A power distribution network electric vehicle and distributed photovoltaic bearing capacity configuration method and system considering space-time distribution

Abstract

The application discloses a power distribution network electric vehicle and distributed photovoltaic bearing capacity configuration method and system considering space-time distribution, and the method comprises the following steps: for a power distribution network containing distributed photovoltaic, electric vehicle and conventional load, based on historical operation data, a K-means clustering algorithm is used to extract typical daily power curves of load and photovoltaic output, and probability characteristics of typical daily scenes corresponding to each typical daily power curve; a multi-dimensional objective function is constructed with economy, safety, electric vehicle bearing capacity and distributed photovoltaic bearing capacity as targets; the weight coefficients of each dimension target are quantified through an analytic hierarchy process, and the multi-objective function is weighted and coupled into a single comprehensive objective function; constraints considering space-time distribution characteristics of electric vehicles and charging piles and general constraints of power distribution networks such as energy storage configuration and reactive power compensation are constructed; the typical daily power curve and the probability characteristics, the comprehensive objective function and the constraint system are integrated to form an optimization model, and an optimal configuration and operation scheme are obtained.

Description

Technical Field

[0001] This invention belongs to the field of distribution network carrying capacity enhancement, specifically relating to a method and system for configuring the carrying capacity of electric vehicles and distributed photovoltaics in distribution networks that considers spatiotemporal distribution. Background Technology

[0002] Electric vehicles and distributed photovoltaic (PV) power, as core carriers of the clean energy transition, are being connected to the power distribution network on a large scale, playing an increasingly crucial role in optimizing the energy structure and reducing carbon emissions. However, the charging load of electric vehicles exhibits significant spatiotemporal randomness: it is concentrated in peak load periods in time and unevenly distributed in space due to the influence of charging pile layout and user travel habits; the output of distributed PV power is constrained by sunlight conditions, exhibiting strong intermittency and spatiotemporal fluctuations. The combination of these two factors can easily lead to problems such as local overload and voltage exceeding limits in the power distribution network, severely limiting the network's capacity to accommodate these two types of resources.

[0003] Current research on improving the carrying capacity of distribution networks largely focuses on the orderly charging of electric vehicles or the optimization of distributed photovoltaic (PV) absorption, failing to fully consider the spatiotemporal coupling characteristics of these two resources. This results in the underutilization of their resource synergy potential. Furthermore, traditional methods often neglect spatiotemporal constraints, making it difficult to balance global capacity improvement with local operational safety. This leaves distribution networks with shortcomings when facing the combined impacts of these two types of resources. Therefore, accurately matching the spatiotemporal distribution characteristics of electric vehicles and distributed PV, overcoming the limitations of improving the carrying capacity of a single resource, and maximizing their synergistic acceptance capacity has become a key challenge for the high-quality adaptation of distribution networks to clean energy development. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this invention provides a method and system for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network that considers spatiotemporal distribution. By optimizing the spatiotemporal dimensions, the complementary potential of the two types of resources is explored, breaking through the bottleneck of the carrying capacity of the distribution network and supporting the stable and efficient operation of the distribution network with a high proportion of distributed power sources and electric vehicles connected.

[0005] According to one aspect of the present invention, a method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution is provided, comprising: For distribution networks that include distributed photovoltaics, electric vehicles and conventional loads, the K-means clustering algorithm is used to extract the typical daily power curves of the load and distributed photovoltaic output of the distribution network based on historical operating data, as well as the probability characteristics of the typical daily scenarios corresponding to each typical daily power curve. Construct a multi-dimensional objective function, which includes: an economic objective function with the overall cost of the distribution network as the objective, a safety objective function with the degree of node voltage deviation as the objective, an electric vehicle carrying capacity objective function with the maximum total number of electric vehicles that can be accommodated as the objective, and a distributed photovoltaic carrying capacity objective function with the maximum total installed capacity of distributed photovoltaic as the objective. The weight coefficients of each dimension of the objective are quantified by the analytic hierarchy process, and the multi-objective functions are weighted and coupled into a single comprehensive objective function. A constraint system is constructed, which includes constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles, as well as general distribution network constraints that consider energy storage configuration and reactive power compensation. By integrating the typical daily power curves and their probabilistic characteristics, a single comprehensive objective function, and a constraint system, an optimization model for enhancing the collaborative carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network is formed. Solving this optimization model yields the optimal configuration and operation scheme.

[0006] As a further technical solution, based on historical operating data, the K-means clustering algorithm is used to extract typical daily power curves of the load and distributed photovoltaic output of the distribution network, as well as the probabilistic characteristics of typical daily scenarios corresponding to each typical daily power curve, including: Collect historical load operation data and distributed photovoltaic power output data within a preset period of the power distribution network; The collected historical operational data is cleaned and normalized. The K-means clustering algorithm was used to perform cluster analysis on the preprocessed historical running data to obtain several clusters; Typical daily power curves are extracted from several clusters, and the sample proportion of each cluster is calculated to obtain the probability of occurrence of the corresponding typical daily scene.

[0007] As a further technical solution, a multi-dimensional objective function is constructed, including: Economic objective functions, safety objective functions, electric vehicle carrying capacity objective functions, and distributed photovoltaic carrying capacity objective functions are constructed respectively. The economic objective function encompasses both the planning-level objective function and the operational-level objective function. The planning-level objective function includes the energy storage configuration cost, the static var compensator configuration cost, the electric vehicle charging pile configuration cost, and the photovoltaic configuration cost. The operational-level objective function includes the energy storage operation and maintenance cost, the distribution network electricity purchase cost, the photovoltaic curtailment penalty cost, the line loss penalty cost, and the electric vehicle dispatch cost. The security objective function is constructed by minimizing the deviation between the actual voltage and the reference voltage; The electric vehicle carrying capacity objective function is constructed with the maximum total number of electric vehicles that the distribution network can accommodate under various operational constraints as the optimization objective. The objective function for distributed photovoltaic carrying capacity is constructed with the maximum total installed capacity of distributed photovoltaic power plants within the safe operation boundary of the distribution network as the optimization objective. This total installed capacity is the sum of the installed capacities of all distributed photovoltaic power plants within the distribution network.

[0008] As a further technical solution, the weight coefficients of the objective functions of each dimension are quantified using the analytic hierarchy process (AHP), and the multiple objective functions are weighted and coupled into a single comprehensive objective function, including: A comparison and judgment matrix is ​​constructed using economic efficiency, safety, electric vehicle carrying capacity, and distributed photovoltaic carrying capacity as criteria. Calculate the largest eigenvalue and corresponding eigenvector of the judgment matrix, and perform a consistency check; The feature vectors that pass the consistency test are normalized to obtain the weight coefficients of each dimension of the objective. The economic objective function, safety objective function, electric vehicle carrying capacity objective function, and distributed photovoltaic carrying capacity objective function are multiplied by their respective weight coefficients, and the weighted objective functions are summed to obtain a single comprehensive objective function.

[0009] As a further technical solution, the constraint considering the spatiotemporal distribution characteristics of electric vehicles and charging piles includes: The temporal distribution can be used to constrain the number of electric vehicles that can be charged and discharged. Constraints on the number of electric vehicles at nodes in spatial distribution; Constraints on the total number of electric vehicles in time and space.

[0010] As a further technical solution, the general constraints of the distribution network considering energy storage configuration and reactive power compensation include: Constraints on energy storage configuration and operation, constraints on static var compensator operation, constraints on switchable capacitor operation, photovoltaic-related constraints, power flow constraints, node voltage constraints, line current constraints, power purchase constraints, and substation transformer capacity constraints.

[0011] As a further technical solution, solving this optimization model yields the optimal configuration and operation scheme, including: The typical daily power curve and its probabilistic characteristics are input into the optimization model for the coordinated carrying capacity improvement of electric vehicles and distributed photovoltaic power in the distribution network, and the optimization model is solved using a solver. The optimal configuration scheme obtained by solving includes the capacity of energy storage and reactive power compensation equipment and the total installed capacity of distributed photovoltaics. The optimal operation scheme includes the spatiotemporal distribution of electric vehicle charging and discharging and the operation strategy of energy storage and reactive power compensation equipment.

[0012] According to one aspect of the present invention, a power grid electric vehicle and distributed photovoltaic carrying capacity configuration system considering spatiotemporal distribution is provided, comprising: The scenario generation module is used to extract typical daily power curves of load and distributed photovoltaic output of a distribution network that includes distributed photovoltaic, electric vehicles and conventional loads, based on historical operating data and using the K-means clustering algorithm, as well as the probability characteristics of typical daily scenarios corresponding to each typical daily power curve. The objective construction module is used to construct multi-dimensional objective functions, which include: an economic objective function with the overall cost of the distribution network as the objective, a safety objective function with the degree of node voltage deviation as the objective, an electric vehicle carrying capacity objective function with the maximum total number of electric vehicles that can be accommodated as the objective, and a distributed photovoltaic carrying capacity objective function with the maximum total installed capacity of distributed photovoltaic as the objective. The weight determination module is used to quantify the weight coefficients of each dimension of the objective through the analytic hierarchy process and to weight and couple the multi-objective functions into a single comprehensive objective function. The constraint construction module is used to construct a constraint system, which includes constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles, as well as general distribution network constraints that consider energy storage configuration and reactive power compensation. The model solving module is used to integrate the typical daily power curve and its probabilistic characteristics, a single comprehensive objective function and constraint system to form an optimization model for improving the carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network. Solving this optimization model yields the optimal configuration and operation scheme.

[0013] As a further technical solution, the constraint construction module is further used to construct constraints that take into account the spatiotemporal distribution characteristics of electric vehicles and charging piles. The constraints include constraints on the number of electric vehicles that can participate in charging and discharging in the time distribution, constraints on the number of electric vehicles at nodes in the spatial distribution, and total balance constraints on the number of electric vehicles in time and space.

[0014] As a further technical solution, the optimal configuration scheme obtained by the model solving module includes the capacity of energy storage and reactive power compensation equipment and the total installed capacity of distributed photovoltaics. The optimal operation scheme includes the spatiotemporal distribution of electric vehicle charging and discharging and the operation strategy of energy storage and reactive power compensation equipment.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power distribution networks considering spatiotemporal distribution.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Significantly enhance the power distribution network's capacity to support the coordinated operation of electric vehicles and distributed photovoltaic power.

[0017] This invention constructs a constraint system that considers the spatiotemporal distribution characteristics of electric vehicles and charging piles, fully exploring the schedulable potential of electric vehicle charging load in both time and space. It shifts charging demand from peak hours to off-peak hours and periods of high photovoltaic power generation, and guides the charging load to nodes near photovoltaic grid connection points, achieving spatiotemporal complementarity between electric vehicle charging load and distributed photovoltaic output. Simultaneously, it directly improves carrying capacity by optimizing the maximum total number of accommodating electric vehicles and the maximum total installed capacity of distributed photovoltaic power.

[0018] 2. Achieve synergistic optimization of multi-dimensional objectives to ensure the safe and economical operation of the power distribution network. This invention constructs a multi-dimensional objective function with economy, safety, electric vehicle carrying capacity, and distributed photovoltaic carrying capacity as its core. It scientifically quantifies the weights of each dimension using the analytic hierarchy process (AHP), transforming the multi-objective optimization problem into a single comprehensive objective function, achieving a synergistic balance across multiple optimization dimensions. While maximizing carrying capacity, it minimizes node voltage deviation through the safety objective function to ensure the stability of the distribution network voltage operation; and it comprehensively optimizes planning and operating costs through the economic objective function to reduce the total system cost. This solves the performance degradation problems caused by single-objective optimization in existing technologies.

[0019] 3. Reduce the overall operating cost of the power distribution network This invention optimizes the spatiotemporal distribution of electric vehicle charging and discharging, shifting charging load to off-peak electricity periods and reducing electricity purchase costs. Through the coordinated optimization of energy storage systems and reactive power compensation devices, it smooths out fluctuations in photovoltaic output, reducing photovoltaic curtailment penalties. Furthermore, it optimizes power flow to reduce network losses and line loss penalties. This comprehensive optimization of the economic objective function enables the distribution network to minimize operating costs while increasing its carrying capacity, resulting in significant economic benefits.

[0020] 4. Improve the grid integration capacity of distributed photovoltaic power. This invention guides electric vehicles to charge during peak photovoltaic (PV) output periods and near PV grid connection points by constraining their spatiotemporal distribution characteristics. This utilizes electric vehicles as mobile energy storage resources to locally absorb PV power generation, effectively mitigating voltage exceedance issues caused by PV backfeeding. Simultaneously, the distributed PV carrying capacity objective function directly optimizes for the maximum total installed capacity, overcoming the bottleneck of PV absorption.

[0021] 5. Enhance the operational safety and power supply quality of the power distribution network. This invention minimizes node voltage deviation through a safety objective function, ensuring the distribution network operates within safe boundaries through node voltage constraints and line current constraints. Simultaneously, by mitigating the spatiotemporal transfer of electric vehicle charging loads, it avoids local node overload and voltage exceedances, thus improving the power supply quality of the distribution network. The optimized configuration and operation strategies of energy storage and reactive power compensation devices further enhance the voltage support and reactive power balance capabilities of the distribution network.

[0022] 6. To provide a scientific basis for the planning and operation of power distribution networks. This invention outputs the optimal configuration scheme (including the capacity of energy storage and reactive power compensation equipment and the total installed capacity of distributed photovoltaics) and the optimal operation scheme (including the spatiotemporal distribution of electric vehicle charging and discharging and the operation strategy of energy storage and reactive power compensation equipment), providing quantitative basis for equipment selection and capacity configuration in the distribution network planning stage, and providing optimization scheme for the formulation of scheduling strategies in the operation stage, which has important engineering application value.

[0023] In summary, this invention fully considers the spatiotemporal distribution characteristics of electric vehicles and distributed photovoltaics. Through multi-dimensional objective collaborative optimization and refined constraint modeling, it realizes the improvement of the distribution network's collaborative carrying capacity for the two types of resources. While ensuring safe and economical operation, it reduces system costs and provides effective technical support for the stable and efficient operation of the distribution network under high proportion of distributed power sources and electric vehicle access. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a power distribution network, considering spatiotemporal distribution, as provided in an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of a power distribution network provided in an embodiment of the present invention;

[0027] Figure 3 This is a spatiotemporal distribution diagram of electric vehicle charging provided for an embodiment of the present invention. Detailed Implementation

[0028] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0030] A flowchart illustrating a method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution, as provided in this embodiment of the invention, is shown below. Figure 1 As shown, it includes: Step 1: For distribution networks that include distributed photovoltaics, electric vehicles, and conventional loads, the K-means clustering algorithm is used to extract typical daily power curves and their probabilistic characteristics of the load and distributed photovoltaic output of the distribution network based on historical operating data; Step 2: Construct a multi-dimensional objective function with economic efficiency, safety, electric vehicle carrying capacity, and distributed photovoltaic carrying capacity as objectives; Step 3: Quantify the weight coefficients of each dimension of the objective using the analytic hierarchy process (AHP), and weight and couple the multi-objective functions into a single comprehensive objective function; Step 4: Construct a constraint system, which specifically includes: constructing constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles, and constructing general constraints for the distribution network that consider energy storage configuration and reactive power compensation; Step 5: Integrate typical daily scenario characteristics, a single comprehensive objective function, and a constraint system to form a complete optimization model for improving the collaborative carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network, and solve for the optimal configuration and operation scheme.

[0031] Step 1 specifically includes: Collect historical operating data of conventional loads and distributed photovoltaic power output from the power distribution network within one year. The time granularity is 1 hour to form a complete power curve for 24 time nodes every day. The data needs to cover different seasons, weather conditions and electricity consumption periods, and include key information such as power values ​​and corresponding timestamps for each time node to ensure that it can fully reflect the operating fluctuation characteristics of the two types of resources.

[0032] The collected historical operational data is cleaned and processed. Interpolation methods are used to fill in missing values ​​caused by data transmission interruptions or temporary equipment failures. Abnormal data caused by measurement errors or extreme abnormal operating conditions are removed based on the normal fluctuation range of the data. Subsequently, the two types of cleaned data are normalized to unify the data value range and eliminate the impact of dimensional differences on the subsequent cluster analysis results.

[0033] Using daily regular load power sequences and distributed photovoltaic power output sequences as joint clustering samples, a complete sample matrix is ​​constructed. After determining the number of clusters and initializing the cluster centers, each sample is assigned to its corresponding cluster based on the similarity between samples. The cluster centers are continuously iterated and updated until the positions of the cluster centers tend to stabilize or reach the preset convergence condition, and finally several clusters are obtained.

[0034] The center vector of each cluster is extracted and denormalized to restore the actual power value, generating typical daily power curves for both conventional load and distributed photovoltaic output for each cluster. The number of sample days in each cluster is counted, and the ratio of this ratio to the total number of sample days in a year is calculated. This ratio represents the probability of occurrence of the typical daily scenario for that cluster. Through these steps, typical daily power curves and their corresponding probabilities for both types of resources are clearly generated, providing precise scenario-based data support for subsequent optimization of the collaborative carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network.

[0035] Step 2 specifically includes: A multi-dimensional objective function system is formed by constructing safety objective functions, economic objective functions, electric vehicle carrying capacity objective functions, and distributed photovoltaic carrying capacity objective functions for optimizing the collaborative carrying capacity of electric vehicles and distributed photovoltaics in the power distribution network.

[0036] 1) Security objective function: The safety objective function aims to minimize the sum of voltage deviations at all nodes throughout a typical daily scenario in a distribution network. By minimizing the deviation between the actual voltage and the reference voltage, it ensures the stability of the distribution network's voltage operation and prevents equipment failures or power quality degradation caused by voltage exceeding limits. The expression for the safety objective function is as follows:

[0037]

[0038] In the formula, This represents the total target value for node voltage deviation. This includes a single typical day scene and a collection of all typical day scenes. This includes both individual time periods and the total set of time periods for each typical day. For a single distribution network node and the set of all nodes in the distribution network; This represents the actual voltage amplitude of node n in the distribution network under a typical daily scenario s and time period t. This is the voltage reference value for the distribution network node; This refers to the highest voltage amplitude that a distribution network node is allowed to operate at. This refers to the minimum voltage amplitude that a distribution network node is allowed to operate at. The probability of each typical daily scenario.

[0039] 2) Economic objective function:

[0040]

[0041] In the formula, The value of the economic objective function; For the economic objective function at the planning level; This is the economic objective function at the operational level.

[0042] The objective function at the planning level includes the configuration costs of energy storage, static var compensator, electric vehicle charging stations, and photovoltaic systems, and its expression is shown below:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] in, These are the configuration costs, static var compensator configuration costs, electric vehicle charging pile configuration costs, and photovoltaic configuration costs, respectively. These are the discount rates for energy storage, static var compensators, electric vehicle charging piles, and photovoltaics, respectively. These refer to the total lifespan of energy storage, static var compensators, electric vehicle charging stations, and photovoltaic systems. The construction costs per unit capacity (number of units) for energy storage, static var compensator, electric vehicle charging piles, and photovoltaic systems, respectively. The power configuration value for the energy storage system at node n; Configure a limit on the energy storage capacity of node n; Here are the configuration values ​​for the static reactive power compensation device at node n; This represents the number of charging stations at the nth node; The photovoltaic installed capacity configured for node n.

[0049] The objective function at the operational level includes energy storage operation and maintenance costs, distribution network electricity purchase costs, photovoltaic curtailment penalty costs, line loss penalty costs, and electric vehicle dispatch costs, expressed as follows:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] in, This represents the overall objective function value at the runtime level. These are energy storage operation and maintenance costs, distribution network electricity purchase costs, photovoltaic curtailment penalty costs, line loss penalty costs, and electric vehicle dispatch costs; These are, respectively, the unit operation and maintenance cost of energy storage, the electricity purchase price of the distribution network, the unit curtailment cost of photovoltaic power, the unit line loss penalty cost, and the unit dispatch cost of electric vehicles; For time period t and typical daily scenario s, the energy storage charging power of node n; Let n be the energy storage discharge power of node n under time period t and typical daily scenario s. This refers to the active power of the substation. Let n be the power curtailment of photovoltaic power at node n under time period t and typical daily scenario s. The square of the current in each line; Line resistance; For time period t and typical daily scenario s, the number of electric vehicles participating in charging at node n; The number of electric vehicles that can be safely connected to the power distribution network; Based on the historical operation data of the power distribution network, the proportion of unscheduled electric vehicle charging at each node in each time period of each typical daily scenario is obtained; This refers to power distribution network lines and line sets.

[0057] 3) Objective function for the load-bearing capacity of electric vehicles:

[0058] The objective function for electric vehicle carrying capacity aims to maximize the global acceptance capacity of the distribution network for electric vehicles, provided that all operational constraints of the distribution network are met. The expression is as follows:

[0059]

[0060] In the formula, This represents the objective function value for the load-bearing capacity of an electric vehicle.

[0061] 4) Objective function for distributed photovoltaic carrying capacity:

[0062]

[0063] In the formula, The objective function value for distributed photovoltaic carrying capacity; This refers to the nodes and the set of nodes for distributed photovoltaic (PV) grid connection. Let be the distributed photovoltaic installed capacity connected to node i.

[0064] Step 3 specifically includes:

[0065] A hierarchical analysis structure is constructed with distribution network carrying capacity improvement as the target layer and safety, economy, electric vehicle carrying capacity, and distributed photovoltaic carrying capacity as criterion layers. Based on the importance of each dimension to distribution network carrying capacity improvement, a pairwise comparison judgment matrix is ​​established between each dimension using the 1-9 scaling method. The largest eigenvalue and corresponding eigenvector of this judgment matrix are calculated. The consistency ratio is calculated using the consistency index and the average random consistency index. If the consistency ratio is less than 0.1, the judgment matrix is ​​considered to meet the consistency requirements; otherwise, the pairwise comparison relationships between each dimension are adjusted until the test is passed. The eigenvectors that pass the consistency test are normalized to obtain the weight coefficients corresponding to each dimension's objective, and the sum of all weight coefficients is 1. For example, let the weight corresponding to the safety objective function be... The weights corresponding to the economic objective function are: The weights corresponding to the objective function of electric vehicle load-bearing capacity are: The weights corresponding to the objective function of distributed photovoltaic carrying capacity are: Then it satisfies .

[0066] Since the objective functions for safety and economy are to be minimized, while the objective functions for electric vehicle carrying capacity and distributed photovoltaic carrying capacity are to be maximized, all objective functions need to be unified to the direction of minimization first: for the objective function for electric vehicle carrying capacity, the difference between its theoretical maximum possible value and the actual value is taken as the standardization term; for the objective function for distributed photovoltaic carrying capacity, the difference between its theoretical maximum possible value and the actual value is taken as the standardization term. Based on this, a single comprehensive objective function is constructed as follows:

[0067]

[0068] This represents the theoretical maximum number of electric vehicles that the power distribution network can accommodate. This represents the theoretical maximum installed capacity of distributed photovoltaic power that the distribution network can absorb. Through this comprehensive objective function, the weighted coupling of the four dimensions of objectives is achieved, providing a unified objective guide for subsequent optimization solutions.

[0069] Step 4 specifically includes:

[0070] 1) First, construct constraints that take into account the spatiotemporal distribution characteristics of electric vehicles and charging piles.

[0071] The number of electric vehicles that can participate in charging at different times is constrained as follows:

[0072]

[0073] in, Let be the number of electric vehicles participating in charging under time period t and typical daily scenario s. This formula means that the number of electric vehicles eligible for charging under time period t and typical daily scenario s cannot be negative, and cannot exceed the total number of charging piles within the power distribution network.

[0074] The number of electric vehicles at each node in the power distribution network is constrained as follows:

[0075]

[0076] This formula means that, under time period t and typical daily scenario s, the number of electric vehicles that can participate in charging at node n cannot be negative, and at the same time, it cannot exceed the total number of charging piles at node n.

[0077] The number of electric vehicles is constrained as follows:

[0078]

[0079]

[0080] Formula (19) represents the expression for all time periods under a typical daily scenario s. The number of electric vehicles participating in charging The summation equals the total number of electric vehicles that can participate in charging under this scenario. This constraint ensures that, within scenario s, the sum of the number of electric vehicles participating in charging at each time period covers all available electric vehicles, achieving a total balance of electric vehicles over time. Formula (20) represents the total number of electric vehicles for all nodes under time period t and typical daily scenario s. The number of electric vehicles participating in charging The summation yields the total number of electric vehicles participating in charging during that time period and under that scenario. This constraint ensures that the statistics on the number of electric vehicles charging at the node level are consistent with the statistics at the system level, achieving a balance in the total number of electric vehicles in the spatial dimension.

[0081] For each distribution network node, due to factors such as space constraints, the number of charging piles that can be configured at each node needs to be limited, namely:

[0082]

[0083] In the formula, The maximum number of charging piles that can be installed at each node.

[0084] The formula for calculating the charging power of an electric vehicle is as follows:

[0085]

[0086] In the formula, The total charging power of electric vehicles connected to node n in time period t and typical daily scenario s; The charging power for a single electric vehicle.

[0087] 2) Construct a distribution network that takes into account common constraints such as energy storage configuration and reactive power compensation.

[0088] The power constraints for energy storage are as follows:

[0089]

[0090]

[0091]

[0092]

[0093] in, The actual energy storage power of node n under time period t and typical daily scenario s; The charging and discharging efficiency of energy storage; For time period t and typical daily scenario s, the binary variable of the energy storage charging and discharging status of node n takes the value of 0 or 1, where 1 represents energy storage charging and 0 represents energy storage discharging. Configure limits for the energy storage power of node n.

[0094] Since formulas (24) and (25) involve the product of binary variables and continuous variables... Therefore, this part is linearized. Let... Then the following constraints apply:

[0095]

[0096]

[0097]

[0098]

[0099] In the formula, M is a sufficiently large constant.

[0100] Therefore, equations (24) and (25) can be transformed into:

[0101]

[0102]

[0103] The energy constraints for energy storage are as follows:

[0104]

[0105]

[0106]

[0107] in, Let n be the energy stored at node n under time period t and typical daily scenario s. The length of a single scheduling period; , These represent the maximum and minimum allowable energy proportions during energy storage operation; , These represent the energy stored at the beginning and end of a scheduling cycle, respectively.

[0108] The constraint modeling of the static reactive power compensation device is as follows:

[0109]

[0110] in, The reactive power of SVC compensation at node n under time period t and typical daily scenario s; This is the minimum reactive power compensation ratio allowed by the static var compensator.

[0111] The constraint modeling for the grouped switching capacitor bank is as follows:

[0112]

[0113] in, For time period t and typical daily scenario s, the reactive power compensation of the grouped switching capacitor bank at node n; Number of units put into operation; The reactive power compensation for each group of switching capacitor banks; The upper limit of the grouped switching capacitor banks connected to node n.

[0114] Because frequent switching of capacitors in groups can reduce the lifespan and economic efficiency of the equipment, the number of times capacitors can be switched is limited.

[0115]

[0116] In the formula, This represents the maximum number of times the capacitors of the n nodes can be switched on and off in a single day.

[0117] The relevant constraints for photovoltaics are as follows:

[0118]

[0119]

[0120] in, The actual power generation of photovoltaics at node n under time period t and typical daily scenario s; This is the per-unit value for photovoltaic power generation.

[0121] To ensure economic viability, the curtailment rate of photovoltaic power needs to be limited, as shown in the following constraints:

[0122]

[0123] in, This represents the maximum allowable curtailment rate for photovoltaic power.

[0124] The AC power flow constraints of the distribution network are shown below:

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] In the formula: Let n be the branch rooted at node n; L is the set of paths. This is a branch with node n as its child node; , The active and reactive power of line l during time periods s and t; Line power; For line reactance; , These are the net active load and net reactive load of the node, respectively; , The active and reactive power of the load at node n during time period t under a typical daily scenario s; is the reactive power of the substation; in equation (46), m and n are the starting point and ending point of the branch, respectively.

[0132] Considering the nonlinearity of power flow constraints in distribution networks, the power flow constraints are transformed into a second-order cone constraint form, as shown in the following transformation formula:

[0133] In the formula, , Line current and node voltage The square of.

[0134] The node voltage constraints are shown below:

[0135]

[0136] in, and These are the upper and lower limits of the allowable node voltage, respectively.

[0137] The line current constraints are shown below:

[0138]

[0139] in, and These are the upper and lower limits of the allowable node voltage, respectively.

[0140] The power purchase constraints are as follows:

[0141]

[0142] in, This refers to the active power limit for substations.

[0143] The transformer capacity constraints of the substation are shown below:

[0144]

[0145] in, This refers to the capacity of the transformer in the substation.

[0146] Step 5 specifically includes:

[0147] By substituting the distributed photovoltaic output curve, load power curve, and corresponding scenario occurrence probability of typical daily scenarios into the model, and combining the comprehensive objective function and all constraints, a complete optimization model for improving the collaborative carrying capacity of electric vehicles and distributed photovoltaics in the distribution network is constructed. The model is then solved using a solver to obtain the optimal configuration scheme and the optimal operation scheme.

[0148] use Figure 2 The improved IEEE 33-node distribution network shown is an example ( Figure 2 In this context, ESS stands for Energy Storage, SVC for Static Var Compensation, PV for Photovoltaics, and CB for Switchable Capacitors. The resulting spatiotemporal distribution of electric vehicle charging quantities is shown below. Figure 3As shown, while keeping the total charging demand of electric vehicles constant, the charging demand in areas with concentrated load during the evening peak hours is shifted to the off-peak hours in the early morning and the peak photovoltaic output hours at noon, and nodes near the photovoltaic grid connection point are guided to increase charging power during peak output periods. The results of the embodiments show that the carrying capacity of distributed photovoltaic power generation is improved after applying the method of this invention. This invention fully utilizes the dispatchable potential of electric vehicle charging behavior and the spatiotemporal complementarity of photovoltaic output, enhancing the carrying capacity of the distribution network for distributed photovoltaic power generation and electric vehicles, and has strong engineering value.

[0149] The implementation of the various embodiments of the present invention is based on programmed processing by a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of the present invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of the present invention provide a distribution network electric vehicle and distributed photovoltaic carrying capacity configuration system considering spatiotemporal distribution. This system is used to execute a distribution network electric vehicle and distributed photovoltaic carrying capacity configuration method considering spatiotemporal distribution from the above method embodiments.

[0150] The system includes:

[0151] The scenario generation module is used to extract typical daily power curves of load and distributed photovoltaic output of a distribution network that includes distributed photovoltaic, electric vehicles and conventional loads, based on historical operating data and using the K-means clustering algorithm, as well as the probability characteristics of typical daily scenarios corresponding to each typical daily power curve.

[0152] The objective construction module is used to construct multi-dimensional objective functions, which include: an economic objective function with the overall cost of the distribution network as the objective, a safety objective function with the degree of node voltage deviation as the objective, an electric vehicle carrying capacity objective function with the maximum total number of electric vehicles that can be accommodated as the objective, and a distributed photovoltaic carrying capacity objective function with the maximum total installed capacity of distributed photovoltaic as the objective.

[0153] The weight determination module is used to quantify the weight coefficients of each dimension of the objective through the analytic hierarchy process and to weight and couple the multi-objective functions into a single comprehensive objective function.

[0154] The constraint construction module is used to construct a constraint system, which includes constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles, as well as general distribution network constraints that consider energy storage configuration and reactive power compensation.

[0155] The model solving module is used to integrate the typical daily power curve and its probabilistic characteristics, a single comprehensive objective function and constraint system to form an optimization model for improving the carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network. Solving this optimization model yields the optimal configuration and operation scheme.

[0156] This invention provides a power distribution network electric vehicle and distributed photovoltaic carrying capacity configuration system that considers spatiotemporal distribution. Addressing the challenge of accurately matching the spatiotemporal distribution characteristics of electric vehicles and distributed photovoltaics, and overcoming the limitations of improving the carrying capacity of a single resource, this system maximizes the synergistic acceptance capacity of both. By employing the aforementioned modules and optimizing the synergistic effects of spatiotemporal dimensions to tap the complementary potential of these two types of resources, the system effectively enhances the power distribution network's capacity to accept and absorb electric vehicles and distributed photovoltaics. This ensures the safe and economical operation of the power distribution network while reducing system operating costs, supporting the stable and efficient operation of the power distribution network with a high proportion of distributed power sources and electric vehicles connected.

[0157] It should be noted that the system embodiments provided by this invention, in addition to implementing the methods in the above method embodiments, are also used to implement the methods in other method embodiments provided by this invention. The difference lies only in setting corresponding functional modules, and their principles are basically the same as those of the above system embodiments provided by this invention. As long as those skilled in the art, based on the above system embodiments and referring to the specific technical solutions in other method embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and improve the modules in the above system embodiments while ensuring the practicality of the technical solutions, they can obtain corresponding system-like embodiments for implementing the methods in other method-like embodiments. For example:

[0158] Based on the above system embodiments, as a preferred embodiment, this invention provides a power grid electric vehicle and distributed photovoltaic carrying capacity configuration system that considers spatiotemporal distribution. The constraint construction module is further used to construct constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles. The constraints include constraints on the number of electric vehicles that can participate in charging and discharging in time distribution, constraints on the number of electric vehicles at nodes in spatial distribution, and total balance constraints on the number of electric vehicles in time and space.

[0159] Based on the above system embodiments, as a preferred embodiment, this invention provides a power grid electric vehicle and distributed photovoltaic carrying capacity configuration system that considers spatiotemporal distribution. The optimal configuration scheme obtained by the model solving module includes the capacity of energy storage and reactive power compensation equipment and the total installed capacity of distributed photovoltaics. The optimal operation scheme includes the spatiotemporal distribution of electric vehicle charging and discharging and the operation strategy of energy storage and reactive power compensation equipment.

[0160] It should be noted that the various modules (scene generation module, target construction module, weight determination module, constraint construction module, and model solving module) in the above system embodiments are used to implement the corresponding steps in the method embodiments of the present invention. Their specific functions, execution processes, and mathematical models have been described in detail in the method embodiments. Those skilled in the art can fully implement the above modules based on the descriptions in the method embodiments. Therefore, for any parts not detailed in the system embodiments, please refer to the relevant records in the method embodiments, which will not be repeated here. The modules can interact with each other via wired or wireless communication and are integrated into the power distribution network dispatch control system or cloud server.

[0161] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for enhancing the carrying capacity of electric vehicles and distributed photovoltaic power distribution networks considering spatiotemporal distribution, as described in any of the above method embodiments.

[0162] Specifically, the computer-readable storage medium can be any medium capable of storing program code, such as: read-only memory (ROM), random access memory (RAM), USB flash drive, portable hard drive, solid-state drive (SSD), disk, optical disk (such as CD-ROM, DVD), or semiconductor memory, etc. The storage medium can exist independently of the electronic device or be integrated inside the electronic device.

[0163] When a processor executes a computer program stored in a storage medium, it can perform the following steps:

[0164] For distribution networks that include distributed photovoltaics, electric vehicles and conventional loads, the K-means clustering algorithm is used to extract the typical daily power curves of the load and distributed photovoltaic output of the distribution network based on historical operating data, as well as the probability characteristics of the typical daily scenarios corresponding to each typical daily power curve.

[0165] Construct a multi-dimensional objective function, which includes: an economic objective function with the overall cost of the distribution network as the objective, a safety objective function with the degree of node voltage deviation as the objective, an electric vehicle carrying capacity objective function with the maximum total number of electric vehicles that can be accommodated as the objective, and a distributed photovoltaic carrying capacity objective function with the maximum total installed capacity of distributed photovoltaic as the objective.

[0166] The weight coefficients of the objective functions of each dimension are quantified by the analytic hierarchy process, and the multiple objective functions are weighted and coupled into a single comprehensive objective function.

[0167] A constraint system is constructed, which includes constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles, as well as general distribution network constraints that consider energy storage configuration and reactive power compensation.

[0168] By integrating the typical daily power curves and their probabilistic characteristics, a single comprehensive objective function, and a constraint system, an optimization model for enhancing the collaborative carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network is formed. Solving this optimization model yields the optimal configuration and operation scheme.

[0169] Those skilled in the art will understand that the above-described computer program may also include instructions for implementing other steps in the embodiments of the method of the present invention, which will not be described in detail here.

[0170] By storing the method of the present invention in the form of a computer program in a readable storage medium, the method of the present invention can be conveniently deployed in various power distribution network dispatch control systems, energy management platforms or cloud servers, realizing the automated optimization and improvement of the power distribution network's capacity to support electric vehicles and distributed photovoltaic power, and has broad application prospects.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution, characterized in that, include: For distribution networks that include distributed photovoltaics, electric vehicles and conventional loads, the K-means clustering algorithm is used to extract the typical daily power curves of the load and distributed photovoltaic output of the distribution network based on historical operating data, as well as the probability characteristics of the typical daily scenarios corresponding to each typical daily power curve. Construct a multi-dimensional objective function, which includes: an economic objective function with the overall cost of the distribution network as the objective, a safety objective function with the degree of node voltage deviation as the objective, an electric vehicle carrying capacity objective function with the maximum total number of electric vehicles that can be accommodated as the objective, and a distributed photovoltaic carrying capacity objective function with the maximum total installed capacity of distributed photovoltaic as the objective. The weight coefficients of each dimension of the objective are quantified by the analytic hierarchy process, and the multi-objective functions are weighted and coupled into a single comprehensive objective function. A constraint system is constructed, which includes constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles, as well as general distribution network constraints that consider energy storage configuration and reactive power compensation. By integrating the typical daily power curves and their probabilistic characteristics, a single comprehensive objective function, and a constraint system, an optimization model for enhancing the collaborative carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network is formed. Solving this optimization model yields the optimal configuration and operation scheme.

2. The method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution, as described in claim 1, is characterized in that... Based on historical operating data, the K-means clustering algorithm was used to extract typical daily power curves of load and distributed photovoltaic output for this distribution network, as well as the probabilistic characteristics of typical daily scenarios corresponding to each typical daily power curve, including: Collect historical load operation data and distributed photovoltaic power output data within a preset period of the power distribution network; The collected historical operational data is cleaned and normalized. The K-means clustering algorithm was used to perform cluster analysis on the preprocessed historical running data to obtain several clusters; Typical daily power curves are extracted from several clusters, and the sample proportion of each cluster is calculated to obtain the probability of occurrence of the corresponding typical daily scene.

3. The method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution, as described in claim 1, is characterized in that... Construct a multi-dimensional objective function, including: Economic objective functions, safety objective functions, electric vehicle carrying capacity objective functions, and distributed photovoltaic carrying capacity objective functions are constructed respectively. The economic objective function encompasses both the planning-level objective function and the operational-level objective function. The planning-level objective function includes the energy storage configuration cost, the static var compensator configuration cost, the electric vehicle charging pile configuration cost, and the photovoltaic configuration cost. The operational-level objective function includes the energy storage operation and maintenance cost, the distribution network electricity purchase cost, the photovoltaic curtailment penalty cost, the line loss penalty cost, and the electric vehicle dispatch cost. The security objective function is constructed by minimizing the deviation between the actual voltage and the reference voltage; The electric vehicle carrying capacity objective function is constructed with the maximum total number of electric vehicles that the distribution network can accommodate under various operational constraints as the optimization objective. The objective function for distributed photovoltaic carrying capacity is constructed with the maximum total installed capacity of distributed photovoltaic power generation within the safe operation boundary of the distribution network as the optimization objective.

4. The method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power grids considering spatiotemporal distribution according to claim 1, characterized in that, The weight coefficients of the objective functions of each dimension are quantified using the analytic hierarchy process (AHP), and the multiple objective functions are weighted and coupled into a single comprehensive objective function, including: A comparison and judgment matrix is ​​constructed using economic efficiency, safety, electric vehicle carrying capacity, and distributed photovoltaic carrying capacity as criteria. Calculate the largest eigenvalue and corresponding eigenvector of the judgment matrix, and perform a consistency check; The feature vectors that pass the consistency test are normalized to obtain the weight coefficients of each dimension of the objective. The economic objective function, safety objective function, electric vehicle carrying capacity objective function, and distributed photovoltaic carrying capacity objective function are multiplied by their respective weight coefficients, and the weighted objective functions are summed to obtain a single comprehensive objective function.

5. The method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution, as described in claim 1, is characterized in that... The constraints considering the spatiotemporal distribution characteristics of electric vehicles and charging stations include: The temporal distribution can be used to constrain the number of electric vehicles that can be charged and discharged. Constraints on the number of electric vehicles at nodes in spatial distribution; Constraints on the total number of electric vehicles in time and space.

6. The method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution according to claim 1, characterized in that, The general constraints of the distribution network considering energy storage configuration and reactive power compensation include: Constraints on energy storage configuration and operation, constraints on static var compensator operation, constraints on switchable capacitor operation, photovoltaic-related constraints, power flow constraints, node voltage constraints, line current constraints, power purchase constraints, and substation transformer capacity constraints.

7. The method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power in a distribution network considering spatiotemporal distribution, as described in claim 1, is characterized in that... Solving this optimization model yields the optimal configuration and operating scheme, including: The typical daily power curve and its probabilistic characteristics are input into the optimization model for the coordinated carrying capacity improvement of electric vehicles and distributed photovoltaic power in the distribution network, and the optimization model is solved using a solver. The optimal configuration scheme obtained by solving includes the capacity of energy storage and reactive power compensation equipment and the total installed capacity of distributed photovoltaics. The optimal operation scheme includes the spatiotemporal distribution of electric vehicle charging and discharging and the operation strategy of energy storage and reactive power compensation equipment.

8. A power distribution network electric vehicle and distributed photovoltaic carrying capacity configuration system considering spatiotemporal distribution, characterized in that, include: The scenario generation module is used to extract typical daily power curves of load and distributed photovoltaic output of a distribution network that includes distributed photovoltaic, electric vehicles and conventional loads, based on historical operating data and using the K-means clustering algorithm, as well as the probability characteristics of typical daily scenarios corresponding to each typical daily power curve. The objective construction module is used to construct multi-dimensional objective functions, which include: an economic objective function with the overall cost of the distribution network as the objective, a safety objective function with the degree of node voltage deviation as the objective, an electric vehicle carrying capacity objective function with the maximum total number of electric vehicles that can be accommodated as the objective, and a distributed photovoltaic carrying capacity objective function with the maximum total installed capacity of distributed photovoltaic as the objective. The weight determination module is used to quantify the weight coefficients of each dimension of the objective through the analytic hierarchy process and to weight and couple the multi-objective functions into a single comprehensive objective function. The constraint construction module is used to construct a constraint system, which includes constraints that consider the spatiotemporal distribution characteristics of electric vehicles and charging piles, as well as general distribution network constraints that consider energy storage configuration and reactive power compensation. The model solving module is used to integrate the typical daily power curve and its probabilistic characteristics, a single comprehensive objective function and constraint system to form an optimization model for improving the carrying capacity of electric vehicles and distributed photovoltaic power in the distribution network. Solving this optimization model yields the optimal configuration and operation scheme.

9. The distribution network electric vehicle and distributed photovoltaic carrying capacity configuration system considering spatiotemporal distribution according to claim 8, characterized in that, The constraint construction module is further used to construct constraints that take into account the spatiotemporal distribution characteristics of electric vehicles and charging piles. The constraints include constraints on the number of electric vehicles that can participate in charging and discharging in the time distribution, constraints on the number of electric vehicles at nodes in the spatial distribution, and total balance constraints on the number of electric vehicles in time and space.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for configuring the carrying capacity of electric vehicles and distributed photovoltaic power distribution networks, taking into account the spatiotemporal distribution, as described in any one of claims 1 to 7.

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