Energy storage charging pile energy management method based on peak-valley electricity price adaptive scheduling

Abstract

The invention relates to the field of new energy automobiles, and discloses an energy storage charging pile energy management method based on peak-valley electricity price adaptive scheduling, which comprises the following steps: analyzing the energy demand distribution characteristics of an energy storage charging pile in different electricity price periods, and calculating the energy utilization efficiency of the energy storage charging pile; identifying an energy supply and demand balance state of the energy storage charging pile, and constructing a scheduling strategy guide set of the energy storage charging pile in combination with the energy utilization efficiency; extracting energy scheduling response characteristics of the energy storage charging pile in the peak-valley electricity price period, and generating a dynamic energy distribution instruction of the energy storage charging pile; setting a scheduling constraint condition of the energy storage charging pile to create a visual energy transfer interface of the energy storage charging pile; and based on the scheduling strategy guide set, generating an adaptive energy management scheme of the energy storage charging pile through the dynamic energy distribution instruction and the visual energy circulation interface. The energy utilization efficiency of the energy storage charging pile can be improved.

Description

Technical Field

[0001] This invention relates to an energy management method for energy storage charging piles based on adaptive scheduling of peak-valley electricity prices, belonging to the field of new energy vehicles. Background Technology

[0002] Energy management of energy storage charging piles refers to the process of systematically managing the energy flow of energy storage charging piles by integrating energy storage battery packs, intelligent energy dispatching units, charging and discharging control modules, and energy consumption monitoring terminals. This process monitors in real time the charging load fluctuations of the charging piles, the remaining capacity of the energy storage batteries, the stability of the power grid supply, and changes in user charging demand. The aim is to improve the efficiency and economy of energy utilization of energy storage charging piles, balance the load pressure on the power grid, and ensure the continuity and reliability of charging services.

[0003] Traditional energy storage charging piles mainly use a fixed priority allocation method for energy management. This method uses preset charging and discharging priorities and fixed energy allocation ratios to schedule energy. Although it can achieve basic energy allocation and simple charging and discharging state switching, it cannot respond in real time to dynamic changes in the external environment, such as a sudden increase in grid load or a concentrated surge in user charging demand, resulting in low energy utilization efficiency of energy storage charging piles.

[0004] Therefore, there is an urgent need for a solution to improve the energy utilization efficiency of energy storage charging piles. Summary of the Invention

[0005] This invention provides an energy management method for energy storage charging piles based on peak-valley electricity price adaptive scheduling, the main purpose of which is to improve the energy utilization efficiency of energy storage charging piles.

[0006] To achieve the above objectives, the present invention provides an energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing, comprising: Real-time data collection of grid interaction data, user charging request data, and energy storage system status data of energy storage charging piles during different electricity price periods is used to analyze the energy demand distribution characteristics of the energy storage charging piles under different electricity price periods. Based on the energy demand distribution characteristics, the optimal charging time window and power change rate of the energy storage charging pile are determined, and the energy utilization efficiency of the energy storage charging pile is calculated based on the optimal charging time window and the power change rate. Based on the energy utilization efficiency, the energy supply and demand balance state of the energy storage charging pile is identified, and a scheduling strategy guidance set for the energy storage charging pile is constructed according to the energy supply and demand balance state. Extract the energy dispatch response characteristics of the energy storage charging pile during peak and valley electricity price periods, and generate dynamic energy allocation instructions for the energy storage charging pile during peak and valley electricity price periods based on the energy dispatch response characteristics and the energy supply and demand balance state. The battery health status and power supply stability indicators of the energy storage charging pile are monitored in real time to set the scheduling constraints of the energy storage charging pile. Based on the scheduling constraints and the energy supply and demand balance, a visual energy flow interface for the energy storage charging pile is created. Based on the scheduling strategy guidance set, an adaptive energy management scheme for the energy storage charging pile is generated through the dynamic energy allocation instructions and the visualized energy flow interface.

[0007] Optionally, the real-time acquisition of grid interaction data, user charging request data, and energy storage system status data of the energy storage charging pile during different electricity price periods, in order to analyze the energy demand distribution characteristics of the energy storage charging pile under the different electricity price periods, includes: Voltage harmonic analysis is performed on the power grid interaction data to obtain a power grid load fluctuation dataset; Extract the time-of-use grid load characteristics from the grid load fluctuation dataset; Based on the time-sharing grid load characteristics, the grid interaction power demand index of the energy storage charging pile is extracted; Spatiotemporal clustering analysis was performed on the user charging request data to obtain the user charging demand clustering results; Based on the clustering results of the user charging demand, a heat map of the spatiotemporal distribution of the charging demand of the energy storage charging pile is generated. Based on the spatiotemporal distribution heatmap of charging demand, analyze the user charging behavior preferences corresponding to the energy storage charging pile; Extract the vehicle's remaining SOC and the user's reservation time corresponding to the energy storage charging pile, and combine them with the user's charging behavior preferences to extract the charging request priority index from the user's charging request data; Based on the energy storage system status data, analyze the health status degradation trend of the energy storage charging pile; The current capacity and power regulation capability of the energy storage charging pile are assessed by the health status decay trend. Based on the current capacity and the power regulation capability, determine the upper limit of the energy storage capacity of the energy storage charging pile; By integrating the power grid interaction power demand index, the charging request priority index, and the energy storage upper limit index, an energy management feature matrix for the energy storage charging pile is constructed. Based on the energy management feature matrix, the energy demand distribution characteristics of the energy storage charging pile under different electricity price periods are generated.

[0008] Optionally, constructing the scheduling strategy guide set for the energy storage charging pile based on the energy supply and demand balance state includes: Extract the state feature parameters from the energy supply and demand balance state, and determine the multi-scenario adaptation type corresponding to the state feature parameters; Perform association matching processing between the state feature parameters and the multi-scenario adaptation types to generate a scenario-state association dataset; Based on the scenario-state association dataset, the strategy allocation type of the energy storage charging pile is determined; The dynamic constraint parameters of the energy storage charging pile are identified, and the strategy adjustment elasticity coefficient of the energy storage charging pile is calculated using the following formula: ; in, This represents the elasticity coefficient for strategy adjustment. This indicates the slackness of the grid constraints corresponding to the energy storage charging pile. This indicates the current health status of the batteries in the energy storage charging station. This indicates the current battery health reference value. Indicates the penalty coefficient. Indicates the current charging power requirement. Indicates the currently available power capacity; By combining the strategy allocation type and the strategy adjustment elasticity coefficient, a scheduling strategy association graph of the energy storage charging pile is constructed. Extract the key strategy nodes from the scheduling strategy association graph; Based on the key strategy nodes, a scheduling strategy guide set for the energy storage charging pile is generated.

[0009] Optionally, constructing the scheduling strategy association graph of the energy storage charging pile by combining the strategy allocation type and the strategy adjustment elasticity coefficient includes: Match the policy action corresponding to the policy assignment type; Based on the strategy allocation type and the strategy action, define the graph decision node of the energy storage charging pile; By utilizing the transformation relationship between the strategy actions and the strategy adjustment elasticity coefficient, a directed connection relationship is established between the decision nodes of the graph. Based on the graph decision nodes and the directed connection relationships, a scheduling strategy association graph for the energy storage charging pile is constructed.

[0010] Optionally, generating dynamic energy allocation instructions for the energy storage charging pile during the peak-valley electricity price period based on the energy dispatch response characteristics and the energy supply-demand balance state includes: Real-time capture of the electricity price time period signal of the energy storage charging pile; By combining the electricity price period signal, the energy dispatch response characteristics, and the energy supply and demand balance state, the current energy allocation decision state of the energy storage charging pile is identified; Obtain the time-of-use electricity price prediction data corresponding to the energy storage charging pile, so as to construct the future electricity price curve corresponding to the energy storage charging pile; Based on the current energy allocation decision state and the future electricity price curve, the phased energy management objectives of the energy storage charging pile are analyzed. Based on the energy dispatch response characteristics and the phased energy management objectives, the current dispatch mode of the energy storage charging pile is determined, and a set of strategy parameters corresponding to the current dispatch mode is generated. Based on the strategy parameter set, the adaptive control parameter sequence of the energy storage charging pile in multiple future time periods is calculated; Based on the adaptive control parameter sequence, a dynamic energy allocation instruction for the energy storage charging pile is generated during the peak-valley electricity price period.

[0011] Optionally, the step of creating a visual energy flow interface for the energy storage charging pile based on the scheduling constraints and the energy supply-demand balance state includes: Real-time collection of dynamic data corresponding to the scheduling constraints and the energy supply and demand balance state, and extraction of dynamic energy flow indicators of the energy storage charging pile from the dynamic data; Based on the dynamic energy flow index, a visualization element for the energy storage charging pile is generated; Based on the energy scheduling relationship between the scheduling constraints and the energy supply and demand balance state, a multi-view monitoring dashboard integrating the visualization elements is constructed. Configure the information visualization range and data interaction permissions of the multi-view monitoring dashboard to generate role-based views corresponding to the visualization elements; By combining the multi-view monitoring dashboard and the role-based view, a visual energy flow interface for the energy storage charging pile is created.

[0012] Optionally, the real-time monitoring of the battery health status and power supply stability indicators of the energy storage charging pile to set scheduling constraints for the energy storage charging pile includes: The geographical location information and grid connection voltage level of the energy storage charging pile are obtained, and the historical operation data patterns and user group characteristics of the energy storage charging pile are parsed out. The application scenario type of the energy storage charging pile is identified by the geographical location information, the grid connection voltage level, the historical operation data mode, and the user group characteristics. Extract the battery body characteristics, grid connection characteristics, and user service characteristics of the energy storage charging pile to determine the correlation index of the energy storage charging pile; Based on the application scenario type and the correlation index, initial constraint conditions are matched from a predefined constraint strategy library; Based on the battery health status and the power supply stability index, the dynamic relaxation factor and adaptive adjustment direction of the initial constraint conditions are defined. Based on the dynamic relaxation factor and the adaptive adjustment direction, the scheduling constraints of the energy storage charging pile are set.

[0013] Optionally, determining the optimal charging time window and power change rate of the energy storage charging pile based on the energy demand distribution characteristics includes: Based on the energy demand distribution characteristics, the time-of-use energy demand data and equipment status time-series data of the energy storage charging pile are obtained; Based on the time-of-use energy demand data, the demand fluctuation characteristics of the energy storage charging piles during different electricity price periods are analyzed. Using the aforementioned demand fluctuation characteristics, the value coefficient for each of the aforementioned electricity price periods is calculated to form a set of period value coefficients; The optimal charging time window for the energy storage charging pile is determined based on the set of time period value coefficients. Based on the device status time-series data, the charging and discharging performance degradation curve of the energy storage charging pile is fitted; Identify the critical decay point in the charge and discharge performance decay curve to define the power regulation safety boundary of the energy storage charging pile; The power change constraint conditions of the energy storage charging pile are established by using the power regulation safety boundary. By combining the set of time period value coefficients and the power change constraints, the power change rate of the energy storage charging pile is determined.

[0014] Optionally, calculating the energy utilization efficiency of the energy storage charging pile based on the optimal charging time window and the power change rate includes: Based on the optimal charging time window, establish the power curve corresponding to the power change rate; The total absorbed electrical energy of the energy storage charging pile is calculated using the optimal charging time window and the power curve. Within the optimal charging time window, the actual output energy of the energy storage charging pile is measured; The energy utilization efficiency of the energy storage charging pile is calculated based on the total absorbed electrical energy and the actual output energy.

[0015] Optionally, identifying the energy supply and demand balance state of the energy storage charging pile based on the energy utilization efficiency includes: Obtain the rated energy utilization efficiency of the energy storage charging pile under rated operating conditions; The stable operating range of the energy storage charging pile is defined based on the rated energy utilization efficiency. Real-time monitoring and recording of the real-time energy utilization efficiency sequence corresponding to the energy utilization efficiency; The mean efficiency, standard deviation efficiency, and efficiency decay gradient corresponding to the energy utilization efficiency are extracted from the real-time energy utilization efficiency sequence to form the efficiency state characteristics of the energy storage charging pile. Based on the stable operating range and the efficiency status characteristics, the energy supply and demand balance status of the energy storage charging pile is identified.

[0016] Compared to the problems described in the background art, the embodiments of the present invention collect real-time grid interaction data, user charging request data, and energy storage system status data of energy storage charging piles during different electricity price periods. This allows for the analysis of the energy demand distribution characteristics of the energy storage charging piles under different electricity price periods, forming a multi-dimensional and interconnected energy demand perception system. This provides a structured framework for real-time capture of the adaptation relationship between energy supply and demand changes and external dynamic environments (sudden increases in grid load, concentrated bursts of user charging demand) under different electricity price periods. Furthermore, the embodiments of the present invention calculate the energy utilization efficiency of the energy storage charging piles based on the optimal charging time window and the power change rate, deeply integrating the scheduling advantages of both to construct a more suitable system. The peak-valley electricity price characteristic energy utilization system creates favorable conditions for the precise allocation of subsequent energy storage resources and grid interaction, improving the stability of energy conversion and utilization of energy storage charging piles during adaptive scheduling. This embodiment of the invention constructs a scheduling strategy guidance set for the energy storage charging piles based on the energy supply and demand balance state, avoiding energy storage redundancy waste and user demand gaps caused by mismatches in the "grid load-user demand-energy storage status" dimension, reducing unnecessary charging and discharging adjustments that damage the lifespan of energy storage batteries, and improving the operational stability and overall benefits of energy management for energy storage charging piles based on peak-valley electricity price adaptive scheduling. Furthermore, this embodiment of the invention, based on the energy scheduling response characteristics and the energy supply and demand balance state... This invention generates dynamic energy allocation instructions for the energy storage charging piles during peak-valley electricity price periods. This transforms abstract scheduling rules and supply-demand matching relationships into quantifiable execution parameters, providing clear numerical basis for precise allocation of energy resources during peak-valley periods. It improves the efficiency of judging the adaptability of the energy storage system's operating status to external conditions, thereby optimizing the dynamic adjustment logic of energy management for energy storage charging piles based on peak-valley electricity price adaptive scheduling. Furthermore, by real-time monitoring of the battery health status and power supply stability indicators of the energy storage charging piles to set scheduling constraints, this invention enhances the safety and reliability of peak-valley electricity price adaptive scheduling. It also provides key data for subsequent optimization of energy storage strategies and construction of dynamic adjustment logic. According to reference, this invention enhances the risk control capabilities of energy management for energy storage charging piles based on peak-valley electricity price adaptive scheduling in complex operating scenarios. Furthermore, this embodiment of the invention creates a visualized energy flow interface for the energy storage charging pile based on the scheduling constraints and the energy supply and demand balance state. This enables global visualization of the energy flow situation under peak-valley electricity price adaptive scheduling scenarios, reduces the information gap between scheduling constraints and supply and demand status in energy storage management, and improves the accuracy and timeliness of energy management decisions. The visualized energy flow interface refers to an integrated operation interface that dynamically reflects the entire process of energy absorption-storage-release of the energy storage charging pile during peak and valley periods, constructed through visualization technologies (such as real-time charts and dynamic flowcharts).Finally, this embodiment of the invention generates an adaptive energy management scheme for the energy storage charging pile based on the scheduling strategy guidance set, the dynamic energy allocation instructions, and the visualized energy flow interface. This avoids the problem of low energy utilization efficiency caused by fixed charging and discharging priorities and energy allocation ratios. Simultaneously, by matching the real-time energy management needs of the energy storage charging pile under scenarios such as peak-valley electricity price periods and supply-demand balance fluctuations through the visualized energy flow interface, it can reduce the risk of equipment operational imbalance and energy scheduling lag caused by changes in the external environment, achieving a precise balance between the economy and stability of energy management under different dynamic scenarios. Therefore, the energy management method for energy storage charging piles based on peak-valley electricity price adaptive scheduling provided by this embodiment of the invention can improve the energy utilization efficiency of energy storage charging piles. Attached Figure Description

[0017] Figure 1 A flowchart illustrating an energy management method for energy storage charging piles based on adaptive scheduling of peak-valley electricity prices, provided as an embodiment of the present invention; Figure 2 A flowchart of time-of-use electricity price calculation for implementing an energy management method for energy storage charging piles based on adaptive peak-valley electricity price scheduling, provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a module for implementing an energy management system for energy storage charging piles based on adaptive scheduling of peak-valley electricity prices, provided as an embodiment of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] This application provides an energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing. The executing entity of this energy management method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0021] Reference Figure 1The diagram shown is a flowchart illustrating an energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing, according to an embodiment of the present invention. In this embodiment, the energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing includes: S1. Real-time collection of grid interaction data, user charging request data, and energy storage system status data of energy storage charging piles during different electricity price periods, in order to analyze the energy demand distribution characteristics of the energy storage charging piles under different electricity price periods.

[0022] This invention collects real-time grid interaction data, user charging request data, and energy storage system status data of energy storage charging piles during different electricity price periods. This allows for the analysis of the energy demand distribution characteristics of the energy storage charging piles under different electricity price periods, forming a multi-dimensional and interconnected energy demand perception system. This provides a structured framework for capturing the adaptation relationship between energy supply and demand changes and external dynamic environments (sudden increases in grid load, concentrated bursts of user charging demand) under different electricity price periods in real time.

[0023] The energy storage charging pile refers to an integrated power interaction device that combines an energy storage unit and a charging module. The energy storage charging pile has a built-in rechargeable and discharging energy storage battery pack (such as a lithium-ion battery pack or a lithium iron phosphate battery pack), a charge / discharge controller for power conversion, and an interface for communication with the power grid and electrical equipment (such as new energy vehicles). The different electricity price periods refer to the time periods in which the power grid company divides a 24-hour day into different electricity price ranges based on the electricity load variation and energy supply and demand. These are typically divided into off-peak electricity price periods, flat electricity price periods, and peak electricity price periods. The power grid interaction data refers to key data reflecting the power grid's operating status generated during the energy exchange process between the energy storage charging pile and the power grid, including real-time power grid load, power supply stability indicators, and electricity price fluctuations. The user charging request data refers to data reflecting user charging requests. Real-time and historical data on demand characteristics include charging request frequency, single charging power demand, and charging waiting time. The energy storage system status data refers to the real-time operating parameters of the battery system inside the energy storage charging pile, including energy storage SOC, charging / discharging power, health status, temperature, voltage, and current. The energy demand distribution characteristics refer to the energy supply and demand patterns of energy storage charging piles under different electricity price periods, obtained by analyzing grid interaction data, user charging request data, and energy storage system status data through a data processing module. These patterns include peak demand periods, energy flow (charging / discharging) proportion, and supply-demand matching degree. For example, analysis shows that during off-peak hours, energy demand is mainly for absorbing grid power (accounting for 80%), while during peak hours, it is mainly for discharging to vehicles (accounting for 70%), and the peak demand on Wednesdays is significantly higher than on other days.

[0024] As an embodiment of the present invention, the real-time acquisition of grid interaction data, user charging request data, and energy storage system status data of the energy storage charging pile during different electricity price periods, in order to analyze the energy demand distribution characteristics of the energy storage charging pile under the different electricity price periods, includes: Voltage harmonic analysis is performed on the power grid interaction data to obtain a power grid load fluctuation dataset; Extract the time-of-use grid load characteristics from the grid load fluctuation dataset; Based on the time-sharing grid load characteristics, the grid interaction power demand index of the energy storage charging pile is extracted; Spatiotemporal clustering analysis was performed on the user charging request data to obtain the user charging demand clustering results; Based on the clustering results of the user charging demand, a heat map of the spatiotemporal distribution of the charging demand of the energy storage charging pile is generated. Based on the spatiotemporal distribution heatmap of charging demand, analyze the user charging behavior preferences corresponding to the energy storage charging pile; Extract the vehicle's remaining SOC and the user's reservation time corresponding to the energy storage charging pile, and combine them with the user's charging behavior preferences to extract the charging request priority index from the user's charging request data; Based on the energy storage system status data, analyze the health status degradation trend of the energy storage charging pile; The current capacity and power regulation capability of the energy storage charging pile are assessed by the health status decay trend. Based on the current capacity and the power regulation capability, determine the upper limit of the energy storage capacity of the energy storage charging pile; By integrating the power grid interaction power demand index, the charging request priority index, and the energy storage upper limit index, an energy management feature matrix for the energy storage charging pile is constructed. Based on the energy management feature matrix, the energy demand distribution characteristics of the energy storage charging pile under different electricity price periods are generated.

[0025] The voltage harmonic analysis and processing refers to the process of extracting, quantifying, and performing feature analysis on the various harmonics (sine waves that are integer multiples of the fundamental frequency) contained in the power grid voltage signal using signal processing methods such as Fourier transform and wavelet analysis. The power grid load fluctuation dataset refers to a structured dataset containing characteristic parameters such as power fluctuation, harmonic distortion rate, and voltage fluctuation amplitude of the power grid load during different time periods (e.g., peak / flat / valley electricity price periods) after voltage harmonic analysis and processing. For example, it could be a time-series dataset composed of active power fluctuation values, THD values, and voltage deviations recorded every 15 minutes during peak hours (8:00-22:00) for a charging pile. The time-of-use power grid load characteristics refer to the inverse characteristics extracted from the power grid load fluctuation dataset. The system includes characteristic quantities reflecting the load variation patterns of the power grid during different electricity price periods (such as peak, high, flat, and low periods), including average load, peak load, fluctuation frequency, and duration; the power grid interaction power demand index refers to the core parameters that quantify the energy exchange demand between energy storage charging piles and the power grid based on time-of-use power grid load characteristics, including maximum power demand, average interaction power, peak-valley power difference, and harmonic mitigation requirements within a given time period; the spatiotemporal clustering analysis refers to the process of grouping user charging request data using clustering algorithms, combining time dimensions (such as charging time periods) and spatial dimensions (such as charging pile locations or user regions), to identify charging behavior patterns with similar spatiotemporal distribution characteristics; the spatiotemporal distribution heatmap of charging demand refers to the process of grouping user charging request data based on time dimensions (such as charging time periods) and spatial dimensions (such as charging pile locations or user regions), in order to identify charging behavior patterns with similar spatiotemporal distribution characteristics; and the heatmap of charging demand spatiotemporal distribution refers to the process of grouping user charging request data using clustering algorithms based on time dimensions (such as charging time periods) and spatial dimensions (such as charging pile locations or user regions), in order to identify charging behavior patterns with similar spatiotemporal distribution characteristics. The empty clustering results, using Python's Matplotlib or folium libraries to overlay a time axis, generate a dynamic heatmap that visually displays the density and intensity distribution of user charging requests at different times (e.g., hours, days) and spaces (e.g., geographical locations). Darker colors indicate more concentrated demand. For example, a city's charging pile heatmap shows that charging demand in the city center is distributed in high density (red) from 18:00 to 20:00 on weekdays, while suburban areas are distributed in low density (blue). User charging behavior preferences refer to the patterns of user charging habits extracted from the spatiotemporal distribution heatmap of charging demand, including preferred charging times, average charging duration, frequently used charging pile locations, and preferred charging power. The remaining SOC of the vehicle refers to... The percentage of the electric vehicle's current remaining battery power relative to its total battery capacity, read through CAN bus communication between the charging pile and the vehicle, reflects the vehicle's range. For example, if a vehicle has a total battery capacity of 70kWh and currently has 21kWh remaining, then the remaining SOC is 30%. The user reservation time refers to the desired charging start time preset by the user through the APP or charging pile terminal, used to allocate energy storage resources. The charging request priority index is an indicator that quantifies the urgency of the user's charging request by comprehensively considering factors such as the vehicle's remaining SOC, user reservation time, and user behavior preferences (e.g., 0-10 points, with higher scores indicating higher priority). For example, a request with a remaining SOC < 20% and a reservation time within 1 hour has a priority of 9 points.Requests with a remaining SOC > 80% and a reservation time more than 24 hours later have a priority of 3 points; the health status decay trend refers to the analysis of the degradation law of energy storage battery capacity and performance over time based on energy storage system status data (such as charge / discharge cycle count, battery temperature, internal resistance changes); the power regulation capability refers to the ability of the energy storage charging pile to quickly adjust the output / input power according to grid demand or user requests during charging and discharging, usually measured by the maximum adjustable power range and response time. For example, a charging pile with a rated power of 60kW can adjust the output power from 60kW to 30kW within 0.5 seconds, with a power regulation range of 0-60kW; the energy storage upper limit index This refers to the maximum safe and efficient energy storage capacity threshold determined by considering the current capacity (based on health status) and power regulation capability of the energy storage charging pile. The calculation formula is: Energy Storage Upper Limit = Nominal Capacity × (Health Status)SOH × Power Regulation Safety Factor. For example, if a charging pile has a nominal capacity of 100kWh, a current health status of 90%, and a power regulation safety factor of 0.8, then the energy storage upper limit is 100 × 90% × 0.8 = 72kWh. The energy management feature matrix refers to a multi-dimensional feature matrix constructed by integrating grid interaction power demand indicators, charging request priority indicators, and energy storage upper limit indicators. It is used to characterize the energy management status of the energy storage charging pile during different electricity price periods.

[0026] Optionally, the time-of-use grid load characteristics of the grid load fluctuation dataset can be extracted using the sliding window statistical method; the health status degradation trend of the energy storage charging pile can be analyzed using an LSTM neural network model.

[0027] S2. Based on the energy demand distribution characteristics, determine the optimal charging time window and power change rate of the energy storage charging pile, and calculate the energy utilization efficiency of the energy storage charging pile based on the optimal charging time window and the power change rate.

[0028] This invention, by determining the optimal charging time window and power change rate of the energy storage charging pile based on the energy demand distribution characteristics, can optimize the selection of charging time periods and power output intensity of the energy storage charging pile in real time, thereby improving the response accuracy to complex electricity consumption scenarios (such as alternating electricity price periods and sudden increases or decreases in charging demand).

[0029] The optimal charging time window refers to the charging (including charging of the energy storage system and discharging to the vehicle) time window determined based on the energy demand distribution characteristics, which maximizes the energy utilization efficiency of the energy storage charging pile, minimizes the operating cost, and minimizes the impact on the power grid. The power change rate refers to the change in power per unit time during the charging and discharging process of the energy storage charging pile, including the charging power increase rate and the discharging power decrease rate.

[0030] As an embodiment of the present invention, determining the optimal charging time window and power change rate of the energy storage charging pile based on the energy demand distribution characteristics includes: Based on the energy demand distribution characteristics, the time-of-use energy demand data and equipment status time-series data of the energy storage charging pile are obtained; Based on the time-of-use energy demand data, the demand fluctuation characteristics of the energy storage charging piles during different electricity price periods are analyzed. Using the aforementioned demand fluctuation characteristics, the value coefficient for each of the aforementioned electricity price periods is calculated to form a set of period value coefficients; The optimal charging time window for the energy storage charging pile is determined based on the set of time period value coefficients. Based on the device status time-series data, the charging and discharging performance degradation curve of the energy storage charging pile is fitted; Identify the critical decay point in the charge and discharge performance decay curve to define the power regulation safety boundary of the energy storage charging pile; The power change constraint conditions of the energy storage charging pile are established by using the power regulation safety boundary. By combining the set of time period value coefficients and the power change constraints, the power change rate of the energy storage charging pile is determined.

[0031] The time-of-use energy demand data refers to the set of energy interaction data of the energy storage charging pile collected in real time by the charging pile energy metering module at preset time intervals (such as 15 minutes or 1 hour), reflecting different electricity price periods (peak / flat / valley), including time-series data such as grid input power, power output to vehicles, and charging and discharging amount of the energy storage system; the equipment status time-series data refers to the operating status parameters of the core components of the energy storage charging pile (such as energy storage battery, charger, controller) recorded in time series, including data on battery temperature, charge and discharge cycle count, internal resistance, output voltage stability, etc., which can be obtained through the charging pile's built-in sensors (temperature sensor, current sensor, etc.) and BMS. The battery management system collects data in real time and stores it by timestamp. The demand fluctuation characteristics refer to the feature parameters extracted from time-of-use energy demand data, reflecting the changing patterns of energy demand during different electricity price periods, including peak load, trough load, fluctuation amplitude, duration, and trend (rising / falling / stable). The value coefficient is a dimensionless parameter (usually ranging from 0 to 1) that quantifies the energy dispatch value of energy storage charging piles during a specific electricity price period. A higher comprehensive value indicates better overall benefits (economic efficiency, grid friendliness, etc.) for charging and discharging dispatch during that period. The formula for calculating the value coefficient is: Value Coefficient = α × (1 - Electricity Price During Period / Highest Electricity Price) + β × (1 - ... The term is defined as follows: (Time period grid load / grid capacity), where α and β are weights (α+β=1), reflecting economic factors and grid-friendliness factors, respectively; the time period value coefficient set is an ordered set composed of value coefficients for each electricity price time period, used to characterize the scheduling value ranking of different time periods, providing a quantitative basis for decision-making on the optimal charging time window; the charging and discharging performance degradation curve is a curve obtained by fitting a power function curve using the least squares method based on equipment state time series data, reflecting the degradation trend of the energy storage charging pile's charging and discharging capacity (such as capacity retention rate, maximum charging and discharging power) with usage time or number of cycles; the critical degradation point is a characteristic point on the charging and discharging performance degradation curve, when the energy storage... When the performance of a charging pile (such as capacity retention rate and maximum charging / discharging power) drops to the threshold corresponding to that point, power regulation limits need to be activated to avoid equipment damage or a sudden drop in performance. The power regulation safety boundary refers to the upper limit of the allowable power regulation range of the energy storage charging pile during charging and discharging, determined based on the critical decay point. This includes the upper limit of the maximum charging power, maximum discharging power, and power change rate. For example, the power regulation safety boundary of a charging pile before the critical decay point is: charging power ≤ 60kW, discharging power ≤ 50kW, and power change rate ≤ 5kW / min; after exceeding the critical decay point, it is adjusted to: charging power ≤ 40kW, discharging power ≤ 30kW, and power change rate ≤ 3kW / min.The power variation constraint refers to a set of constraint inequalities that limit the rate of power variation, based on the power regulation safety boundary, combined with grid stability requirements (such as power fluctuation limits) and user demand response speed. The power variation constraint can be expressed as a system of inequalities, including: a lower limit constraint: ensuring that user charging timeliness requirements are met (e.g., minimum power variation rate ≥ 2kW / min); and an upper limit constraint: set based on the power regulation safety boundary and grid standards (e.g., GB / T19963-2011) (e.g., maximum power variation rate ≤ 5kW / min). For example, the power variation constraint could be: 2kW / min ≤ charging power variation rate ≤ 5kW / min and |discharging power variation rate| ≤ 4kW / min.

[0032] Optionally, the optimal charging time window for the energy storage charging pile can be determined using an integer linear programming (ILP) algorithm. An economically optimal objective function is constructed based on the set of time-period value coefficients, with constraints including the grid load ceiling, charging demand, and time continuity. Binary decision variables characterize whether each time period is included in the charging window, and the optimal charging time window is obtained by solving the linear programming model. The critical decay point in the charge-discharge performance degradation curve can be identified using a threshold method. For example, according to industry standards (such as GB / T36276-2018), a threshold for the energy storage battery capacity retention rate can be set. The critical threshold is 80%. By performing linear interpolation on the charge-discharge performance decay curve, when the capacity retention rate corresponding to a certain number of cycles in the curve first reaches 80%, that point is the critical decay point. The power change rate of the energy storage charging pile can be solved using a multi-objective optimization algorithm. For example, based on NSGA-II (Non-dominated sorting genetic algorithm II), a multi-objective function is constructed that includes minimizing grid impact, minimizing battery life loss, and maximizing user demand response. With power regulation safety boundary and time period value coefficient as constraints, the Pareto optimal solution set is obtained through population iterative optimization.

[0033] Furthermore, by calculating the energy utilization efficiency of the energy storage charging pile based on the optimal charging time window and the power change rate, this embodiment of the invention can deeply integrate the scheduling advantages of both, construct an energy utilization system that better suits the characteristics of peak and valley electricity prices, create favorable conditions for the subsequent precise allocation of energy storage resources and grid interaction and coordination, and improve the stability of energy conversion and utilization of the energy storage charging pile in the adaptive scheduling process.

[0034] The energy utilization efficiency refers to the ratio of the effective electrical energy actually used by the energy storage charging pile to meet the user's charging needs during the adaptive scheduling process based on peak-valley electricity prices to the total electrical energy absorbed by the energy storage charging pile from the power grid (including losses during the energy storage and discharge stages). It is a core indicator for quantifying the effectiveness of energy conversion and utilization of energy storage charging piles, and is usually expressed as a percentage (%).

[0035] As an embodiment of the present invention, calculating the energy utilization efficiency of the energy storage charging pile based on the optimal charging time window and the power change rate includes: Based on the optimal charging time window, establish the power curve corresponding to the power change rate; The total absorbed electrical energy of the energy storage charging pile is calculated using the optimal charging time window and the power curve. Within the optimal charging time window, the actual output energy of the energy storage charging pile is measured; The energy utilization efficiency of the energy storage charging pile is calculated based on the total absorbed electrical energy and the actual output energy.

[0036] The power curve refers to a continuous curve representing the dynamic change of the charging and discharging power of the energy storage charging pile over time, constructed based on the time dimension (horizontal axis, unit: h or min) of the optimal charging time window and the power change rate of the corresponding period. It includes the power rise / stable / fall segments during the charging phase and the power adjustment segment during the discharging phase. The total absorbed electrical energy refers to the total electrical energy absorbed by the energy storage charging pile from the power grid under ideal operating conditions (no energy loss, such as no line loss, battery polarization loss, or grid harmonic loss), calculated by integration based on the optimal charging time window and the power curve, with the unit being kWh. The actual output energy refers to the effective electrical energy ultimately delivered to the user vehicle by the energy storage charging pile through actual metering equipment (such as an electricity meter) within the scheduling cycle corresponding to the optimal charging time window (including the absorption of electrical energy from the power grid during the charging phase and the output of electrical energy to the user vehicle during the discharging phase), after deducting all actual losses (line loss, battery polarization loss, equipment self-loss, etc.), with the unit being kWh.

[0037] Optionally, the power curve corresponding to the power change rate can be established by a piecewise linear interpolation algorithm; the total absorbed electrical energy of the energy storage charging pile can be calculated using the trapezoidal integral method.

[0038] S3. Based on the energy utilization efficiency, identify the energy supply and demand balance state of the energy storage charging pile, and construct the scheduling strategy guidance set of the energy storage charging pile according to the energy supply and demand balance state.

[0039] This invention identifies the energy supply and demand balance state of the energy storage charging pile based on the energy utilization efficiency. It can dynamically adjust the energy storage and charging / discharging strategies according to the balance state parameters, improve the adaptability to complex electricity usage scenarios (such as fluctuations in user demand and changes in grid load), and enhance the stability and economy of energy management of the energy storage charging pile under the peak-valley electricity pricing mechanism.

[0040] The energy supply and demand balance state refers to the system energy supply and demand matching type based on the matching relationship between the efficiency state characteristics and stable operating range of the energy storage charging pile, including high-efficiency balance state, fluctuating balance state, recovery transition state, imbalance and decline state, and severe instability state.

[0041] As an embodiment of the present invention, identifying the energy supply and demand balance state of the energy storage charging pile based on the energy utilization efficiency includes: Obtain the rated energy utilization efficiency of the energy storage charging pile under rated operating conditions; The stable operating range of the energy storage charging pile is defined based on the rated energy utilization efficiency. Real-time monitoring and recording of the real-time energy utilization efficiency sequence corresponding to the energy utilization efficiency; The mean efficiency, standard deviation efficiency, and efficiency decay gradient corresponding to the energy utilization efficiency are extracted from the real-time energy utilization efficiency sequence to form the efficiency state characteristics of the energy storage charging pile. Based on the stable operating range and the efficiency status characteristics, the energy supply and demand balance status of the energy storage charging pile is identified.

[0042] The rated operating condition refers to the working state of the energy storage charging pile under the standard test conditions specified by the manufacturer. For example, the design documents of a certain model of energy storage charging pile (model: ECP-600) specify that the rated operating condition is when the ambient temperature is 25℃, the grid input is AC380V / 50Hz, the energy storage battery voltage is DC650V, and 8 out of 10 charging ports are outputting at full power of 60kW (total output power 480kW, load rate 80%). The rated energy utilization efficiency refers to the maximum and most ideal energy conversion efficiency that the energy storage charging pile can achieve under the above rated operating conditions. The formula for calculating the rated energy utilization efficiency is: Rated efficiency = (Rated output energy / Rated input energy) × 100%. The stable operating range refers to a high-efficiency and reliable efficiency range defined based on the rated efficiency. The real-time energy utilization efficiency sequence refers to the sequence of energy utilization efficiency over a period of time at fixed time intervals (e.g., per second, per...). The system collects and arranges a series of real-time energy utilization efficiency values ​​(in milliseconds). The efficiency mean refers to the average value calculated by arithmetic averaging the real-time energy utilization efficiency (η_t, where t is the time sampling point and the sampling interval is 1 minute) of the energy storage charging pile within a set statistical period T (usually 1 hour, which can be adjusted to 0.5 hours or 2 hours depending on the application scenario). The efficiency standard deviation is an indicator that measures the degree of dispersion between the real-time efficiency value and the efficiency mean. The efficiency decay gradient is the ratio of the difference between the efficiency mean of the later period and the efficiency mean of the previous period to the time interval (T2-T1) of the two consecutive adjacent statistical periods (T1, T2, with the same period length). The efficiency state characteristics refer to a set of multi-dimensional parameters used to characterize the overall state of energy utilization efficiency of the energy storage charging pile, which is composed of the efficiency mean, efficiency standard deviation, and efficiency decay gradient.

[0043] Furthermore, by constructing a scheduling strategy guide set for the energy storage charging pile based on the energy supply and demand balance state, this embodiment of the invention can avoid the waste of energy storage redundancy and the gap in user demand caused by the mismatch of the "grid load-user demand-energy storage status" dimension, reduce the loss of energy storage battery life due to unnecessary charging and discharging adjustments, and improve the operational stability and comprehensive benefits of energy management of energy storage charging piles based on peak-valley electricity price adaptive scheduling.

[0044] The scheduling strategy guidance set refers to a structured, state-aware decision mapping framework. Its core function is to intelligently map the real-time diagnosed system state into corresponding optimized operation suggestions, thereby realizing the intelligent and adaptive scheduling of energy storage charging piles.

[0045] As an embodiment of the present invention, the step of constructing a scheduling strategy guidance set for the energy storage charging pile based on the energy supply and demand balance state includes: Extract the state feature parameters from the energy supply and demand balance state, and determine the multi-scenario adaptation type corresponding to the state feature parameters; Perform association matching processing between the state feature parameters and the multi-scenario adaptation types to generate a scenario-state association dataset; Based on the scenario-state association dataset, the strategy allocation type of the energy storage charging pile is determined; The dynamic constraint parameters of the energy storage charging pile are identified in order to calculate the strategy adjustment elasticity coefficient of the energy storage charging pile. By combining the strategy allocation type and the strategy adjustment elasticity coefficient, a scheduling strategy association graph of the energy storage charging pile is constructed. Extract the key strategy nodes from the scheduling strategy association graph; Based on the key strategy nodes, a scheduling strategy guide set for the energy storage charging pile is generated.

[0046] The state characteristic parameters refer to the core parameter set extracted from the energy supply and demand balance state, used to quantify the "energy storage-demand" matching characteristics of energy storage charging piles, including three key indicators: supply-demand difference rate, demand satisfaction rate, and efficiency trend slope. The multi-scenario adaptation types refer to scenario classifications based on the electricity consumption characteristics (such as user behavior and time-of-day demand patterns) of the area where the energy storage charging pile is located, combined with differences in state characteristic parameters. These classifications are used to adapt to differences in electricity demand in different areas and at different times. The multi-scenario adaptation types can be divided into five core types based on "regional attributes + time attributes." This includes residential area-weekday, residential area-weekend, commercial area-weekday, commercial area-weekend, and highway service area-holiday scenarios. For example, the highway service area-holiday scenario typically exhibits status characteristic parameters such as "supply-demand gap rate -8%, demand satisfaction rate 82%, and efficiency trend slope 0.25% / cycle," requiring a focus on "peak-hour replenishment" strategies. The associated matching process refers to the data analysis process that logically associates status characteristic parameters with multiple scenario adaptation types. The scenario-state association dataset refers to the dataset formed after association matching processing, containing "multiple scenario adaptation types and status..." The structured dataset, consisting of "feature parameters and historical strategy execution effects," includes a "scenario-state" combination and past scheduling feedback for each data point. This data is used to determine the strategy allocation type. The data format is typically a table (e.g., CSV, database table). The strategy allocation type refers to the strategy priority category based on the scenario-state association dataset and the core scheduling objectives (grid stability, user satisfaction, operating costs). This includes grid priority, user priority, and cost-optimal strategies. For example, a commercial area-weekend scenario (concentrated user demand, tight grid load) corresponds to a "user priority" strategy, prioritizing user charging needs; a residential area-weekday off-peak scenario (low grid load, cheap electricity) corresponds to a "cost-optimal" strategy, prioritizing reducing grid interaction costs. The dynamic constraint parameters refer to the set of external and internal constraint parameters that affect the feasibility of the energy storage charging pile scheduling strategy in real time. These include current available power capacity (unit: kW), current charging power demand (unit: kW), current battery health (SOH), and regional electricity price policy adjustment coefficients. These parameters ensure that the strategy does not exceed safety and compliance boundaries during actual operation. The formula for calculating the current available power capacity is: ,in, Indicates the maximum allowable power on the grid side, Indicates the current total load power corresponding to the energy storage charging pile. Indicates the rated power of the energy storage charging pile, The current battery health is indicated. The strategy adjustment elasticity coefficient is a dimensionless parameter (range 0.6-1.2) calculated based on dynamic constraint parameters, quantifying the adjustable space of the energy storage charging pile scheduling strategy. A larger coefficient indicates a looser constraint and a higher degree of freedom in strategy adjustment; conversely, a smaller coefficient requires a tighter strategy. The scheduling strategy association graph is a visual topology graph that associates "state characteristic parameters, dynamic constraint parameters, and key strategy nodes" with strategy allocation type as the core. Nodes represent parameters or strategy elements, and edges represent the association rules between elements. It is used to intuitively present the logical link of strategy generation and supports quick location and adjustment of strategy nodes. The key strategy nodes are the core strategy elements in the scheduling strategy association graph that directly affect the achievement of strategy objectives. They include three types: off-peak energy storage duration adjustment nodes, peak energy replenishment power nodes, and user priority allocation nodes. For example, the key strategy nodes of the "cost-optimal" strategy are "off-peak energy storage duration adjustment node (reduced from 6 hours to 4 hours), peak energy replenishment power node (maintained at 2kW / min), and user priority allocation node (low-priority power limit of 15kW)".

[0047] Optionally, the multi-scenario adaptation type corresponding to the state feature parameters can be determined using the K-means clustering algorithm; the association matching processing of the state feature parameters and the multi-scenario adaptation type can be implemented using a decision tree model; and the strategy allocation type of the energy storage charging pile can be determined using the analytic hierarchy process.

[0048] In an optional embodiment of the present invention, the strategy adjustment elasticity coefficient of the energy storage charging pile is calculated by the following formula: ; in, This represents the elasticity coefficient for strategy adjustment. This indicates the slackness of the grid constraints corresponding to the energy storage charging pile. This indicates the current health status of the batteries in the energy storage charging station. This indicates the current battery health reference value. Indicates the penalty coefficient. Indicates the current charging power requirement. This indicates the currently available power capacity.

[0049] It needs to be explained that, This parameter measures the degree of leniency with which the power grid imposes constraints on the operation of energy storage charging piles. It reflects the margin that the power grid allows for flexible adjustments to energy storage charging piles deviating from theoretical constraints (such as rated power, maximum charge and discharge limits) under current operating conditions. It is expressed by the formula... = Calculate, where, Indicates the maximum power allowed by the power grid. This indicates the current total load of the charging station corresponding to the energy storage charging pile; This is a coefficient used to "penalize" behaviors that deviate from the desired state (such as a mismatch between charging power demand and available power capacity, or excessive deviation from the appropriate range of battery health). It reflects the system's level of attention and constraint on unreasonable deviations during strategy adjustments. For example, in the scheduling of energy storage charging piles, if it is desired to strictly control the deviation between charging power demand and available power capacity to ensure the stable operation of the power grid and batteries, a larger penalty coefficient can be set. =5, when When the relative deviation between power demand and available capacity is large, This will decrease rapidly, thereby reducing the policy adjustment elasticity coefficient r and limiting excessive policy adjustments; if the system has a high tolerance for a certain degree of deviation, a smaller value can be set. =2, which allows the strategy to attempt adjustments over a larger range of deviations.

[0050] As another embodiment of the present invention, the step of constructing the scheduling strategy association map of the energy storage charging pile by combining the strategy allocation type and the strategy adjustment elasticity coefficient includes: Match the policy action corresponding to the policy assignment type; Based on the strategy allocation type and the strategy action, define the graph decision node of the energy storage charging pile; By utilizing the transformation relationship between the strategy actions and the strategy adjustment elasticity coefficient, a directed connection relationship is established between the decision nodes of the graph. Based on the graph decision nodes and the directed connection relationships, a scheduling strategy association graph for the energy storage charging pile is constructed.

[0051] The "strategy action" refers to the specific control command that can be directly executed in the energy management system (EMS) of the energy storage charging pile corresponding to the strategy allocation type (grid priority / user priority / cost optimal), such as power reduction, peak shifting, valley filling, and priority scheduling. The "graph decision node" refers to the core node unit in the scheduling strategy association graph that carries the strategy logic, including target nodes representing high-level strategy allocation types and action nodes representing specific executable strategy actions. For example, the strategy allocation type of the user priority graph decision node can be user priority, and the core strategy action could be increasing the peak charging power change rate to 5kJ / m³. W / min; The transformation relationship refers to the logical association and execution order rules between different strategy actions, including "progressive relationship" (the next action is triggered after the previous action is completed), "mutually exclusive relationship" (two actions cannot be executed simultaneously), and "compensation relationship" (another action is started when an action fails to achieve its goal); The directed connection relationship refers to the directed logical edges in the scheduling strategy association graph that connect different graph decision nodes. The direction of the edge represents the flow order of the strategy logic, and the weight of the edge represents the priority of the connection (calculated based on the strategy adjustment elasticity coefficient). It is used to characterize the activation order and importance between nodes to ensure that the graph executes the strategy according to the optimal logical path.

[0052] Optionally, the strategy actions corresponding to the strategy allocation type can be matched using an association rule mining algorithm, such as the Apriori algorithm; the conversion relationship between the strategy actions can be sorted out through semantic analysis based on the functional attributes of the strategy actions (such as energy storage regulation and power control).

[0053] S4. Extract the energy dispatch response characteristics of the energy storage charging pile during peak and valley electricity price periods, and generate dynamic energy allocation instructions for the energy storage charging pile during peak and valley electricity price periods based on the energy dispatch response characteristics and the energy supply and demand balance state.

[0054] This invention improves the comprehensive scheduling perception capability of energy storage charging piles under complex electricity price and load conditions by extracting the energy dispatch response characteristics of the energy storage charging piles during peak and valley electricity price periods. At the same time, it lays the foundation for subsequent extraction of key scheduling optimization parameters, and enhances the dynamic adaptability and decision-making accuracy of energy management of energy storage charging piles based on peak and valley electricity price adaptive scheduling. The energy dispatch response characteristics refer to the regular response pattern of energy storage charging piles to changes in external input conditions (electricity price fluctuations, grid load changes, user demand adjustments) and internal operating status (battery health, energy storage capacity) during peak and valley electricity price periods (low valley energy storage period, peak discharge period).

[0055] Optionally, time series clustering algorithms can be used to extract the energy dispatch response characteristics of the energy storage charging pile during peak and valley electricity price periods. For example, by clustering time series data (such as energy storage power time series and discharge power time series) during peak and valley periods, similar response patterns in different time periods can be discovered.

[0056] Furthermore, this embodiment of the invention generates dynamic energy allocation instructions for the energy storage charging pile during peak-valley electricity price periods based on the energy dispatch response characteristics and the energy supply-demand balance state. This transforms abstract dispatch rules and supply-demand matching relationships into quantifiable execution parameters, providing clear numerical basis for the accurate allocation of energy resources during peak-valley periods. It also improves the efficiency of judging the degree of adaptation between the operating status of the energy storage system and external conditions, thereby optimizing the dynamic adjustment logic of energy management for energy storage charging piles based on peak-valley electricity price adaptive dispatch.

[0057] The dynamic energy allocation instruction refers to a set of structured execution instructions generated based on the energy dispatch response characteristics and the energy supply and demand balance, used to precisely regulate the energy absorption, storage, and release behavior of energy storage charging piles during peak and off-peak electricity price periods.

[0058] As an embodiment of the present invention, generating a dynamic energy allocation instruction for the energy storage charging pile during the peak-valley electricity price period based on the energy dispatch response characteristics and the energy supply-demand balance state includes: Real-time capture of the electricity price time period signal of the energy storage charging pile; By combining the electricity price period signal, the energy dispatch response characteristics, and the energy supply and demand balance state, the current energy allocation decision state of the energy storage charging pile is identified; Obtain the time-of-use electricity price prediction data corresponding to the energy storage charging pile, so as to construct the future electricity price curve corresponding to the energy storage charging pile; Based on the current energy allocation decision state and the future electricity price curve, the phased energy management objectives of the energy storage charging pile are analyzed. Based on the energy dispatch response characteristics and the phased energy management objectives, the current dispatch mode of the energy storage charging pile is determined, and a set of strategy parameters corresponding to the current dispatch mode is generated. Based on the strategy parameter set, the adaptive control parameter sequence of the energy storage charging pile in multiple future time periods is calculated; Based on the adaptive control parameter sequence, a dynamic energy allocation instruction for the energy storage charging pile is generated during the peak-valley electricity price period.

[0059] The electricity price period signal refers to a real-time signal used to identify the current electricity price period (valley / flat / peak) of the energy storage charging pile, including the period type (such as "valley period" and "peak period"), the start and end time of the period, and the current electricity price; the current energy allocation decision status refers to the current energy allocation priority status of the energy storage charging pile determined by combining the electricity price period signal, energy dispatch response characteristics (such as the electricity price fluctuation-energy storage response pattern), and energy supply and demand balance status (such as the supply and demand difference rate), including priority energy storage, priority discharge, priority energy replenishment, and balanced allocation; The time-of-use electricity price forecast data refers to a dataset that predicts electricity prices for different time periods (usually in 1-hour increments) within the next 24-72 hours based on historical electricity price data, electricity market supply and demand forecasts, and policy adjustment information. It includes information such as the forecast period, forecast price, and forecast confidence level. The future electricity price curve refers to a continuous curve plotted using Python's matplotlib library, drawing the time-of-use electricity price forecast data along a time dimension (horizontal axis, unit: hour) and a price dimension (vertical axis, unit: yuan / kWh). This curve visually represents the fluctuations in electricity prices over future periods. The trends (e.g., peak electricity price periods, valley periods, and stable periods); the phased energy management objectives refer to the core energy management objectives for multiple future time periods (e.g., the next 6 hours, 12 hours) based on the current energy allocation decision-making status and future electricity price curves, including energy storage capacity targets during valley periods, discharge satisfaction rate targets during peak periods, and energy consumption control targets during stable periods; the current scheduling mode refers to the current core scheduling priority mode for energy storage charging piles determined by combining energy scheduling response characteristics (e.g., load fluctuation-power adjustment patterns) and phased energy management objectives, including economic priority mode, stability priority mode, and demand satisfaction priority mode; the strategy parameter set refers to the set of core parameters set to achieve phased energy management objectives corresponding to the current scheduling mode, including energy storage parameters, discharge parameters, and replenishment parameters; the adaptive control parameter sequence refers to a set of dynamically adjustable control parameters calculated based on the strategy parameter set for multiple future time periods (e.g., 1 hour / segment), which are adaptively optimized with time and operating conditions, including time period identifiers, charging / discharging power, energy storage capacity thresholds, and replenishment trigger conditions.

[0060] Optionally, the current energy allocation decision state of the energy storage charging pile can be identified using the fuzzy comprehensive evaluation method; the phased energy management target of the energy storage charging pile can be analyzed using the target decomposition algorithm; and the adaptive control parameter sequence of the energy storage charging pile in multiple future time periods can be calculated using the model predictive control (MPC) algorithm.

[0061] To gain a more intuitive understanding of the dynamic energy allocation command generation process, please refer to [link / reference]. Figure 2The diagram shown is a flowchart of time-of-use electricity price calculation for an energy management method for energy storage charging piles based on adaptive scheduling of peak and valley electricity prices, provided by an embodiment of the present invention. The diagram presents the complete logic from initializing particle swarm parameters, through demand-side response, multi-entity revenue constraints, to particle fitness calculation and iterative updates. It simulates the optimization process of energy allocation decision through particle swarm algorithm, assisting in the analysis of the current energy allocation decision state, constructing the future electricity price curve, and generating adaptive control parameter sequences, among other key steps. It should be noted that this flowchart focuses on the algorithm logic for generating dynamic energy allocation instructions during peak and valley electricity price periods. In actual applications, it can be adapted and adjusted according to the scenario, and the specific execution form of the process is not limited.

[0062] S5. Monitor the battery health status and power supply stability indicators of the energy storage charging pile in real time, so as to set the scheduling constraints of the energy storage charging pile, and create a visual energy flow interface of the energy storage charging pile according to the scheduling constraints and the energy supply and demand balance status.

[0063] This invention improves the safety and reliability of peak-valley electricity price adaptive scheduling by real-time monitoring of the battery health status and power supply stability indicators of the energy storage charging pile, thereby setting scheduling constraints for the energy storage charging pile. It also provides key data references for subsequent optimization of energy storage strategies and construction of dynamic adjustment logic, enhancing the risk control capability of energy management of energy storage charging piles based on peak-valley electricity price adaptive scheduling in complex operating scenarios.

[0064] The battery health status refers to the degree to which the performance of the energy storage battery (such as lithium battery or vanadium redox flow battery) used in the energy storage charging pile is maintained relative to its brand-new state. The battery health status is determined by capacity health, efficiency health, and safety health. For example, a lithium battery energy storage charging pile with a rated capacity of 200kWh and a charge / discharge efficiency of 92% when brand-new, after two years of operation, real-time monitoring shows a current actual capacity of 170kWh and a charge / discharge efficiency of 88%, with no safety hazards such as leakage or bulging. Therefore, its battery health status is: capacity health 85% (170 / 200×100%), efficiency health 95.6% (88% / 92%×100%), and safety health 100%, comprehensively judged as "good health status," capable of maintaining normal dispatch intensity. The power supply stability index refers to the measurement of the power supply quality of the energy storage charging pile connected to the grid and its relationship with the grid. The set of parameters for output stability reflects the reliability of power supply on the grid side and the consistency of power output on the charging pile side. These parameters include grid-side indicators (such as voltage fluctuation, frequency fluctuation, and harmonic distortion rate) and equipment-side indicators (such as output power deviation and current fluctuation amplitude). The grid-side indicators can be collected in real time using a power quality analyzer (such as FLUKE435-II); the equipment-side indicators can be collected using current and voltage sensors at the charging pile's output end. The scheduling constraints refer to a set of rules with clearly defined numerical ranges or prohibited / permitted behaviors, set based on battery health status and power supply stability indicators, to ensure that the energy storage charging pile does not exceed the equipment safety boundary and grid stability boundary during peak-valley electricity price scheduling. These rules include equipment constraints (such as energy storage capacity, power change rate, and depth of discharge) and grid constraints (such as power upper limit, replenishment threshold, and fluctuation control).

[0065] As an embodiment of the present invention, the real-time monitoring of the battery health status and power supply stability indicators of the energy storage charging pile to set scheduling constraints for the energy storage charging pile includes: The geographical location information and grid connection voltage level of the energy storage charging pile are obtained, and the historical operation data patterns and user group characteristics of the energy storage charging pile are parsed out. The application scenario type of the energy storage charging pile is identified by the geographical location information, the grid connection voltage level, the historical operation data mode, and the user group characteristics. Extract the battery body characteristics, grid connection characteristics, and user service characteristics of the energy storage charging pile to determine the correlation index of the energy storage charging pile; Based on the application scenario type and the correlation index, initial constraint conditions are matched from a predefined constraint strategy library; Based on the battery health status and the power supply stability index, the dynamic relaxation factor and adaptive adjustment direction of the initial constraint conditions are defined. Based on the dynamic relaxation factor and the adaptive adjustment direction, the scheduling constraints of the energy storage charging pile are set.

[0066] The geographical location information refers to spatial data used to identify the physical installation location of the energy storage charging pile, including latitude and longitude coordinates, administrative division (province / city / district / street), and surrounding environmental attributes (such as residential area / commercial area / highway service area); the grid connection voltage level refers to the voltage standard level of the energy storage charging pile when it is connected to the power grid, obtained by querying the grid connection registration information of the charging pile; the historical operation data mode refers to the regular operation characteristics extracted by data mining based on the operation data of the energy storage charging pile over the past 3-12 months (such as charging and discharging power, energy storage capacity, and user charging time distribution), including power fluctuation mode, time-of-day load mode, and energy storage-discharging cycle mode; the user group The "user attributes" refer to user attribute features extracted from charging behavior data (such as charging duration, charging power demand, charging frequency, and vehicle type) of the energy storage charging pile service recipients, including demand concentration, power preference, and time period preference. The "battery attributes" refer to the inherent attributes and performance parameters of the energy storage batteries used in the charging pile, reflecting the battery's safe operating boundaries and scheduling adaptability, including core parameters such as battery type (lithium battery / vanadium redox flow battery), rated capacity / power, cycle life, and safety thresholds (such as temperature / voltage limits). The "grid access attributes" refer to user attribute features extracted from grid operation data (such as voltage fluctuation, frequency fluctuation, power limit, and short-circuit capacity) at the energy storage charging pile's grid connection point. The power grid attribute characteristics reflect the grid's ability to schedule and constrain charging piles, including core indicators such as voltage stability, power carrying capacity margin, and harmonic tolerance. The user service characteristics refer to features extracted based on user service objectives and service quality requirements of energy storage charging piles, reflecting the charging piles' responsiveness to user needs, including core indicators such as demand fulfillment rate targets, response time requirements, and service priority. The correlation index refers to a comprehensive set of indicators reflecting the relationship between "equipment-grid-user" based on the battery characteristics, grid connection characteristics, and user service characteristics of the energy storage charging pile, including equipment constraint correlation index, grid adaptability correlation index, and user demand correlation index. The device constraint correlation degree is the absolute value of the Pearson correlation coefficient between the battery safety threshold and the scheduling parameters; the grid adaptation correlation degree is the absolute value of the correlation coefficient between the grid operation index and the scheduling parameters; the user demand correlation degree is the absolute value of the correlation coefficient between the service target and the scheduling parameters; the constraint strategy library refers to a predefined structured database containing various scheduling constraint templates based on the matching relationship of "application scenario type - associated object characteristics", storing the mapping rules of "scenario - feature - constraint", covering three types of templates: device constraints (such as energy storage capacity limit, power change rate), grid constraints (such as voltage fluctuation limit, power limit), and user constraints (such as demand satisfaction rate threshold);The initial constraints refer to the basic constraint templates obtained from the constraint strategy library based on application scenario type and correlation index matching, without dynamic adjustment. These templates include specific constraint parameter values ​​(e.g., energy storage capacity limit 80%, power limit 50kW) and constraint application scope (e.g., effective only during peak hours). They serve as the baseline template for subsequent dynamic adjustment of constraints. For example, the initial constraints for a peak discharge scenario in a commercial area might be: "Equipment constraints: energy storage capacity limit 80% (corresponding to 160kWh), charging power change rate ≤3kW / min, battery temperature ≤42℃; grid constraints: voltage fluctuation limit ±3%, discharge power limit 60kW; user constraints: demand satisfaction rate ≥95%, high-priority user response time ≤5 minutes." These conditions do not consider real-time battery health status and power supply stability and require further dynamic adjustment. The dynamic relaxation factor refers to... Real-time battery health status and power supply stability indicators are dimensionless coefficients (range 0.8-1.2) used to adjust the tightness of initial constraints. A coefficient > 1 indicates relaxed constraints, and a coefficient < 1 indicates tightened constraints. These are core adjustment parameters for achieving dynamic adaptation of constraints and can be determined using a weighted summation algorithm. Specifically, the relaxation factor corresponding to battery health status is 1 + 0.2 × (SOH - 80%) / 20% (coefficient ≥ 1 when SOH ≥ 80%, coefficient < 1 when SOH < 80%), and the relaxation factor corresponding to grid stability is 1 + 0.2 × (5% - voltage fluctuation amplitude) / 5% (coefficient ≥ 1 when voltage fluctuation ≤ 5%, coefficient < 1 when fluctuation > 5%). The adaptive adjustment direction refers to determining the adjustment trend (tightening / relaxing / maintaining) of the initial constraints based on the real-time battery health status and power supply stability indicators.

[0067] Optionally, time series pattern mining algorithms, such as the matrix contour algorithm of the STUMPY library, can be used to parse the historical operation data patterns of the energy storage charging pile; a constraint strategy library containing "scenario-feature-constraint" mapping rules can be constructed using a rule engine (such as Drools) based on historical scheduling data (more than 3 years) and expert experience; or a fuzzy matching algorithm, such as the Levenshtein distance algorithm, can be used to match the initial constraint conditions from the predefined constraint strategy library.

[0068] Furthermore, this embodiment of the invention creates a visualized energy flow interface for the energy storage charging pile based on the scheduling constraints and the energy supply and demand balance state. This enables global visualization of the energy flow situation under peak-valley electricity price adaptive scheduling scenarios, reduces the information gap between scheduling constraints and supply and demand status in energy storage management, and improves the accuracy and timeliness of energy management decisions. The visualized energy flow interface refers to an integrated operation interface that dynamically reflects the entire process of energy absorption-storage-release of the energy storage charging pile during peak and valley periods, constructed through visualization technologies (such as real-time charts and dynamic flowcharts).

[0069] As an embodiment of the present invention, the step of creating a visual energy flow interface for the energy storage charging pile based on the scheduling constraints and the energy supply and demand balance state includes: Real-time collection of dynamic data corresponding to the scheduling constraints and the energy supply and demand balance state, and extraction of dynamic energy flow indicators of the energy storage charging pile from the dynamic data; Based on the dynamic energy flow index, a visualization element for the energy storage charging pile is generated; Based on the energy scheduling relationship between the scheduling constraints and the energy supply and demand balance state, a multi-view monitoring dashboard integrating the visualization elements is constructed. Configure the information visualization range and data interaction permissions of the multi-view monitoring dashboard to generate role-based views corresponding to the visualization elements; By combining the multi-view monitoring dashboard and the role-based view, a visual energy flow interface for the energy storage charging pile is created.

[0070] The dynamic data refers to a structured dataset that dynamically updates over time, reflecting real-time changes in the scheduling constraints and energy supply-demand balance of energy storage charging piles. This includes dynamic constraint parameters and dynamic supply-demand data. The dynamic constraint parameters include parameters such as real-time energy storage capacity limits, current power limits, and real-time battery temperature. The dynamic supply-demand data includes data such as the current supply-demand difference rate, peak-hour demand satisfaction rate, and real-time charging and discharging power. The dynamic energy flow indicators are core parameters extracted from the dynamic data that quantify the dynamic characteristics of the entire energy "absorption-storage-release" process of energy storage charging piles. These include flow indicators (such as grid input power, energy storage battery charging power, vehicle discharge power, and power fed back to the grid). The system includes status indicators (such as real-time SOC of the energy storage battery, energy conversion efficiency, and equipment health (SOH)) and boundary indicators (such as current power constraint limits, SOC safety boundaries, and operating modes). The visualization elements refer to graphical elements generated using visualization technology based on dynamic energy flow indicators, which can intuitively present the characteristics of energy flow. These are the basic units constituting the multi-view monitoring dashboard, including flow direction animation elements (using dynamic arrows (thickness representing power, color representing efficiency, and direction representing energy flow) to intuitively display the real-time path of electrical energy from the grid → energy storage → vehicle or grid) and status indicator elements (using dashboards, progress bars, or ring charts to display SOC and SOH, and using numerical cards to display grid voltage and frequency in real time). Constraint boundary elements (dynamically marked on power curves and SOC history graphs, using background color bands, warning lines, or draggable boundaries to indicate safe operating areas defined by scheduling constraints); the energy scheduling relationship refers to the logical connection between scheduling constraints and the energy supply and demand balance state, reflecting the two-way logic of "how constraints limit supply and demand, and how supply and demand adjust constraints in turn," including "the constraint's limiting relationship on supply and demand" and "the supply and demand's adaptation relationship to constraints"; the multi-view monitoring dashboard refers to a structured data display carrier that integrates visualization elements according to "data dimensions" and "business logic" based on energy scheduling relationships, used to centrally present the energy flow status, and is an aggregation platform for visualization elements. The platform includes a main dashboard and a detailed analysis dashboard. The main dashboard uses the primary system wiring diagram as its base map and integrates dynamic energy flow diagrams and energy storage status instruments to globally display energy sources, destinations, intensity, and status in real time. The detailed analysis dashboard provides interactive power band curves and historical energy flow queries, and supports analysis of the interaction between energy flow trends and constraints over time. The information visualization range refers to the display boundary determined after filtering and integrating the visualization elements and data in the multi-view monitoring dashboard according to the core needs of different roles. For example, the information visualization range of a charging pile is set as follows: Administrator: can see the full view (equipment + grid + user) and all elements (including peak and valley revenue statistics charts).Maintenance personnel: Only the voltage fluctuation curves in the equipment status view (including alarm elements) and the grid adaptation view are visible; charging cost data in the user demand view is hidden. Users: Only the charging progress bar, estimated charging time, and available charging station icons in the user demand view are visible. The data interaction permissions refer to granting different roles operational permissions on the data and elements in the multi-view monitoring dashboard based on their responsibilities. The role-based view refers to a personalized view customized for different roles, containing "exclusive visible elements" and "exclusive operational functions," based on the information visualization scope and data interaction permissions. For example, a charging station... The role-based views of the charging piles include: Administrator view: Displays all three views, with a top menu bar containing "Policy Editing" and "Permission Management" buttons. Clicking "Policy Editing" allows dragging a slider to adjust the energy storage capacity limit, and the bottom of the interface displays peak and off-peak revenue statistics charts; Maintenance personnel view: Displays only the equipment and grid views, with a "Alarm Handling" entry in the left toolbar. Clicking the alarm icon brings up a "Fault Handling Guide" window, but there is no revenue statistics module; User view: Simplified into three modules: "Charging Progress + Cost + Charging Pile Location," with a "Reserve Charging" button at the bottom, allowing users to reserve charging during off-peak hours.

[0071] Optionally, the visualization elements of the energy storage charging pile can be developed using ECharts (a front-end visualization library); the energy scheduling relationship between the scheduling constraints and the energy supply and demand balance state can be determined using causal graph analysis; and the role-based view corresponding to the visualization elements can be generated based on a front-end framework (Vue.js).

[0072] S6. Based on the scheduling strategy guidance set, an adaptive energy management scheme for the energy storage charging pile is generated through the dynamic energy allocation command and the visualized energy flow interface.

[0073] This invention, through the scheduling strategy guidance set, the dynamic energy allocation instructions, and the visualized energy flow interface, generates an adaptive energy management scheme for the energy storage charging pile. This avoids the problem of low energy utilization efficiency caused by fixed charging and discharging priorities and energy allocation ratios. At the same time, by matching the real-time energy management needs of the energy storage charging pile under scenarios such as peak and off-peak electricity price periods and supply and demand balance fluctuations through the visualized energy flow interface, it can reduce the risk of equipment operation imbalance and energy scheduling lag caused by changes in the external environment, and achieve a precise balance between the economy and stability of energy management under different dynamic scenarios.

[0074] The adaptive energy management scheme refers to an intelligent energy management scheme based on the scheduling strategy guide set of energy storage charging piles. Through the synergy of dynamic energy allocation instructions and a visualized energy flow interface, it can adapt in real time to dynamic changes in the external environment (such as grid load fluctuations, surges in user demand, and electricity price adjustments) and the status of internal equipment (such as battery health status and changes in constraints). This achieves dynamic optimization of the entire energy process. For example, 8:30 a.m. is a peak period for user charging demand. The visualized energy flow interface monitors that "actual demand has increased by 20% compared to the forecast, the demand satisfaction rate has dropped to 83% (below the 85% threshold), and grid voltage fluctuations have risen to ±4% (approaching the 5% critical value)," and feeds the data back to the scheduling system in real time. The strategy set is immediately fine-tuned, such as adding a "stability supplement strategy" while maintaining the core objective of "demand satisfaction orientation". The dynamic energy allocation command is updated synchronously by reducing the upper limit of discharge power from 45kW to 42kW (to avoid aggravating grid fluctuations), activating the grid energy supplementation backup channel (the upper limit of energy supplementation power is 5kW / min, which is only activated when the demand gap exceeds 10%), and relaxing the energy storage discharge depth from 80% to 85% (releasing an additional 7.5kWh of capacity). The visualized energy flow interface simultaneously highlights "demand satisfaction rate 83% (warning)" in red and pops up a prompt "grid energy supplementation backup has been activated, current demand gap 9%" so that managers can intuitively understand the energy adjustment logic.

[0075] Compared to the problems described in the background art, the embodiments of the present invention collect real-time grid interaction data, user charging request data, and energy storage system status data of energy storage charging piles during different electricity price periods. This allows for the analysis of the energy demand distribution characteristics of the energy storage charging piles under different electricity price periods, forming a multi-dimensional and interconnected energy demand perception system. This provides a structured framework for real-time capture of the adaptation relationship between energy supply and demand changes and external dynamic environments (sudden increases in grid load, concentrated bursts of user charging demand) under different electricity price periods. Furthermore, the embodiments of the present invention calculate the energy utilization efficiency of the energy storage charging piles based on the optimal charging time window and the power change rate, deeply integrating the scheduling advantages of both to construct a more suitable system. The peak-valley electricity price characteristic energy utilization system creates favorable conditions for the precise allocation of subsequent energy storage resources and grid interaction, improving the stability of energy conversion and utilization of energy storage charging piles during adaptive scheduling. This embodiment of the invention constructs a scheduling strategy guidance set for the energy storage charging piles based on the energy supply and demand balance state, avoiding energy storage redundancy waste and user demand gaps caused by mismatches in the "grid load-user demand-energy storage status" dimension, reducing unnecessary charging and discharging adjustments that damage the lifespan of energy storage batteries, and improving the operational stability and overall benefits of energy management for energy storage charging piles based on peak-valley electricity price adaptive scheduling. Furthermore, this embodiment of the invention, based on the energy scheduling response characteristics and the energy supply and demand balance state... This invention generates dynamic energy allocation instructions for the energy storage charging piles during peak-valley electricity price periods. This transforms abstract scheduling rules and supply-demand matching relationships into quantifiable execution parameters, providing clear numerical basis for precise allocation of energy resources during peak-valley periods. It improves the efficiency of judging the adaptability of the energy storage system's operating status to external conditions, thereby optimizing the dynamic adjustment logic of energy management for energy storage charging piles based on peak-valley electricity price adaptive scheduling. Furthermore, by real-time monitoring of the battery health status and power supply stability indicators of the energy storage charging piles to set scheduling constraints, this invention enhances the safety and reliability of peak-valley electricity price adaptive scheduling. It also provides key data for subsequent optimization of energy storage strategies and construction of dynamic adjustment logic. According to reference, this invention enhances the risk control capabilities of energy management for energy storage charging piles based on peak-valley electricity price adaptive scheduling in complex operating scenarios. Furthermore, this embodiment of the invention creates a visualized energy flow interface for the energy storage charging pile based on the scheduling constraints and the energy supply and demand balance state. This enables global visualization of the energy flow situation under peak-valley electricity price adaptive scheduling scenarios, reduces the information gap between scheduling constraints and supply and demand status in energy storage management, and improves the accuracy and timeliness of energy management decisions. The visualized energy flow interface refers to an integrated operation interface that dynamically reflects the entire process of energy absorption-storage-release of the energy storage charging pile during peak and valley periods, constructed through visualization technologies (such as real-time charts and dynamic flowcharts).Finally, this embodiment of the invention generates an adaptive energy management scheme for the energy storage charging pile based on the scheduling strategy guidance set, the dynamic energy allocation instructions, and the visualized energy flow interface. This avoids the problem of low energy utilization efficiency caused by fixed charging and discharging priorities and energy allocation ratios. Simultaneously, by matching the real-time energy management needs of the energy storage charging pile under scenarios such as peak-valley electricity price periods and supply-demand balance fluctuations through the visualized energy flow interface, it can reduce the risk of equipment operational imbalance and energy scheduling lag caused by changes in the external environment, achieving a precise balance between the economy and stability of energy management under different dynamic scenarios. Therefore, the energy management method for energy storage charging piles based on peak-valley electricity price adaptive scheduling provided by this embodiment of the invention can improve the energy utilization efficiency of energy storage charging piles.

[0076] like Figure 3 The diagram shown is a functional block diagram of an energy management system for energy storage charging piles based on adaptive scheduling of peak and valley electricity prices according to the present invention.

[0077] The energy management system 200 for energy storage charging piles based on adaptive peak-valley electricity pricing described in this invention can be installed in an electronic device. Depending on the functions implemented, the energy management system may include a feature analysis module 201, an efficiency calculation module 202, a strategy guidance module 203, an energy allocation module 204, a visualization flow module 205, and a scheme output module 206. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0078] In this embodiment of the invention, the functions of each module / unit are as follows: The feature analysis module 201 is used to collect grid interaction data, user charging request data and energy storage system status data of the energy storage charging pile in real time during different electricity price periods, so as to analyze the energy demand distribution characteristics of the energy storage charging pile under the different electricity price periods. The efficiency calculation module 202 is used to determine the optimal charging time window and power change rate of the energy storage charging pile according to the energy demand distribution characteristics, and to calculate the energy utilization efficiency of the energy storage charging pile based on the optimal charging time window and the power change rate.

[0079] The strategy guidance module 203 is used to identify the energy supply and demand balance state of the energy storage charging pile based on the energy utilization efficiency, and to construct a scheduling strategy guidance set for the energy storage charging pile according to the energy supply and demand balance state. The energy allocation module 204 is used to extract the energy dispatch response characteristics of the energy storage charging pile during peak and valley electricity price periods, and generate dynamic energy allocation instructions for the energy storage charging pile during peak and valley electricity price periods based on the energy dispatch response characteristics and the energy supply and demand balance state. The visualization flow module 205 is used to monitor the battery health status and power supply stability indicators of the energy storage charging pile in real time, so as to set the scheduling constraints of the energy storage charging pile, and create a visualization energy flow interface of the energy storage charging pile according to the scheduling constraints and the energy supply and demand balance status. The scheme output module 206 is used to generate an adaptive energy management scheme for the energy storage charging pile based on the scheduling strategy guidance set, through the dynamic energy allocation instructions and the visualized energy flow interface.

[0080] In detail, the modules in the energy management system 200 for energy storage charging piles based on adaptive peak-valley electricity pricing described in this embodiment of the invention adopt the same approach as described above. Figure 1 The method used is the same as the energy management method for energy storage charging piles based on peak-valley electricity price adaptive scheduling described in the article, and can produce the same technical effect, so it will not be elaborated here.

[0081] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0082] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An energy management method for energy storage charging piles based on adaptive scheduling of peak-valley electricity prices, characterized in that, The method includes: Real-time data collection of grid interaction data, user charging request data, and energy storage system status data of energy storage charging piles during different electricity price periods is used to analyze the energy demand distribution characteristics of the energy storage charging piles under different electricity price periods. Based on the energy demand distribution characteristics, the optimal charging time window and power change rate of the energy storage charging pile are determined, and the energy utilization efficiency of the energy storage charging pile is calculated based on the optimal charging time window and the power change rate. Based on the energy utilization efficiency, the energy supply and demand balance state of the energy storage charging pile is identified, and a scheduling strategy guidance set for the energy storage charging pile is constructed according to the energy supply and demand balance state. Extract the energy dispatch response characteristics of the energy storage charging pile during peak and valley electricity price periods, and generate dynamic energy allocation instructions for the energy storage charging pile during peak and valley electricity price periods based on the energy dispatch response characteristics and the energy supply and demand balance state. The battery health status and power supply stability indicators of the energy storage charging pile are monitored in real time to set the scheduling constraints of the energy storage charging pile. Based on the scheduling constraints and the energy supply and demand balance, a visual energy flow interface for the energy storage charging pile is created. Based on the scheduling strategy guidance set, an adaptive energy management scheme for the energy storage charging pile is generated through the dynamic energy allocation instructions and the visualized energy flow interface.

2. The energy management method for energy storage charging piles based on adaptive scheduling of peak-valley electricity prices as described in claim 1, characterized in that, The real-time acquisition of grid interaction data, user charging request data, and energy storage system status data of the energy storage charging piles during different electricity price periods is used to analyze the energy demand distribution characteristics of the energy storage charging piles under different electricity price periods, including: Voltage harmonic analysis is performed on the power grid interaction data to obtain a power grid load fluctuation dataset; Extract the time-of-use grid load characteristics from the grid load fluctuation dataset; Based on the time-sharing grid load characteristics, the grid interaction power demand index of the energy storage charging pile is extracted; Spatiotemporal clustering analysis was performed on the user charging request data to obtain the user charging demand clustering results; Based on the clustering results of the user charging demand, a heat map of the spatiotemporal distribution of the charging demand of the energy storage charging pile is generated. Based on the spatiotemporal distribution heatmap of charging demand, analyze the user charging behavior preferences corresponding to the energy storage charging pile; Extract the vehicle's remaining SOC and the user's reservation time corresponding to the energy storage charging pile, and combine them with the user's charging behavior preferences to extract the charging request priority index from the user's charging request data; Based on the energy storage system status data, analyze the health status degradation trend of the energy storage charging pile; The current capacity and power regulation capability of the energy storage charging pile are assessed by the health status decay trend. Based on the current capacity and the power regulation capability, determine the upper limit of the energy storage capacity of the energy storage charging pile; By integrating the power grid interaction power demand index, the charging request priority index, and the energy storage upper limit index, an energy management feature matrix for the energy storage charging pile is constructed. Based on the energy management feature matrix, the energy demand distribution characteristics of the energy storage charging pile under different electricity price periods are generated.

3. The energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing as described in claim 1, characterized in that, The step of constructing a scheduling strategy guide set for the energy storage charging pile based on the energy supply and demand balance state includes: Extract the state feature parameters from the energy supply and demand balance state, and determine the multi-scenario adaptation type corresponding to the state feature parameters; Perform association matching processing between the state feature parameters and the multi-scenario adaptation types to generate a scenario-state association dataset; Based on the scenario-state association dataset, the strategy allocation type of the energy storage charging pile is determined; The dynamic constraint parameters of the energy storage charging pile are identified, and the strategy adjustment elasticity coefficient of the energy storage charging pile is calculated using the following formula: ； in, This represents the elasticity coefficient for strategy adjustment. This indicates the slackness of the grid constraints corresponding to the energy storage charging pile. This indicates the current health status of the batteries in the energy storage charging station. This indicates the current battery health reference value. Indicates the penalty coefficient. Indicates the current charging power requirement. Indicates the currently available power capacity; By combining the strategy allocation type and the strategy adjustment elasticity coefficient, a scheduling strategy association graph of the energy storage charging pile is constructed. Extract the key strategy nodes from the scheduling strategy association graph; Based on the key strategy nodes, a scheduling strategy guide set for the energy storage charging pile is generated.

4. The energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing as described in claim 3, characterized in that, The step of constructing a scheduling strategy association graph for the energy storage charging pile by combining the strategy allocation type and the strategy adjustment elasticity coefficient includes: Match the policy action corresponding to the policy assignment type; Based on the strategy allocation type and the strategy action, define the graph decision node of the energy storage charging pile; By utilizing the transformation relationship between the strategy actions and the strategy adjustment elasticity coefficient, a directed connection relationship is established between the decision nodes of the graph. Based on the graph decision nodes and the directed connection relationships, a scheduling strategy association graph for the energy storage charging pile is constructed.

5. The energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing as described in claim 1, characterized in that, The step of generating dynamic energy allocation instructions for the energy storage charging pile during peak-valley electricity price periods based on the energy dispatch response characteristics and the energy supply-demand balance state includes: Real-time capture of the electricity price time period signal of the energy storage charging pile; By combining the electricity price period signal, the energy dispatch response characteristics, and the energy supply and demand balance state, the current energy allocation decision state of the energy storage charging pile is identified; Obtain the time-of-use electricity price prediction data corresponding to the energy storage charging pile, so as to construct the future electricity price curve corresponding to the energy storage charging pile; Based on the current energy allocation decision state and the future electricity price curve, the phased energy management objectives of the energy storage charging pile are analyzed. Based on the energy dispatch response characteristics and the phased energy management objectives, the current dispatch mode of the energy storage charging pile is determined, and a set of strategy parameters corresponding to the current dispatch mode is generated. Based on the strategy parameter set, the adaptive control parameter sequence of the energy storage charging pile in multiple future time periods is calculated; Based on the adaptive control parameter sequence, a dynamic energy allocation instruction for the energy storage charging pile is generated during the peak-valley electricity price period.

6. The energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing as described in claim 1, characterized in that, The step of creating a visual energy flow interface for the energy storage charging pile based on the scheduling constraints and the energy supply-demand balance state includes: The system collects dynamic data corresponding to the scheduling constraints and the energy supply and demand balance in real time, and extracts the dynamic energy flow index of the energy storage charging pile from the dynamic data. Based on the dynamic energy flow index, a visualization element for the energy storage charging pile is generated; Based on the energy scheduling relationship between the scheduling constraints and the energy supply and demand balance state, a multi-view monitoring dashboard integrating the visualization elements is constructed. Configure the information visualization range and data interaction permissions of the multi-view monitoring dashboard to generate role-based views corresponding to the visualization elements; By combining the multi-view monitoring dashboard and the role-based view, a visual energy flow interface for the energy storage charging pile is created.

7. The energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing as described in claim 1, characterized in that, The real-time monitoring of the battery health status and power supply stability indicators of the energy storage charging pile, in order to set the scheduling constraints of the energy storage charging pile, includes: The geographical location information and grid connection voltage level of the energy storage charging pile are obtained, and the historical operation data patterns and user group characteristics of the energy storage charging pile are parsed out. The application scenario type of the energy storage charging pile is identified by the geographical location information, the grid connection voltage level, the historical operation data mode, and the user group characteristics. Extract the battery body characteristics, grid connection characteristics, and user service characteristics of the energy storage charging pile to determine the correlation index of the energy storage charging pile; Based on the application scenario type and the correlation index, initial constraint conditions are matched from a predefined constraint strategy library; Based on the battery health status and the power supply stability index, the dynamic relaxation factor and adaptive adjustment direction of the initial constraint conditions are defined. Based on the dynamic relaxation factor and the adaptive adjustment direction, the scheduling constraints of the energy storage charging pile are set.

8. The energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing as described in claim 1, characterized in that, The step of determining the optimal charging time window and power change rate of the energy storage charging pile based on the energy demand distribution characteristics includes: Based on the energy demand distribution characteristics, the time-of-use energy demand data and equipment status time-series data of the energy storage charging pile are obtained; Based on the time-of-use energy demand data, the demand fluctuation characteristics of the energy storage charging piles during different electricity price periods are analyzed. Using the aforementioned demand fluctuation characteristics, the value coefficient for each of the aforementioned electricity price periods is calculated to form a set of period value coefficients; The optimal charging time window for the energy storage charging pile is determined based on the set of time period value coefficients. Based on the device status time-series data, the charging and discharging performance degradation curve of the energy storage charging pile is fitted; Identify the critical decay point in the charge and discharge performance decay curve to define the power regulation safety boundary of the energy storage charging pile; The power change constraint conditions of the energy storage charging pile are established by using the power regulation safety boundary. By combining the set of time period value coefficients and the power change constraints, the power change rate of the energy storage charging pile is determined.

9. The energy management method for energy storage charging piles based on adaptive peak-valley electricity pricing as described in claim 1, characterized in that, The calculation of the energy utilization efficiency of the energy storage charging pile based on the optimal charging time window and the power change rate includes: Based on the optimal charging time window, establish the power curve corresponding to the power change rate; The total absorbed electrical energy of the energy storage charging pile is calculated using the optimal charging time window and the power curve. Within the optimal charging time window, the actual output energy of the energy storage charging pile is measured; The energy utilization efficiency of the energy storage charging pile is calculated based on the total absorbed electrical energy and the actual output energy.

10. The energy management method for energy storage charging piles based on adaptive peak-valley electricity price scheduling as described in claim 1, characterized in that, The step of identifying the energy supply and demand balance state of the energy storage charging pile based on the energy utilization efficiency includes: Obtain the rated energy utilization efficiency of the energy storage charging pile under rated operating conditions; The stable operating range of the energy storage charging pile is defined based on the rated energy utilization efficiency. Real-time monitoring and recording of the real-time energy utilization efficiency sequence corresponding to the energy utilization efficiency; The mean efficiency, standard deviation efficiency, and efficiency decay gradient corresponding to the energy utilization efficiency are extracted from the real-time energy utilization efficiency sequence to form the efficiency state characteristics of the energy storage charging pile. Based on the stable operating range and the efficiency status characteristics, the energy supply and demand balance status of the energy storage charging pile is identified.

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