Community EV charging and discharging simulation method and system considering bearing capacity change and multi-day charging

Abstract

The invention discloses a community EV charging and discharging simulation method and system considering bearing capacity change and multi-day charging, and the method comprises the steps: obtaining the arrival time, departure time and probability distribution of daily mileage of a community user, and obtaining parameters of time-of-use electricity price, basic load and power distribution network bearing capacity changing at any time; the method comprises the following steps: sampling vehicle travel behaviors on a multi-day time scale, and establishing a cross-day evolution model of the state of charge (SOC) of an electric vehicle group; on the basis of a charging window constraint, upper and lower limits of charging and discharging power and a charging and discharging efficiency constraint, introducing an energy demand constraint of a starting SOC covering mileage, and setting a charging and discharging mode selection constraint for a vehicle supporting V2G; superposing the basic load and the net charging and discharging power of the electric vehicle group to form a total load of the community; and constructing an optimization model with the goal of minimizing the comprehensive cost in one week, and guiding to form a charging behavior of charging once in multiple days. According to the method, peak load shifting can be realized, the out-of-limit risk is reduced, and support is provided for power distribution network planning and electric vehicle access evaluation.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging and discharging technology, specifically relating to a simulation method and system for EV charging and discharging in a residential area that considers changes in load capacity and charging every few days. Background Technology

[0002] Against the backdrop of rapid development of new energy sources and a continuous increase in the number of electric vehicles, the large-scale integration of electric vehicles into community power distribution networks is becoming an important research direction in power distribution network planning and operation. Existing research on electric vehicle integration into power distribution networks mainly focuses on two categories: one emphasizes constructing the temporal characteristics of electric vehicle charging load based on traffic flow and residents' travel behavior, typically simulating the charging start time and charging demand by statistically analyzing arrival time, departure time, and daily mileage; the other focuses on conducting orderly charging or vehicle-grid interaction optimization scheduling at the power distribution network level, aiming to minimize peak-valley electricity prices or network losses, and coordinating and optimizing the charging and discharging power of electric vehicles within the day.

[0003] However, existing technologies have significant shortcomings. First, many studies, when establishing electric vehicle load models, typically assume that vehicles are charged "once a day" or follow a fixed charging strategy, ignoring the actual daily charging habits of residents. This makes it difficult to accurately reflect the daily evolution of electric vehicle charge status and its cumulative impact on the distribution network load. Second, existing methods for optimizing orderly charging and vehicle-grid interaction focus more on economic efficiency or overall load smoothing. While they consider transformer capacity constraints and line current limitations to some extent, their characterization of the overall distribution network capacity is often coarse, lacking a mechanism for systematically evaluating the electric vehicle access capacity and operational safety margin on a multi-day timescale. Third, although some literature proposes simulation or scheduling models that consider distribution network constraints, these often focus on a single day or a few typical days, failing to fully incorporate the statistical characteristics of residents' travel and charging behavior over a week or even longer periods. This makes it difficult to provide reliable long-term references for distribution network planning, renovation, and electric vehicle charging and discharging strategy formulation.

[0004] Therefore, existing technologies struggle to accurately reflect the multi-day charging behavior of electric vehicles in residential communities while comprehensively characterizing the evolution of charging and discharging loads and the available scale of grid connection under the constraints of distribution network capacity. This introduces uncertainties into the safe and economical operation of the community distribution network. To address these issues, it is necessary to propose a method for simulating electric vehicle charging and discharging loads that simultaneously considers both distribution network capacity and multi-day charging characteristics. This method would provide a more refined multi-day-scale analysis and assessment of the impact of electric vehicle grid connection on the community distribution network, more closely reflecting actual user behavior. Summary of the Invention

[0005] The purpose of this invention is to provide a simulation method and system for EV charging and discharging in residential communities that considers changes in carrying capacity and multiple-day charging, in order to overcome the shortcomings of existing modeling methods that only consider a single-day perspective and fail to simultaneously depict the evolution of charging behavior over multiple days and the constraints of distribution network carrying capacity. By uniformly describing the changes in the state of charge of electric vehicles, charging and discharging decisions, and the upper limit of distribution network carrying capacity over a one-week timescale, this invention can more realistically reflect the impact of EV access in residential communities on distribution network operation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The simulation method for EV charging and discharging in a residential area considering variations in load-bearing capacity and multiple charging cycles includes the following steps: Step 1: Obtain the probability functions of the departure time, arrival time and driving distance of users in the community, and obtain the time-of-use electricity price curve, base load curve and relevant parameters of the distribution network carrying capacity of the community; Step 2: Sampling of multi-day travel behavior based on probability functions to obtain the arrival time, departure time, and mileage samples of each electric vehicle within a week; combining the power balance relationship and the battery state of charge (SOC) evolution equation, a multi-day state evolution model of the electric vehicle group is established. Step 3: Based on the multi-day state evolution model, introduce electric vehicle charging and discharging power variables and their upper and lower limits, charging mode selection variables, multi-day SOC evolution constraints, and user travel energy demand constraints to construct a time-of-use pricing-guided electric vehicle charging and discharging model for residential communities. At the same time, integrate the community's basic load and the charging and discharging power of the electric vehicle group into the distribution network carrying capacity constraints. By limiting the total load in each time period to not exceed the upper limit of the distribution network carrying capacity, explicit control of the distribution network overload risk can be achieved. Step 4: With the goal of minimizing the total cost of charging and discharging over a week, the time-of-use pricing-guided electric vehicle charging and discharging model for residential areas is restricted to the range allowed by changes in the distribution network's carrying capacity, thus obtaining the simulation results of electric vehicle charging and discharging load in residential areas that take into account the distribution network's carrying capacity.

[0007] A further improvement of the present invention is that, in step one, the relevant parameters of the distribution network carrying capacity include transformer capacity, safety margin, and upper limit of carrying capacity for each time period.

[0008] A further improvement of the present invention is that, in step two, the charging mode includes a normal charging mode and a V2G discharging mode.

[0009] A further improvement of this invention is that, in step three, both the normal charging mode and the V2G discharging mode are considered simultaneously, so that the bidirectional energy flow between the vehicle and the network is fully utilized; the various charging and discharging powers, SOC evolution, charging and discharging modes, and the distribution network carrying capacity constraints are expressed as a set of linear constraints, and the specific state constraints are shown in the following formula: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, For vehicles i In the k SOC of arrival time in days For vehicles i In the k SOC at the time of departure For vehicles i In the k The journey of the day, For vehicles i Electricity consumption per 100 kilometers For vehicles i Battery capacity; , vehicles i exist Charge and discharge power during the period This is the maximum power of the charging gun. This is a binary variable indicating whether charging or discharging is enabled. It means it can be charged. It means that it can discharge electricity. For vehicles i In the k Arrival time of day For vehicles i In the k+ One day's leave time; for Time-of-day vehicles i SOC, , These represent charge and discharge efficiencies, For users i Expected SOC , , They are respectively Total charging power, discharging power, and net charging power during the period. For the basic load of the community, This refers to the rated capacity of the transformer. For safety margin, This refers to the maximum power purchase capacity allowed by the distribution network. Vehicles during the last time period of the week i SOC, For vehicles in the first time period of the week i SOC.

[0010] A further improvement of the present invention is that, in step four, the impact of the number of charging cycles on the electric vehicle battery is considered, and the objective function is charging expenditure, V2G benefits, and frequent charging penalty. (11) (12) in It is a binary vector. For sufficiently small numbers close to 0, For a sufficiently large number; The objective function is to minimize the overall charging and discharging cost within one week. Cost per unit of charging For unit discharge revenue, This is the penalty coefficient for frequent charging.

[0011] The simulation system for EV charging and discharging in a residential community, considering variations in load-bearing capacity and multiple charging cycles, includes the following steps: Data acquisition unit: acquires the probability functions of departure time, arrival time and driving distance of users in the community, and acquires the time-of-use electricity price curve, base load curve and relevant parameters of distribution network carrying capacity of the community; Model building unit: Based on the probability function, multi-day travel behavior is sampled to obtain the arrival time, departure time and driving mileage samples of each electric vehicle within a week; combined with the power balance relationship and the battery state of charge (SOC) evolution equation, a multi-day state evolution model of the electric vehicle group is established. Explicit control unit of the power grid: Based on the multi-day state evolution model, the charging and discharging power variables of electric vehicles and their upper and lower limits, charging mode selection variables, multi-day SOC evolution constraints, and user travel energy demand constraints are introduced to construct a time-of-use pricing-guided electric vehicle charging and discharging model for residential communities. At the same time, the basic load of the community and the charging and discharging power of the electric vehicle group are uniformly incorporated into the distribution network carrying capacity constraints. By limiting the total load in each time period to not exceed the upper limit of the distribution network carrying capacity, explicit control of the distribution network overload risk is achieved. Charging and discharging load simulation unit: With the goal of minimizing the total cost of charging and discharging over a week, the charging and discharging model of electric vehicles in the community guided by time-of-use pricing is restricted to the range allowed by changes in the carrying capacity of the distribution network, and the charging and discharging load simulation results of electric vehicles in the community considering the carrying capacity of the distribution network are obtained.

[0012] A further improvement of the present invention is that the data acquisition unit includes parameters related to the carrying capacity of the distribution network, such as transformer capacity, safety margin, and upper limit of carrying capacity for each time period.

[0013] A further improvement of the present invention is that the charging mode in the model building unit includes a normal charging mode and a V2G discharging mode.

[0014] A further improvement of this invention lies in the fact that the power grid explicit control unit simultaneously considers both the normal charging mode and the V2G discharging mode, thereby fully utilizing the bidirectional energy flow between the vehicle and the grid; the various charging and discharging powers, SOC evolution, charging and discharging modes, and distribution network carrying capacity constraints are expressed as a set of linear constraints, the specific state constraints of which are shown in the following formula: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, For vehicles i In the k SOC of arrival time in days For vehicles i In the k SOC at the time of departure For vehicles i In the k The journey of the day, For vehicles i Electricity consumption per 100 kilometers For vehicles i Battery capacity; , vehicles i exist Charge and discharge power during the period This is the maximum power of the charging gun. This is a binary variable indicating whether charging or discharging is enabled. It means it can be charged. It means that it can discharge electricity. For vehiclesi In the k Arrival time of day For vehicles i In the k+ One day's leave time; for Time-of-day vehicles i SOC, , These represent charge and discharge efficiencies, For users i Expected SOC , , They are respectively Total charging power, discharging power, and net charging power during the period. For the basic load of the community, This refers to the rated capacity of the transformer. For safety margin, This refers to the maximum power purchase capacity allowed by the distribution network. Vehicles during the last time period of the week i SOC, For vehicles in the first time period of the week i SOC.

[0015] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned simulation method for charging and discharging EVs in a residential community, taking into account variations in load capacity and multiple charging cycles.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a simulation method and system for EV charging and discharging in residential communities, considering variations in load capacity and multiple-day charging. It fully characterizes the voltage constraints of distribution network nodes and the capacity constraints of lines and transformers. It integrates the base load and the charging and discharging power of the EV fleet into the distribution network load capacity model. By setting upper limits for load capacity in each time period, it guides EVs to suppress concentrated charging during periods of low distribution network load capacity and to centrally schedule charging and V2G discharging during periods of high load capacity or low electricity prices, achieving orderly charging control that matches the distribution network's capacity. This invention comprehensively considers electricity purchase costs, V2G grid connection revenue, and charging frequency penalties in the objective function. While ensuring vehicle travel energy demand and SOC safety boundaries, it effectively reduces the number of charging and discharging cycles and slows down battery performance degradation. Simultaneously, by reconstructing the EV load time-series through distribution network load capacity constraints, it significantly reduces system load peaks and peak-valley differences, avoiding distribution network overload and voltage exceedances. This improves the safety and economy of distribution network operation in scenarios with large-scale EV access, providing an efficient and practical solution tool for distribution network planning and operation optimization. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is an overall flowchart of the method of the present invention.

[0019] Figure 2 This is the empirical probability density curve.

[0020] Figure 3 This section compares the optimization results for two different scenarios.

[0021] Figure 4 This is a graph showing the impact of average mileage on the number of times a single vehicle needs to be charged per week.

[0022] Figure 5 This is a graph showing the impact of battery capacity on the number of times a single vehicle is charged per week.

[0023] Figure 6 The graph shows the effects of normalized fluctuation and average slope change rate under different example sizes.

[0024] Figure 7 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0025] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0026] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0030] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0031] Example 1 like Figure 1 As shown, the EV charging and discharging simulation method for residential communities considering load-bearing capacity changes and multiple-day charging provided by this invention includes the following steps: Step 1: Obtain the probability distribution function of the departure time, arrival time, and driving distance of users in the community, and obtain the time-of-use electricity price curve and the change of distribution network carrying capacity in the community.

[0032] This simulation example uses a typical residential community as the subject, with a total of 900 charging vehicles, of which 100 are capable of V2G charging, and the rest are for regular charging. The maximum rated power of a single charging gun is set to 15kW, with charging and discharging efficiencies of 0.97 and 0.95, respectively. The energy consumption per 100 kilometers is 20kWh, and the battery capacity is 46kWh. A one-week simulation period is selected, and the 24 hours are discretized into several time periods. Statistical data from NHTS is used, and after data fitting, it is found that the arrival and departure times of users follow a normal distribution function, and the daily mileage follows a log-normal distribution. At the same time, based on the distribution network planning and operation data, the basic load of the community and the corresponding maximum acceptable charging and discharging power limit for each time period are obtained. The time-of-use electricity price curve is discretized to a time scale consistent with the time periods, providing input parameters for subsequent sampling modeling and optimized scheduling.

[0033] Step 2: Sample the probability distribution function from Step 1 to establish a state evolution model of the electric vehicle group, providing a benchmark scenario for subsequent ordered charging and discharging optimization.

[0034] The Monte Carlo method was used to sample all vehicles over multiple days. The analysis period was discretized according to a preset time interval, generating arrival time, departure time, and mileage samples for each vehicle on each day of the week. The corresponding empirical probability density curves are shown below. Figure 2As shown, based on the energy balance relationship between battery charging and discharging power and battery capacity, the battery capacity state changes of each electric vehicle in various discrete time periods are described, forming a feasible state space that reflects the charging and discharging behavior of the electric vehicle group, providing a benchmark scenario for subsequent orderly charging and discharging optimization.

[0035] Step 3: Based on the community user charging model obtained in Step 2, and according to the time-of-use electricity price parameters obtained in Step 1, establish a community electric vehicle charging and discharging model guided by time-of-use electricity price.

[0036] This model considers both normal charging and vehicle-to-grid (V2G) interaction modes. It represents whether each vehicle can charge or discharge at different times using decision variables. By limiting the charging and discharging power of vehicles to no more than the rated power of the charging facilities, and updating the vehicle's state of charge hourly based on the power balance relationship, it ensures that the state of charge remains within a safe range and that the total power is sufficient to meet the user's travel needs before their scheduled departure time. By superimposing the hourly charging and discharging power of all vehicles, the total charging power, total discharging power, and net charging power of the community at each time period are obtained. These, along with the base load, constitute the community's total load. Combined with the transformer's rated capacity and safety margin, power constraints are formed to achieve orderly charging and discharging control guided by time-of-use pricing. The above relationships correspond to the state constraint formulas given in the instruction manual.

[0037] Step 4: With the goal of minimizing the total cost of charging and discharging over a week, the time-of-use pricing-guided electric vehicle charging and discharging model in Step 3 is restricted to the range allowed by changes in the distribution network's carrying capacity, resulting in simulation results of the electric vehicle charging and discharging load in the community that take into account the distribution network's carrying capacity.

[0038] like Figure 3 As shown, within a one-week simulation period, the upper limit of transformer capacity, the upper limit of distribution network carrying capacity, the basic load curve of the community, and the total load curve of the community under two scheduling strategies are presented. Scenario 1 is an ordered charging and discharging scheme considering the carrying capacity constraints of the distribution network, while Scenario 2 is a scheme that only uses time-of-use pricing guidance and does not consider the carrying capacity constraints of the distribution network. It can be seen that in Scenario 2, the total load approaches or even exceeds the upper limit of the distribution network carrying capacity during multiple evening peak periods. In contrast, Scenario 1, by uniformly coordinating the charging and discharging sequence of electric vehicles, keeps the total load of the community consistently below the carrying capacity curve, significantly reducing load peaks and smoothing out intraday fluctuations. This provides an intuitive basis for subsequent quantitative evaluation based on peak-to-valley ratio, normalized fluctuation, average ramp rate, and over-limit indicators.

[0039] Figure 4This paper illustrates the changing patterns of electric vehicle (EV) group charging and discharging behavior under different battery capacities. As battery capacity gradually increases from smaller to larger capacities, the available energy for daytime support significantly improves, enabling vehicles to complete concentrated charging during more low-electricity-price periods, thus alleviating the immediate charging demand during peak hours. The figure clearly shows that under larger battery capacity scenarios, the peak of the total load in the community is further weakened, and the load curve exhibits stronger smoothness throughout the day. However, under smaller battery capacity conditions, vehicles are more prone to insufficient power, leading to charging activity occurring more frequently during the evening peak hours when users return home, resulting in significantly increased system load fluctuations. This invention, through a multi-day SOC evolution model, can realistically capture the impact of battery capacity on the flexibility of charging and discharging scheduling, and provides a more refined reference for assessing the carrying capacity of the distribution network.

[0040] Figure 5 The paper presents a comparison of the load characteristics of electric vehicle groups under varying mileage statistical characteristics. Compared to the short-mileage scenario, the dependence of vehicles on energy replenishment is significantly increased in the long-mileage scenario, causing vehicles to approach low SOC intervals more frequently during multi-day SOC evolution, resulting in a significant compression of the charging time window. The figure shows that in the long-mileage scenario, the total load of the community experiences more concentrated and steeper inclines during multiple evening peak periods; while in the short-mileage scenario, vehicles can maintain normal travel with less energy replenishment for most days, resulting in a flatter load curve. This figure illustrates that the multi-day charging mechanism proposed in this invention can effectively reflect the true impact of mileage on vehicle energy demand, providing a more reliable multi-scenario evaluation basis for distribution network operation planning.

[0041] like Figure 6As shown, the rate of change is calculated based on the normalized fluctuation index and average ramp rate corresponding to 600 vehicles, with negative values ​​indicating that the index is less than the benchmark value. As the number of electric vehicles increases from 600 to 700, 800, and 900, under Scenario 1 (an orderly charging scheme considering distribution network capacity constraints), both the normalized fluctuation index and average ramp rate show an overall downward trend. The decrease in average ramp rate is most significant when N=900, indicating that the method of this invention can effectively reduce short-term fluctuations in the total system load and ramp rate when the electric vehicle penetration rate is high. In contrast, under Scenario 2 (a comparative scheme using only time-of-use pricing and not considering distribution network capacity constraints), the changes in both indicators are significantly smaller, and even the average ramp rate shows a slight increase at some scales such as N=700, making it difficult to fully suppress the power ramp caused by concentrated charging of electric vehicles. It can be seen that, under different scales of electric vehicle access, the orderly charging / discharging strategy proposed in this invention, which considers the carrying capacity constraints of the distribution network, has a stronger load smoothing and peak shaving and valley filling capability compared to the disorderly response scheme that only relies on time-of-use pricing. In particular, it can significantly reduce system power fluctuations and ramp-up in large-scale access scenarios, and has obvious technical effects on ensuring the safe and stable operation of the distribution network.

[0042] The effectiveness of this invention is evaluated using the following criteria.

[0043] (1) Peak-to-valley ratio (13) (14) (15) (2) Normalized volatility index (16) (17) (18) (3) Average gradient (19) (20) (4) Time and energy exceeding the limit (twenty one) (twenty two) To verify the effectiveness of the proposed method for simulating the charging and discharging load of electric vehicles in residential communities, which considers the carrying capacity of the distribution network, two comparative scenarios were constructed based on the aforementioned typical residential community example: Scenario 1, an ordered charging and discharging scenario under time-of-use pricing while simultaneously considering distribution network carrying capacity constraints; and Scenario 2, a residential community electric vehicle charging and discharging scenario without considering distribution network carrying capacity constraints under the same time-of-use pricing and travel sample conditions. In both scenarios, the vehicle arrival time, departure time, mileage samples, base load, and charging / discharging equipment parameters remain completely consistent; the only difference lies in whether or not distribution network carrying capacity constraints are introduced.

[0044] To comprehensively characterize the impact of the method of this invention on the temporal characteristics of community load and the operational safety of the distribution network, this embodiment uses the peak-to-valley ratio, normalized fluctuation index, and average ramp rate to evaluate the smoothness of the total load curve. Furthermore, it statistically analyzes the number of hours and energy exceeding the upper limit of the distribution network's carrying capacity to reflect the overload risk of the distribution network. The calculation results of the above indicators are shown in Table 1.

[0045] Table 1 Comparison of solution results for two ordered charging and discharging scenarios.

[0046] As shown in Table 1, under the same travel scenarios and time-of-use pricing conditions, the peak-to-valley difference and fluctuation level of the total load of the community are significantly improved after introducing the distribution network carrying capacity constraint. The peak-to-valley ratio, normalized fluctuation, and average ramp rate all decrease significantly. This indicates that the present invention effectively weakens the peaks and sharp ramps caused by concentrated charging by uniformly coordinating the charging and discharging behavior of electric vehicles, making the load curve smoother. At the same time, in Scenario 1, the total load of the community did not exceed the carrying capacity limit throughout the entire simulation period. However, in Scenario 2, the number of over-limit hours reached 10.500h, corresponding to an over-limit energy of approximately 3967.395kWh. This indicates that if only time-of-use pricing is relied upon without constraining the carrying capacity of the distribution network, it will lead to long-term and significant overload operation, with significant voltage over-limit and equipment thermal stability risks.

[0047] Based on verifying the effectiveness of this invention in improving the smoothness of the total load curve of a residential area and the operational safety of the distribution network, this embodiment further compares and analyzes the differences before and after considering the carrying capacity constraints of the distribution network from the perspectives of economy and user charging behavior. To this end, this embodiment statistically analyzes the total charging and discharging expenditure of electric vehicles within a week and the average number of charging times per vehicle per week in two scenarios. The former reflects the comprehensive impact of the method of this invention on electricity purchase costs and V2G benefits under the premise of meeting the energy demand for vehicle travel, while the latter can serve as a direct representation of the charging frequency penalty, used to evaluate the "charging once every few days" preference and the battery life protection effect. The calculation results of the above indicators in the two scenarios are shown in Table 2.

[0048] Table 2 Comparison of economic efficiency and charging behavior in two ordered charging / discharging scenarios.

[0049] As shown in Table 2, after introducing the distribution network capacity constraint, the total weekly charging and discharging expenditure of electric vehicles in the community is basically on the same order of magnitude as when the distribution network capacity is not considered, with only slight fluctuations. This indicates that the present invention, while ensuring the safe operation of the distribution network and reducing load peaks and fluctuations, does not significantly increase the overall electricity cost on the user side, demonstrating good economic efficiency. Meanwhile, the average number of charging times per vehicle per week is similar in both scenarios, remaining at a low level. This indicates that the present invention, by introducing a charging frequency penalty term into the objective function, can effectively suppress frequent charging behavior, maintaining a "charge every few days" charging habit without sacrificing user travel needs, which is beneficial for slowing down the performance degradation of the power battery.

[0050] The comprehensive simulation results from multiple dimensions show that the method of this invention exhibits stable and consistent advantages in terms of distribution network security, load smoothing capability, user behavior rationality, and overall economy. By uniformly introducing a multi-day SOC evolution mechanism, charging frequency constraints, and time-varying carrying capacity limits into the model, the charging and discharging behavior of the electric vehicle fleet is effectively reconstructed. The total load of the community exhibits lower peak values, weaker rapid ramp-up, and smaller intraday fluctuations throughout the entire simulation period, avoiding any form of limit-breaking risk. Simultaneously, regardless of changes in vehicle battery capacity or resident mileage, the optimized charging and discharging strategy maintains good adaptability. Vehicles spontaneously form a charging pattern concentrated during low-load periods while meeting travel needs, achieving a unification of peak shaving and valley filling with multi-day charging behavior. Furthermore, the overall system cost remains close to that of scenarios without carrying capacity constraints, and the number of charging times per vehicle remains low and reasonable, achieving a balance between distribution network operation safety, load friendliness, and user-side economy. Therefore, the method of the present invention is fully applicable to various electric vehicle penetration rates and usage scenarios, and can provide highly reliable load simulation and optimization results for community power distribution networks that are feasible, scalable, and have engineering operability.

[0051] Example 2 like Figure 7 As shown, the EV charging and discharging simulation system for residential communities considering load-bearing capacity changes and multiple-day charging provided by this invention includes the following steps: Data acquisition unit: acquires the probability functions of departure time, arrival time and driving distance of users in the community, and acquires the time-of-use electricity price curve, base load curve and relevant parameters of distribution network carrying capacity of the community; Model building unit: Based on the probability function, multi-day travel behavior is sampled to obtain the arrival time, departure time and driving mileage samples of each electric vehicle within a week; combined with the power balance relationship and the battery state of charge (SOC) evolution equation, a multi-day state evolution model of the electric vehicle group is established. Explicit control unit of the power grid: Based on the multi-day state evolution model, the charging and discharging power variables of electric vehicles and their upper and lower limits, charging mode selection variables, multi-day SOC evolution constraints, and user travel energy demand constraints are introduced to construct a time-of-use pricing-guided electric vehicle charging and discharging model for residential communities. At the same time, the basic load of the community and the charging and discharging power of the electric vehicle group are uniformly incorporated into the distribution network carrying capacity constraints. By limiting the total load in each time period to not exceed the upper limit of the distribution network carrying capacity, explicit control of the distribution network overload risk is achieved. Charging and discharging load simulation unit: With the goal of minimizing the total cost of charging and discharging over a week, the charging and discharging model of electric vehicles in the community guided by time-of-use pricing is restricted to the range allowed by changes in the carrying capacity of the distribution network, and the charging and discharging load simulation results of electric vehicles in the community considering the carrying capacity of the distribution network are obtained.

[0052] In the data acquisition unit of this embodiment, the relevant parameters of the distribution network carrying capacity include transformer capacity, safety margin, and upper limit of carrying capacity for each time period.

[0053] In the model building unit of this embodiment, the charging modes include normal charging mode and V2G discharging mode.

[0054] In the power grid explicit control unit of this embodiment, both the normal charging mode and the V2G discharging mode are considered simultaneously, so that the bidirectional energy flow between the vehicle and the grid can be fully utilized; the various charging and discharging powers, SOC evolution, charging and discharging modes, and distribution network carrying capacity constraints are represented as a set of linear constraints, and the specific state constraints are shown in the following formula: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, For vehicles i In the kSOC of arrival time in days For vehicles i In the k SOC at the time of departure For vehicles i In the k The journey of the day, For vehicles i Electricity consumption per 100 kilometers For vehicles i Battery capacity; , vehicles i exist Charge and discharge power during the period This is the maximum power of the charging gun. This is a binary variable indicating whether charging or discharging is enabled. It means it can be charged. It means that it can discharge electricity. For vehicles i In the k Arrival time of day For vehicles i In the k+ One day's leave time; for Time-of-day vehicles i SOC, , These represent charge and discharge efficiencies, For users i Expected SOC , , They are respectively Total charging power, discharging power, and net charging power during the period. For the basic load of the community, This refers to the rated capacity of the transformer. For safety margin, This refers to the maximum power purchase capacity allowed by the distribution network. Vehicles during the last time period of the week i SOC, For vehicles in the first time period of the week i SOC.

[0055] Example 3 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned simulation method for EV charging and discharging in a residential area considering changes in load capacity and charging once every few days.

[0056] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0058] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0059] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0060] The advantages of this invention are as follows: This invention addresses the problem that existing technologies struggle to accurately reflect the actual multi-day charging habits of residents. By explicitly introducing a multi-day evolution mechanism of the electric vehicle's state of charge and charging frequency constraints into the modeling process, vehicles can accumulate electricity consumption over a period before being charged in a concentrated manner, thus more closely resembling real-world travel and charging behavior. Furthermore, this invention considers the combined impact of battery capacity differences and mileage fluctuations on load evolution, enabling the charging and discharging behavior of the electric vehicle fleet to exhibit continuous, stable, and predictable characteristics on a multi-day scale, significantly improving the realism and generalization ability of the load model.

[0061] This invention embeds the carrying capacity constraints of the power distribution network into the simulation and optimization process of electric vehicle charging and discharging loads, enabling multi-day simulations and scheduling while ensuring the safe operation of the power distribution network. Compared to traditional methods that only roughly set capacity limits, this invention can quantitatively assess the safety margin and access scale of the power distribution network under different electric vehicle penetration rates and charging strategies during multi-day continuous operation. This provides a more reliable theoretical basis and decision-making reference for the planning and design of community power distribution networks, equipment capacity expansion, and electric vehicle access schemes, effectively avoiding safety risks such as overload and voltage exceeding limits caused by insufficient assessment.

[0062] Furthermore, this invention introduces a penalty term related to the number of charging cycles into the objective function, and combined with multi-day energy balance constraints, it guides the preference for charging once every few days: under the premise of meeting users' travel needs and battery energy constraints, it automatically reduces unnecessary frequent charging, so that charging behavior is concentrated as much as possible during periods of low load and low electricity price in the distribution network, thereby achieving the comprehensive effect of peak shaving and valley filling, reducing users' overall electricity costs and improving the economic efficiency of distribution network operation.

[0063] Because the method of this invention adopts a unified modeling framework and scalable parameter settings, it maintains good adaptability and stability for different battery capacity structures, different travel mileage statistical characteristics and different electric vehicle penetration rates. Therefore, it can be adapted to different community sizes, different user models, different load levels and different electricity pricing mechanisms. It has the advantages of flexible modeling, strong scalability and high engineering application value, and can effectively overcome the shortcomings of existing technologies such as overly idealistic model assumptions and imprecise assessment of distribution network carrying capacity.

[0064] The inventive points protected by this invention are: A multi-day simulation method for electric vehicle charging and discharging load is proposed, which is oriented towards the community scenario and explicitly considers the "one charge every multiple days" usage habit. The method describes the cross-day state evolution of the vehicle by serializing the multi-day SOC and the one-week energy constraint.

[0065] The carrying capacity of the community distribution network (including the daily time-varying maximum carrying capacity) is explicitly embedded into the multi-day charging and discharging simulation and ordered optimization model of electric vehicles, so as to realize the quantitative assessment of the scale of electric vehicles that can be connected and the safety margin of the distribution network.

[0066] By introducing a "charging frequency penalty" term on a daily basis into the objective function, and combining it with the hard constraint of the mileage covered by the departure SOC, an orderly charging strategy oriented towards "charging once every few days" is spontaneously formed under the premise of meeting travel needs.

[0067] A multi-scenario, weekly-scale simulation framework is constructed, which combines "base load + electric vehicle load based on random travel behavior" to compare and analyze the peak-shaving and valley-filling effects and economic efficiency under different electricity pricing mechanisms and carrying capacity levels.

[0068] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0069] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for simulating EV charging and discharging in a cell considering changes in carrying capacity and multi-day charging, characterized in that, The method comprises the following steps: Step one: obtaining probability functions of cell user departure time, arrival time and driving mileage, obtaining cell time-of-use electricity price curve, basic load curve and distribution network carrying capacity related parameters; Step two: sampling multi-day travel behavior according to the probability functions to obtain arrival time, departure time and driving mileage samples of each electric vehicle within a week; combining the electric energy balance relationship and the battery state of charge SOC evolution equation, a multi-day state evolution model of the electric vehicle group is established; Step three: on the basis of the multi-day state evolution model, the electric vehicle charging and discharging power variable and its upper and lower limits, the charging mode selection variable, the multi-day SOC evolution constraint and the user travel energy demand constraint are introduced to construct a time-of-use electricity price guided cell electric vehicle charging and discharging model; at the same time, the cell basic load and the electric vehicle group charging and discharging power are uniformly included in the distribution network carrying capacity constraint, and by limiting the total load in each period to be less than the upper limit of the distribution network carrying capacity, the explicit control of the distribution network overload risk is realized; Step four: taking the minimum total charging and discharging cost in a week as the target, the time-of-use electricity price guided cell electric vehicle charging and discharging model is limited within the range allowed by the distribution network carrying capacity to obtain the cell electric vehicle charging and discharging load simulation result considering the distribution network carrying capacity. 2.The method of claim 1, wherein the method further comprises, In step one, the distribution network carrying capacity related parameters include transformer capacity, safety margin and upper limit of carrying capacity in each period. 3.The method of claim 1, wherein the method further comprises: In step two, the charging mode includes ordinary charging mode and V2G discharging mode.

4. The cell EV charging and discharging simulation method considering the change of the bearing capacity and the one-day charging according to claim 3, characterized in that, In step three, both ordinary charging mode and V2G discharging mode are considered, so that the bidirectional energy flow between vehicle and network is fully utilized; various charging and discharging power, SOC evolution, charging and discharging mode and distribution network carrying capacity constraint relationship are represented as a set of linear constraints, and the specific state constraints are shown in the following formula: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, For vehicles i In the k SOC of arrival time in days For vehicles i In the k SOC at the time of departure For vehicles i In the k The journey of the day, For vehicles i Electricity consumption per 100 kilometers For vehicles i Battery capacity; , vehicles i exist Charge and discharge power during the period This is the maximum power of the charging gun. This is a binary variable indicating whether charging or discharging is enabled. It means it can be charged. It means that it can discharge electricity. For vehicles i In the k Arrival time of day For vehicles i In the k+ One day's leave time; for Time-of-day vehicles i SOC, , These represent charge and discharge efficiencies, For users i Expected SOC , , They are respectively Total charging power, discharging power, and net charging power during the period. For the basic load of the community, This refers to the rated capacity of the transformer. For safety margin, This refers to the maximum power purchase capacity allowed by the distribution network. Vehicles during the last time period of the week i SOC, For vehicles in the first time period of the week i SOC.

5. The cell EV charging and discharging simulation method considering the change of the bearing capacity and multi-day charging according to claim 4, characterized in that, In step four, the influence of charging frequency on the electric vehicle battery is considered, and the objective function is the charging expenditure, V2G income and frequent charging penalty; (11) (12) wherein is a binary vector, is a sufficiently small number close to 0, is a sufficiently large number; is an objective function with the optimization goal of the lowest comprehensive cost of charging and discharging within a week, is a unit charging cost, is a unit discharging income, is a frequent charging penalty coefficient.

6. A cell EV charging and discharging simulation system considering the change of bearing capacity and multi-day charging, characterized in that, The method comprises the following steps: A data acquisition unit: obtains probability functions of cell user departure time, arrival time and driving mileage, obtains cell time-of-use electricity price curve, basic load curve and distribution network carrying capacity related parameters; A model establishment unit: sampling multi-day travel behavior according to the probability functions to obtain arrival time, departure time and driving mileage samples of each electric vehicle within a week; combining the electric energy balance relationship and the battery state of charge SOC evolution equation, a multi-day state evolution model of the electric vehicle group is established; A power grid explicit control unit: on the basis of the multi-day state evolution model, the electric vehicle charging and discharging power variable and its upper and lower limits, the charging mode selection variable, the multi-day SOC evolution constraint and the user travel energy demand constraint are introduced to construct a time-of-use electricity price guided cell electric vehicle charging and discharging model; at the same time, the cell basic load and the electric vehicle group charging and discharging power are uniformly included in the distribution network carrying capacity constraint, and by limiting the total load in each period to be less than the upper limit of the distribution network carrying capacity, the explicit control of the distribution network overload risk is realized; The charge-discharge load simulation unit limits the charge-discharge model of the electric vehicles in the community guided by the time-of-use electricity price to the range allowed by the change of the power distribution network carrying capacity, and obtains the charge-discharge load simulation result of the electric vehicles in the community considering the power distribution network carrying capacity.

7. The cell EV charging and discharging simulation system considering the change of the bearing capacity and multi-day charging according to claim 6, wherein The power distribution network carrying capacity related parameters in the data acquisition unit include transformer capacity, safety margin and upper limit of carrying capacity in each period. 8.The cell EV charging and discharging simulation method considering the change of the bearing capacity and multi-day charging according to claim 6, wherein, The charging mode in the model establishment unit includes ordinary charging mode and V2G discharging mode. 9.The cell EV charging and discharging simulation system considering the change of the bearing capacity and multi-day charging according to claim 8, wherein, The grid explicit control unit simultaneously considers the ordinary charging mode and the V2G discharging mode, so that the bidirectional energy flow between the vehicle and the grid is fully utilized; the constraint relationship among various charging and discharging powers, SOC evolution, charging and discharging modes and the power distribution network carrying capacity is represented as a set of linear constraints, and the specific state constraint is shown in the following formula: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, For vehicles i In the k SOC of arrival time in days For vehicles i In the k SOC at the time of departure For vehicles i In the k The journey of the day, For vehicles i Electricity consumption per 100 kilometers For vehicles i Battery capacity; , vehicles i exist Charge and discharge power during the period This is the maximum power of the charging gun. This is a binary variable indicating whether charging or discharging is enabled. It means it can be charged. It means that it can discharge electricity. For vehicles i In the k Arrival time of day For vehicles i In the k+ One day's leave time; for Time-of-day vehicles i SOC, , These represent charge and discharge efficiencies, For users i Expected SOC , , They are respectively Total charging power, discharging power, and net charging power during the period. For the basic load of the community, This refers to the rated capacity of the transformer. For safety margin, This refers to the maximum power purchase capacity allowed by the distribution network. Vehicles during the last time period of the week i SOC, For vehicles in the first time period of the week i SOC.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program realizes the steps of the community EV charge-discharge simulation method considering the carrying capacity change and multi-day charging in any one of claims 1-6 when executed by the processor.

Images