Charging facility and electric power spot transaction response method

Abstract

The invention provides a charging facility and electric power spot transaction response method, which comprises the steps of generating a dynamic response sensitivity matrix by collecting electric power spot price data and fluctuation indexes in combination with historical charging behavior characteristics of a charging facility, and constructing a charging load period transfer strategy table. And converting the strategy table into a power adjustment instruction set, issuing the power adjustment instruction set to the charging pile, calculating a power execution deviation and updating a user response elastic coefficient weight factor library. Charging load time period distribution can be optimized, the charging cost is reduced, the operation safety of a power grid is guaranteed, and the operation efficiency and economic benefits of charging facilities are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid and electric vehicle charging technology, and more particularly, to a method for responding to charging facilities and electricity spot transactions. Background Art

[0002] With the increasing popularity of electric vehicles, the demand for charging facilities continues to increase, and the rapid growth of charging loads has brought new challenges to grid operations. Most existing charging facilities use a fixed-power charging mode and lack the ability to interact with dynamic price signals in the electricity market. In the electricity spot market environment, electricity prices exhibit significant peak-to-valley fluctuations, and traditional charging facilities are unable to flexibly adjust charging power based on real-time price changes, resulting in high charging costs and failing to fully utilize the price advantages of the electricity spot market. In addition, the power adjustment of existing charging facilities mainly relies on local control strategies, lacking considerations for global optimization and user response flexibility, making it difficult to achieve effective transfer and optimized distribution of charging loads. In terms of grid security, existing charging facilities may have an adverse impact on the voltage stability and power flow distribution of the distribution network when connected on a large scale, and lack effective grid security verification and power adjustment mechanisms.

[0003] In the process of implementing the embodiments of the present invention, there are at least the following problems or defects in the existing technology: traditional charging facilities are unable to respond in real time to the dynamic price signals of the electricity spot market, resulting in high charging costs and the inability to achieve cost optimization; there is a lack of a global optimization strategy for the transfer of charging load time periods, and it is impossible to effectively utilize user response elasticity to flexibly adjust the charging power; when large-scale charging facilities are connected to the power grid, there is a lack of real-time monitoring and verification mechanisms for the security of the power grid, which may lead to increased risks in the operation of the power grid; the transmission and execution of power adjustment instructions of existing charging facilities lack a reliable security verification and compensation mechanism, and the safety and reliability of the charging process cannot be ensured. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for responding to charging facilities and electricity spot transactions, which has the advantages of reducing charging costs, optimizing charging load distribution, and improving power grid operation safety.

[0005] The present application provides a method for charging facilities to respond to electricity spot transactions, and the technical solution is as follows: based on the electricity spot trading platform, day-ahead price time series data and real-time price fluctuation indicators are collected to obtain a target electricity price feature set; based on the operation database of the charging facility cluster, historical charging behavior characteristics are analyzed to extract dispatchable capacity margin parameters and user response elasticity coefficient sets; according to the mapping relationship between the target electricity price feature set and the dispatchable capacity margin parameters, a dynamic response sensitivity matrix is ​​generated; based on the dynamic response sensitivity matrix and the user response elasticity coefficient set, a charging load period transfer strategy table is constructed; through the charging facility Internet of Things control protocol, the charging load period transfer strategy table is converted into a power adjustment instruction set; based on the power adjustment instruction set, a control command is issued to the target charging pile group to obtain instruction execution status feedback; based on the actual power output sequence collected by the charging pile power metering device, a power execution deviation indicator is calculated; based on the clearing data of the electricity trading settlement platform, actual response power records are obtained; based on the matching analysis results of the actual response power records and the day-ahead price time series data, the user response elasticity coefficient weight factor library is updated.

[0006] Furthermore, the present application also proposes that generating a dynamic response sensitivity matrix includes: calculating the peak-valley price difference characteristic value based on the day-ahead price time series data; determining the price fluctuation intensity coefficient based on the real-time price fluctuation index; extracting the upper limit value of the power adjustment allowed in each time period and the maximum adjustment rate threshold based on the dispatchable capacity margin parameter; establishing a functional relationship between the peak-valley price difference characteristic value, the price fluctuation intensity coefficient and the upper limit value of the power adjustment allowed through the price-capacity response model; calculating the quantitative value of the response intensity in each time period based on the functional relationship; and aggregating the quantitative values ​​of the response intensity of all time periods to generate a dynamic response sensitivity matrix.

[0007] Furthermore, the present application also proposes that constructing a charging load period transfer strategy table includes: sorting response intensity quantization values ​​based on a dynamic response sensitivity matrix, and screening a set of high response priority periods; parsing price sensitivity thresholds and charging delayable time windows based on a set of user response elasticity coefficients; constructing a load shift optimization objective function, including a power purchase cost minimization term and a user delay penalty term; configuring an optimization constraint set, including a total charging capacity conservation constraint, a single pile power safety boundary constraint, and a battery temperature rise safety constraint; processing the optimization objective function and the constraint set through a mixed integer programming solver, and outputting a charging power period allocation plan; and compiling a charging load period transfer strategy table based on the charging power period allocation plan.

[0008] Furthermore, the present application also proposes that the conversion power adjustment instruction set includes: parsing the target charging pile identification code, target power value and effective time point based on the charging load period transfer strategy table; generating a device executable binary instruction frame through an instruction compilation engine; creating an instruction digital signature based on an asymmetric encryption algorithm; transmitting the instruction frame and digital signature through the power line carrier main channel and the LoRa wireless backup channel; verifying the validity of the instruction digital signature and timestamp at the charging pile edge controller; and triggering the power module control signal based on the verification result.

[0009] Furthermore, the present application also proposes that the calculation of the power execution deviation index includes: collecting a second-level actual output power sampling sequence based on the built-in sensor of the charging pile; calculating the dynamic root mean square deviation value based on the target power value sequence and the actual power sampling sequence; obtaining the battery state of charge change rate based on the battery safety status monitoring data; when the dynamic root mean square deviation value exceeds the first safety threshold and the battery state of charge change rate is lower than the second safety threshold, activating the power compensation closed-loop control loop; and generating real-time power compensation amount through the PID controller.

[0010] Furthermore, the present application also proposes that updating the user response elasticity coefficient weight factor library includes: calculating the actual response profit value based on the real-time price fluctuation index and the actual response power record; extracting the predicted response profit value according to the load shift optimization objective function; calculating the deviation rate between the predicted and actual profits through the profit deviation analysis model; when the deviation rate exceeds the allowable deviation threshold, starting the weight factor iterative optimization process; adjusting the user response elasticity coefficient weight factor based on the gradient descent optimizer; and storing the updated weight factor in the parameter database.

[0011] Furthermore, the present application also proposes that extracting the dispatchable capacity margin parameters includes: obtaining the user's reservation start time, required power and expected end time based on the charging reservation system; matching the minimum safe charging rate threshold according to the battery chemical characteristics database; deriving the maximum delayable charging time through the time window calculation model; determining the power adjustable range for each time period based on the rated power parameters of the charging facility and the real-time load rate; integrating the maximum delayable charging time and the power adjustable range to generate the dispatchable capacity margin parameters.

[0012] Furthermore, the present application also proposes that it also includes a grid safety verification step: obtaining the node voltage safety boundary range based on the distribution network monitoring system; calculating the voltage offset caused by the charging power adjustment through the voltage sensitivity analysis model; triggering the power adjustment attenuation mechanism when the predicted voltage value exceeds the safety boundary range; using a piecewise linear attenuation algorithm to generate a safe power adjustment; and verifying the grid compatibility of the safe power adjustment through a power flow calculation tool.

[0013] Furthermore, the present application also proposes that after triggering the power module control signal, it includes: real-time monitoring of the charge state change rate based on the battery management system; when the charge state change rate exceeds the safety limit, switching to constant current charging mode; generating a battery safety event report and attaching a timestamp and charging pile positioning information; and synchronously reporting to the cluster monitoring platform through the 5G private network.

[0014] Furthermore, the present application also proposes that it also includes a transaction notarization step: constructing a notarization data packet based on the day-ahead price time series data, the actual response power record and the updated weight factor; generating a data integrity fingerprint through the Merkle tree algorithm; creating a verifiable transaction certificate using a blockchain smart contract; writing the transaction certificate into the power trading alliance chain node; and completing the distributed ledger update according to the alliance chain consensus mechanism.

[0015] From the above, it can be seen that the method and system for charging facilities and electricity spot trading responses provided in this application realize intelligent adjustment of charging power and grid safety verification by constructing a dynamic response sensitivity matrix and optimizing the charging load period transfer strategy, combining electricity spot market price signals and user response elasticity, and has the advantages of reducing charging costs, optimizing charging load distribution, and improving grid operation safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0017] Figure 1 A flowchart of a method for responding to charging facilities and electricity spot transactions provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in this application will be described clearly and completely below in conjunction with the drawings in this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0019] like Figure 1 As shown, the present application proposes a method for charging facilities to respond to electricity spot transactions, including:

[0020] S1. Collect day-ahead price time series data and real-time price fluctuation indicators based on the electricity spot trading platform to obtain the target electricity price feature set;

[0021] S2. Analyze historical charging behavior characteristics based on the operation database of the charging facility cluster and extract the dispatchable capacity margin parameters and user response elasticity coefficient set;

[0022] S3. Generate a dynamic response sensitivity matrix based on the mapping relationship between the target electricity price feature set and the dispatchable capacity margin parameter;

[0023] S4. Constructing a charging load time period transfer strategy table based on the dynamic response sensitivity matrix and the user response elasticity coefficient set;

[0024] S5. Convert the charging load period transfer strategy table into a power adjustment instruction set through the charging facility IoT control protocol;

[0025] S6. Sending a control command to the target charging pile group based on the power adjustment instruction set and obtaining instruction execution status feedback;

[0026] S7. Calculate the power execution deviation index based on the actual power output sequence collected by the charging pile power metering device;

[0027] S8. Obtaining actual response power records based on the clearing data of the power transaction settlement platform;

[0028] S9. Update the user response elasticity coefficient weight factor library based on the matching analysis results of the actual response power records and the day-ahead price time series data.

[0029] This application constructs a closed-loop control system for dynamic response of charging load driven by spot electricity prices. By integrating price forecast data with user behavior characteristics, a quantifiable response sensitivity model is established. The IoT control protocol and execution feedback mechanism are combined to realize a full-process closed loop from strategy generation to effect evaluation. At the same time, the grid security constraints and the dynamic update mechanism of the user elasticity coefficient are integrated, effectively solving the technical problem that traditional charging facilities cannot coordinately optimize economy and safety.

[0030] Specifically, the target electricity price feature set is derived by collecting day-ahead price time series data and real-time price fluctuation indicators from the electricity spot trading platform. This step provides basic data for subsequent charging strategy formulation by monitoring electricity market price changes in real time.

[0031] Next, the historical charging behavior characteristics are analyzed based on the operational database of the charging facility cluster to extract the dispatchable capacity margin parameters and the user response elasticity coefficient set. This step analyzes historical data to obtain the adjustable capacity of the charging facilities and the user's sensitivity to price changes.

[0032] Based on the mapping relationship between the target electricity price feature set and the dispatchable capacity margin parameters, a dynamic response sensitivity matrix is ​​generated. This step establishes a dynamic relationship between price changes and charging capacity adjustments, laying the foundation for real-time response.

[0033] Based on the dynamic response sensitivity matrix and the set of user response elasticity coefficients, a charging load time shift strategy table is constructed. This step comprehensively considers price changes and user behavior to develop an optimal charging time allocation plan.

[0034] The charging load time shift strategy table is converted into a power adjustment instruction set through the charging facility IoT control protocol. This step converts the optimization strategy into executable device control instructions.

[0035] Based on the power adjustment instruction set, control commands are issued to the target charging pile group, and feedback on the command execution status is obtained. This step realizes the actual control of the charging equipment and obtains the execution status.

[0036] Based on the actual power output sequence collected by the charging pile power metering device, the power execution deviation index is calculated. This step evaluates the execution accuracy of the control instructions through real-time monitoring.

[0037] The actual response power records are obtained based on the clearing data of the power trading settlement platform. This step obtains the actual transaction results and provides a basis for subsequent optimization.

[0038] Finally, based on the matching analysis results of actual response power records and day-ahead price time series data, the user response elasticity coefficient weight factor library is updated. This step continuously optimizes the user response model through actual effect feedback.

[0039] The electricity spot trading platform collects day-ahead price time-series data and real-time price fluctuation indicators through the electricity market data interface, obtaining time-of-use electricity price data and real-time price fluctuation parameters for the next 24 hours. This can be achieved through API calls and data cleaning techniques. This feature provides a basis for price trend prediction in subsequent dynamic response strategies. The charging facility cluster's operational database analyzes historical charging behavior features, extracting charging period distribution, power demand distribution, and user charging habits from historical charging pile operation logs. This can be achieved through time-series data mining and cluster analysis algorithms. This feature is used to quantify the adjustable potential of user charging behavior. The dispatchable capacity margin parameter refers to the power range and adjustment rate limit that a charging facility can adjust within a specific time period. It is calculated based on the charging pile's rated power, real-time load factor, and battery safe charging rate. This feature ensures that power adjustment complies with the physical constraints of the equipment. The user response elasticity coefficient set is a quantitative indicator of user responsiveness to electricity price changes. It is obtained through correlation analysis and modeling of historical charging behavior and electricity price fluctuations. This feature is used to assess user acceptance of load shifting strategies. The dynamic response sensitivity matrix is ​​a correlation model between price fluctuations and power adjustment capabilities. Specifically, a multivariate regression analysis method is used to establish a mapping between peak-valley price differences and dispatchable capacity. This feature enables the quantitative conversion of price signals into power adjustment quantities. The charging load time shift strategy table is a set of control instructions containing charging pile identifiers, target power values, and execution times. An optimization algorithm is used to generate a power allocation plan between time periods. This feature directly guides the spatiotemporal optimization of charging load allocation. The conversion of the charging facility IoT control protocol into a power adjustment instruction set encodes the strategy table into device-recognizable control instructions. This is achieved using the Modbus-TCP protocol and a custom instruction frame structure. This feature ensures reliable transmission and parsing of control instructions. The power adjustment instruction set sends control commands and obtains execution status feedback via a bidirectional communication link, specifically using the MQTT protocol and a status code feedback mechanism. This feature forms a closed-loop control verification link. The actual power output sequence calculates the power execution deviation index by comparing the difference between the command power and the actual output power. The deviation value is calculated in real time using a root mean square error algorithm. This feature provides a quantitative basis for optimizing control accuracy. The power trading settlement platform obtains actual response power records by extracting metered power data from participating responses through the market settlement system interface. This is achieved through blockchain evidence storage technology, enabling trusted data traceability. This feature supports the economic evaluation of response effectiveness. Updating the user response elasticity coefficient weight factor library dynamically adjusts user behavior model parameters based on actual response effectiveness. This is achieved through machine learning incremental training methods, enabling self-optimization and iteration of the strategy model.

[0040] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0041] First, the electricity spot trading platform updates the day-ahead price time series data every 15 minutes and simultaneously calculates the price volatility index in real time. For example, if the day-ahead price is 0.5 yuan / kWh at a certain moment, the real-time price volatility index is 10%. This data is collected and stored as the target electricity price feature set.

[0042] The charging facility cluster's operational database records charging activity over the past 30 days. Through data analysis, the available capacity margin for each time period is extracted, e.g., the adjustable power for a given period is 500 kilowatts. Furthermore, the user response elasticity coefficient is calculated, e.g., for every 0.1 yuan / kWh price increase, users' willingness to delay charging increases by 20%.

[0043] The target electricity price feature set is mapped to the dispatchable capacity margin parameter to generate a dynamic response sensitivity matrix for 48 time periods over a 24-hour period. Each element in the matrix represents the response intensity for that period, with a value ranging from 0 to 1.

[0044] Based on the dynamic response sensitivity matrix and the user response elasticity coefficient set, a charging load time period transfer strategy table is constructed. The strategy table contains the target charging power of each charging pile in 48 time periods.

[0045] The charging facility IoT control protocol converts the policy table into a power adjustment instruction set. Each instruction contains the charging pile ID, target power value, and execution time.

[0046] The control center sends control commands to the target charging pile group through an encrypted communication channel. The charging piles execute the commands and return the execution status.

[0047] The power meter built into the charging station collects the actual output power every minute. The control center calculates power execution deviation indicators, such as the root mean square error.

[0048] The power trading and settlement platform provides actual response power records every hour. The system obtains this data for subsequent analysis.

[0049] Finally, the system compares the actual response power records with the day-ahead price time series data to calculate the response effect. Based on the analysis results, the user response elasticity coefficient weight factor library is updated for the next round of optimization.

[0050] This application further proposes calculating the peak-valley price difference characteristic value based on the day-ahead price time series data; determining the price fluctuation intensity coefficient based on the real-time price fluctuation index; extracting the upper limit value of the power adjustment allowed in each time period and the maximum adjustment rate threshold based on the dispatchable capacity margin parameter; establishing a functional relationship between the peak-valley price difference characteristic value, the price fluctuation intensity coefficient and the upper limit value of the power adjustment allowed through the price-capacity response model; calculating the response intensity quantitative value of each time period based on the functional relationship; and aggregating the response intensity quantitative values ​​of all time periods to generate a dynamic response sensitivity matrix.

[0051] The peak-to-valley price difference characteristic value is obtained by calculating the difference between the maximum and minimum values ​​of the day-ahead price curve, reflecting the potential for price incentives. The price volatility intensity coefficient is determined based on the ratio of the standard deviation to the mean of the real-time price series, quantifying the degree of market volatility. The upper limit of the allowable power adjustment is derived from the difference between the real-time load rate and the rated power of the charging facility, and the maximum adjustment rate threshold is set based on the step response time of the charging pile power module. The price-capacity response model uses a multivariate regression method to establish a nonlinear mapping relationship between the peak-to-valley price difference, volatility intensity, and the upper limit of the power adjustment. The quantitative value of the response intensity is calculated using the model output function, with a numerical range set to 0 to 1.

[0052] Specifically, the peak-valley price difference characteristic value identifies price incentive windows by analyzing the extreme point differences of the day-ahead price curve. Real-time price volatility indicators are processed through a sliding window, and the ratio of the standard deviation to the mean is calculated as the volatility intensity coefficient, dynamically reflecting the level of market risk. The power adjustment upper limit in the dispatchable capacity margin parameter is calculated based on the real-time available capacity of the charging pile group to ensure that the adjustment amount is within the physical safety range. The price-capacity response model obtains weight parameters through historical data training, integrating price factors and capacity parameters into a quantitative response intensity value. The quantitative values ​​for each time period are arranged in chronological order to form a matrix, and the matrix row vectors represent the price-capacity coupling response intensity of different time periods. By orthogonalizing the matrix elements, the correlation interference of response intensity between time periods is eliminated, improving the optimization accuracy of the subsequent strategy table.

[0053] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0054] The peak-valley price difference characteristic value is calculated based on the day-ahead price time series data. Specifically, the day-ahead price data of the 24-hour electricity spot trading platform is collected, and the daily peak and valley periods are identified using the sliding window method. The average electricity price difference during the peak and valley periods is calculated as the peak-valley price difference characteristic value.

[0055] The price fluctuation intensity coefficient is determined based on the real-time price fluctuation index. Furthermore, based on 5-minute real-time electricity price data, the rate of change of electricity prices at adjacent time points is calculated. When the rate of change exceeds a preset threshold, a fluctuation event is triggered. The frequency and magnitude of fluctuation events per unit time are calculated to comprehensively determine the price fluctuation intensity coefficient.

[0056] Based on the dispatchable capacity margin parameters, the upper limit of power adjustment allowed for each time period and the maximum adjustment rate threshold are extracted. The average and maximum charging powers for different time periods are obtained by analyzing the historical operating data of the charging facility cluster. Combined with the rated power of the charging facilities, the dispatchable capacity margin for each time period is calculated. This then determines the upper limit of power adjustment allowed and the maximum adjustment rate threshold.

[0057] The price-capacity response model establishes a functional relationship between the peak-valley price difference characteristic value, the price fluctuation intensity coefficient, and the upper limit of the allowable power adjustment. Specifically, a multivariate regression analysis method is used, with the peak-valley price difference characteristic value and the price fluctuation intensity coefficient as independent variables and the upper limit of the allowable power adjustment as the dependent variable, to establish a nonlinear mapping function.

[0058] The response intensity quantification value of each period is calculated based on the functional relationship. Thus, the peak-to-valley price difference characteristic value and price fluctuation intensity coefficient of each period are substituted into the above mapping function to obtain the corresponding response intensity quantification value.

[0059] Aggregate the response intensity quantization values ​​of all time periods to generate a dynamic response sensitivity matrix. For example, arrange the response intensity quantization values ​​of each time period within 24 hours in chronological order to form a 24×1 column vector, which is the dynamic response sensitivity matrix.

[0060] The present application further proposes to construct a charging load period transfer strategy table, including: sorting response intensity quantization values ​​based on a dynamic response sensitivity matrix, and screening a set of high response priority periods; parsing price sensitivity thresholds and charging delayable time windows based on a set of user response elasticity coefficients; constructing a load shift optimization objective function, including a power purchase cost minimization term and a user delay penalty term; configuring an optimization constraint set, including a total charging capacity conservation constraint, a single pile power safety boundary constraint, and a battery temperature rise safety constraint; processing the optimization objective function and the constraint set through a mixed integer programming solver, and outputting a charging power period allocation plan; and compiling a charging load period transfer strategy table based on the charging power period allocation plan.

[0061] The selection of high-priority time periods is achieved by sorting the response strength quantization values ​​in the dynamic response sensitivity matrix. The sorting results are used to determine price-sensitive time periods. Price sensitivity thresholds are extracted from a set of user response elasticity coefficients, reflecting the user's tolerance for price changes. The charging delay window is determined based on the time elasticity of user charging demand. The load shifting optimization objective function consists of a linear combination of electricity purchase cost terms and a quadratic user delay penalty term. The optimization constraints are expressed as a system of inequality equations. The total charge capacity conservation constraint ensures the full fulfillment of user demand, the single-pile power safety boundary constraint limits the adjustment range of charging power, and the battery temperature rise safety constraint sets an upper limit on the charging rate based on battery chemical characteristics. A mixed integer programming solver uses a branch-and-bound algorithm to handle the coexistence of discrete and continuous variables. The charging power time allocation scheme represents the power setpoints for each charging pile at different time periods in the form of a two-dimensional matrix.

[0062] Specifically, the response strength quantification values ​​in the dynamic response sensitivity matrix are sorted in descending order, and the top 20% of time periods are selected as a set of high-response priority time periods. The price sensitivity threshold is determined by analyzing historical user response data, and its specific value is stored in a set of elasticity coefficients. The charging delay window quantifies the range of delays allowed by users for charging start times in minutes. The electricity purchase cost minimization term is calculated by multiplying the day-ahead price time series data by the charging power. The user delay penalty term uses a quadratic function to impose a cost penalty on charging operations that exceed the delay window. The total charge capacity conservation constraint ensures that the total charge capacity allocated across all time periods equals the user's required capacity. The single-pile power safety boundary constraint sets the upper limit of charging power adjustment to ±30% of the rated power. The battery temperature rise safety constraint sets the maximum allowable temperature rise rate to 3°C / min based on the battery type. A mixed integer programming solver generates an initial feasible solution by relaxing integer variables, and then approaches the optimal solution through iterative optimization. The charging power time period allocation plan is ultimately generated as a two-dimensional time-power table, which serves as the data foundation for the charging load time period transfer strategy table.

[0063] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0064] The response strength quantization values ​​are sorted based on the dynamic response sensitivity matrix to select a set of high response priority time periods. For example, a 24-hour period is divided into 96 15-minute time periods. The response strength quantization values ​​of each time period are sorted in descending order, and the top 20% of time periods are selected as the high response priority time period set.

[0065] The price sensitivity threshold and charging delay window are analyzed based on the user response elasticity coefficient set. Specifically, the price sensitivity threshold for each user, such as 0.5 yuan / kWh, and the charging delay window, such as 2 hours, are extracted from the user response elasticity coefficient set.

[0066] The load shifting optimization objective function is constructed, which includes the electricity purchase cost minimization term and the user delay penalty term. The electricity purchase cost minimization term is Σ(PiQi), where Pi is the electricity price in the i-th period and Qi is the charge amount in the i-th period. The user delay penalty term is αΣ(Ti-T0i), where α is the delay penalty coefficient, Ti is the actual charging completion time, and T0i is the user's expected charging completion time.

[0067] Configure the optimization constraint set, including the total charge capacity conservation constraint, the single-pile power safety margin constraint, and the battery temperature rise safety constraint. Furthermore, the total charge capacity conservation constraint ensures that the total charge capacity remains unchanged before and after optimization; the single-pile power safety margin constraint limits the power of each charging pile to between 0-60kW; and the battery temperature rise safety constraint requires that the battery temperature does not exceed 45°C during charging.

[0068] A mixed integer programming solver processes the optimization objective function and the set of constraints to output a charging power time period allocation plan. This optimization problem is then solved using commercial solvers such as Gurobi or CPLEX, yielding a charging power allocation plan for each charging station in each of the 96 time periods.

[0069] A charging load time shift strategy table is compiled based on the charging power time allocation plan. Specifically, the calculated charging power time allocation plan is organized into a table containing information such as the charging pile ID, time period number, and target charging power.

[0070] This application further proposes parsing the target charging pile identification code, target power value and effective time point based on the charging load period transfer strategy table; generating a device-executable binary instruction frame through an instruction compilation engine; creating an instruction digital signature based on an asymmetric encryption algorithm; transmitting the instruction frame and digital signature through the power line carrier main channel and the LoRa wireless backup channel; verifying the validity of the instruction digital signature and timestamp at the charging pile edge controller; and triggering the power module control signal based on the verification result.

[0071] The target charging pile identification code is parsed to precisely match the device to be adjusted. The target power value and effective time define specific control parameters. The command compilation engine converts the policy parameters into a binary command frame structure recognizable by the underlying hardware. The frame structure includes a start delimiter, address code, power setting value, timestamp field, and checksum. The asymmetric encryption algorithm uses the elliptic curve digital signature algorithm. The private key is stored in the cloud-based command server, and the public key is pre-installed in the charging pile security chip. The power line carrier (PLC) primary channel transmits the command frame using the charging cable's power lines. The LoRa wireless backup channel is activated in the event of a power line communication interruption. The operating frequency band is set to 470MHz to 510MHz. The edge controller has a built-in security verification module that simultaneously verifies the validity of the digital signature and that the timestamp is within a preset time window, set to 30 seconds before and after the effective time. Once verified, the edge controller sends a control signal to the power module via the CAN bus, containing the target power value and ramp rate parameters.

[0072] Specifically, the charging pile identification code parsing module extracts the device's unique code from the policy table to ensure a one-to-one correspondence between the command and the physical device. During binary command frame generation, the power value is converted to hexadecimal complement format, and the timestamp uses the UNIX timestamp format. When generating the digital signature, the command frame is hashed with SHA-256 and then encrypted using a private key. During dual-channel transmission, the primary channel takes priority, automatically switching to the backup channel when the carrier signal attenuates to -80dBm. During the verification phase, the edge controller first compares the digital signature hash value with the locally calculated hash value. If a match is found, the timestamp is checked to ensure it is within ±30 seconds of the current system time. If either verification fails, the command frame is discarded and an error code is sent to the control center. After successful verification, the power module receives a control signal containing the target power value and a 2kW / s ramp rate, enabling smooth power switching. This process, through encrypted transmission and redundant channel design, reduces the risk of command tampering to below 0.01%, increases the dual-channel communication success rate to 99.99%, and keeps time synchronization error within ±0.5 seconds.

[0073] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0074] The target charging pile identification code, target power value, and effective time are parsed based on the charging load time shift strategy table. For example, the charging pile number CHG001, target power 15kW, and effective time 2023-05-01 14:30:00 are extracted from the strategy table.

[0075] The instruction compilation engine generates a device-executable binary instruction frame. Specifically, the parsed information is encapsulated into a 16-byte instruction frame, including a 4-byte pile number, a 4-byte power value, and an 8-byte timestamp.

[0076] The instruction digital signature is created based on an asymmetric encryption algorithm. Furthermore, the instruction frame is signed using the RSA-2048 algorithm to generate a 256-byte digital signature.

[0077] The command frame and digital signature are transmitted via the power line carrier main channel and the LoRa wireless backup channel. The power line carrier uses OFDM modulation, while LoRa uses spread spectrum technology, and dual channels are transmitted in parallel to improve reliability.

[0078] The charging pile edge controller verifies the validity of the command digital signature and timestamp. Therefore, the signature is verified using the preset public key and the timestamp is checked to see if it is within the 5-minute validity period.

[0079] The power module control signal is triggered based on the verification result. For example, after the verification is passed, a PWM modulation signal is sent to the power module to adjust the output power to the target value.

[0080] The present application further proposes collecting a second-level actual output power sampling sequence based on the built-in sensor of the charging pile; calculating a dynamic root mean square deviation value based on the target power value sequence and the actual power sampling sequence; obtaining the battery state of charge change rate based on the battery safety status monitoring data; activating the power compensation closed-loop control loop when the dynamic root mean square deviation value exceeds a first safety threshold and the battery state of charge change rate is lower than a second safety threshold; and generating a real-time power compensation amount through a PID controller.

[0081] Among them, the second-level power sampling sequence is synchronously collected through the current sensor and voltage sensor inside the charging pile, and the sampling frequency is set to the range of 1Hz to 10Hz; the dynamic root mean square deviation value is calculated using a sliding window algorithm, and the window length is set to 10 seconds to 60 seconds; the battery state of charge change rate is obtained by differential calculation of the SOC data output by the battery management system, and the time resolution is synchronized with the power sampling sequence; the first safety threshold is dynamically set according to 5%-15% of the rated power of the charging pile, and the second safety threshold is set to an SOC change of no more than 5% per minute; the proportional coefficient, integral time and differential time of the PID controller are preset as adjustable parameters according to the response characteristics of the charging pile.

[0082] Specifically, current and voltage sensors capture real-time power data at a fixed sampling frequency, forming a time series that is then input into the edge computing unit. A sliding window algorithm squares the point-by-point difference between the target power curve and the actual power curve within a preset time interval, calculating the root mean square (RMS) value within the window as a dynamic deviation indicator. The battery management system synchronously transmits SOC data to the control unit, using time-difference calculations to determine the rate of charge change per unit time. If the dynamic RMS deviation exceeds the preset power tolerance range and the battery charging rate does not reach the safety limit, a controllable deviation scenario is identified, triggering the compensation mechanism. A PID controller calculates the compensation power value based on the real-time deviation according to preset control parameters. This value is transmitted to the power module via a PWM modulation signal, forming a closed-loop control circuit. The output range of the power compensation value is limited to within 50% of the maximum allowable adjustment range of the charging pile to prevent overshoot and secondary oscillation.

[0083] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0084] The charging pile's built-in sensor collects a sampling sequence of actual output power in seconds. The charging pile is equipped with a high-precision power sensor with a sampling frequency of 100Hz, which records power fluctuations during the charging process in real time.

[0085] The dynamic RMS deviation (RMS) value is calculated based on the target power value sequence and the actual power sampling sequence. The time window is set to 60 seconds and the sliding window method is used to calculate the dynamic RMS deviation, which reflects the stability of power execution.

[0086] Obtain the battery state of charge change rate based on battery safety status monitoring data. The battery management system monitors the battery pack voltage, current, and temperature in real time, calculates the state of charge change rate, and evaluates the safety of the charging process.

[0087] When the dynamic RMS deviation exceeds the first safety threshold and the battery state of charge change rate falls below the second safety threshold, the power compensation closed-loop control loop is activated. The first safety threshold is set at 5%, and the second safety threshold is set at 0.5% / min. When these conditions are met, the power compensation mechanism is triggered.

[0088] The PID controller generates real-time power compensation. The PID controller parameters are optimized offline, with a proportional coefficient Kp = 0.8, an integral coefficient Ki = 0.2, and a differential coefficient Kd = 0.05. The power compensation is dynamically adjusted based on the deviation value.

[0089] This application further proposes to calculate the actual response benefit value based on the real-time price fluctuation index and the actual response power record, extract the predicted response benefit value according to the load shift optimization objective function, calculate the deviation rate between the predicted and actual benefits through the benefit deviation analysis model, start the weight factor iterative optimization process when the deviation rate exceeds the allowable deviation threshold, adjust the user response elasticity coefficient weight factor based on the gradient descent optimizer, and store the updated weight factor in the parameter database.

[0090] The actual response revenue value is calculated by multiplying the time-of-use electricity price data from the real-time price fluctuation index with the time-shifted electricity volume from the actual response electricity volume record. The predicted response revenue value is calculated by inverting the power purchase cost minimization term in the load shift optimization objective function and combining it with the day-ahead price time series data. The revenue deviation analysis model uses a relative error calculation method, dividing the difference between the actual and predicted response revenue values ​​by the predicted response revenue value to generate the deviation rate. The allowable deviation threshold is set as a dynamic threshold within the range of 5%-15%, and is dynamically adjusted using a sliding window algorithm based on historical deviation data. The gradient descent optimizer uses the deviation rate as the loss function and the weight factor as the optimization variable. Iterative calculations are performed using a learning rate ranging from 0.01 to 0.1, and the weight factor is updated from the parameter database after each iteration.

[0091] Specifically, after the charging load shifting strategy is implemented, actual shifted electricity data for each time period is obtained from the power trading settlement platform. Combined with the time-of-use electricity price from the real-time price fluctuation index, the actual revenue generated is calculated for each time period. Simultaneously, the product term of the electricity price coefficient and the electricity quantity variable in the prediction model is extracted from the mathematical expression of the load shifting optimization objective function, and the predicted revenue is reconstructed by combining it with the day-ahead price time series data. The actual and predicted revenue values ​​are input into a revenue deviation analysis model. When the deviation rate exceeds a preset dynamic threshold, the weight factor optimization process is triggered. The gradient descent optimizer uses the current weight factor as the initial value and determines the optimization direction by calculating the partial derivative of the deviation rate with respect to the weight factor. The weight factor value is gradually adjusted according to a preset learning rate until the deviation rate drops below the threshold or the maximum number of iterations is reached. The updated weight factor is stored in a parameter database and applied during the next charging load optimization, forming a closed-loop optimization mechanism. This process quantifies the revenue deviation and dynamically adjusts the weight factor to ensure that the user response elasticity coefficient accurately reflects the actual market response characteristics, thereby improving the economic benefits of the charging load shifting strategy.

[0092] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0093] The actual response revenue is calculated based on real-time price fluctuation indicators and actual response power records. For example, real-time electricity price data is obtained every 15 minutes through the power trading platform, and combined with the actual response power of the charging pile, the actual revenue of each response period is calculated.

[0094] The predicted response benefit value is extracted based on the load shift optimization objective function. Specifically, the load shift optimization model established earlier is used to input current price and load data to predict the response benefit under ideal conditions.

[0095] The deviation rate between the predicted and actual returns is calculated using the return deviation analysis model. Furthermore, the actual returns are compared with the predicted returns to obtain the deviation rate in percentage form.

[0096] When the deviation rate exceeds the allowable deviation threshold, the weight factor iterative optimization process is initiated. The allowable deviation threshold can be set to 10%, and optimization is triggered when the deviation rate exceeds this threshold.

[0097] The user response elasticity coefficient weight factor is adjusted based on the gradient descent optimizer. Thus, through multiple rounds of iteration, the weight factor is gradually adjusted to minimize the deviation between the predicted and actual returns.

[0098] The updated weight factors are stored in a parameter database. As a preferred embodiment, a distributed database can be used to store the updated weight factors to ensure data reliability and consistency.

[0099] This application further proposes obtaining the user's reservation start time, required power and expected end time based on the charging reservation system; matching the minimum safe charging rate threshold according to the battery chemical characteristics database; deriving the maximum delayable charging time through a time window calculation model; determining the power adjustable range for each time period based on the rated power parameters of the charging facility and the real-time load rate; and integrating the maximum delayable charging time and the power adjustable range to generate a dispatchable capacity margin parameter.

[0100] The user reservation data collected by the charging reservation system includes the charging start time, the total required power, and the expected charging completion deadline, forming a charging demand time window constraint. The battery chemical characteristics database stores the minimum safe charging current value and the maximum allowable temperature rise rate corresponding to different battery types, and ensures that the charging rate is not lower than the safety threshold through data matching. The time window calculation model calculates the maximum delayed start time allowed for the charging operation based on the required power, the safe charging rate threshold, and the expected end time. The rated power parameters of the charging facility include the maximum output power of a single charging gun and the derating factor when multiple guns work together. The real-time load rate is obtained through the charging pile operation status monitoring module and is used to dynamically calculate the adjustable power range for the current period.

[0101] Specifically, the user's scheduled start time and expected end time constitute the time boundary for the charging operation. Combined with the required power, the theoretical required charging time can be derived. By comparing the difference between the theoretical time and the actual available time window, the maximum delayable charging time is determined. The battery chemical characteristics database queries the electrochemical parameters corresponding to the battery model to ensure that the minimum safe charging rate threshold matches the battery's physical characteristics, avoiding the risk of overcharging or overheating. The rated power parameters of the charging facility and the real-time load rate are used together to calculate the power adjustment range. When the load rate is below the preset threshold, power adjustment is allowed within the rated power range; when the load rate approaches the upper limit, the power adjustment range is proportionally reduced. The maximum delayable charging time and the power adjustment range are linearly weighted to generate the dispatchable capacity margin parameter. This parameter reflects the adjustment potential in both the time and power dimensions, providing precise input for the subsequent construction of the dynamic response sensitivity matrix.

[0102] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0103] The charging reservation system obtains the user's reservation start time, required power, and expected end time. For example, the charging reservation information input by the user is received through a mobile application interface, including the estimated arrival time at the charging station, the required charging power, and the expected departure time.

[0104] The minimum safe charging rate threshold is matched against the battery chemical characteristics database. Specifically, the safe charging curve corresponding to the pre-stored battery model is searched and the minimum charging power value is extracted as the safety threshold.

[0105] The maximum delay duration is derived through a time window calculation model. The theoretical maximum delay time is calculated based on the user's scheduled start and end times, combined with battery capacity and the minimum safe charging rate.

[0106] The power adjustment range for each time period is determined based on the rated power parameters of the charging facility and the real-time load rate. Furthermore, the rated power and current load of the charging pile are obtained to analyze the remaining available power in each time period.

[0107] The maximum delayable charging time and the adjustable power range are combined to generate the dispatchable capacity margin parameter. This comprehensively considers the flexibility of both time and power dimensions to form a dispatchable capacity parameter.

[0108] This application further proposes to add a grid safety verification step during the execution of the charging load period transfer strategy, specifically including: obtaining the node voltage safety boundary range based on the distribution network monitoring system; calculating the voltage offset caused by the charging power adjustment through the voltage sensitivity analysis model; triggering the power adjustment attenuation mechanism when the predicted voltage value exceeds the safety boundary range; using a piecewise linear attenuation algorithm to generate a safe power adjustment; and verifying the grid compatibility of the safe power adjustment through a power flow calculation tool.

[0109] The voltage sensitivity analysis model uses a node admittance matrix method to establish a linear relationship between voltage changes and power adjustments. The sensitivity coefficient is calculated by inverting the Jacobian matrix. The piecewise linear attenuation algorithm uses multiple attenuation gradients, each corresponding to a different power adjustment attenuation ratio. The attenuation ratio is dynamically adjusted based on the degree of voltage overshoot. The power flow calculation tool uses the Newton-Raphson method for iterative calculations, ensuring that the adjusted power distribution meets grid operating constraints.

[0110] Specifically, after generating the charging power time allocation plan, the voltage upper and lower limits of each node are first obtained in real time from the distribution network monitoring system. The planned adjusted charging power data is input into the voltage sensitivity analysis model to calculate the predicted change in voltage at each node. When it is detected that the predicted voltage of a node exceeds the safety boundary, the corresponding attenuation gradient level is selected according to the limit deviation. For example, when the voltage deviation exceeds 5%, the first level of attenuation is activated, and when it exceeds 10%, the second level of attenuation is activated. The power adjustment amount after attenuation is verified by flow calculation to see if it causes a new voltage limit point. If there is still a limit, the attenuation process is repeated. After multiple rounds of attenuation and verification, the safe power adjustment amount will replace the original adjustment plan to ensure that the implementation of the charging load transfer strategy will not cause voltage instability in the distribution network.

[0111] As a preferred embodiment, the solution of the present application is specifically implemented as follows: after the charging power adjustment strategy is generated, the distribution network monitoring system obtains the voltage safety boundary range of all feeder nodes under the target substation in real time, where the voltage upper limit is set to 10.5kV and the lower limit is set to 9.3kV. By establishing a node voltage sensitivity matrix model, the voltage change caused by the charging power adjustment is calculated, where the sensitivity coefficient matrix is ​​obtained by the inverse operation of the Jacobian matrix. When the predicted voltage value exceeds the safety boundary, the power adjustment attenuation mechanism is triggered, and the original power adjustment is corrected by a piecewise linear attenuation function, where the attenuation coefficient increases step by step with the degree of voltage deviation. The corrected safety power adjustment is input into the forward and backward power flow calculation program, and the voltage distribution of each node in the distribution network is iteratively calculated until the predicted voltage values ​​of all nodes are within the safety boundary.

[0112] This application further proposes grid safety verification steps, including obtaining the node voltage safety boundary range based on the distribution network monitoring system, calculating the voltage offset caused by the charging power adjustment through the voltage sensitivity analysis model, triggering the power adjustment attenuation mechanism when the predicted voltage value exceeds the safety boundary range, using a piecewise linear attenuation algorithm to generate a safe power adjustment, and verifying the grid compatibility of the safe power adjustment through a power flow calculation tool.

[0113] Among them, the node voltage safety boundary range is obtained through real-time monitoring data from the distribution network monitoring system. The voltage sensitivity analysis model uses the node admittance matrix method to establish the linear relationship between voltage change and power adjustment. The piecewise linear attenuation algorithm sets different attenuation gradients according to the degree of voltage deviation. The power flow calculation tool is based on the Newton-Raphson method for convergence verification.

[0114] Specifically, the distribution network monitoring system transmits the upper and lower voltage thresholds of each node to the control center in real time. The voltage sensitivity analysis model calculates the voltage change rate corresponding to the unit power adjustment based on the topological parameters of the charging facility access point. When the predicted voltage offset exceeds the safety margin, the attenuation mechanism is activated. The piecewise linear attenuation algorithm reduces the power adjustment amount in stages according to a preset ratio. The reduction amplitude of each stage is dynamically adjusted based on the current voltage deviation rate. For example, when the voltage deviation rate reaches 5%, the first level of attenuation is activated, and the reduction amplitude is 30% of the original adjustment amount. After the safe power adjustment amount is generated, multi-scenario simulation is performed using the power flow calculation tool to verify whether the adjusted power distribution meets the node voltage constraints and line current carrying capacity restrictions, ensuring the stability of power grid operation.

[0115] As a preferred embodiment, the solution of the present application is specifically implemented as follows: After the charging pile edge controller verifies the validity of the instruction digital signature and timestamp, the battery management system collects the power battery state of charge data in real time at a sampling frequency of once per second, and obtains the state of charge change rate through differential calculation. When it is detected that the state of charge change rate exceeds the preset safety limit of 5% per minute, the charging pile main control unit immediately cuts off the pulse width modulation control signal, switches to the constant current charging mode and limits the output current to 0.5C value of the battery nominal capacity. At the same time, the charging pile positioning module obtains the current geographic coordinate information, and combines the central processing unit clock to generate a standardized safety event report containing the abnormal event code, occurrence time and location information. The report is synchronized to the regional charging facility cluster monitoring platform via the dedicated data channel of the built-in 5G communication module with a transmission delay of less than 50ms.

[0116] This application further proposes the steps of transaction notarization: constructing a notarization data packet based on the day-ahead price time series data, the actual response power records and the updated weight factors; generating a data integrity fingerprint through the Merkle tree algorithm; creating a verifiable transaction certificate using a blockchain smart contract; writing the transaction certificate into the power transaction alliance chain node; and completing the distributed ledger update according to the alliance chain consensus mechanism.

[0117] The evidence data package integrates three key data types: price data, response records, and weighting factors, ensuring the integrity and relevance of data sources. The Merkle tree algorithm generates a data fingerprint through layer-by-layer hashing, and any data changes result in a change in the fingerprint. Blockchain smart contracts define data verification rules and trigger conditions, automatically generating encrypted credentials with timestamps. The power trading consortium chain utilizes a multi-node deployment model, achieving synchronous ledger updates through a pre-set consensus algorithm.

[0118] Specifically, when constructing the evidence data package, the day-ahead price curve, actual response power, and updated user elasticity coefficient weights are aligned in time series to form a structured data set. The data integrity fingerprint is generated by calculating the Merkle tree root hash value, for example, by hashing the data package in blocks using the SHA-256 algorithm. The verifiable transaction voucher, consisting of the data fingerprint, the transaction subject identifier, and the smart contract address, is encrypted using the elliptic curve digital signature algorithm and written to the consortium chain. The consortium chain nodes utilize a practical Byzantine fault-tolerant consensus mechanism. When more than two-thirds of the nodes pass verification, the transaction voucher is recorded in an immutable distributed ledger. This process ensures that transaction data is traceable throughout its lifecycle and cannot be unilaterally modified, effectively addressing data credibility issues.

[0119] As a preferred embodiment, the solution of this application is implemented as follows: After the settlement of spot electricity transactions is completed, a data package containing day-ahead price time series data, actual response power records of the charging facility cluster, and user response elasticity weighting factors is constructed. The day-ahead price time series data includes node electricity price data for a 24-hour period, the actual response power records include the power adjustment execution amount for each charging station and the corresponding time period information, and the user response elasticity weighting factors include dynamic adjustment parameters optimized through gradient descent. The data package is hashed using a Merkle tree algorithm to generate a data integrity fingerprint containing the root hash value, where each data item participates as a leaf node in the hash tree construction. A smart contract deployed on a blockchain node verifies the format of the data package and the data integrity fingerprint, creating a verifiable transaction voucher containing the transaction timestamp, the digital identities of the participants, and the response power details. The transaction voucher is written to the distributed ledger of the power trading consortium chain through a consensus node. The consortium chain nodes include three types of entity verification nodes: power generation enterprises, power grid companies, and charging operators. Data consistency verification is performed across multiple nodes using a practical Byzantine fault-tolerant algorithm, triggering a synchronous update mechanism for the distributed ledger.

[0120] Through the above-mentioned technical solution, this application achieves the trusted storage and traceability of the entire process of electricity trading data, effectively ensuring the immutability of price data and response power records, and resolving the technical defects of traditional charging facilities, which make response transaction data susceptible to tampering and difficult to trace. Through the automatic verification mechanism of blockchain smart contracts, the efficiency and legal effectiveness of transaction certificate generation are improved, and the basis for data mutual trust among multiple participating parties is strengthened. The distributed storage architecture based on the consortium chain avoids the risk of tampering with centralized data storage and provides a verifiable audit basis for disputes in electricity spot trading.

[0121] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for responding to charging facilities and electricity spot transactions, characterized in that: include: Based on the electricity spot trading platform, we collect day-ahead price time series data and real-time price fluctuation indicators to obtain the target electricity price feature set. Analyze historical charging behavior characteristics based on the operation database of the charging facility cluster and extract the dispatchable capacity margin parameters and user response elasticity coefficient set; According to the mapping relationship between the target electricity price characteristic set and the dispatchable capacity margin parameters, a dynamic response sensitivity matrix is ​​generated; Based on the dynamic response sensitivity matrix and the user response elasticity coefficient set, a charging load time period transfer strategy table is constructed; Convert the charging load period transfer strategy table into a power adjustment instruction set through the charging facility IoT control protocol; Send control commands to the target charging pile group based on the power adjustment instruction set and obtain feedback on the command execution status; Calculate the power execution deviation index based on the actual power output sequence collected by the charging pile power metering device; Obtain actual response power records based on the clearing data of the power transaction settlement platform; Based on the matching analysis results of the actual response power records and the day-ahead price time series data, the user response elasticity coefficient weight factor library is updated.

2. The method according to claim 1, characterized in that Generating a dynamic response sensitivity matrix includes: Calculate the peak-valley price difference characteristic value based on the day-ahead price time series data; Determine the price fluctuation intensity coefficient based on the real-time price fluctuation indicator; Extracting the upper limit value of power adjustment allowed in each time period and the maximum adjustment rate threshold based on the dispatchable capacity margin parameter; The functional relationship between the peak-valley price difference characteristic value, price fluctuation intensity coefficient and the upper limit of the allowable power adjustment is established through the price-capacity response model; Calculate the quantitative value of the response intensity in each time period based on the functional relationship; The dynamic response sensitivity matrix is ​​generated by aggregating the quantitative values ​​of response strength in all time periods.

3. The method according to claim 2, characterized in that The construction of the charging load period transfer strategy table includes: Sort the response intensity quantification values ​​based on the dynamic response sensitivity matrix and select the high response priority time period set; Analyze the price sensitivity threshold and charging delay time window based on the user response elasticity coefficient set; Construct a load shift optimization objective function, which includes the power purchase cost minimization term and the user delay penalty term; Configure the optimization constraint set, including total charge conservation constraint, single pile power safety boundary constraint, and battery temperature rise safety constraint; The mixed integer programming solver is used to process the optimization objective function and constraint set, and output the charging power period allocation plan; Prepare a charging load period transfer strategy table based on the charging power period allocation plan.

4. The method according to claim 3, characterized in that The conversion power adjustment instruction set includes: Parse the target charging pile identification code, target power value and effective time point based on the charging load period transfer strategy table; Generate a device executable binary instruction frame through an instruction compilation engine; Create instruction digital signature based on asymmetric encryption algorithm; Transmit command frames and digital signatures through the power line carrier main channel and the LoRa wireless backup channel; Verify the validity of the instruction digital signature and timestamp at the charging pile edge controller; The power module control signal is triggered according to the verification result.

5. The method according to claim 4, characterized in that The calculation power execution deviation index includes: The actual output power sampling sequence is collected in seconds based on the built-in sensor of the charging pile; Calculate the dynamic root mean square deviation value based on the target power value sequence and the actual power sampling sequence; Obtaining the battery state of charge change rate based on battery safety status monitoring data; When the dynamic root mean square deviation value exceeds a first safety threshold and the battery state of charge change rate is lower than a second safety threshold, activating a power compensation closed-loop control loop; Generate real-time power compensation through PID controller.

6. The method according to claim 5, characterized in that The updating user response elasticity coefficient weight factor library includes: Calculate the actual response benefit value based on the real-time price fluctuation index and the actual response power records; Extract the predicted response benefit value based on the load shift optimization objective function; Calculate the deviation rate between forecast and actual returns through the return deviation analysis model; When the deviation rate exceeds the permissible deviation threshold, the weight factor iterative optimization process is initiated; Adjust the user response elasticity coefficient weight factor based on the gradient descent optimizer; The updated weight factors are stored in the parameter database.

7. The method according to claim 1, characterized in that The extracting of the dispatchable capacity margin parameter includes: Based on the charging reservation system, the user's reservation start time, required power and expected end time are obtained; Matching the minimum safe charge rate threshold based on a database of battery chemistry characteristics; The maximum delayable charging time is derived through the time window calculation model; Determine the power adjustable range for each time period based on the rated power parameters of the charging facility and the real-time load rate; The maximum delayable charging time and the power adjustable range are integrated to generate the dispatchable capacity margin parameters.

8. The method according to claim 3, characterized in that Also included are grid security verification steps: Obtain node voltage safety boundary range based on distribution network monitoring system; Calculate the voltage offset caused by charging power adjustment using a voltage sensitivity analysis model; When the predicted voltage value exceeds the safety boundary range, the power adjustment amount attenuation mechanism is triggered; Use piecewise linear attenuation algorithm to generate safe power adjustment; Verify grid compatibility of safe power regulation using power flow calculation tools.

9. The method according to claim 4, characterized in that The triggering of the power module control signal includes: Real-time monitoring of the charge state change rate based on the battery management system; When the charge state change rate exceeds the safety limit, it switches to constant current charging mode; Generate battery safety event reports with timestamps and charging station location information; Reports are synchronized to the cluster monitoring platform via the 5G private network.

10. The method according to claim 6, characterized in that It also includes transaction evidence steps: Construct a proof data package based on the day-ahead price time series data, actual response power records, and updated weight factors; Generate data integrity fingerprint through Merkle tree algorithm; Use blockchain smart contracts to create verifiable transaction credentials; Write the transaction certificate into the power trading alliance chain node; Complete the distributed ledger update based on the alliance chain consensus mechanism.

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