Electric vehicle V2G microgrid energy storage capacity optimization method based on dynamic planning

Abstract

The invention discloses an electric vehicle V2G micro-grid energy storage capacity optimization method based on dynamic planning, particularly relates to the technical field of micro-grid energy storage optimization, and aims to solve the problem of energy storage capacity optimization defects caused by spatial dynamic characteristics caused by mobility of an existing electric vehicle. Performing geographic grid division on the node position, generating a space-time matrix by combining with a timestamp, and identifying a high-frequency access node; constructing a state transition equation based on the inter-node impedance matrix, and setting a node-level safety boundary by taking a line capacity change rate as a dynamic constraint; a staged reverse dynamic programming algorithm is adopted, spatial dimension optimization is preferentially executed on the high-frequency access nodes, and charging and discharging constraints fusing voltage deviation and capacity overrun penalty terms are generated; carrying out global optimization by taking the minimization of the total operation cost as a target after the constraints are integrated, and outputting an energy storage capacity configuration strategy of each time period; and finally generating an energy storage deployment scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid energy storage optimization. More specifically, the present invention relates to a method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming. Background Art

[0002] With the deep integration of electric vehicle V2G technology and the microgrid, the dynamic programming method is widely used in energy storage capacity optimization to achieve economic dispatch and supply-demand balance. Existing technologies usually divide the optimization stage based on the time dimension. By predicting the charging and discharging demands of electric vehicles and the output of renewable energy, the capacity configuration and charging and discharging strategies of energy storage devices are generated. Such methods default that the physical location of electric vehicles connected to the microgrid is fixed during the optimization period and assume that the grid topology parameters (such as node voltage and line capacity) are evenly distributed in the spatial dimension. It is difficult to adapt to the conduction constraints of the microgrid access stability for AC transmission systems above 750 kV, so the complex microgrid is simplified to a single-node or homogeneous multi-node model for solution.

[0003] In practice, due to the lack of consideration of the spatial dynamic characteristics caused by the mobility of electric vehicles, there are defects in participating in large-scale grid collaborative dispatch: the dynamic programming strategy ignores the differential impacts of the changes in the access positions of electric vehicles on the grid node voltage and line capacity, resulting in spatial mismatch problems in the actual multi-node microgrid scenario, triggering the conduction of chain safety risks to the backbone network. Specifically, the energy storage capacity configuration and dispatch strategy cause voltage over-limit and line overload at some grid nodes, while there is redundant capacity at other nodes, leading to a reduction in the safety defense margin of the intelligent dispatch system and causing a double deterioration of grid safety risks and investment costs. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming includes the following steps:

[0007] S1. Collect the grid topology parameters and electric vehicle movement trajectory data of the target microgrid. The grid topology parameters include the impedance matrix between nodes, the node voltage limit value, and the line capacity threshold;

[0008] S2. Divide the node positions in the electric vehicle movement trajectory data into geographical grids, generate a spatio-temporal matrix in combination with the time stamp, and identify high-frequency access nodes;

[0009] S3. Construct a state transition equation based on the inter-node impedance matrix, taking the line capacity change rate caused by electric vehicle movement as a constraint, and set the node-level safety boundary based on the node voltage limit and line capacity threshold;

[0010] S4. Using a phased inverse dynamic programming algorithm, with node voltage deviation and line capacity exceeding the limit as penalty items, prioritize spatial dimension optimization for high-frequency access nodes to generate node charge and discharge constraints;

[0011] S5. Integrate node charging and discharging constraints, perform global optimization with the goal of minimizing total operating costs, and output a global strategy for energy storage capacity configuration in each time period;

[0012] S6. Generate an energy storage deployment plan based on the global strategy and the distribution of high-frequency access nodes.

[0013] In a preferred embodiment, S1 includes:

[0014] S1a. Obtaining grid topology parameters in real time through the microgrid monitoring system. The grid topology parameters include an inter-node impedance matrix, a node voltage limit, and a line capacity threshold. The node voltage limit and line capacity threshold are extracted from a preset safe operation database of the microgrid management platform.

[0015] S1b, collecting electric vehicle movement trajectory data through a vehicle-mounted positioning device, the movement trajectory data including the node location and corresponding timestamp of the electric vehicle connected to the microgrid in consecutive time periods;

[0016] S1c, based on the inter-node connection relationship in the power grid topology parameters, calculate the numerical update result of the inter-node impedance matrix in real time, and spatially correlate and map the numerical update result of the inter-node impedance matrix with the node position in the electric vehicle movement trajectory data;

[0017] S1d. Perform data verification on the numerical update results of the node voltage limit, line capacity threshold, and inter-node impedance matrix, and eliminate abnormal parameters that exceed the preset deviation range.

[0018] In a preferred embodiment, S2 includes:

[0019] S2a, dynamically gridding the node positions in the electric vehicle movement trajectory data according to a preset geographic grid density to generate geographic grid areas corresponding to the grid node distribution;

[0020] S2b, dividing the electric vehicle access events in each geographic grid area into continuous time periods according to the timestamp, counting the number of access events in each time period, and generating a spatiotemporal matrix;

[0021] S2c. Cluster the number of access events in the spatio-temporal matrix based on a preset clustering analysis rule, identify the geographical grid areas where the number of access events exceeds the preset high-frequency threshold, and mark them as high-frequency access nodes;

[0022] S2d. Conduct spatio-temporal association verification on the high-frequency access nodes. If the same geographical grid area is marked as a high-frequency access node in more than a certain number of consecutive time periods, it is confirmed as a valid high-frequency access node.

[0023] In a preferred embodiment, S3 includes:

[0024] S3a. Construct a state transition equation based on the dynamic change characteristics of the inter-node impedance matrix. The numerical update result of the inter-node impedance matrix is dynamically associated with the node positions in the electric vehicle movement trajectory data through a weighting factor to generate a spatio-temporal coupled state transition weight;

[0025] S3b. According to the line capacity change rate caused by the movement of electric vehicles, combined with historical load data and line capacity thresholds, establish a linear constraint relationship between the line capacity change rate and the node charge and discharge power to generate dynamic constraint conditions;

[0026] S3c. Set node-level safety boundaries based on the node voltage limit and line capacity threshold. The node-level safety boundaries include the allowable range of node voltage deviation and the tolerance of line capacity overrun, and embed the node-level safety boundaries into the state transition equation as a hard constraint.

[0027] In a preferred embodiment, S4 includes:

[0028] S4a. Divide the optimization stage into spatial dimension optimization and time dimension recursion, and determine the priority order of spatial dimension optimization based on the distribution of high-frequency access nodes;

[0029] S4b. In the spatial dimension optimization stage, use the node voltage deviation and line capacity overrun as penalty terms, and adopt the reverse dynamic programming algorithm to generate the initial charge and discharge power constraints of high-frequency access nodes;

[0030] S4c. Dynamically adjust the penalty term weight according to the inter-node impedance matrix in the power grid topology parameters, and iteratively correct the initial charge and discharge power constraints to generate optimization constraints that meet the node-level safety boundaries;

[0031] S4d. In the time dimension recursion stage, integrate the optimization constraints of all high-frequency access nodes, eliminate the local constraints that conflict with the global strategy, and generate the final node charge and discharge constraints.

[0032] In a preferred embodiment, in the spatial dimension optimization stage, the reverse dynamic programming algorithm is used to perform backward recursive calculation starting from the node with the lowest priority. Taking the node voltage deviation and line capacity overlimit as penalty terms, an objective function is constructed. The specific form of the objective function includes:

[0033] The voltage deviation penalty term and the line capacity overlimit penalty term are weighted and summed proportionally. The voltage deviation penalty term is the percentage of the absolute difference between the actual voltage and the rated voltage to the rated voltage, and the line capacity overlimit penalty term is the square of the ratio of the actual line current to the capacity threshold; the weighting ratio is dynamically adjusted according to the grid topology parameters.

[0034] In a preferred embodiment, S5 includes:

[0035] S5a. Summarize the charge and discharge constraints of all high-frequency access nodes to generate a node-level charge and discharge feasible region set, which includes the power upper and lower limits and time validity ranges of each node;

[0036] S5b. Based on the node impedance matrix in the grid topology parameters, construct a charge and discharge cost coefficient matrix, which includes the comprehensive influence weights of node charge and discharge power on line loss, equipment depreciation, and peak-valley electricity prices;

[0037] S5c. Taking the minimum total operating cost as the objective function, input the charge and discharge cost coefficient matrix and the node-level charge and discharge feasible region set into a linear programming model to solve the global charge and discharge power allocation scheme that satisfies the time period coupling constraints;

[0038] S5d. Perform N-1 security verification on the global charge and discharge power allocation scheme, eliminate the schemes that cause node voltage overlimit or capacity overlimit when a single line fails, and output the global strategy for energy storage capacity configuration in each time period.

[0039] In a preferred embodiment, S6 includes:

[0040] S6a. Integrate the energy storage capacity configuration schemes in the global strategy and the high-frequency access node distribution data to generate a node energy storage demand matrix, which includes the capacity demand, charge and discharge power demand of each node, and the time distribution characteristics matching the high-frequency time periods;

[0041] S6b. Based on the node energy storage demand matrix and the node impedance matrix, calculate the energy storage deployment priority of each node, and the energy storage deployment priority is dynamically adjusted according to the access frequency in the high-frequency time periods and the node electrical centrality;

[0042] S6c. Taking the node-level safety boundary as a constraint, combining the charge and discharge power limits and the adaptive priority rules, use a multi-objective optimization algorithm to generate the deployment scheme of the capacity, power limit, and geographical location of the energy storage device.

[0043] S6d. Perform node-level voltage stability verification on the energy storage deployment plan, eliminate the plans that exceed the charge-discharge power limit or voltage deviation tolerance, and output the final energy storage deployment plan.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. Through geographical grid division and spatio-temporal matrix construction, the present invention dynamically maps the moving trajectories of electric vehicles to the physical topology of the power grid, realizes the accurate identification and spatio-temporal distribution quantification of high-frequency access nodes, and adjusts the charge-discharge constraints in combination with the dynamically updated node impedance matrix, solves the spatial mismatch defect of the traditional single-node model, ensures the spatial consistency between the energy storage capacity allocation and the load demand, and effectively suppresses the risks of local voltage over-limit and line overload;

[0046] 2. Through the staged reverse dynamic programming algorithm, the present invention deeply couples the spatial optimization of high-frequency nodes with the global safety objective, solves the problem that it is difficult to coordinate local optimization and global constraints. On the basis of prior optimization in the spatial dimension, a node-level safety boundary and an N-1 fault pre-verification mechanism are embedded, a hierarchical and progressive global strategy generation framework is constructed, and multi-objective coordination of economy, safety and operability is realized through a multi-objective optimization algorithm, overcomes the strategy oscillation and safety hazards caused by the lack of closed-loop verification in the prior art, ensures the robustness of the energy storage deployment plan in complex power grid scenarios, and provides a systematic solution that takes into account both efficiency and reliability for the microgrid energy storage capacity optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of the method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Embodiment: Figure 1 The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to the present invention is given, and it includes the following steps:

[0050] S1. Collect the power grid topology parameters and the moving trajectory data of electric vehicles of the target microgrid. The power grid topology parameters include the node impedance matrix, the node voltage limit value and the line capacity threshold;

[0051] S2. Divide the node positions in the electric vehicle movement trajectory data into geographical grids, generate a spatio-temporal matrix in combination with the time stamps, and identify high-frequency access nodes;

[0052] S3. Based on the impedance matrix between nodes, construct a state transition equation, use the line capacity change rate caused by the movement of electric vehicles as a constraint condition, and set the node-level safety boundary according to the node voltage limit and the line capacity threshold;

[0053] S4. Adopt a phased reverse dynamic programming algorithm, use the node voltage deviation and the line capacity overlimit as penalty terms, and preferentially perform spatial dimension optimization on high-frequency access nodes to generate node charge and discharge constraints;

[0054] S5. Integrate the node charge and discharge constraints, perform global optimization with the goal of minimizing the total operating cost, and output the global strategy for the energy storage capacity configuration in each time period;

[0055] S6. Generate an energy storage deployment plan according to the global strategy and the distribution of high-frequency access nodes.

[0056] S1. Collect the power grid topology parameters of the target microgrid and the electric vehicle movement trajectory data. The power grid topology parameters include the impedance matrix between nodes, the node voltage limit, and the line capacity threshold. Specifically:

[0057] Obtain the power grid topology parameters of the target microgrid in real time through the microgrid monitoring system. The power grid topology parameters include the impedance matrix between nodes, the node voltage limit, and the line capacity threshold. The node voltage limit and the line capacity threshold are extracted from the preset safe operation database of the microgrid management platform. The preset safe operation database is a standardized database preset according to the power grid equipment parameters and operation regulations in the microgrid planning and design stage. For example, the node voltage limit is ±10% of the rated voltage, and the line capacity threshold is 85% of the maximum transmission power of the line design.

[0058] Collect the electric vehicle movement trajectory data through the vehicle-mounted positioning device. The movement trajectory data includes the node positions where the electric vehicle accesses the microgrid in continuous time periods and the corresponding time stamps. The vehicle-mounted positioning device uses the Global Positioning System or the Beidou Navigation System, and the time stamp accuracy is at the second level to ensure the accurate spatial correspondence between the electric vehicle position and the microgrid nodes.

[0059] Based on the connection relationship between nodes in the power grid topology parameters, calculate the numerical update result of the impedance matrix between nodes in real time. Specifically, the calculation of the impedance matrix between nodes is based on the connection relationship between nodes in the power grid topology parameters, and uses the node admittance calculation method in circuit topology to update the matrix numerical values according to the electrical distance between adjacent nodes and the line impedance parameters. For example, when the impedance value of a certain node changes due to line transformation, the numerical values of the corresponding rows and columns in the impedance matrix between nodes will be updated synchronously.

[0060] The numerical update results of the inter-node impedance matrix are spatially associated with the node positions in the electric vehicle movement trajectory data. Spatial association mapping refers to matching the location information of the electric vehicle with the geographic coordinates of the power grid node. According to the geographic grid division rules, the grid area where the electric vehicle is located is mapped to the corresponding power grid node, thereby establishing a dynamic association relationship between the electric vehicle movement trajectory and the power grid node impedance matrix.

[0061] Data verification is performed on the updated values of node voltage limits, line capacity thresholds, and internode impedance matrices to eliminate abnormal parameters that exceed the preset deviation range. The preset deviation range is set based on historical operating data and equipment error ranges. For example, the allowable deviation of node voltage limits is ±2%, the allowable deviation of line capacity thresholds is ±3%, and the internode impedance matrix is considered abnormal if the numerical deviation exceeds ±5%.

[0062] During the data verification process, if a node voltage limit is found to be outside the preset tolerance range, the node voltage limit is re-extracted from the preset safe operation database. If the line capacity threshold or the updated value of the inter-node impedance matrix exceeds the preset tolerance range, an alarm signal is sent to the microgrid monitoring system and a manual review is requested. These steps ensure the accuracy and reliability of the input grid topology parameters and electric vehicle movement trajectory data, providing a high-quality data foundation for the subsequent construction of the dynamic programming model.

[0063] During the implementation process, it is necessary to pay attention to the following: First, when the microgrid monitoring system obtains the grid topology parameters in real time, it needs to be synchronized with the data interface of the microgrid management platform to avoid parameter expiration due to communication delays; Second, the node position data collected by the on-board positioning device needs to be calibrated with the microgrid's geographic information system to ensure the spatial mapping accuracy of the electric vehicle position and the grid node; Third, the real-time calculation of the impedance matrix between nodes needs to take into account the dynamic changes in the grid operation status, such as changes in impedance parameters caused by distributed power supply switching or load fluctuations; Fourth, the preset deviation range of the data verification link needs to be dynamically adjusted according to the actual operating environment. For example, the deviation range of the line capacity threshold can be appropriately relaxed in high or low temperature environments to avoid mistakenly rejecting normal parameters; Fifth, the processing of abnormal parameters requires recording operation logs, including abnormal values, occurrence time and processing measures, to facilitate subsequent tracing and analysis.

[0064] S2. Divide the node locations in the electric vehicle movement trajectory data into geographic grids, generate a spatiotemporal matrix based on the timestamps, and identify high-frequency access nodes. Specifically:

[0065] The node positions in the electric vehicle movement trajectory data are dynamically gridded according to the preset geographic grid density to generate geographic grid areas corresponding to the grid node distribution.

[0066] The preset geographical grid density is set according to the historical access frequency distribution of electric vehicles in the target microgrid coverage area. The specific rules are as follows: In the urban commercial area, a high-density division of 10×10 grids per square kilometer is adopted, and in the residential area, a low-density division of 5×5 grids per square kilometer is adopted.

[0067] During the dynamic grid division process, if the number of power grid nodes in a certain geographical grid area exceeds 3, the grid density of this area will be automatically increased to 15×15 per square kilometer to ensure that each geographical grid area is only associated with one power grid node. For example, if the initial division of a commercial area grid is 10×10 and 4 power grid nodes are detected in the area, the grid density is automatically adjusted to 15×15.

[0068] The electric vehicle access events in each geographical grid area are divided into continuous time periods according to the time stamp, and the number of access events in each period is counted to generate a spatio-temporal matrix.

[0069] The division rule of continuous time periods is a fixed time interval, which can be set to 15 minutes per period, and the time stamp accuracy is consistent with the collected electric vehicle movement trajectory data.

[0070] The rows of the spatio-temporal matrix represent geographical grid areas, the columns represent continuous time periods, and the matrix element values are the number of electric vehicle access events in the corresponding geographical grid area in the corresponding period. For example, the element value N at the i-th row and j-th column in the spatio-temporal matrix ij represents the number of access events in the i-th geographical grid area in the j-th period, where i = 1, 2,..., M (M is the total number of geographical grid areas), and j = 1, 2,..., T (T is the total number of periods).

[0071] Based on statistical analysis methods, the number of access events in the spatio-temporal matrix is clustered to identify high-frequency access nodes. Specifically, for each period j, calculate the average value μ of the number of access events in all geographical grid areas in this period j and the standard deviation σ j , and set the preset high-frequency threshold as μ j +2σ j . If the number of access events N of geographical grid area i in period j ij is greater than the preset high-frequency threshold, then this area is marked as a high-frequency access node in period j.

[0072] Perform spatio-temporal correlation verification on high-frequency access nodes. The verification rule is as follows: If the same geographical grid area is marked as a high-frequency access node in more than three consecutive time periods, it is confirmed as a valid high-frequency access node. The determination criterion for consecutive time periods is a sequence of adjacent and non-interval time periods. For example, if the geographical grid area A is marked as a high-frequency access node in time periods t1, t2, and t3 respectively, it is confirmed as a valid high-frequency access node; if it is only marked in time periods t1 and t3, the continuous condition is not met. During the verification process, if the proportion of high-frequency marking times of a certain area in consecutive time periods exceeds 80%, the length of the consecutive time periods is automatically relaxed to four time periods to adapt to traffic flow fluctuations.

[0073] The following details need to be noted during the implementation: The adjustment of the preset geographical grid density needs to be updated in real time according to the changes in the microgrid topology. For example, when a new power grid node is added, the associated geographical grid area is automatically re-divided; the setting of the high-frequency threshold can be dynamically adjusted in combination with the microgrid load capacity. For example, the threshold is reduced by 10% during the peak electricity consumption period to identify more potential high-frequency nodes; the length of the consecutive time periods in the spatio-temporal correlation verification can be adjusted according to actual needs. For example, it is shortened to two consecutive time periods during the traffic peak period and extended to four consecutive time periods during the low valley period.

[0074] S3. Construct a state transition equation based on the impedance matrix between nodes, use the line capacity change rate caused by the movement of electric vehicles as a constraint condition, and set the node-level safety boundary according to the node voltage limit and the line capacity threshold. Specifically:

[0075] Construct a state transition equation based on the dynamic change characteristics of the impedance matrix between nodes. The numerical update result of the impedance matrix between nodes is dynamically associated with the node positions in the electric vehicle movement trajectory data through a weighting factor.

[0076] The calculation method of the weighting factor is as follows: Take the numerical update result of the impedance matrix between power grid nodes (representing the real-time impedance value from node x to node y, unit: ohm) and the electric vehicle access frequency within the corresponding geographical grid area (representing the number of electric vehicle movements from node x to node y, unit: times / hour) for linear weighting ( and Both are dimensionless processed before linear weighting), generating a spatio-temporal coupled state transition weight The calculation formula is: where α and β are preset balance coefficients, satisfying α + β = 1. For example, α is 0.6 and β is 0.4, which are used to adjust the contribution ratio of the impedance value and the movement frequency to the weight; is obtained through the real-time calculation in step S1c, is obtained by statistically analyzing the high-frequency access node recognition results in step S2c.

[0077] By calculating the state transition weight of spatio-temporal coupling Realize the spatio-temporal coupling of the physical characteristics of the power grid and the moving behavior of electric vehicles.

[0078] According to the line capacity change rate caused by the movement of electric vehicles, combined with historical load data and line capacity thresholds, establish a linear constraint relationship between the line capacity change rate and the node charge and discharge power. Specifically, for each line k, its capacity change rate And the node charge and discharge power The relationship of is obtained by fitting historical data to get a linear equation: Among them, And Represent the fitting coefficients, which are obtained by performing regression analysis on the charge and discharge power and capacity change rate in the historical operation data through the least squares method; k represents the line number.

[0079] For example, through the analysis of the historical data of a certain commercial area line, it is obtained that Then when the node charge and discharge power When

[0080] Based on this linear relationship, generate dynamic constraint conditions: Among them, Is the capacity threshold of line k (unit: kW), which is obtained through the power grid topology parameters in step S1 to ensure that the adjustment of the charge and discharge power will not cause the line capacity to exceed the limit.

[0081] Set the node-level safety boundary based on the node voltage limit and the line capacity threshold, including the allowable range of node voltage deviation and the tolerance of line capacity overrun.

[0082] The allowable range of voltage deviation is set to ±5% of the node voltage limit. For example, when the rated node voltage is 380V, the allowable deviation is ±19V; the tolerance of line capacity overrun is set to 10% of the line capacity threshold. For example, when the capacity threshold is 100kW, it is allowed to temporarily exceed the limit to 110kW. Embed the safety boundary into the state transition equation as a hard constraint. Specifically, a penalty term is introduced in the state transition equation. When the node voltage deviation or line capacity overrun exceeds the safety boundary, the penalty term value is infinite, forcing the optimization algorithm to avoid such state transition paths. For example, if the voltage deviation of node x reaches 20V (exceeding the allowable 19V), then the weight Of the corresponding path in the state transition equation is set to infinity, and this path is prohibited from being selected.

[0083] It should be noted that the balance coefficients α and β can be dynamically adjusted according to the microgrid operation scenarios. For example, β can be increased to 0.5 during peak hours to enhance the influence of the electric vehicle movement frequency on the weight; α can be increased to 0.7 during off-peak hours to prioritize the grid impedance characteristics; and it can be refitted monthly based on the latest historical data and The update period is synchronized with the microgrid operation and maintenance plan to ensure that the linear relationship is consistent with the actual operation status; under extreme weather conditions (such as high temperature or low temperature), the tolerance of line capacity overlimit can be temporarily reduced to 5% to prevent equipment overload; in areas with frequent voltage fluctuations, the allowable range of voltage deviation is tightened to ±3%; when the voltage deviation or capacity overlimit exceeds the safety boundary, the current optimization calculation is immediately interrupted and the state transition equation is re-initialized to avoid ineffective iteration. When re-initializing, the path weights that have not triggered the penalty term are preferentially retained to improve the calculation efficiency.

[0084] By deeply integrating the dynamic changes of the impedance matrix between grid nodes with the spatio-temporal characteristics of the electric vehicle movement trajectories, a spatio-temporal coupled state transition equation is constructed, which solves the problem of energy storage optimization mismatch caused by traditional dynamic programming methods ignoring the differences in spatial dimension parameters. Specifically, the weighting factor dynamically correlates the impedance value with the electric vehicle movement frequency, and reflects the impact of grid topology changes on the charge and discharge strategy in real time; based on the linear constraint relationship obtained by historical data regression, the complex line capacity change rate is converted into a computable charge and discharge power limit, significantly reducing the model complexity; the hard constraint mechanism of the node-level safety boundary, through the dual control of the allowable range of voltage deviation and the tolerance of capacity overlimit, ensures that the optimized path strictly complies with the grid safe operation standard.

[0085] S4. Adopt a phased reverse dynamic programming algorithm, with the node voltage deviation and line capacity overlimit as penalty terms, and perform spatial dimension optimization on the high-frequency access nodes first to generate node charge and discharge constraints, specifically:

[0086] Divide the optimization process into two stages: spatial dimension optimization and time dimension recursion. Based on the distribution of high-frequency access nodes identified in step S2, determine the priority order of spatial dimension optimization. The priority sorting rule is: comprehensively score according to the electrical position weight of the node in the grid topology (calculated from the impedance matrix between nodes) and the access frequency.

[0087] For example, for high-frequency nodes located at the end of the main line (electrical weight ≥ 0.8) and with an access frequency ≥ 10 times / hour, the priority is upgraded to the highest level; for branch nodes (electrical weight < 0.5) and with an access frequency < 5 times / hour, the priority is set to the lowest level.

[0088] After generating the priority list, process the spatial optimization problems of each node in order from high to low.

[0089] In the spatial dimension optimization stage, the reverse dynamic programming algorithm is used to perform backward recursive calculations starting from the node with the lowest priority. Taking the node voltage deviation and line capacity overlimit as penalty terms, an objective function is constructed. The specific forms of the objective function include but are not limited to the following two:

[0090] Linear combination form: The voltage deviation penalty term and the line capacity overlimit penalty term are weighted and summed proportionally. Among them, the voltage deviation penalty term is the percentage of the absolute difference between the actual voltage and the rated voltage to the rated voltage, and the line capacity overlimit penalty term is the square of the ratio of the actual line current to the capacity threshold. The weighting ratio is dynamically adjusted according to the grid topology parameters (such as the node impedance matrix).

[0091] Piecewise function form: When the degree of line capacity overlimit is within 10% of the threshold, a linear penalty coefficient is adopted; when the overlimit exceeds 10%, the penalty coefficient increases according to an exponential law to strengthen the suppression of severe overlimits. The voltage deviation penalty term sets asymmetric weights according to the deviation direction (too high or too low). For example, the weight when the voltage is too high is 1.2 times that when the voltage is too low.

[0092] For each high-frequency access node, traverse its charge and discharge power feasible interval (such as -1.5 MW to +1.5 MW), and screen out the power range with a penalty value lower than the preset threshold as the initial charge and discharge constraint. For example, if the comprehensive penalty value of all power points within the power interval [-1.2 MW, +1.0 MW] of a certain node is lower than the preset threshold, an initial constraint is generated.

[0093] The penalty term weights are dynamically adjusted according to the node impedance matrix in the grid topology parameters. For nodes with closely electrical coupling (such as the impedance between nodes is less than 0.1 ohm), increase the voltage deviation penalty weight; for lines with low capacity margins (such as the line load rate exceeds 90%), increase the capacity overlimit penalty weight. After adjustment, the iterative method is used to correct the constraint boundary. For example, the feasible power interval is gradually shrunk through the bisection method until the node-level safety boundaries (voltage deviation ≤ 5%, line capacity overlimit ≤ 10%) are met.

[0094] In the time dimension recursive stage, the optimization constraints of all high-frequency access nodes are integrated. A global conflict detection rule is established: If the charge and discharge power constraints of a certain node cause the voltage of adjacent nodes to exceed the limit or the line capacity to exceed the limit, then this constraint is removed. For example, when the constraint of node A causes the voltage deviation of adjacent node B to exceed 5%, the power upper limit of node A is reduced to below the conflict threshold. Finally, a set of charge and discharge constraints consistent with the global optimization goal is generated.

[0095] It should be noted that in the setting of the objective function parameters, the initial value of the voltage deviation penalty weight is set according to the electrical centrality of the node in the power grid. The higher the centrality (such as closer to the main transformer), the greater the weight; the capacity over-limit penalty weight is dynamically adjusted according to the historical load rate of the line. When the load rate exceeds 80%, the weight is increased by 20%;

[0096] In the dynamic rule of the penalty term, when the node voltage deviation exceeds 3% for three consecutive time periods, the voltage deviation penalty weight is increased by 20%; when the probability of line capacity over-limit exceeds 15%, the capacity over-limit penalty weight is increased by 30%;

[0097] In the conflict resolution mechanism, a relaxation factor is introduced for the conflicting constraints, allowing the constraint boundary to be fine-tuned within a preset tolerance range (such as ±5%) to ensure the compatibility of local optimization and the global objective.

[0098] The Backward Dynamic Programming algorithm recursively calculates from the final time period to the initial time period of the optimization cycle. At each time period, based on the optimal solution of the subsequent stage, the node voltage deviation, and the line capacity over-limit penalty term, the current charge-discharge strategy is calculated to ensure the consistency of local decisions and the global safety boundary, and finally, an optimization result that meets the node-level constraints is generated.

[0099] It should be noted that the node voltage limit is the preset safe operating range of the power grid node voltage (such as rated voltage ±10%), which is set by the power grid design specifications or management regulations and is used to define the allowable voltage fluctuation boundary (for example, 380V ± 38V). The node voltage deviation is the real-time difference between the actual operating voltage and the rated voltage (such as +25V or -20V), which reflects the degree of deviation of the node voltage from the rated value and needs to be maintained within the limit range through monitoring and control means.

[0100] It should be noted that the line capacity threshold is the maximum power or current value that the line is allowed to carry (such as 1000A), which is determined by the line design parameters or safety regulations and is used to ensure that the equipment is not overloaded. The line capacity over-limit refers to the state where the actual operating power or current exceeds the threshold (such as 1050A), which is an abnormal operating condition and needs to be restored within the threshold by adjusting the charge-discharge strategy or cutting off the load.

[0101] S5. Integrate the charge-discharge constraints of the nodes, perform global optimization with the goal of minimizing the total operating cost, and output the global strategy for the energy storage capacity configuration in each time period. Specifically:

[0102] Summarize the charge-discharge constraints of all high-frequency access nodes output in step S4 to generate a set of node-level charge-discharge feasible regions.

[0103] Each node constraint in the feasible region set includes the upper and lower power limits and the time validity range. The upper and lower power limits are directly obtained from the spatial optimization results in step S4 (for example, the upper power limit of node A is +1.2 MW and the lower limit is -0.8 MW), and the time validity range is determined according to the time period markings of the high-frequency access nodes in step S2 (for example, the charging and discharging operations of node B are only valid from 9:00 to 12:00 and from 18:00 to 21:00 every day).

[0104] To generate the feasible region set, the compatibility between node constraints needs to be verified. If there is an overlapping interval in the upper and lower power limits of adjacent nodes (such as [-0.5 MW, +1.0 MW] of node C and [-0.8 MW, +0.9 MW] of node D), the overlapping part is automatically merged to form a joint feasible region.

[0105] Based on the inter-node impedance matrix in the power grid topology parameters, a charging and discharging cost coefficient matrix is constructed. Each element of the cost coefficient matrix represents the comprehensive influence weight of the node charging and discharging power on the power grid operation cost, including line loss cost, equipment depreciation cost, and peak-valley electricity price difference cost.

[0106] The line loss cost weight is calculated from the mutual impedance elements of the inter-node impedance matrix. For example, the greater the mutual impedance from node M to node N, the higher the line loss cost weight; the equipment depreciation cost weight is set according to the rated power and historical usage frequency of the node charging and discharging equipment. For example, if the rated power of a node's charging and discharging equipment is 2 MW and the average daily usage times ≥ 10 times, its depreciation cost weight is increased by 20%; the peak-valley electricity price difference weight is dynamically adjusted according to the real-time electricity price data collected in step S1. For example, during the valley electricity price period (0:00 - 8:00), the weight is reduced to 0.7, and during the peak period (18:00 - 22:00), it is increased to 1.3.

[0107] Taking the minimum total operation cost as the objective function, the charging and discharging cost coefficient matrix and the node-level feasible region set are input into a linear programming model to solve the global charging and discharging power allocation scheme that satisfies the time period coupling constraints. The specific form of the objective function is to minimize the weighted sum of the line loss cost, equipment depreciation cost, and peak-valley electricity price cost. The time period coupling constraints include the adjacent time period power change rate limit (for example, the charging and discharging power change between adjacent time periods does not exceed ±20%), the node charging and discharging power within the feasible region range, and the time validity constraint of high-frequency access nodes.

[0108] During the solution process, the simplex method is used to perform iterative calculations on the linear programming model until all constraint conditions are met and converge to the optimal solution. For example, the global allocation scheme for a certain time period requires the charging and discharging power of node A to be +0.9 MW from 9:00 to 10:00, node B to be -0.3 MW, and the power change rates of both nodes do not exceed 20%.

[0109] Perform N-1 security verification on the global charging and discharging power distribution scheme, that is, simulate the grid operation state under the fault scenario of any single line, and detect whether the node voltage exceeds the limit or the line capacity exceeds the limit. The verification rules include:

[0110] Node voltage verification: If the voltage deviation of a certain node exceeds its safety boundary (such as ±5% of the rated voltage) under the fault scenario, it is determined that the scheme is unqualified;

[0111] Line capacity verification: If the load rate of a certain line exceeds 10% of the capacity threshold (such as the load > 110kW when the threshold is 100kW) under the fault scenario, it is determined to be unqualified.

[0112] For unqualified schemes, eliminate or adjust the charging and discharging power distribution of relevant nodes. For example, when the voltage deviation of node E reaches 6% due to the fault of line L1, adjust the power upper limit of node E from +1.0MW to +0.8MW, and recalculate the global strategy.

[0113] Finally, output the global strategy of the energy storage capacity configuration for each period that passes the verification, including the charging and discharging power plan, time arrangement, and cost budget of each node.

[0114] It should be noted that the interval intersection algorithm can be used to detect the overlap of power constraints of adjacent nodes. If the proportion of the overlapping interval < 30%, the independent constraints are maintained; if ≥ 30%, the joint constraints are generated; in the scenario of frequent electricity price fluctuations, update the peak-valley electricity price weights once an hour; within the equipment maintenance cycle (such as every quarter), update the equipment depreciation cost weights; for the charging and discharging power change rate limit, if the power change of a node is lower than 5% in three consecutive periods, relax the limit to ±30% to improve the scheduling flexibility; give priority to verifying the fault scenarios of the main lines and the lines associated with high-frequency access nodes. For example, conduct a full-scale verification on the lines associated with nodes with an access frequency ≥ 20 times per day, and sample other lines at a ratio of 10%.

[0115] By integrating the node-level charging and discharging constraints and multi-dimensional cost modeling, the collaborative improvement of the global optimization strategy in terms of safety, economy, and operability is realized. The charging and discharging cost coefficient matrix integrates multiple factors such as line losses, equipment depreciation, and peak-valley electricity prices, solving the problem of the one-sidedness of the strategy caused by single-cost optimization in traditional methods; the time-period coupling constraints (such as the charging and discharging power change rate limit between adjacent periods) are embedded through a linear programming model, suppressing the drastic fluctuations of the charging and discharging power and avoiding the losses caused by frequent start-stop of equipment; the N-1 security verification mechanism is deeply coupled with the global optimization process, and high-risk strategies under fault scenarios are eliminated in real time, significantly improving the grid robustness.

[0116] S6. Generate an energy storage deployment plan according to the global strategy and the distribution of high-frequency access nodes, specifically:

[0117] Integrate the global energy storage capacity configuration strategy for each time period output by step S5 and the high-frequency access node distribution data generated by step S2 to generate a node energy storage demand matrix. Each element of this matrix corresponds to a power grid node and contains the following characteristics:

[0118] Capacity demand: Calculated by accumulating the charge and discharge power demands of this node in each time period in the global strategy. For example, if a node needs to provide a cumulative discharge of 10 MWh during the peak period (18:00 - 22:00) of each day, the capacity demand is 10 MWh; during the valley period (0:00 - 8:00), a charging capacity of 8 MWh needs to be reserved;

[0119] Charge and discharge power demand: Directly reference the node charge and discharge power limits generated in step S4. For example, the power upper limit of node A is +1.2 MW (discharge), the lower limit is -0.8 MW (charging), and it is only valid from 9:00 to 12:00 and from 18:00 to 21:00 every day;

[0120] Time distribution characteristics: The time validity range matching the high-frequency time periods. For example, the high-frequency time periods of node B are the morning and evening rush hours on weekdays (7:00 - 9:00, 17:00 - 19:00), and its energy storage operation is only allowed during this time period.

[0121] Based on the node energy storage demand matrix and the node impedance matrix in step S1, calculate the energy storage deployment priority for each node. The priority scoring rules are as follows:

[0122] The higher the access frequency during the high-frequency time periods, the greater the weight. For example, the access frequency of node C during the peak period is 15 times per hour, and the weight coefficient is set to 1.2; the access frequency of node D is 5 times per hour, and the weight coefficient is set to 0.6;

[0123] According to the electrical position of the node in the power grid topology (calculated from the node impedance matrix), the weight of the node close to the main transformer (electrical centrality ≥ 0.8) is set to 1.5, and the weight of the end node (electrical centrality ≤ 0.3) is set to 0.5;

[0124] When the node capacity demand exceeds 20% of its historical average, the priority score is increased by 10%; when the node impedance value changes due to power grid transformation, recalculate the electrical centrality weight. For example, if the self-impedance of a node decreases by 30% due to line upgrade, its electrical centrality weight increases from 0.7 to 0.9.

[0125] Taking the node-level safety boundary defined in step S3 as a hard constraint (such as voltage deviation ≤ 5%, line capacity overlimit ≤ 10%), combining the charge and discharge power limits generated in step S4 and the adaptive priority rules, use a multi-objective optimization algorithm to generate an energy storage deployment plan. The specific optimization logic includes:

[0126] Minimize the investment cost and operation and maintenance cost of energy storage devices. For example, the unit capacity cost of lithium-ion batteries is 1000 yuan / kWh, and that of lead-acid batteries is 800 yuan / kWh. Prioritize the type with the best cost-benefit ratio.

[0127] Maximize the charge and discharge efficiency, and prioritize the energy storage device types with an efficiency ≥ 95%. For example, the alternative options for a certain node include a lithium-ion battery with an efficiency of 97% and a flow battery with an efficiency of 92%. The former is preferred.

[0128] For nodes with a priority score ≥ 0.8, force the deployment of at least one energy storage device; for nodes with a score < 0.5, allocate according to demand elasticity. For example, the priority score of node E is 0.85, and an energy storage device with a capacity ≥ 5 MWh needs to be deployed; the score of node F is 0.4, and the capacity is allocated according to the actual demand.

[0129] Prioritize the deployment of energy storage devices in areas with low impedance values and close to high-frequency access nodes. For example, the electrical centrality of node G is 0.9 and it is adjacent to three high-frequency access nodes. The deployment location of its energy storage device is set at the grid intersection with the lowest impedance value.

[0130] Conduct a voltage stability check on the energy storage deployment plan to ensure that the voltage deviation of all nodes is within the node-level safety boundary defined in step S3 (such as ±5%). The check rules include:

[0131] Simulate the full-power charge and discharge scenario of the energy storage device to detect whether the node voltage exceeds the limit. For example, when node H discharges at full power, the voltage drops to 363 V (rated 380 V), and the deviation is -4.47%, which meets the safety boundary; if the deviation reaches -5.3%, the power upper limit needs to be adjusted.

[0132] Simulate the scenario of a single energy storage device failing and dropping out, and detect whether the remaining devices can still meet the voltage stability requirements. For example, after the main energy storage device of node I fails, the standby device automatically switches, and the node voltage deviation remains at 4.8%.

[0133] If the voltage deviation of a certain node exceeds the tolerance, adjust the capacity or location of its energy storage device. For example, the deviation of node J reaches 5.2%, and its energy storage capacity is increased from 3 MWh to 3.5 MWh, and re-check until it meets the standard.

[0134] It should be noted that during the off-peak electricity price period (0:00 - 8:00), the weight of the access frequency in the priority score can be reduced to 0.8 to encourage energy storage charging during the off-peak period; during extreme weather warnings, the weight of the electrical centrality can be increased to 1.1 to strengthen the coverage of key nodes.

[0135] When selecting the type of energy storage device, introduce the life cycle cost model and preferentially select devices with a service life of ≥ 10 years; when deploying geographically, combine GIS coordinates with the power grid topology map to ensure the physical accessibility of the device.

[0136] For a solution that fails the voltage stability check, automatically trigger the deployment parameter adjustment process, such as increasing the capacity by 5% or tightening the power limit by 10%; the adjusted solution needs to re - execute the full - process check until all nodes meet the safety boundary.

[0137] It should be noted that in this embodiment, through node - level safety boundaries and penalty term constraints, the risk of microgrid voltage fluctuations conducting to the AC transmission network above 750 kV is effectively suppressed, providing spatial collaborative optimization support for the large - scale power grid defense system, and enhancing the boundary control ability of the intelligent dispatching system for distributed resource clusters.

[0138] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0139] The above - mentioned embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above - mentioned embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general - purpose computer, a special - purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer - readable storage medium or transmitted from one computer - readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer - readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center containing one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid - state drive.

[0140] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above - described systems, devices, and modules can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0141] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or modules can be in electrical, mechanical, or other forms.

[0142] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0144] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0145] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0146] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization method for the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming, characterized in that The steps include: S1. Collect the grid topology parameters of the target microgrid and the electric vehicle movement trajectory data. The grid topology parameters include the inter-node impedance matrix, node voltage limit and line capacity threshold; S2, geographically grid the node locations in the electric vehicle movement trajectory data, generate a spatiotemporal matrix based on the timestamps, and identify high-frequency access nodes; S3. Construct a state transition equation based on the inter-node impedance matrix, taking the line capacity change rate caused by electric vehicle movement as a constraint, and set the node-level safety boundary based on the node voltage limit and line capacity threshold; S4. Using a phased inverse dynamic programming algorithm, with node voltage deviation and line capacity exceeding the limit as penalty items, prioritize spatial dimension optimization for high-frequency access nodes to generate node charge and discharge constraints; S5. Integrate node charging and discharging constraints, perform global optimization with the goal of minimizing total operating costs, and output a global strategy for energy storage capacity configuration in each time period; S6. Generate an energy storage deployment plan based on the global strategy and the distribution of high-frequency access nodes.

2. The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to claim 1, wherein S1 includes: S1 a. Obtain grid topology parameters in real time through the microgrid monitoring system. The grid topology parameters include the inter-node impedance matrix, node voltage limit, and line capacity threshold. The node voltage limit and line capacity threshold are extracted from the preset safe operation database of the microgrid management platform. S1 b. Collecting the electric vehicle's movement trajectory data through the vehicle-mounted positioning device. The movement trajectory data includes the node location and corresponding timestamp of the electric vehicle's access to the microgrid in consecutive time periods; S1 c. Based on the inter-node connection relationship in the power grid topology parameters, the numerical update results of the inter-node impedance matrix are calculated in real time, and the numerical update results of the inter-node impedance matrix are spatially correlated with the node positions in the electric vehicle movement trajectory data; S1 d. Perform data verification on the numerical update results of the node voltage limit, line capacity threshold, and inter-node impedance matrix, and eliminate abnormal parameters that exceed the preset deviation range.

3. The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to claim 1, wherein S2 include: S2a, dynamically gridding the node positions in the electric vehicle movement trajectory data according to a preset geographic grid density to generate geographic grid areas corresponding to the grid node distribution; S2b, dividing the electric vehicle access events in each geographic grid area into continuous time periods according to the timestamp, counting the number of access events in each time period, and generating a spatiotemporal matrix; S2c, clustering the number of access events in the spatiotemporal matrix based on a preset clustering analysis rule, identifying geographic grid areas where the number of access events exceeds a preset high-frequency threshold, and marking them as high-frequency access nodes; S2d. Perform spatiotemporal correlation verification on the high-frequency access node. If the same geographical grid area is marked as a high-frequency access node in more than a plurality of consecutive time periods, it is confirmed as a valid high-frequency access node.

4. The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to claim 1, wherein S3 include: S3a, constructing a state transfer equation based on the dynamic change characteristics of the inter-node impedance matrix. The numerical update result of the inter-node impedance matrix is dynamically associated with the node position in the electric vehicle movement trajectory data through a weighting factor to generate a spatiotemporal coupled state transfer weight. S3b. Based on the line capacity change rate caused by the movement of the electric vehicle, combined with historical load data and the line capacity threshold, establish a linear constraint relationship between the line capacity change rate and the node charge-discharge power to generate dynamic constraint conditions; S3c. Set the node-level safety boundary based on the node voltage limit and the line capacity threshold. The node-level safety boundary includes the allowable range of node voltage deviation and the tolerance of line capacity overrun, and embed the node-level safety boundary into the state transition equation as a hard constraint.

5. The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to claim 1, wherein S4 Including: S4a. Divide the optimization stage into spatial dimension optimization and time dimension recursion, and determine the priority order of spatial dimension optimization based on the distribution of high-frequency access nodes; S4b. In the spatial dimension optimization stage, using the node voltage deviation and line capacity overrun as penalty terms, adopt the reverse dynamic programming algorithm to generate the initial charge-discharge power constraints of high-frequency access nodes; S4c. Dynamically adjust the penalty term weight according to the node impedance matrix in the grid topology parameters, and iteratively correct the initial charge-discharge power constraints to generate optimization constraints that meet the node-level safety boundary; S4d. In the time dimension recursion stage, integrate the optimization constraints of all high-frequency access nodes, eliminate local constraints that conflict with the global strategy, and generate the final node charge-discharge constraints.

6. The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to claim 5, characterized in that, In the spatial dimension optimization stage, adopt the reverse dynamic programming algorithm to perform reverse recursive calculation starting from the node with the lowest priority. Using the node voltage deviation and line capacity overrun as penalty terms, construct the objective function. The specific form of the objective function includes: Sum the voltage deviation penalty term and the line capacity overrun penalty term proportionally weighted. Among them, the voltage deviation penalty term is the percentage of the absolute difference between the actual voltage and the rated voltage to the rated voltage, and the line capacity overrun penalty term is the square of the ratio of the actual line current to the capacity threshold; the weighting ratio is dynamically adjusted according to the grid topology parameters.

7. The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to claim 1, wherein S5 Including: S5a. Summarize the charge-discharge constraints of all high-frequency access nodes to generate a set of node-level charge-discharge feasible regions. The set of node-level charge-discharge feasible regions includes the upper and lower power limits and the time validity range of each node; S5b. Construct a charge-discharge cost coefficient matrix based on the node impedance matrix in the grid topology parameters. The charge-discharge cost coefficient matrix contains the comprehensive influence weights of node charge-discharge power on line loss, equipment depreciation, and peak-valley electricity prices; S5c. Taking the minimum total operating cost as the objective function, input the charge-discharge cost coefficient matrix and the set of node-level charge-discharge feasible regions into the linear programming model to solve the global charge-discharge power distribution scheme that meets the time period coupling constraints; S5d. Perform N-1 safety verification on the global charge-discharge power distribution scheme, eliminate the schemes that cause node voltage overlimit or capacity overrun when a single line fails, and output the global strategy for energy storage capacity configuration in each time period.

8. The method for optimizing the energy storage capacity of an electric vehicle V2G microgrid based on dynamic programming according to claim 1, wherein S6 Including: S6a. Integrate the energy storage capacity configuration schemes and high-frequency access node distribution data in the global strategy to generate a node energy storage demand matrix. The node energy storage demand matrix contains the capacity demand, charge-discharge power demand of each node, and the time distribution characteristics matching the high-frequency time periods; S6b. Calculate the energy storage deployment priorities of each node based on the node energy storage demand matrix and the impedance matrix between nodes. The energy storage deployment priorities are dynamically adjusted according to the access frequency during high-frequency periods and the electrical centrality of the nodes. S6c. With the node-level safety boundary as a constraint, combined with the charge and discharge power limits and the adaptive priority rules, use a multi-objective optimization algorithm to generate a deployment plan for the capacity, power limit, and geographical location of the energy storage devices. S6d. Conduct a node-level voltage stability check on the energy storage deployment plan, eliminate the plans that exceed the charge and discharge power limits or the voltage deviation tolerance, and output the final energy storage deployment plan.

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