] The above constraint conditions can improve the reliability and flexibility of the power grid, reduce the risk of load over-limit, and optimize resource allocation; ensure the reasonable distribution of the substation area capacity, prevent equipment damage or unstable power supply caused by excessive load; optimize the construction and operation of V2G charging and discharging stations, enhance the load regulation ability of the power grid and the battery use efficiency, and reduce over-investment and resource waste; improve the energy utilization efficiency of photovoltaic power stations, and at the same time reasonably control the installed capacity and quantity to avoid over-investment or under-investment.

[0056] Among them, for the constraints on the new construction and upgrade of line types considering conventional load growth, it is required that at most one type of line can be selected for new construction or upgrade for each branch to avoid duplicate investment. The specific formula is: ; When current or voltage over-limit feedback occurs in a branch or at the end node, the constraints on the new construction and upgrade of line types need to be corrected, and the set of voltage and current over-limit branches in the lower-layer operation feedback The relevant branches are synchronously updated to In the upper-layer planning layer, the planning or upgrade planning of the line type of this branch is mainly considered.

[0057] For the constraints on the new construction type and capacity of the substation area considering conventional load growth, the substation area capacity needs to cover the basic load and the future new load growth in the substation area (including basic load growth and V2G charging and discharging demand), and at the same time, it needs to meet the main transformer capacity limit. The specific formula is: ; ; In the formula, Ω Total are the sets of all nodes in the distribution network respectively, and the quantity is N Total ; is the basic capacity of the substation area. If it is a newly built substation area, the basic capacity is 0; is the safety margin coefficient of the substation area, which is usually set to [0.6, 0.8] according to the importance of the nodes; 、 are the current basic load of the substation area and the future new load growth in the substation area respectively;E SS is the capacity of the main transformer in the distribution network.

[0058] When load over - limit feedback occurs at a node, it is necessary to correct the constraints on the new construction type and capacity of the sub - region. The set of nodes with load over - limit feedback from the lower - layer operation Ω Node,over is synchronously updated to the Ω TR set. When planning the type and capacity of the sub - region of this node, the specific formula is: ; In the formula, is the power shortage due to power over - limit of the node feedback from the lower layer i

[0059] When building a new V2G charging and discharging station, it is necessary to consider the location constraints, type constraints, quantity constraints, and service capacity constraints of the new V2G charging and discharging station. Specifically: To avoid overlapping service areas, improve the economy of planning, and enhance the user experience, a minimum geographical distance needs to be maintained between charging stations d min . Obtain the actual geographical coordinates between charging station i and charging station j , and calculate the geographical location distance d ij . The location constraint formula for the V2G charging and discharging station is obtained as: ; Due to the limited urban land resources, it is necessary to avoid duplicate construction and optimize the space utilization rate. Only one type of V2G charging and discharging station is allowed to be built at the same node, and the quantity does not exceed one. The type and quantity constraint formula for the new V2G charging and discharging station is: ; The capacity of the charging station needs to match the total charging demand of electric vehicles within the service area. The local demand weight coefficient i of node is determined by factors such as population density, traffic flow, and commercial activities at the node and is a constant. The attraction weight i of node for the charging demand of all electric vehicles in the power supply area of the distribution network i is jointly determined by the local demand weight of node i and the actual geographical distance between node j and

[0060] ; ; In the formula,​ For nodes within the power supply area of the distribution network i The electric vehicle charging demand of the charging station; For node i The attraction weight of the charging station for all electric vehicle charging demands within the power supply area; For the total attraction weight of the charging stations of all nodes for all electric vehicle charging demands within the area; For the l th node, the attraction weight for all electric vehicle charging demands within the power supply area of the distribution network; First, the intelligent vehicle networking system collects basic data, and then the total electric vehicle charging demand in the future power supply area of the distribution network is obtained by using the clustering algorithm and the extrapolation method.

[0061] The constraints of the newly built PVG station include site selection and type constraints, and installed capacity constraints. Specifically: Only one type of PVG station is allowed to be installed at the same node, which simplifies operation and maintenance management and reduces equipment compatibility problems. The photovoltaic capacity needs to match the substation area capacity to prevent over-generation and light curtailment.

[0062] The formula for the site selection and installation type constraints of the PVG station is: ; The formula for the installed capacity constraints of the PVG station is: .

[0063] S104: Establish a lower-level planning model with the goal of minimizing the annual comprehensive operation cost.

[0064] In this embodiment, the objective function of the annual comprehensive operation cost of the lower-level planning model is: ; In the formula, Ω S Is the set of typical daily scenarios that may occur in a year; Is the natural daily constant in a year, = 365; Is the probability of occurrence of the typical daily scenario s ; ; , , , , Respectively represent the power purchase cost, network loss cost, light curtailment penalty cost, V2G battery degradation cost, and safety over-limit penalty cost of the distribution network; Is the penalty factor adaptability coefficient.

[0065] The annual comprehensive operation cost of the lower - layer operation layer includes the power purchase cost of the distribution network, the network loss cost, the penalty cost for light curtailment, the V2G battery degradation cost, and the penalty cost for safety over - limit. Specifically: Figure 1 In the double - layer collaborative planning model shown, the formula for the power purchase cost of the power grid in the lower - layer operation layer model is: ; In the formula, T is the number of hours in a typical day, T = 24; is t the time - of - use price of the main grid at time is the typical - day scenario s in t the active power transmitted by the main transformer of the distribution network at time

[0066] Quantify the economic impact of line losses and reduce line impedance and voltage drop. The formula for the network loss cost is: ; In the formula, Θ Line is the set of all lines in the distribution network, ij represents the branch from the head node i to the end node j ; is the unit network - loss cost; is the current of branch s at time t under scenario ij ; is the resistance of branch ij ;

[0067] The amount of light curtailment is the generated electricity that the system is forced to abandon due to technical or economic reasons. The formula for the penalty cost of light curtailment is: ; In the formula, is the unit light - curtailment penalty coefficient; is the theoretical maximum PV active - power output power of node s at time i under scenario t ; represents the actual - dispatch - allowed PV active - power output power of node s at time i under scenario t ;

[0068] Quantify the cost of the impact of charge - discharge on battery life. The formula for the V2G - mode battery degradation cost is: ; In the formula, is the unit charge-discharge degradation cost and coefficient of the battery; , are respectively the s at the node i under the scenario t of the V2G charging and discharging power station at time

[0069] When the distribution network operates under different typical daily scenarios and voltage, current or load overlimits occur, the safety overlimit penalty should be incorporated into the operating cost. The safety overlimit penalty cost formula is: ; In the formula, c Vol , c Cur , c Load are respectively the unit penalty coefficients for voltage, current and load overlimits; ΔU i,t,s , Δ I ij,t,s , ΔS i,t,s are respectively the amounts of voltage, current and load overlimits.

[0070] In this embodiment, in multiple typical daily scenarios s , due to different levels of solar radiation and the response of electric vehicles, the upper limits of the decision variables are different. The allowable PVG output power s at time t at node i under multiple typical daily scenarios and its power factor angle , as well as the discharging and charging powers s at node i of the V2G charging and discharging power station at time t under scenario , will also be different (these parameters are all decision variables of the lower-level operation layer); this results in inconsistent minimum operating costs for each scenario. By statistically analyzing the probabilities of each scenario and performing weighted summation, the minimum annual comprehensive operating cost can be obtained.

[0071] In this embodiment, the constraint conditions of the lower-level operation layer include distribution network power flow and safety constraints (DistFlow branch power flow constraints, current and voltage overlimit and equipment load overload safety constraints); V2G charging and discharging power station operation constraints (maximum schedulable real-time charging and discharging power constraints); PVG station operation constraints (PVG station output power constraints, reactive power output constraints).

[0072] These constraints can effectively reduce potential safety hazards in the operation of power systems, such as line overload and voltage over-limit, improve the reliability and stability of the distribution network, and reduce the risk of power outages or equipment damage. It maximizes the charging and discharging benefits of V2G stations, enhances the grid's regulation ability, and balances the supply and demand relationship. It effectively improves the stability of photovoltaic power generation and its support for the grid, avoids grid voltage instability caused by reactive power imbalance, and improves the overall operating efficiency of the power system.

[0073] Among them, the distribution network power flow constraint is the DistFlow branch power flow constraint, specifically: ; In the formula, Ω H(j) represents the set of the head nodes of the branches with j as the end nodes; Ω B(j) represents the set of the end nodes of the branches with j as the head nodes; P ij,t,s , Q ij,t,s , P jl,t,s , Q jl,t,s are the active and reactive powers of branch s at time t and branch ij and branch jl under scenario P i,t,s , Q i,t,s are the active and reactive powers injected into node s at time t under scenario i ; U i,t,s , U j,t,s are the voltages of node s at time t and node i and j under scenario , are the reactive powers of the photovoltaic power source, the base and the growing load respectively; is the reactance of branch ij . , are the base and growing load active powers of node s at time t under scenario i respectively.

[0074] The lower-layer operation security constraints of the distribution network include voltage and current overlimits or equipment load overloads. The security constraints are modified to soft constraints, allowing overlimits but imposing penalties, and continuous iteration is required to re-plan the upper-layer equipment types and capacities. The soft security constraints for current and voltage overlimits or equipment load overloads are as follows: ; If the above security constraints occur, immediately mark the relevant nodes or branches and summarize them into the voltage and current overlimit branch set Θ Line,over and the node load overlimit set Ω Node,over , and the specific formula is: ; In the formula, , are the marked voltage and current overlimit branches and the load overlimit nodes respectively; , , are the overlimits of voltage, current and load respectively.

[0075] The operation constraints of the V2G charging and discharging station include the maximum schedulable real-time charging and discharging power constraints, specifically: The Monte Carlo algorithm and the improved K-means clustering algorithm are used to generate the maximum schedulable real-time charging power and discharging power of the V2G charging and discharging station under various typical daily scenarios in the future. The charging and discharging power actually called by the intelligent vehicle networking system should be less than or equal to the maximum schedulable real-time charging and discharging power. The specific formula is: ; In the formula, , are the maximum schedulable charging and discharging powers of the V2G charging and discharging station at node s at time t under scenario i respectively. , are the actual charging and discharging powers called by the V2G charging and discharging station at node s at time t under scenario i respectively.

[0076] The operation constraints of the PVG station include the PVG station output constraint and the reactive power output constraint, specifically: The Monte Carlo algorithm and the improved K-means clustering algorithm are used to generate the maximum schedulable PV output power of the PVG station under various typical daily scenarios in the future. The actual scheduled PV output power does not exceed the maximum schedulable PV output power . The PVG station output constraint formula is: ; In the formula, β is the curtailment rate, which needs to be lower than the 5% threshold.

[0077] When the system needs to curtail light due to safety or economic reasons, is restricted to be lower than , and it is allowed to utilize the remaining capacity of the inverter to provide reactive power support. The reactive power output constraint formula for the PVG station is: ; ; In the formula, is the power factor angle in the limit state of the PVG station, taking ; is the scenario s under the node i The power factor angle during the actual operation of the PVG station.

[0078] S105: Use a nested iterative algorithm to solve the bi-level programming model. Through the upper-level programming model, decide on the types of new construction / upgrading of lines, the capacity and location of the distribution transformer area, and the site selection and capacity determination schemes of V2G charging and discharging stations and PVG stations. Output a preliminary planning scheme that meets the load growth demand and has the lowest investment cost, and transfer the distribution network topology and equipment information to the lower-level model; Solve the annual optimal operation results through the lower-level programming model, and synchronously generate the over-limit branch, node marker set, and penalty cost feedback to the upper-level programming model; Iteratively calculate until the total investment-operation cost fluctuates stably and the safety index meets the standard, and output the collaborative planning scheme for the distribution network source website.

[0079] In this embodiment, an adaptive hybrid particle swarm optimization algorithm is used to solve the upper-level programming model, decide on the types of new construction / upgrading of lines, the capacity and location of the distribution transformer area, and the site selection and capacity determination schemes of V2G charging and discharging stations and PVG stations. Output a preliminary planning scheme that meets the load growth demand and has the lowest investment cost, and transfer the distribution network topology and equipment information to the lower-level model.

[0080] The adaptive hybrid particle swarm optimization algorithm is as Figure 2 shown, and the specific steps and processes are as follows: S1. Algorithm initialization. It includes three aspects: setting algorithm parameters, initializing the hybrid particle swarm, and preliminarily evaluating the fitness.

[0081] Set algorithm parameters. Set the size of the hybrid particle swarm (population size) denoted as ; The maximum number of iterations is denoted as ; The current iteration count is denoted as g , g = 1, 2,..., ; The particle index is denoted asp , p = 1, 2, …, ; r rand 、 r 1, r 2 are random numbers drawn from a uniform distribution in the interval [0, 1] and are used in processes such as the update of the velocity of the hybrid particle swarm; the inertia weight ω (g) , which is used in the g th generation to balance global search and local convergence; the local search scaling factor ζ d , which will be reduced when the node / branch corresponding to a certain dimension exceeds the limit severely, so that the particle reduces the moving step in this direction; the target convergence threshold ε , if the change rate of the global optimum is less than ε within several generations, the iteration can be terminated in advance; is the position vector of the g th hybrid particle in the p th generation, which contains the values of all decision variables of the upper-layer model; is the velocity vector of the g th hybrid particle in the p th generation and is used to update the position during iteration; represents the record of the optimal position vector of the upper-layer particle p in history; represents the optimal particle position of the entire upper-layer population in history; represents the representation of the global fitness optimum value of the upper layer.

[0082] Initialize the hybrid particle swarm. Set the 0-1 site selection variables, integer variables, and continuous variables included in the scheme. 0-1 site selection variables: line construction flag , line upgrade flag , substation installation node flag , V2G charging and discharging station site selection flag , PVG station site selection flag . Integer variables: substation installation quantity , V2G charging and discharging station quantity , PVG station quantity . Continuous variables: lengths of newly built and upgraded lines , , substation installation capacity , V2G charging and discharging station capacity , PVG station capacity 。Encode and design various decision variable particles for the upper-layer planning layer. To enable each hybrid particle to completely represent an upper-layer planning scheme, all decision variables, including 0-1 variables, integer variables, and continuous variables, need to be written out in sequence in the same hybrid particle vector. Initialize the velocity vector , usually 0 can be taken; hybrid particle index p = 1, 2, …, , randomly generate initial particles within the feasible region, expressed as: ; In the formula, d is the hybrid particle vector dimension variable, and the total dimension size of the hybrid particle is D up , d = 1, 2, …, ; is the initial vector of the p th dimension of the d th hybrid particle; , 、 、 respectively represent the sets of initial hybrid particle positions for newly built and upgraded lines, newly built substations, newly built V2G charging and discharging stations, and newly built PVG stations; the superscript (0) of other parameters represents the initial hybrid particle positions of the corresponding parameters; || represents vector concatenation; initialize the hybrid particle temporarily as the individual optimal position of the particle .

[0083] Calculate the initial fitness. Based on the initial hybrid particle scheme calculate the annualized investment cost C inv ( ); set the initial operating cost C ope ( ) as a relatively large constant; obtain the initial fitness of each hybrid particle; count the hybrid particle corresponding to the global optimal fitness, and set it as the optimal hybrid particle position , and the initial optimal fitness is .

[0084] S2. Iterative solution of the algorithm. During iterative solution, it includes three parts: dynamically adjusting the inertia weight, updating the velocity and position of each particle, and calling the lower-layer model and calculating the fitness.

[0085] Dynamically adjust the inertia weight. Dynamically adjust the inertia weight using a linear decreasing strategy , larger in the early stage ω Enhance global search, smaller in the later stage ω Improve the local convergence accuracy, and the formula is: ; In the formula, , are the maximum and minimum values within the set range of the inertia weight respectively; the maximum number of iterations is denoted as ; is the number of iterations.

[0086] Update the velocity and position of the hybrid particles one by one. For particle p = 1, 2, …, , update the continuous, discrete and integer variables and their adaptive local search scaling factors respectively. For continuous variables, for each dimension d in the hybrid particle, the velocity update and its position movement update are calculated by the formula: ; ; In the formula, is the adaptive local search scaling factor, indicating the adaptive adjustment of the degree of exceeding the limit (the more serious the exceeding the limit, the larger it is, taking > 1, prompting the particle to make a greater correction in the area where the limit is exceeded); if the problem of exceeding the limit is less serious or has been alleviated, can be appropriately reduced, taking < 1, and the search range is more refined; if there is no node or branch exceeding the limit in the running layer, then set = 1 for normal update. c 1. c 2 are acceleration factors, generally taking values from 1 to 2; r 1. r 2 are random numbers drawn from a uniform distribution in the interval [0, 1]; , are the velocity and position of each dimension d in the hybrid particle respectively; is the optimal parameter of the p th particle in the d th dimension; is the optimal parameter of the d th dimension in the particle population.

[0087] Integer and 0 - 1 variables. For integer variables, they can be corrected to integers through the "nearest integer" strategy. For 0 - 1 variables, the velocity can be mapped to the flipping probability , and the formula is: ; ; In the formula, The change rule of

[0088] Call the lower - layer model and calculate the fitness. Pass the updated to the lower - layer operation layer for multi - scenario power flow and operation scheduling calculation, and return the optimal operation result under this planning scheme C ope ( ), the fitness function The updated calculation formula is: = C inv ( ) + C ope ( ); S3. Update the individual best and the global best. If is better than the historical best of the hybrid particle itself, then refresh the individual best of the hybrid particle, expressed as: = , = ; If the fitness of this hybrid particle is better than the current global best, then refresh the position of the best hybrid particle in the entire population's history and the global fitness optimal value .

[0089] S4. Termination determination If the maximum number of iterations has been reached or the global best improves less than the target convergence threshold ε up within several iterations, then stop the iteration.

[0090] The V2G control in the micro - grid mode adopts a "centralized - decentralized combination" scheduling method to achieve the orderly scheduling and control of the entire distribution network through the centralized scheduling layer.

[0091] In this embodiment, the V2G control in the microgrid mode refers to the orderly charging and discharging control of electric vehicles as "dispatchable energy storage units" in the microgrid, enabling electric vehicles to form a local power system together with other resources such as photovoltaic, energy storage, and the power grid. In the microgrid mode, electric vehicles can support various functions within the microgrid through charging and discharging behaviors, such as voltage control, supply-demand balance, and peak shaving and valley filling. The core idea is to regard V2G not only as "the two-way interaction between vehicles and the large power grid", but further incorporate it into the overall scheduling and operation of the microgrid to achieve the combination of microcosmic local autonomy and overall global planning.

[0092] In this embodiment, the orderly scheduling and control of the entire distribution network are realized through the centralized scheduling layer and the decentralized execution layer. The centralized scheduling layer and the decentralized execution layer are the object subjects in the lower-layer operation model, namely the intelligent vehicle networking system and each V2G charging and discharging station respectively; specifically: Centralized scheduling layer: Make unified scheduling decisions based on the economic and safety objectives of the overall microgrid. Collect the maximum dispatchable data in all V2G charging and discharging stations, and the centralized scheduling layer conducts unified optimal scheduling to obtain the charging and discharging requirements of electric vehicles at the distribution network nodes under different scenarios, and send the charging and discharging instructions to the decentralized execution layer.

[0093] Decentralized execution layer: The microgrid mode means that each charging and discharging station or node can make certain autonomous decisions locally. If the voltage of the current node is low and the load is high, V2G can give priority to discharging to support the local voltage; conversely, if the load is low or the electricity price is cheap, the vehicle can be charged or G2V can be carried out using the surplus photovoltaic power. The Monte Carlo simulation method is used to generate the maximum dispatchable charging and discharging power of each V2G charging and discharging station under different scenarios, which is summarized by the decentralized execution layer and uploaded to the centralized scheduling layer.

[0094] Considering multiple typical daily scenarios, the lower-layer operation layer uses the parallel particle swarm optimization algorithm to solve the annual optimal operation results, calculate the operation costs such as network loss, light curtailment, and battery degradation, synchronously detect potential safety hazards such as voltage over-limit and line overload, and generate the over-limit branch, node marker set and penalty cost feedback to the upper layer.

[0095] In this embodiment, the parallel particle swarm optimization algorithm specifically includes two parts: scenario parallel optimization and particle swarm optimization algorithm, as Figure 2 shown.

[0096] Scenario parallel optimization adopts the Spark parallel computing framework. The master computing node distributes s typical daily scenarios to multiple computing nodes, and the slave computing nodes independently run the particle swarm optimization algorithm sub-solver. The main tasks of the master computing node include distributing different typical daily scenarios to the slave nodes, monitoring the computing progress of the slave nodes, and calculating the minimum annual operation cost under this planning scheme according to the probability of the scenarios occurring C ope, and feedback the branch and node over - limit conditions to the upper - layer planning model. The computing slave nodes are responsible for executing specific computing tasks, running the particle swarm optimization algorithm to perform optimization calculations for each typical - day scenario. Each computing slave node independently processes the scenarios assigned to it and calculates the optimal operating cost under that scenario. C ope , s . In addition, for each scenario, each computing slave node checks the grid state under the optimal operating state. If there are over - limit problems (such as current overload, low node voltage, high load rate of the sub - station area, etc.), these over - limit branches and nodes need to be recorded.

[0097] Particle swarm optimization algorithm: In the lower - layer operation optimization, according to the improved K - means clustering method and the s generated typical - day scenarios of the distribution network, their probabilities, and the distribution network structure and equipment configuration parameters determined by the upper - layer planning layer, a parallel particle swarm solver is constructed. The continuous decision variables are encoded as multi - dimensional particle position vectors according to the time series, and the optimal particle position and its fitness are iteratively searched to solve the minimum operating cost under each scenario.

[0098] The specific process of the parallel particle swarm algorithm is as follows: S1. Input data processing. The input data is the upper - layer planning parameters and the set of typical - day scenarios of the corresponding nodes. The upper - layer planning parameters include line topology structure, sub - station area capacity, siting and equipment capacity upper limits of V2G charging and discharging stations and PVG stations, etc. The set of typical - day scenarios of the corresponding nodes includes the typical - day load curve set, the maximum adjustable charging and discharging power set of electric vehicles, and the maximum adjustable power generation power set of photovoltaic under this planning parameter. S typical - day scenarios s =1,2,…, S and their probabilities π s . In the computing slave nodes, for each scenario s start a particle swarm optimization algorithm solver to search for the optimal operating cost under this scenario. After each parallel particle swarm completes the optimization, upload the results (including the optimal operating cost and over - limit conditions) to the master node for summary.

[0099] S2. Particle encoding and initialization. Taking the particle swarm algorithm under scenario s as an example, solve the operation - layer model. The decision variables of the lower - layer operation layer are the actual charging and discharging power, the actual power generation power of the PVG station, and their power factor angles. Under scenario s , these types of decision variables are still two - dimensional variables, which are not suitable for particle swarm solution. Therefore, perform particle - swarm initial particle position encoding for each station obtained from the upper - layer planning , expressed as: ; In the formula, the variable superscript (0) represents the initial mixed particle position of the corresponding parameter; d is the dimension variable of the particle vector, and the total dimension size of the particle vector is D down , that is d = 1, 2, …, ; is the initial vector of the p th particle in the d th dimension; N EVS represents the number of V2G charging and discharging power stations obtained from the upper-level planning; N PVG represents the number of PVG stations obtained from the upper-level planning; , are respectively the charging and discharging powers of the N EVS th EVS station at the s th moment in the scenario t ; , are respectively the active power and power factor angle of the N PVG th PVG station at the s th moment in the scenario t ;

[0100] Assume that the number of particles is ; the maximum number of iterations is ; the particle index is p = 1, 2, …, ; the individual best and the global best ; for each dimension d in the particle, the g +1st speed update and position update ; the particle speed in the g th generation is defined as . For easy understanding, the basic particle swarm parameters such as the inertia weight , the acceleration constants c 1, c 2, etc. are consistent with the upper-level solution algorithm.

[0101] S3. Fitness function calculation. For any particle position in the scenario s , the DistFlow branch power flow calculation needs to be carried out, and then its minimum operating cost (fitness) is obtained. The formula is: ; In the formula, , , , , are the power purchase cost, power loss cost, curtailment penalty cost, V2G battery degradation cost, and security violation penalty cost of the distribution network when the particle position is s under the scenario respectively; is the adaptive penalty coefficient under the scenario s .

[0102] S4. Velocity and position update. Under the scenario s , the particle position and velocity of the g +1 generation are updated according to the formula: ; ; S5. Adjustment of the adaptive penalty coefficient . By performing a power flow calculation to check whether there are any over-limit branches or nodes under the operating scenario s , if there are, record the positions of the over-limit branches and nodes, as well as their voltage, current, and load over-limit values. If a severe over-limit occurs, increase so that the fitness of the violating particles deteriorates significantly, driving the population away from the infeasible solutions; if the over-limit situation improves, decrease to enable the algorithm to search widely.

[0103] S6. Update the individual optimal and global optimal. For each particle position s under the scenario , calculate the new fitness . If is better than the historical optimal of this particle, then update to . If is better than the global optimal fitness , then update to . If the maximum number of iterations has been reached or the global optimal has improved less than the target convergence threshold ε down in several iterations, then stop the iteration.

[0104] S7. Calculate the global annual operating cost for all scenarios at the main node layer by weighted calculation according to the π s probability. The formula is: ; In addition, calculate the positions of the over-limit branches and nodes, voltage, current, and load over-limit values that appear in the optimal operating mode among all scenarios at the main node and feedback them to the upper-layer planning layer.

[0105] In this embodiment, a nested iterative algorithm is used to solve the two-layer collaborative planning model. Through the "decision transfer - cost feedback - scheme correction" strategy, the collaborative planning scheme of the source website is iteratively optimized. Iterative calculations are performed until the total investment - operation cost fluctuates stably and the safety index meets the standard, and finally a collaborative planning scheme with excellent economy is output.

[0106] In this embodiment, the "decision transfer - state feedback - scheme correction" strategy for optimizing the collaborative planning scheme of the source website is specifically as follows: Decision transfer: According to the load and the demand for electric vehicle charging load in the areas covered by the future distribution network, and assuming that the lower-layer operation cost is a relatively large constant, the upper-layer planning layer preliminarily plans the decision variable parameters and transfers them to the lower-layer operation layer.

[0107] State feedback: Based on multiple typical day scenarios and their occurrence probabilities, the lower-layer operation layer performs optimal operation solution of the distribution network, calculates the annual operation cost of the lower layer considering the penalty for safety over-limit, and records the set of over-limit branches Θ Line,over and the set of over-limit nodes Ω Node,over , and feeds them back to the upper-layer planning layer together.

[0108] Scheme correction: For the set of over-limit branches and nodes that appear in the lower-layer operation scenario, the upper-layer planning layer adjusts the update mechanism of the position and velocity of the corresponding hybrid particles, recalculates the planning cost, and transfers the new distribution network planning parameters to the lower-layer operation layer, repeating the process. Until the economic condition and the safety condition both meet the convergence threshold of the upper-layer planning layer, finally a collaborative planning scheme with strong robustness and excellent economy is output.

[0109] Embodiment 2 In one or more embodiments, a collaborative planning system for the source website of a distribution network considering the V2G mode is disclosed, including: A data acquisition module, used to acquire the existing topological structure of the distribution network, traffic road network structure data, spatio-temporal distribution data of the number of electric vehicles, photovoltaic resource distribution data, and historical daily load power curve sets; A scenario generation module, used to confirm the preliminary spatial position constraints of each facility in the collaborative planning of the source website; cluster the historical daily load power curves into typical daily load power curves, and then predict the node load power sets, the maximum dispatchable power sets of V2G charging and discharging stations, and the maximum output power sets of PVG stations under various future typical day scenarios; A model construction module, used to establish an upper-layer planning model with the goal of minimizing the total economic cost of the system and a lower-layer planning model with the goal of minimizing the annual comprehensive operation cost; The model optimization and solution module is used to solve the bilevel programming model by using the nested iteration algorithm. It determines the types of new construction / upgrading of lines, the capacity and location of substations, and the site selection and capacity determination plans of V2G charging and discharging stations and PVG stations through the upper-level programming model, outputs a preliminary planning plan that meets the load growth requirements and has the lowest investment cost, and transfers the distribution network topology and equipment information to the lower-level model. It solves the annual optimal operation results through the lower-level programming model, generates the over-limit branch, node marker set, and penalty cost, and feeds them back to the upper-level programming model. Iteratively calculate until the total investment-operation cost fluctuates stably and the safety index reaches the standard, and output the collaborative planning plan for the distribution network source website.

[0110] Visualization module: Generate a collaborative planning diagram of the distribution network, mark the site selection and capacity determination plans of V2G charging and discharging stations and photovoltaic power sources, and the line upgrade / new construction paths; Dynamically display the node voltage distribution, V2G charging and discharging power curves, and safety over-limit warning information under multiple typical daily scenarios.

[0111] Although the specific implementation manners of the present invention have been described in conjunction with the accompanying drawings above, they do not limit the protection scope of the present invention. Those skilled in the art should understand that based on the technical solutions of the present invention, various modifications or deformations that can be made without creative efforts by those skilled in the art are still within the protection scope of the present invention.

Claims

1. A distribution network source site collaborative planning method considering V2G mode, characterized in that: include: Obtain the existing topological structure of the distribution network, traffic network structure data, spatiotemporal distribution data of the number of electric vehicles, photovoltaic resource distribution data, and historical daily load power curve sets; Confirm the preliminary spatial location constraints of each facility in the source site collaborative planning; cluster the historical daily load power curves into typical daily load power curves, and then predict the node load power set, the maximum dispatchable power set of the V2G charging and discharging station, and the maximum output power set of the PVG station under various typical daily scenarios in the future; The upper-level planning model is established with the goal of minimizing the total economic cost of the system, and the lower-level planning model is established with the goal of minimizing the annual comprehensive operating cost; The nested iterative algorithm is used to solve the two-level planning model. The upper-level planning model determines the type of new line construction / upgrade, the capacity and location of the substation, and the site selection and sizing plan of the V2G charging and discharging station and the PVG station. The initial planning plan that meets the load growth demand and has the lowest investment cost is output, and the distribution network topology and equipment information are passed to the lower-level model. The annual optimal operation results are solved by the lower-level planning model, and the over-limit branches, node marking sets and penalty costs are simultaneously generated and fed back to the upper-level planning model. Iterate the calculation until the fluctuation of total investment-operation cost is stable and the safety index meets the standard, and output the distribution network source site collaborative planning plan.

2. A distribution network source site collaborative planning method considering V2G mode as claimed in claim 1, characterized in that: The historical daily load power curves are clustered into typical daily load power curves through the improved K-means clustering algorithm, and the node load power sets under various typical daily scenarios in the future are obtained by the recursive method; the maximum dispatchable power set of V2G charging and discharging stations and the maximum output power set of PVG stations under various typical daily scenarios in the future are generated by the Monte Carlo algorithm.

3. A distribution network source site collaborative planning method considering the V2G mode as claimed in claim 2, characterized in that: The improved K-means clustering algorithm is used to cluster the historical daily load power curve into a typical daily load power curve, specifically: Randomly select a curve as the initial cluster center curve and set the threshold parameter T 1 and T 2. Set the distance to be less than T The curve with a value of 1 is the curve within the cluster, and the distance is T 1 to T The curves within the range of 2 are suspicious curves, and the distance is T The curves outside the 2 circles are the cluster outer curves; select new cluster center curves from the data marked as cluster outer curves, and repeat the iteration until all cluster outer curves are correctly classified; get the number of clusters as the initial K value; and randomly select K initial cluster center curves; By iteratively updating the center curves of all objects in each cluster, K corresponding cluster center curves are obtained; thus, the distribution network nodes are obtained. i Typical day scene s Load curve below and its probability of occurrence .

4. A distribution network source site collaborative planning method considering V2G mode as claimed in claim 2, characterized in that: The recursive method is used to obtain the node load power set under various typical daily scenarios in the future, specifically: Assume that the overall load growth rate in the future planning year is relative to the historical year and scenario occurrence probability correction factor It is known that the formula for the simple daily load recursion model is: ； ； in, , Distribution network nodes i Typical day scene s Down load curves and their probabilities; and Respectively for scenes s Next Node i The predicted values ​​of the future typical daily load curve set and its probability set.

5. A distribution network source site collaborative planning method considering V2G mode as claimed in claim 1, characterized in that: The upper-level planning model is established with the goal of minimizing the total economic cost of the system, specifically: min F = C inv + C ope ; in, F is the total economic cost of the system, C inv The annual construction investment cost includes the annualized sum of the investment costs for new construction / upgrade of lines, new construction of substations, PVG stations, and V2G charging and discharging stations. C ope The annual operating cost.

6. A distribution network source site collaborative planning method considering V2G mode as claimed in claim 5, characterized in that: The decision variables of the upper-level planning model include: , , , , They are 0-1 decision variables for new construction of lines, upgraded lines, new construction of substations, new construction of V2G power stations, and new construction of PVG stations, where 0 means no new construction and 1 means new construction; , are the lengths of new and upgraded lines, , , Node i Newly built substations, new V2G power stations and new PVG stations k Type equipment rated capacity, both are continuous decision variables greater than 0; , , They are the new substations, new V2G power stations and new PVG stations. k Type of equipment i The number of nodes, an integer variable greater than 0.

7. A distribution network source site collaborative planning method considering V2G mode as claimed in claim 1, characterized in that: The lower-level planning model is established with the goal of minimizing the annual comprehensive operating cost, specifically: ； in, is the annual comprehensive operating cost, A collection of typical daily scenes that may occur in a year; is the natural daily number of the year; Typical day scene s The probability of occurrence ; , , , , They represent the power purchase cost of the distribution network, network loss cost, penalty cost for abandoned solar power, V2G battery degradation cost and penalty cost for safety over-limit respectively; is the penalty factor adaptability coefficient.

8. A distribution network source site collaborative planning method considering V2G mode as claimed in claim 7, characterized in that: The decision variables of the lower operation layer include: multiple typical daily scenarios s Next Node i At the moment t The actual charging power used by the V2G charging and discharging station and discharge power , PVG actual output power Its power factor angle .

9. A distribution network source site collaborative planning method considering V2G mode as claimed in claim 1, characterized in that: Adaptive hybrid particle swarm algorithm is used to solve the upper-level planning model, and parallel particle swarm algorithm is used to solve the lower-level planning model; According to the load volume of the area covered by the future distribution network and the demand for electric vehicle charging load, and assuming that the lower-level operating cost is a large constant, the upper-level planning model preliminarily plans the decision variable parameters and passes them to the lower-level planning model; based on multiple typical daily scenarios and their occurrence probabilities, the lower-level planning model solves the optimal operation of the distribution network, calculates the lower-level annual operating cost considering the safety over-limit penalty, records the over-limit branch set and the over-limit node set, and feeds them back to the upper-level planning model; the upper-level planning model adjusts the update mechanism of the position and speed of the corresponding hybrid particles, recalculates the planning cost, and passes the new distribution network planning parameters to the lower-level planning model, and repeats the cycle; The iteration continues until both the economic and safety conditions meet the convergence threshold of the upper planning layer, and finally the optimal collaborative planning solution is output.

10. A distribution network source site collaborative planning system considering V2G mode, characterized in that: include: The data acquisition module is used to obtain the existing topological structure of the distribution network, the traffic network structure data, the temporal and spatial distribution data of the number of electric vehicles, the photovoltaic resource distribution data, and the historical daily load power curve set; The scenario generation module is used to confirm the preliminary spatial location constraints of each facility in the source site collaborative planning; cluster the historical daily load power curves into typical daily load power curves, and then predict the node load power set, the maximum dispatchable power set of the V2G charging and discharging station, and the maximum output power set of the PVG station under various typical daily scenarios in the future; Model building module, used to establish the upper-level planning model with the goal of minimizing the total economic cost of the system, and to establish the lower-level planning model with the goal of minimizing the annual comprehensive operating cost; The model optimization solution module is used to solve the two-level planning model using a nested iterative algorithm. The upper-level planning model determines the type of new line construction / upgrade, the capacity and location of the substation, and the site selection and capacity determination plan of the V2G charging and discharging station and the PVG station. The module outputs a preliminary planning plan that meets the load growth demand and has the lowest investment cost, and transmits the distribution network topology and equipment information to the lower-level model. The lower-level planning model is used to solve the annual optimal operation results, and the over-limit branches, node marking sets and penalty costs are simultaneously generated and fed back to the upper-level planning model. Iterate the calculation until the fluctuation of total investment-operation cost is stable and the safety index meets the standard, and output the distribution network source site collaborative planning plan.

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