A charging station energy management system and method

Abstract

The application discloses a charging station energy management system and method, relates to the technical field of management analysis, and comprises the following steps: based on charging station operation data, the space-time distribution of charging demand is predicted, and a charging load distribution prediction atlas is generated; the topological structure of a charging station power grid connection node is analyzed, power grid topological parameters are obtained, charging and discharging strategy optimization is carried out in combination with real-time electricity price fluctuation data and energy storage system state data, and an optimal charging and discharging strategy is generated; based on the optimal charging and discharging strategy, the charging and discharging path of the energy storage system is planned, a charging and discharging path is obtained, the charging and discharging path is adjusted according to a power grid load balancing index, and a power grid safety interaction strategy is generated; the power grid safety interaction strategy is input into a charging station energy management digital twin model for strategy verification, an optimized energy management strategy is obtained, and the optimized energy management strategy is issued to a charging station control terminal for execution. The application has the effect of improving the energy management efficiency of the charging station.

Description

Technical Field

[0001] This application relates to the field of management analysis technology, and in particular to an energy management system and method for charging stations. Background Technology

[0002] With the rapid development of new energy vehicles, the large-scale deployment of charging stations, as the core infrastructure for energy replenishment, has placed higher demands on grid stability and energy utilization efficiency.

[0003] In related technologies, existing energy management methods for charging stations are mostly based on fixed-period electricity demand forecasting, achieving basic load control through static allocation of charging pile power or simple peak-shaving strategies. For example, some solutions use historical charging data to train regression models to predict short-term load and combine them with energy storage systems for peak shaving and valley filling; other technologies propose using time-of-use pricing strategies to guide users to charge during off-peak hours to reduce operating costs. However, these methods generally have two major limitations:

[0004] 1. Insufficient integration of dynamic variables (such as real-time electricity price fluctuations and changes in grid topology) leads to a large deviation between the prediction results and the actual scenario;

[0005] 2. The lack of linkage analysis on the interaction characteristics of charging piles, energy storage systems and power grid makes it difficult to adapt to the optimization needs under complex operating conditions, and makes the ability to assess the load risk of the connection node between charging station and power grid insufficient, which can easily lead to problems such as local overload or equipment lifespan reduction. There are areas for improvement. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this application provides an energy management system and method for charging stations.

[0007] In a first aspect, this application provides a method for energy management in charging stations, comprising the following steps:

[0008] Step S1: Collect data on the operating status of the charging station to obtain charging station operating data, and predict the spatiotemporal distribution of charging demand based on the charging station operating data to generate a charging load distribution prediction map.

[0009] Step S2: Based on the charging load distribution prediction map, perform topology analysis on the charging station grid connection nodes to obtain grid topology parameters, and combine real-time electricity price fluctuation data and energy storage system status data to optimize the charging and discharging strategy and generate the optimal charging and discharging strategy.

[0010] Step S3: Based on the optimal charging and discharging strategy, plan the charging and discharging path of the energy storage system to obtain the charging and discharging path, and perform secondary optimization and adjustment of the charging and discharging path according to the grid load balancing index to generate the grid security interaction strategy.

[0011] Step S4: Input the power grid security interaction strategy into the digital twin model of the charging station energy management for strategy verification to obtain the optimized energy management strategy, and then send the optimized energy management strategy to the charging station control terminal for execution.

[0012] Preferably, step S1 includes the following steps:

[0013] The charging station's operating status is collected by the embedded sensors of the charging pile, thereby obtaining charging station operating data. The charging station operating data includes charging pile output power data, battery temperature data, charging current data, voltage fluctuation data, and user charging time period record data.

[0014] The charging station operation data is subjected to noise filtering and outlier correction to generate standardized charging time series data. Charging behavior features are extracted from the standardized charging time series data, and then charging behavior feature data is confirmed based on the results of the charging behavior feature extraction. The charging behavior feature data includes charging time period concentration, power demand fluctuation coefficient and user charging habit correlation index.

[0015] The charging behavior feature data is input into a preset spatiotemporal prediction model to predict the charging demand distribution data corresponding to a preset time period in the future, and then a charging load distribution prediction map is generated based on the charging demand distribution data corresponding to the preset time period in the future.

[0016] Preferably, step S2 includes the following steps:

[0017] The connection nodes between the charging station and the power grid are identified based on the charging load distribution prediction map, and the topology of the connection nodes is analyzed. Then, the power grid topology parameters are confirmed based on the results. The power grid topology parameters include power transmission path, capacity limitation parameters, and topology correlation parameters.

[0018] Based on the power grid topology parameters, a first optimization objective function is established with the goal of minimizing the peak-valley difference of the power grid. Real-time electricity price fluctuation data is used as a constraint condition to calculate the charging load transfer priority corresponding to the peak electricity price period.

[0019] Acquire energy storage system status data, including current remaining power, charging and discharging efficiency, and battery health status. Construct an energy storage dynamic scheduling model based on the energy storage system status data, and generate a charging and discharging power allocation scheme by combining the charging load transfer priority with the grid topology parameters.

[0020] The charging and discharging power allocation scheme is iteratively optimized to generate the optimal charging and discharging strategy.

[0021] Preferably, the specific steps for constructing an energy storage dynamic scheduling model are as follows:

[0022] Based on the historical operating data of the energy storage system, the nonlinear relationship between battery charging and discharging efficiency and temperature and charging and discharging rate is fitted to generate an efficiency decay curve.

[0023] Based on the efficiency decay curve and battery health status data, power adjustment thresholds are set for different charge and discharge rates.

[0024] A second optimization objective function is established, which takes maximizing the life cycle of the energy storage system as the second optimization objective function. The power adjustment thresholds under different charge and discharge rates in the grid topology parameters, such as capacity limitation parameters, charging load transfer priority, and power adjustment thresholds, are used as constraints to solve the charging and discharging power allocation scheme under the second optimization objective function.

[0025] Preferably, step S3 includes the following steps:

[0026] Based on the optimal charging and discharging strategy, the power demand time series data corresponding to the charging pile group and the charging and discharging window period corresponding to the energy storage system are extracted, and then the charging and discharging path is planned based on the power demand time series data corresponding to the charging pile group and the charging and discharging window period corresponding to the energy storage system.

[0027] Obtain the power grid load balancing index, and calculate the load deviation of the power grid nodes corresponding to each charging and discharging path based on the power grid load balancing index. Generate an initial optimized path based on the load deviation of the power grid nodes corresponding to each charging and discharging path.

[0028] A comprehensive evaluation function is established that includes battery aging costs, grid overload risk coefficients, and user waiting time. The initial optimization path is analyzed based on the comprehensive evaluation function, and a grid safety interaction strategy is generated based on the analysis results.

[0029] Preferably, the process involves obtaining a power grid load balancing index and calculating the load deviation of each power grid node corresponding to each charging and discharging path based on the power grid load balancing index, specifically including:

[0030] Obtain power grid load balancing indicators, which include real-time load rate, historical peak load data and line transmission capacity limit for each node of the power grid.

[0031] Based on the charging load distribution prediction map, the load increment of each node in the power grid in the future preset period is predicted, the difference between the load increment and the upper limit of the line transmission capacity is calculated, and the difference is used as the load margin.

[0032] Based on load margin, the load risk level of power grid nodes is divided, and the charging and discharging power adjustment coefficient for each load risk level is set.

[0033] The load deviation of grid nodes is quantified by multiplying the load margin by the charging and discharging power adjustment coefficient for each load risk level.

[0034] Preferably, step S4 includes the following steps:

[0035] A digital twin model for energy management of charging stations is constructed, in which charging station operation data, grid topology parameters and energy storage system status are synchronized in real time, and a virtual simulation environment is established.

[0036] The power grid security interaction strategy is loaded into the digital twin model of energy management of the charging station to simulate the power grid response characteristics data and energy storage system operation status change data under different charging scenarios;

[0037] Based on grid response characteristic data and energy storage system operation status change data under different charging scenarios, the grid load stability index, energy storage system life loss rate and user charging satisfaction are evaluated, and a strategy verification report is generated.

[0038] Based on the evaluation results in the strategy verification report, the parameters of the power grid security interaction strategy are corrected and the path is optimized to generate an optimized energy management strategy, which is then sent to the charging station control terminal.

[0039] Secondly, this application provides an energy management system for charging stations, comprising:

[0040] The data acquisition module is used to collect data on the operating status of the charging station, obtain charging station operating data, predict the spatiotemporal distribution of charging demand based on the charging station operating data, and generate a charging load distribution prediction map.

[0041] The analysis module is used to perform topology analysis on the charging station grid connection nodes based on the charging load distribution prediction map, obtain grid topology parameters, and optimize the charging and discharging strategy by combining real-time electricity price fluctuation data and energy storage system status data to generate the optimal charging and discharging strategy.

[0042] The optimization module is used to plan the charging and discharging path of the energy storage system based on the optimal charging and discharging strategy, obtain the charging and discharging path, and perform secondary optimization and adjustment of the charging and discharging path according to the grid load balancing index to generate the grid safety interaction strategy.

[0043] The execution module is used to input the power grid security interaction strategy into the digital twin model of the charging station energy management for strategy verification, obtain the optimized energy management strategy, and send the optimized energy management strategy to the charging station control terminal for execution.

[0044] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any of the above-described energy management methods for charging stations.

[0045] In summary, this application includes the following beneficial technical effects:

[0046] This application provides a method for energy management of charging stations. Based on charging station operation data, it predicts the spatiotemporal distribution of charging demand, generating a charging load distribution prediction map. Using this map, it analyzes the topology of the charging station's grid connection nodes to obtain grid topology parameters. Combining real-time electricity price fluctuation data and energy storage system status data, it optimizes charging and discharging strategies to generate an optimal strategy. This effectively reduces the occurrence of large deviations between prediction results and actual scenarios due to insufficient integration of dynamic variables. Based on the optimal strategy, it plans charging and discharging paths for the energy storage system, obtaining charging and discharging paths. These paths are then further optimized and adjusted according to grid load balancing indicators to generate a grid safety interaction strategy. This strategy is input into a digital twin model of the charging station's energy management for verification, resulting in an optimized energy management strategy. The optimized strategy is then sent to the charging station control terminal for execution. This effectively improves the linkage analysis of the interaction characteristics between charging piles, energy storage systems, and the grid, and effectively reduces the occurrence of local overloads or equipment lifespan reductions caused by insufficient load risk assessment capabilities at the charging station and grid connection nodes. Therefore, it effectively improves the energy management efficiency of charging stations. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a method for energy management of a charging station according to an embodiment of this application.

[0049] Figure 2 This is a schematic diagram of a system for energy management of a charging station according to an embodiment of this application. Detailed Implementation

[0050] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0051] Example 1

[0052] This application discloses an energy management method for charging stations.

[0053] Reference Figure 1 A method for energy management in charging stations includes the following steps:

[0054] Step S1: Collect data on the operating status of the charging station to obtain charging station operating data, and predict the spatiotemporal distribution of charging demand based on the charging station operating data to generate a charging load distribution prediction map.

[0055] Step S2: Based on the charging load distribution prediction map, perform topology analysis on the charging station grid connection nodes to obtain grid topology parameters, and combine real-time electricity price fluctuation data and energy storage system status data to optimize the charging and discharging strategy and generate the optimal charging and discharging strategy.

[0056] Step S3: Based on the optimal charging and discharging strategy, plan the charging and discharging path of the energy storage system to obtain the charging and discharging path, and perform secondary optimization and adjustment of the charging and discharging path according to the grid load balancing index to generate the grid security interaction strategy.

[0057] Step S4: Input the power grid security interaction strategy into the digital twin model of the charging station energy management for strategy verification to obtain the optimized energy management strategy, and then send the optimized energy management strategy to the charging station control terminal for execution.

[0058] It should be noted that step S1 includes the following steps:

[0059] The charging station's operating status is collected by the embedded sensors of the charging pile, thereby obtaining charging station operating data. The charging station operating data includes charging pile output power data, battery temperature data, charging current data, voltage fluctuation data, and user charging time period record data.

[0060] The charging station operation data is subjected to noise filtering and outlier correction to generate standardized charging time series data. Charging behavior features are extracted from the standardized charging time series data, and then charging behavior feature data is confirmed based on the results of the charging behavior feature extraction. The charging behavior feature data includes charging time period concentration, power demand fluctuation coefficient and user charging habit correlation index.

[0061] The charging behavior feature data is input into a preset spatiotemporal prediction model to predict the charging demand distribution data corresponding to a preset time period in the future, and then a charging load distribution prediction map is generated based on the charging demand distribution data corresponding to the preset time period in the future.

[0062] In this embodiment, during the data acquisition phase, multiple types of embedded sensors (e.g., Hall current sensors, thermocouple temperature sensors, power transmitters, etc.) built into the charging pile are used to capture real-time charging station operation data: current sensors collect charging current data, temperature sensors continuously monitor temperature changes at the battery interface, and power transmitters synchronously record output power data. Simultaneously, user login information is used to associate user charging time period records, forming a multi-dimensional dataset containing power, temperature, current, voltage fluctuations, and time period records, laying the foundation for subsequent analysis. In the data processing and feature extraction phase, Kalman filtering is used to smooth noise in the charging station operation data, outliers are identified and interpolated based on data from adjacent time periods to generate standardized charging time series data. Subsequently, charging behavior features are extracted from the standardized charging time series data: the concentration of charging time periods is calculated by... The charging frequency ratio is used to determine the power demand fluctuation coefficient, which is calculated by the ratio of the power standard deviation to the mean within a certain period (e.g., a ratio > 0.3 indicates large fluctuations). The user charging habit correlation index is determined by analyzing the Pearson correlation coefficient of power demand during the periods of multiple consecutive charging by the same user. In the prediction and application stage, the above features are input into a preset spatiotemporal prediction model. The preset spatiotemporal prediction model integrates a long short-term memory network and a graph convolutional neural network. The long short-term memory network captures the temporal dependence of power demand in each period, while the graph convolutional neural network models the spatial correlation between different charging piles. Finally, it outputs the charging demand distribution data corresponding to the preset time period in the future (the charging demand distribution data includes, but is not limited to, the expected charging volume of each charging pile and peak periods). Based on this data, a charging load distribution prediction map is generated, which intuitively displays the load intensity of different periods and regions in the form of a heat map.

[0063] By adopting the above technical solutions, the accuracy of the analysis basis is ensured through refined data collection and processing, providing reliable input for subsequent predictions. Feature extraction directly identifies key patterns in charging behavior, improving the interpretability and accuracy of the prediction model. Spatiotemporal prediction and visualization maps provide decision-making basis for charging station operation, such as: increasing the number of maintenance personnel in advance during high-load periods, dynamically adjusting the power allocation of charging piles to avoid overload, and pushing off-peak charging suggestions to users, which optimizes operational efficiency, improves user experience, and can also provide early warning of equipment aging risks through long-term analysis of battery temperature data, extending service life.

[0064] It should be noted that step S2 includes the following steps:

[0065] The connection nodes between the charging station and the power grid are identified based on the charging load distribution prediction map, and the topology of the connection nodes is analyzed. Then, the power grid topology parameters are confirmed based on the results. The power grid topology parameters include power transmission path, capacity limitation parameters, and topology correlation parameters.

[0066] Based on the power grid topology parameters, a first optimization objective function is established with the goal of minimizing the peak-valley difference of the power grid. Real-time electricity price fluctuation data is used as a constraint condition to calculate the charging load transfer priority corresponding to the peak electricity price period.

[0067] Acquire energy storage system status data, including current remaining power, charging and discharging efficiency, and battery health status. Construct an energy storage dynamic scheduling model based on the energy storage system status data, and generate a charging and discharging power allocation scheme by combining the charging load transfer priority with the grid topology parameters.

[0068] The charging and discharging power allocation scheme is iteratively optimized to generate the optimal charging and discharging strategy.

[0069] In this embodiment of the invention, firstly, based on the charging load distribution prediction map, a graph feature matching algorithm (such as node feature extraction based on convolutional neural networks) is used to identify the connection nodes between the charging station and the power grid. The charging load distribution prediction map contains the spatial distribution and intensity information of the charging load in each time period, and the connection points can be located through the voltage and current change characteristics of the nodes. Subsequently, graph theory analysis is used to analyze the topology of the connection nodes. By constructing a node adjacency matrix, the electrical connection relationships between nodes are traversed to clarify the power transmission path; and the capacity limitation parameters of each line segment are obtained, including but not limited to maximum transmission power and current carrying capacity. The power coupling degree between nodes is analyzed through an association rule mining algorithm to obtain topology correlation parameters, including but not limited to node voltage sensitivity coefficient and power transmission correlation degree, which are finally integrated to form the power grid topology parameters. In the process of calculating the charging load transfer priority, a first optimization objective function is constructed based on the power grid topology parameters. The objective function expression is: ,in, Let t represent the grid load at time t. The first optimization objective function aims to minimize the peak-valley difference. Real-time electricity price fluctuation data (such as time-of-use electricity price curves and fluctuation amplitude thresholds) are used as constraints. By establishing an electricity price-load response model, the marginal benefit of load transfer for different charging stations during peak electricity price periods is calculated. The load transfer marginal benefits are sorted from high to low to determine the priority of charging load transfer. Energy storage dynamic scheduling model construction and power allocation: Real-time status data is collected through sensors in the energy storage system, including the current remaining power, charge / discharge efficiency curves, and battery health status. Based on this data, an energy storage dynamic scheduling model is constructed. Combining charging load transfer priorities and grid topology parameters (such as power transmission paths and capacity limitation parameters), a mixed integer programming algorithm is used to allocate the charging and discharging power of each energy storage unit, generating a charging and discharging power allocation scheme. The particle swarm optimization algorithm is then used to iteratively optimize the charging and discharging power allocation scheme, using charging and discharging cost, grid peak-to-valley difference, and energy storage lifetime loss as comprehensive evaluation indicators. In each iteration, the power allocation coefficient is adjusted to verify whether the scheme meets the grid topology parameter constraints and energy storage status limitations (such as the current remaining power being within a safe range), until the indicators converge, ultimately generating the optimal charging and discharging strategy.

[0070] By adopting the above technical solutions, the precise identification of grid topology parameters provides a physical constraint basis for load dispatching, avoiding line overload or voltage instability caused by blindly transferring loads. By minimizing peak-to-valley differences and combining them with real-time electricity price constraints, the system can improve grid operation stability and reduce charging costs, achieving a balance between economy and reliability. Integrating energy storage system status data to construct a dynamic dispatching model can fully leverage the "peak shaving and valley filling" function of energy storage, improving energy storage utilization and extending battery life. The iterative optimization mechanism ensures that the strategy can adapt to dynamic changes in grid load, electricity price, and energy storage status, significantly improving the efficiency of coordinated operation between charging stations and the grid, and providing technical support for the large-scale application of new energy vehicle charging networks.

[0071] Furthermore, the specific steps for constructing the energy storage dynamic scheduling model are as follows:

[0072] Based on the historical operating data of the energy storage system, the nonlinear relationship between battery charging and discharging efficiency and temperature and charging and discharging rate is fitted to generate an efficiency decay curve.

[0073] Based on the efficiency decay curve and battery health status data, power adjustment thresholds are set for different charge and discharge rates.

[0074] A second optimization objective function is established, which takes maximizing the life cycle of the energy storage system as the second optimization objective function. The power adjustment thresholds under different charge and discharge rates in the grid topology parameters, such as capacity limitation parameters, charging load transfer priority, and power adjustment thresholds, are used as constraints to solve the charging and discharging power allocation scheme under the second optimization objective function.

[0075] Specifically, firstly, historical operational data of the energy storage system is collected, covering battery charge / discharge efficiency and corresponding time-series battery health status under different temperatures and charge / discharge rates. A machine learning algorithm is used to fit the nonlinear relationship between these three factors, generating an efficiency decay curve. This curve visually illustrates the efficiency decay pattern with temperature fluctuations and changes in charge / discharge rate. Secondly, combining the efficiency decay curve with battery health status, power adjustment thresholds are set for different charge / discharge rates: when the actual charge / discharge power reaches the threshold, a power limiting mechanism is triggered to prevent irreversible damage caused by excessively low efficiency. For example, when the battery health status drops to 80%, the power threshold corresponding to discharge rates above 1.5C is lowered by 20%. Finally, a second optimization objective function is constructed, maximizing the energy storage system's lifespan. Capacity limiting parameters in the grid topology, charging load transfer priorities, and the aforementioned power adjustment thresholds are simultaneously incorporated as constraints. This is solved using particle swarm optimization or a genetic algorithm to obtain the charge / discharge power allocation scheme that maximizes battery cycle life within the constraints.

[0076] It should be noted that step S3 includes the following steps:

[0077] Based on the optimal charging and discharging strategy, the power demand time series data corresponding to the charging pile group and the charging and discharging window period corresponding to the energy storage system are extracted, and then the charging and discharging path is planned based on the power demand time series data corresponding to the charging pile group and the charging and discharging window period corresponding to the energy storage system.

[0078] Obtain the power grid load balancing index, and calculate the load deviation of the power grid nodes corresponding to each charging and discharging path based on the power grid load balancing index. Generate an initial optimized path based on the load deviation of the power grid nodes corresponding to each charging and discharging path.

[0079] A comprehensive evaluation function is established that includes battery aging costs, grid overload risk coefficients, and user waiting time. The initial optimization path is analyzed based on the comprehensive evaluation function, and a grid safety interaction strategy is generated based on the analysis results.

[0080] In this embodiment of the invention, power demand time-series data corresponding to the charging pile group and the charging / discharging window period corresponding to the energy storage system are extracted according to the optimal charging / discharging strategy, and then the charging / discharging path is planned. Specifically, the power demand time-series data is obtained by time-series modeling of the historical charging power of the charging pile group at different time periods (such as peak, normal, and valley hours) and the charging demand of the connected vehicles in real time. The average power demand at 15-minute intervals is calculated using a sliding window algorithm to form power demand time-series data. The charging / discharging window period is determined by combining the current remaining power of the energy storage system, battery cycle life constraints, and peak and valley electricity price periods of the power grid, and the effective time interval for the energy storage system to charge (valley hours) and discharge (peak hours) is determined by a dynamic programming algorithm. When planning the charging / discharging path, a directed graph model including charging pile locations, energy storage system interfaces, and grid nodes is constructed based on the spatiotemporal matching degree between the power demand time-series data and the window period. The Dijkstra algorithm is used to solve for the shortest path to ensure that the path meets the power demand while highly matching the energy storage system window period, avoiding resource mismatch.

[0081] Furthermore, a grid load balancing index is obtained, and the load deviation of grid nodes corresponding to each charging and discharging path is calculated based on this index. On this basis, a comprehensive evaluation function is established, incorporating battery aging costs, grid overload risk coefficients, and user waiting times. Specifically, battery aging costs are calculated using the rainflow counting method to determine the capacity decay cost corresponding to the number of charge-discharge cycles of the energy storage battery, quantified by combining this with the unit capacity replacement cost. The grid overload risk coefficient is based on the ratio of the maximum load to the rated load of the grid nodes in the initial optimized path, mapped to a risk value between 0 and 1 using the Sigmoid function. User waiting time is estimated using a queuing theory model based on the queue length of charging piles, charging power, and vehicle battery capacity in the path. The comprehensive evaluation function uses the analytic hierarchy process (AHP) to determine the weights of each index (e.g., aging cost 0.3, overload risk 0.4, waiting time 0.3), scores and ranks the initial optimized paths, selecting the path with the highest score as the optimal path, and then generating a grid safety interaction strategy. This strategy includes real-time adjustment of charging and discharging power commands, dynamic allocation of energy storage system output, and priority scheduling of user charging demands.

[0082] The above-mentioned solution improves the spatiotemporal utilization of charging and discharging resources by accurately matching power demand timing with window periods; it effectively reduces the impact of paths on the power grid based on the initial optimization of the power grid load balancing index; and the comprehensive evaluation function balances economic costs, power grid security, and user experience, so that the generated power grid security interaction strategy can not only ensure the stable operation of the power grid, but also reduce operating costs and improve user satisfaction. It is suitable for scenarios where large-scale charging pile groups and energy storage systems operate in synergy.

[0083] Furthermore, the power grid load balancing index is obtained, and the load deviation of the power grid nodes corresponding to each charging and discharging path is calculated based on the power grid load balancing index, specifically including:

[0084] Obtain power grid load balancing indicators, which include real-time load rate, historical peak load data and line transmission capacity limit for each node of the power grid.

[0085] Based on the charging load distribution prediction map, the load increment of each node in the power grid in the future preset period is predicted, the difference between the load increment and the upper limit of the line transmission capacity is calculated, and the difference is used as the load margin.

[0086] Based on load margin, the load risk level of power grid nodes is divided, and the charging and discharging power adjustment coefficient for each load risk level is set.

[0087] The load deviation of grid nodes is quantified by multiplying the load margin by the charging and discharging power adjustment coefficient for each load risk level.

[0088] In the embodiments of the present invention, a power grid load balance index is obtained. The power grid load balance index includes the real-time load rate corresponding to each node of the power grid, historical peak load data, and the upper limit of line transmission capacity. Specifically, the real-time load rate is collected in real time by intelligent measurement terminals deployed at each power grid node, and the ratio of the current load to the rated capacity of the node is calculated; the historical peak load data is extracted from the historical database of the power dispatching system and corrected in combination with seasonal factors (such as air-conditioning load in summer and heating load in winter); the upper limit of line transmission capacity is calculated based on the line type, laying method, and ambient temperature, ensuring that the data can comprehensively reflect the real-time bearing state, historical limit, and physical transmission limit of the node. Further, according to the charging load distribution prediction map, the load increment of each node of the power grid in a preset future period is predicted, the difference between it and the upper limit of line transmission capacity is calculated, and the difference is used as the load margin. When predicting the load increment, first, the charging load concentration area of each node in the future period is located through the charging load distribution prediction map, and the load increment ΔP caused by charging is calculated in combination with the rated power and the expected access quantity of the charging piles in this area; then, in combination with the current basic load P0 of the node, the predicted total load Ppre = P0 + ΔP is obtained; finally, the load margin M = the upper limit of line transmission capacity P额 - Ppre. When M is positive, it indicates that there is still load redundancy at the node, and when it is negative, it indicates an overloading risk. Based on the load margin, the load risk levels of the power grid nodes are divided, and the charge-discharge power adjustment coefficients for each load risk level are set. The specific division criteria are as follows: when M ≥ 20%P额, it is determined as a low risk level, indicating that the node has a small load pressure; when 10%P额 ≤ M < 20%P额, it is determined as a medium risk level, and the load change needs to be concerned; when 0 < M < 10%P额, it is determined as a high risk level, and the load is close to saturation; when M ≤ 0, it is determined as an emergency risk level, with an immediate overloading risk. The corresponding charge-discharge power adjustment coefficients are set as follows: low risk 1.0 (no limit on charge-discharge power), medium risk 0.8 (allow 80% of the rated power), high risk 0.5 (limit to 50% of the rated power), and emergency risk 0.2 (only allow 20% of the emergency power). The charge-discharge power adjustment coefficient is dynamically calibrated through historical overload handling experience.

[0089] The deviation degree of the power grid node load is quantified by the product of the load margin and the charge-discharge power adjustment coefficient of each load risk level, that is, the load deviation degree D = M × K (K is the adjustment coefficient). When D is positive, the larger the value, the safer the node load is and the more charging load can be accepted; when D is negative, the larger the absolute value, the farther away from the safe state, and the charge-discharge power needs to be restricted first.

[0090] By adopting the above technical solutions, the collection of multi-dimensional load balancing indicators ensures a comprehensive understanding of the status of power grid nodes; the calculation of load increments based on predictive maps enables forward-looking judgment of future load changes, avoiding passive responses; the risk level classification and adjustment coefficient setting provide a hierarchical basis for load management, taking into account both power grid safety and charging service continuity; and the quantification of load deviation provides accurate numerical references for subsequent charging and discharging strategy optimization, making power grid dispatching more scientific, effectively reducing the probability of line overload, and improving power supply reliability.

[0091] It should be noted that step S4 includes the following steps:

[0092] A digital twin model for energy management of charging stations is constructed, in which charging station operation data, grid topology parameters and energy storage system status are synchronized in real time, and a virtual simulation environment is established.

[0093] The power grid security interaction strategy is loaded into the digital twin model of energy management of the charging station to simulate the power grid response characteristics data and energy storage system operation status change data under different charging scenarios;

[0094] Based on grid response characteristic data and energy storage system operation status change data under different charging scenarios, the grid load stability index, energy storage system life loss rate and user charging satisfaction are evaluated, and a strategy verification report is generated.

[0095] Based on the evaluation results in the strategy verification report, the parameters of the power grid security interaction strategy are corrected and the path is optimized to generate an optimized energy management strategy, which is then sent to the charging station control terminal.

[0096] In this embodiment of the invention, a digital twin model for charging station energy management is constructed. This model synchronizes charging station operation data, grid topology parameters, and energy storage system status in real time. A virtual simulation environment is established, and the grid security interaction strategy is loaded into the model to simulate grid response characteristics and energy storage system operation status changes under different charging scenarios. The grid security interaction strategy is transformed into executable control logic through modular programming and loaded into the control layer of the virtual simulation environment. Typical scenarios are simulated, and the simulation process calculates grid response characteristics (voltage fluctuation values, line current distortion rate, frequency deviation) and energy storage system status change data (SOC change curve, charge / discharge efficiency, battery cycle count) in real time. Based on the grid response characteristics and energy storage system operation status change data under different charging scenarios, the grid load stability index, energy storage system lifespan loss rate, and user charging satisfaction are evaluated, and a strategy verification report is generated. Grid load stability indicators are calculated as follows: voltage fluctuation range (±5% of rated voltage is acceptable), frequency deviation (≤±0.2Hz is acceptable), and node overload duration (≤5 minutes is acceptable). The evaluation is based on the frequency of indicator exceedances in various scenarios. The energy storage system's lifespan loss rate is calculated using the rainflow counting method to determine the capacity decay corresponding to each charge-discharge cycle, and then converted to a percentage of lifespan loss based on the battery cycle life curve. User charging satisfaction is evaluated using a weighted score of charging waiting time (≤10 minutes is satisfactory), charging completion rate (≥95% is satisfactory), and fault response time (≤3 minutes is satisfactory). The strategy verification report includes the indicator compliance rate for each scenario, bottlenecks in strategy execution (e.g., voltage fluctuation exceeding the limit 3 times in a certain scenario), and sensitivity analysis of key parameters (e.g., the impact of charging and discharging power adjustment on the results). Based on the evaluation results in the strategy verification report, the grid safety interaction strategy is modified in terms of parameters and optimized in terms of path, generating an optimized energy management strategy, which is then distributed to the charging station control terminal. Parameter adjustments address weaknesses identified in the report: if voltage fluctuations exceed limits, the maximum charging power of charging piles during high-load periods is reduced; if energy storage lifespan deteriorates too rapidly, the charge / discharge depth threshold is adjusted. Path optimization reduces line transmission losses by improving the connection path algorithm between charging piles and the energy storage system. The optimized strategy is translated into executable control commands, which are then sent to the charging station control terminal via a 5G edge computing gateway to achieve closed-loop execution of the strategy.

[0097] By adopting the above technical solutions, the virtual-real synchronization of the digital twin model for charging station energy management achieves accurate mapping of the charging station's operating status, avoiding the high cost and high risk of physical experiments; multi-scenario simulation comprehensively verifies the applicability of the grid safety interaction strategy under complex operating conditions, improving the robustness of the strategy; multi-dimensional evaluation indicators take into account grid safety, energy storage life, and user experience, ensuring the overall optimality of the strategy; dynamic correction and optimization mechanisms enable the strategy to continuously adapt to changes in actual operating conditions, significantly improving the intelligence level of charging station energy management, reducing grid overload risk, extending energy storage life, and improving user satisfaction.

[0098] Example 2

[0099] This application also discloses an energy management system for charging stations.

[0100] Reference Figure 2 An energy management system for charging stations, comprising:

[0101] The data acquisition module is used to collect data on the operating status of the charging station, obtain charging station operating data, predict the spatiotemporal distribution of charging demand based on the charging station operating data, and generate a charging load distribution prediction map.

[0102] The analysis module is used to perform topology analysis on the charging station grid connection nodes based on the charging load distribution prediction map, obtain grid topology parameters, and optimize the charging and discharging strategy by combining real-time electricity price fluctuation data and energy storage system status data to generate the optimal charging and discharging strategy.

[0103] The optimization module is used to plan the charging and discharging path of the energy storage system based on the optimal charging and discharging strategy, obtain the charging and discharging path, and perform secondary optimization and adjustment of the charging and discharging path according to the grid load balancing index to generate the grid safety interaction strategy.

[0104] The execution module is used to input the power grid security interaction strategy into the digital twin model of the charging station energy management for strategy verification, obtain the optimized energy management strategy, and send the optimized energy management strategy to the charging station control terminal for execution.

[0105] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0106] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0107] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for energy management in charging stations, characterized in that, Includes the following steps: Step S1: Collect data on the operating status of the charging station to obtain charging station operating data, and predict the spatiotemporal distribution of charging demand based on the charging station operating data to generate a charging load distribution prediction map. Step S2: Based on the charging load distribution prediction map, perform topology analysis on the charging station grid connection nodes to obtain grid topology parameters, and combine real-time electricity price fluctuation data and energy storage system status data to optimize the charging and discharging strategy and generate the optimal charging and discharging strategy. Step S3: Based on the optimal charging and discharging strategy, plan the charging and discharging path of the energy storage system to obtain the charging and discharging path, and perform secondary optimization and adjustment of the charging and discharging path according to the grid load balancing index to generate a grid security interaction strategy. Step S3 includes the following steps: Based on the optimal charging and discharging strategy, the power demand time series data corresponding to the charging pile group and the charging and discharging window period corresponding to the energy storage system are extracted, and then the charging and discharging path is planned based on the power demand time series data corresponding to the charging pile group and the charging and discharging window period corresponding to the energy storage system. Obtain the power grid load balancing index, and calculate the load deviation of the power grid nodes corresponding to each charging and discharging path based on the power grid load balancing index. Generate an initial optimized path based on the load deviation of the power grid nodes corresponding to each charging and discharging path. A comprehensive evaluation function is established that includes battery aging cost, grid overload risk coefficient and user waiting time. The initial optimization path is analyzed based on the comprehensive evaluation function, and a grid safety interaction strategy is generated based on the analysis results. Obtain the power grid load balancing index, and calculate the load deviation of the power grid nodes corresponding to each charging and discharging path based on the power grid load balancing index, specifically including: Obtain power grid load balancing indicators, which include real-time load rate, historical peak load data and line transmission capacity limit for each node of the power grid. Based on the charging load distribution prediction map, the load increment of each node in the power grid in the future preset period is predicted, the difference between the load increment and the upper limit of the line transmission capacity is calculated, and the difference is used as the load margin. Based on load margin, the load risk level of power grid nodes is divided, and the charging and discharging power adjustment coefficient for each load risk level is set. The load deviation of the grid node is quantified by multiplying the load margin by the charging and discharging power adjustment coefficient of each load risk level. Step S4: Input the power grid security interaction strategy into the digital twin model of the charging station energy management for strategy verification to obtain the optimized energy management strategy, and send the optimized energy management strategy to the charging station control terminal for execution; Step S4 includes the following steps: A digital twin model for energy management of charging stations is constructed, in which charging station operation data, grid topology parameters and energy storage system status are synchronized in real time, and a virtual simulation environment is established. The power grid security interaction strategy is loaded into the digital twin model of energy management of the charging station to simulate the power grid response characteristics data and energy storage system operation status change data under different charging scenarios; Based on grid response characteristic data and energy storage system operation status change data under different charging scenarios, the grid load stability index, energy storage system life loss rate and user charging satisfaction are evaluated, and a strategy verification report is generated. Based on the evaluation results in the strategy verification report, the parameters of the power grid security interaction strategy are corrected and the path is optimized to generate an optimized energy management strategy, which is then sent to the charging station control terminal.

2. The energy management method for charging stations according to claim 1, characterized in that, Step S1 includes the following steps: The charging station's operating status is collected by the embedded sensors of the charging pile, thereby obtaining charging station operating data. The charging station operating data includes charging pile output power data, battery temperature data, charging current data, voltage fluctuation data, and user charging time period record data. The charging station operation data is subjected to noise filtering and outlier correction to generate standardized charging time series data. Charging behavior features are extracted from the standardized charging time series data, and then charging behavior feature data is confirmed based on the results of the charging behavior feature extraction. The charging behavior feature data includes charging time period concentration, power demand fluctuation coefficient and user charging habit correlation index. The charging behavior feature data is input into a preset spatiotemporal prediction model to predict the charging demand distribution data corresponding to a preset time period in the future, and then a charging load distribution prediction map is generated based on the charging demand distribution data corresponding to the preset time period in the future.

3. The energy management method for charging stations according to claim 1, characterized in that, Step S2 includes the following steps: The connection nodes between the charging station and the power grid are identified based on the charging load distribution prediction map, and the topology of the connection nodes is analyzed. Then, the power grid topology parameters are confirmed based on the results. The power grid topology parameters include power transmission path, capacity limitation parameters, and topology correlation parameters. Based on the power grid topology parameters, a first optimization objective function is established with the goal of minimizing the peak-valley difference of the power grid. Real-time electricity price fluctuation data is used as a constraint condition to calculate the charging load transfer priority corresponding to the peak electricity price period. Acquire energy storage system status data, including current remaining power, charging and discharging efficiency, and battery health status. Construct an energy storage dynamic scheduling model based on the energy storage system status data, and generate a charging and discharging power allocation scheme by combining the charging load transfer priority with the grid topology parameters. The charging and discharging power allocation scheme is iteratively optimized to generate the optimal charging and discharging strategy.

4. The energy management method for charging stations according to claim 3, characterized in that, The specific steps for constructing a dynamic energy storage scheduling model are as follows: Based on the historical operating data of the energy storage system, the nonlinear relationship between battery charging and discharging efficiency and temperature and charging and discharging rate is fitted to generate an efficiency decay curve. Based on the efficiency decay curve and battery health status data, power adjustment thresholds are set for different charge and discharge rates. A second optimization objective function is established, which takes maximizing the life cycle of the energy storage system as the second optimization objective function. The power adjustment thresholds under different charge and discharge rates in the grid topology parameters, such as capacity limitation parameters, charging load transfer priority, and power adjustment thresholds, are used as constraints to solve the charging and discharging power allocation scheme under the second optimization objective function.

5. An energy management system for charging stations, applied to the energy management method for charging stations described in any one of claims 1-4, characterized in that, include: The data acquisition module is used to collect data on the operating status of the charging station, obtain charging station operating data, predict the spatiotemporal distribution of charging demand based on the charging station operating data, and generate a charging load distribution prediction map. The analysis module is used to perform topology analysis on the charging station grid connection nodes based on the charging load distribution prediction map, obtain grid topology parameters, and optimize the charging and discharging strategy by combining real-time electricity price fluctuation data and energy storage system status data to generate the optimal charging and discharging strategy. The optimization module is used to plan the charging and discharging path of the energy storage system based on the optimal charging and discharging strategy, obtain the charging and discharging path, and perform secondary optimization and adjustment of the charging and discharging path according to the grid load balancing index to generate a grid security interaction strategy. The execution module is used to input the power grid security interaction strategy into the digital twin model of the charging station energy management for strategy verification, obtain the optimized energy management strategy, and send the optimized energy management strategy to the charging station control terminal for execution.

6. A computer-readable storage medium, characterized in that: The device stores instructions that, when executed on a computer, cause the computer to perform a method for energy management of a charging station as described in any one of claims 1 to 4.

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