Rural power grid friendly type flexible charging pile intelligent power adjusting system and method

Abstract

The invention relates to the technical field of electric power regulation and control, and provides a rural power grid friendly type flexible charging pile intelligent power regulation system which comprises a flexible power regulation terminal, a multi-source state sensing module, a rural power grid situation evaluation and capacity margin calculation module, a double-layer optimization decision module, an instruction issuing and collaborative execution module and a user interaction and excitation module. The multi-source state sensing module collects the charging demand, the rural power grid operation state and new energy output prediction data in real time. The rural power grid situation assessment module calculates available capacity margin and dynamic safety risk indexes of the nodes; the double-layer optimization decision-making module carries out primary power distribution by taking maximization of user comprehensive satisfaction as a target, and then carries out secondary adjustment by taking smoothing of load fluctuation and improvement of new energy local consumption as targets. According to the method, multi-target collaborative optimization of charging service satisfaction, power grid safety and new energy consumption is realized, the operation pressure of a rural power distribution network is relieved, energy structure transformation is assisted, and the method has good practicability and economical efficiency.

Description

Technical Field

[0001] This invention relates to the field of power regulation technology, specifically to an intelligent power regulation system and method for rural-friendly flexible charging piles. Background Technology

[0002] With the advancement of the rural revitalization strategy, the number of new energy vehicles in rural areas is growing rapidly, making the installation of charging stations in rural areas a key focus of infrastructure construction. However, rural power distribution networks suffer from weak grid structures, limited capacity, and large load fluctuations. Furthermore, the widespread adoption of distributed photovoltaic and wind power in rural areas further exacerbates the pressure on power grid operations due to the intermittent and random nature of their power output. Traditional charging stations mostly operate in a constant power output mode, lacking flexible adjustment capabilities. The disorderly connection of numerous charging stations can easily lead to problems such as voltage deviations and three-phase imbalances in the power distribution network, hindering the large-scale promotion of charging infrastructure and affecting the efficiency of local consumption of new energy.

[0003] Current technologies related to power regulation of rural charging piles have significant shortcomings: First, existing regulation schemes often focus on a single objective, either prioritizing charging efficiency while neglecting grid safety, or simply pursuing grid stability at the expense of user experience. They fail to achieve multi-objective synergistic optimization and cannot balance charging service satisfaction with grid operation safety. Second, there is a lack of precise response mechanisms for distributed renewable energy output. The matching degree between charging load and renewable energy output is low, forcing a large amount of clean electricity to be abandoned or transmitted over long distances, resulting in energy waste. At the same time, traditional regulation methods do not fully consider the differences in user needs and lack effective interactive incentive mechanisms, making it difficult to guide users to participate in flexible regulation, further reducing the overall efficiency of system operation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a smart power regulation system and method for flexible charging piles that are network-friendly in rural areas. This system solves the problems of high grid security risks, poor user charging experience, and insufficient local consumption of new energy caused by the disorderly connection of traditional charging piles in rural areas, where the rural power distribution network is weak and the output of new energy fluctuates greatly.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent power adjustment system and method for a rural network-friendly flexible charging pile, comprising: Flexible power adjustment terminal, deployed inside the charging pile or in the front-end line, is used to receive and execute power adjustment commands to continuously or in stages adjust the charging power. The multi-source state perception module is used to collect and integrate three types of dynamic data in real time: charging demand data of the charging pile itself, real-time operation status data of the local rural power distribution network, and real-time output and short-term forecast data of local distributed new energy. The rural power grid status assessment and capacity margin calculation module, connected to the multi-source status sensing module, is configured to: calculate the current available capacity margin of the charging pile access node based on the real-time operating status data of the local rural power distribution network, and generate a dynamic safety risk indicator that reflects the level of power grid operation risk. The two-layer optimization decision module, connecting the multi-source state perception module and the rural power grid situation assessment and capacity margin calculation module, is configured to perform two-stage optimization decisions: First-level optimization: With maximizing charging service satisfaction as the primary goal and using the dynamic safety risk indicators as key constraints, the initial power allocation is performed on the current charging demand. The second layer of optimization aims to smooth the total load fluctuation of the charging pile access node and maximize the local consumption of the real-time output of the local distributed new energy. The initial power allocation result is then adjusted in a second coordinated manner to generate the final flexible power adjustment command. The instruction issuance and collaborative execution module connects the dual-layer optimization decision module and the flexible power adjustment terminal. It is used to decompose the flexible power adjustment instruction and issue it to the flexible power adjustment terminal of the target charging pile, and coordinate the timing of the adjustment actions of multiple charging piles in the area.

[0006] Preferably, the real-time operating status data of the local rural power distribution network includes at least: voltage deviation rate, three-phase imbalance, line load rate of the access node, and real-time load rate and short-term load forecast data of the upstream transformer.

[0007] Preferably, in the rural power grid status assessment and capacity margin calculation module, the dynamic security risk indicator Calculated using the following formula: , in, Real-time voltage of the access node. Rated voltage; This refers to the three-phase imbalance. Critical path load rate; The load factor of the upstream transformer; , , , The rural power grid characteristic weighting coefficients are adaptively adjusted based on the power grid topology, equipment parameters, and historical fault data, and satisfy the following conditions: .

[0008] Preferably, in the two-layer optimization decision module, the objective function of the first layer optimization is to maximize overall satisfaction. : , The constraints include: ,and , in, Number of pending charging requests; , The first The power allocated to each request is equal to the power of the original request. , These are the estimated completion time under the current power allocation and the user's expected completion time, respectively. To match the elasticity coefficient of user demand Positively correlated service priority weights: requests with high demand elasticity receive higher priority weights. ; To and Negative correlation delay penalty coefficient; Dynamic security risk threshold; This represents the available capacity margin for the current node.

[0009] Preferably, the objective function of the second layer of optimization for: , in, To optimize the total load of nodes within a time period The variance of the load is used to measure load smoothness. Contribute to distributed new energy With total charging load The correlation coefficient of the curve is used to measure the level of local consumption. and These are the normalized weight coefficients, and The optimization variable is the adjustable power of each flexible charging pile, and the floating adjustment is based on the first-level optimization allocation result.

[0010] Preferably, it also includes a user interaction and incentive module, connected to the two-layer optimization decision module, for generating gradient charging strategy options including different adjustment ranges, expected delay times and corresponding incentive integrals based on the preliminary results of the second-layer optimization, and pushing them to the user client; and feeding back the user-confirmed options to the two-layer optimization decision module to correct the relevant parameters in the service satisfaction objective function and determine the final adjustment instruction.

[0011] Preferably, a smart power adjustment method for a rural internet-friendly flexible charging pile includes the following steps: S1: Through the multi-source state sensing module, charging demand data, rural power grid operation status data and new energy output prediction data are collected in real time; S2: Using the rural power grid status assessment and capacity margin calculation module, calculate the available capacity margin and dynamic security risk indicators of the current node based on the rural power grid operation status data; S3: Perform two-stage optimization decision-making through the aforementioned two-layer optimization decision-making module: S31: Perform the first layer of optimization, with the core goal of maximizing overall user satisfaction, and with the dynamic safety risk indicators and node capacity margin as constraints, perform the initial power allocation for charging demand. S32: Perform the second-level optimization, with the core objectives of smoothing the total load curve of nodes and improving the local consumption of new energy, establish a multi-objective optimization model, perform secondary coordinated adjustment on the initial allocation results, and generate the final adjustment command. S4: Through the instruction issuance and collaborative execution module, the final adjustment instruction is issued to the flexible power adjustment terminal of the relevant charging pile to perform flexible power adjustment.

[0012] Preferably, in step S31, the calculation of the overall user satisfaction incorporates a demand elasticity coefficient. This is used to quantify a user's tolerance for charging delay; for For users with low scores, the delay penalty term in the satisfaction function The increased capacity ensures that their charging needs are prioritized during the initial allocation.

[0013] Preferably, in step S32, the improvement of local consumption of new energy is achieved by constructing a matching degree function between new energy output and charging load, specifically by maximizing the Pearson correlation coefficient between the two in the same time series, and guiding the adjustable charging load to shift to the period of high new energy output.

[0014] Preferably, after step S3 and before step S4, step S3a is also included: through the user interaction and incentive module, the secondary adjustment scheme is converted into an incentive-based gradient option and pushed to the user; the satisfaction model parameters are adjusted according to user feedback; and the adjustment instruction is finally confirmed and fine-tuned.

[0015] This invention provides an intelligent power regulation system and method for rural network-friendly flexible charging piles. It has the following beneficial effects: This invention achieves a dynamic balance between charging service satisfaction and grid security through multi-source state perception and a two-layer optimization decision-making mechanism. The first layer of optimization is based on the elasticity of user demand, and differentiates power allocation for users with different urgent needs, prioritizing the charging experience of users with urgent needs. At the same time, dynamic safety risk indicators are used as core constraints to effectively avoid grid operation risks such as voltage deviation and three-phase imbalance, solving the drawbacks of single-objective optimization in traditional technologies. This improves user charging satisfaction and ensures the stable operation of rural distribution networks. The second layer of optimization smooths load fluctuations and optimizes the matching of renewable energy output with charging load, significantly improving the local consumption level of distributed renewable energy. Combined with the gradient strategy of user interaction and incentive modules, it fully mobilizes users' enthusiasm for participating in flexible regulation, achieving the dual goals of load peak shaving and valley filling and efficient utilization of clean energy, alleviating the pressure of rural distribution network expansion, and contributing to energy structure transformation. Attached Figure Description

[0016] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: Please see the appendix Figure 1 This invention provides a smart power regulation system and method for flexible charging piles suitable for rural areas, adapted to a rural distribution network scenario. This distribution network connects 10 flexible charging piles, along with a distributed photovoltaic power station with a total installed capacity of 500kW and one 10kV / 0.4kV distribution transformer with a rated capacity of 800kVA. The specific configuration and operating logic of each module in the system are as follows: The flexible power adjustment terminal adopts a modular design, with a built-in bidirectional controllable rectifier and power controller. It is deployed at the inlet of each charging pile and supports continuous power adjustment from 0-7kW as well as three-level adjustment modes of 3kW, 5kW and 7kW. It can receive power adjustment commands transmitted through the RS485 communication interface with a response delay of no more than 100ms and an adjustment accuracy error of ≤±2%.

[0019] The multi-source state perception module integrates three types of sensing and data acquisition units: First, a local acquisition unit for charging piles, which collects real-time data on charging request power, remaining battery power, and user-expected completion time for each charging pile, with an acquisition cycle of 5 seconds. Second, a distribution network state acquisition unit, which collects data such as access node voltage, three-phase current, line current, and transformer load through voltage transformers, current transformers, and transformer load monitoring devices installed on the transformer substation busbars. The voltage deviation rate calculation accuracy reaches 0.1%, the three-phase imbalance is calculated using the negative sequence current method, and the line load rate and transformer load rate are basically... The system uses three main components: a real-time current to rated current ratio, and an LSTM model to generate short-term (within 1 hour) load prediction data for the transformer, with a prediction error of ≤5%; a new energy output acquisition and prediction unit, which acquires real-time output data through the photovoltaic power station inverter data interface, and uses a gradient boosting tree model to predict short-term (within 1 hour) output based on environmental sensor data such as irradiance and temperature and historical output curves, with a prediction step size of 15 minutes and a prediction error of ≤8%. After the three types of data are fused and processed by the edge computing gateway, they are uploaded to the system main controller through the 5G communication module.

[0020] The rural power grid status assessment and capacity margin calculation module is connected to the multi-source status sensing module via industrial Ethernet. It analyzes and calculates based on the collected real-time operating status data of the distribution network: the available capacity margin of a node is determined by subtracting the current actual load from the transformer's rated capacity and the current load from the line's allowable current carrying capacity, combined with the distribution network's safe operation constraints. The calculation cycle is consistent with the data acquisition cycle; the dynamic safety risk index R is calculated using the following formula: Among them, rated voltage Weighting coefficient , , , Based on the power grid topology, equipment parameters, and historical fault data from the past three years, the initial value was adaptively adjusted. , , , ,satisfy Furthermore, the system updates and calibrates the weighting coefficients quarterly based on the latest fault data, dynamically adjusting the security risk threshold. The value is set to 0.7, based on the N-1 safety criteria of the distribution network and historical safe operation data.

[0021] The two-layer optimization decision module is deployed on the system's main controller and implemented using an embedded industrial computer. Its first layer of optimization aims to maximize overall satisfaction. The objective function is expressed as follows: The number of pending charging requests The service priority weight is dynamically determined based on real-time charging requests from charging piles. Elasticity coefficient of user demand Positive correlation By analyzing users' charging urgency preferences (urgent, neutral, or lenient) filled in during registration, and their historical charging behavior data (such as whether they frequently cancel delayed charging orders), users with urgent needs can be quantified and identified. , , General users , , users with relaxed needs , , The constraints are and , The available capacity margin for the current node; the second layer of optimization minimizes the objective function. To achieve the goal, the formula is: Among them, the total load of the nodes The total load on the secondary side of the transformer includes charging load from charging piles, residential load, and agricultural production load, etc., and the output of distributed renewable energy. Real-time power output data and total charging load of photovoltaic power plants The sum of the actual charging power of all flexible charging piles, with a weighting factor. , Based on the load smoothing requirements of the distribution network in the transformer area and the priority setting of new energy consumption, the optimization variable is the adjustable power of each charging pile. Its adjustment range is based on the first-level optimization allocation result, and the fluctuation shall not exceed 30%.

[0022] The instruction issuance and collaborative execution module establishes two-way communication with each flexible power adjustment terminal through the 5G communication network. It decomposes and issues the final flexible power adjustment instruction generated by the dual-layer optimization decision module according to the charging pile number. At the same time, based on the geographical location and line impedance parameters of each charging pile, it adopts a time-sequence adjustment strategy to coordinate the adjustment actions of 10 charging piles in the area. This avoids voltage fluctuations in the distribution network caused by multiple charging piles adjusting their power significantly at the same time. The timing interval of the adjustment action is set to 200ms to ensure a smooth transition in the adjustment process.

[0023] The user interaction and incentive module establishes interaction with users through a mobile app. Based on the preliminary results of the second-layer optimization, it generates three tiered charging strategy options: Option 1 is no adjustment (0% adjustment range, estimated delay time of 0 minutes, incentive points of 0); Option 2 is slight adjustment (10%-20% adjustment range, estimated delay time of 15-30 minutes, incentive points of 50-100); Option 3 is deep adjustment (20%-30% adjustment range, estimated delay time of 30-60 minutes, incentive points of 100-200). These points can be used to offset subsequent charging costs or redeem maintenance services. After the user confirms their selection through the app, the module transmits feedback information to the two-layer optimization decision module to correct the service satisfaction objective function. and Parameters, such as the user's selection of depth adjustment, will affect the next charge. Increase by 0.1 Reduce by 0.1 and determine the final adjustment command.

[0024] Example 2: Please see the appendix Figure 2 This embodiment uses the intelligent power adjustment system for rural network-friendly flexible charging piles described in Embodiment 1 above, and performs the following steps to achieve intelligent power adjustment: S1: The multi-source status sensing module starts real-time data acquisition. The charging pile local acquisition unit collects the charging request power of each charging pile every 5 seconds, such as charging pile 1 requesting power of 7kW and expected completion time of 2 hours, charging pile 2 requesting power of 5kW and expected completion time of 3 hours, and the remaining battery power, etc. The distribution network status acquisition unit synchronously collects the real-time voltage of the access node, such as 0.395kV, the three-phase current such as phase A 320A, phase B 310A, phase C 305A, the line current such as 180A, the real-time load of the transformer such as 550kVA, and short-term load forecast data, such as the maximum load of 600kVA in the next hour. The new energy output acquisition and forecasting unit collects the real-time output of the photovoltaic power station, such as 320kW, and short-term output forecast data, such as the maximum output of 380kW and the minimum output of 250kW in the next hour. The three types of data are merged and processed before being uploaded to the system main controller.

[0025] S2: The rural power grid status assessment and capacity margin calculation module receives the data collected by S1 and calculates the available capacity margin of the current node. The transformer's rated capacity is 800kVA, the current actual load is 550kVA, the line's allowable current carrying capacity is 250A, and the current line load is 180A. Considering the safety constraints of the distribution network, the following parameters are determined: Simultaneously press Calculate dynamic security risk indicators , Voltage deviation rate Three-phase imbalance Critical path load rate Upstream transformer load rate Substitute the weighting coefficients , , , Calculated less than the threshold The power grid operation risk is at a safe level.

[0026] S3: The two-layer optimization decision module executes a two-stage optimization decision: S31: Execute the first-layer optimization, the number of current pending charging requests. This means that 8 charging stations initiate charging requests, and the demand is adjusted according to the elasticity coefficient of each user. Sure and Two of them were for users with urgent needs. , , Four units are for general users. , , Two units are for users with relaxed needs. , , The objective function is to maximize The constraints are and The initial power allocation results were obtained by solving the problem using a linear programming algorithm: for users with urgent needs... They are 7kW and 7kW respectively, suitable for general user needs. The power outputs are 5kW, 5kW, 4.5kW, and 4.5kW respectively, catering to users with more flexible power requirements. They are 4kW and 3.5kW respectively. The constraints are satisfied.

[0027] S32: Perform the second level of optimization, objective function The optimization period is 1 hour. Based on the initial allocation result of S31, the adjustable power of each charging pile fluctuates by no more than 30%. Based on the photovoltaic output prediction data: the output for the next 1 hour 15 minutes, 30 minutes, 45 minutes, and 60 minutes are 350kW, 380kW, 330kW, and 280kW respectively. A multi-objective optimization algorithm is used to guide the charging load to shift towards the peak photovoltaic output period: During the 30-minute period of 380kW photovoltaic output, the power of 4 charging piles for general demand users is increased to 5.5kW (an increase of 10%), and the power of 2 charging piles for users with moderate demand is increased to 5.2kW and 4.6kW (an increase of 30% and 31.4% respectively); During the 60-minute period of 280kW photovoltaic output, the power of 2 charging piles for users with moderate demand is decreased to 2.8kW and 2.45kW (a decrease of 30%), and the initial allocation power remains unchanged for the remaining periods. After optimization... Reduced by 25% compared to before optimization. The value was increased to 0.82, achieving the dual goals of load smoothing and local consumption of new energy.

[0028] S3a: The user interaction and incentive module transforms the secondary adjustment scheme of S32 into tiered options and pushes them to the mobile apps of 8 charging pile users. Users with urgent needs only receive Option 1 without adjustment; users with general needs receive Options 1 and 2; and users with lenient needs receive all three options. User feedback results are as follows: 1 user with general needs chose Option 2, 2 users with lenient needs chose Option 3, and the remaining users chose Option 1. The module transmits the feedback information to the two-layer optimization decision module to correct the relevant users' decisions. and Users who choose option two generally have the following needs. Increased to 0.6, Reduced to 0.7; users with relaxed requirements who choose option three. Increased to 0.4, Reduce to 0.3 and fine-tune the adjustment command to determine the final adjustment command.

[0029] S4: The instruction issuance and collaborative execution module decomposes the final adjustment instruction according to the charging pile number and sends it to each flexible power adjustment terminal via the 5G communication network. It coordinates the adjustment actions of the eight charging piles according to a time-series adjustment strategy, with an adjustment interval of 200ms: first, it adjusts the power of charging piles for users with relaxed demand, then adjusts the power of charging piles for users with general demand, and keeps the power of charging piles for users with urgent demand unchanged. After receiving the instruction, the flexible power adjustment terminal executes the power adjustment through its built-in power controller. During the adjustment process, it provides real-time feedback of the actual output power to the system main controller. The main controller performs closed-loop monitoring of the adjustment effect. If the power deviation exceeds ±2%, it promptly issues a correction instruction to ensure that the adjustment accuracy meets the requirements.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart power adjustment system for a rural internet-friendly flexible charging pile, characterized in that, include: Flexible power adjustment terminal, deployed inside the charging pile or in the front-end line, is used to receive and execute power adjustment commands to continuously or in stages adjust the charging power. The multi-source state perception module is used to collect and integrate three types of dynamic data in real time: charging demand data of the charging pile itself, real-time operation status data of the local rural power distribution network, and real-time output and short-term forecast data of local distributed new energy. The rural power grid status assessment and capacity margin calculation module, connected to the multi-source status sensing module, is configured to: calculate the current available capacity margin of the charging pile access node based on the real-time operating status data of the local rural power distribution network, and generate a dynamic safety risk indicator that reflects the level of power grid operation risk. The two-layer optimization decision module, connecting the multi-source state perception module and the rural power grid situation assessment and capacity margin calculation module, is configured to perform two-stage optimization decisions: First-level optimization: With maximizing charging service satisfaction as the primary goal and using the dynamic safety risk indicators as key constraints, the initial power allocation is performed on the current charging demand. The second layer of optimization aims to smooth the total load fluctuation of the charging pile access node and maximize the local consumption of the real-time output of the local distributed new energy. The initial power allocation result is then adjusted in a second coordinated manner to generate the final flexible power adjustment command. The instruction issuance and collaborative execution module connects the dual-layer optimization decision module and the flexible power adjustment terminal. It is used to decompose the flexible power adjustment instruction and issue it to the flexible power adjustment terminal of the target charging pile, and coordinate the timing of the adjustment actions of multiple charging piles in the area.

2. The intelligent power adjustment system for a rural network-friendly flexible charging pile according to claim 1, characterized in that, The real-time operating status data of the local rural power distribution network includes at least: voltage deviation rate, three-phase imbalance, line load rate of the access node, and real-time load rate and short-term load forecast data of the upstream transformer.

3. The intelligent power adjustment system for a rural network-friendly flexible charging pile according to claim 1, characterized in that, In the rural power grid status assessment and capacity margin calculation module, the dynamic security risk indicators Calculated using the following formula: ， in, Real-time voltage of the access node. Rated voltage; This refers to the three-phase imbalance. Critical path load rate; The load factor of the upstream transformer; , , , The rural power grid characteristic weighting coefficients are adaptively adjusted based on the power grid topology, equipment parameters, and historical fault data, and satisfy the following conditions: .

4. The intelligent power adjustment system for a rural network-friendly flexible charging pile according to claim 1, characterized in that, In the dual-level optimization decision module, the objective function of the first level of optimization is to maximize overall satisfaction. : ， The constraints include: ,and , in, Number of pending charging requests; , The first The power allocated to each request is equal to the power of the original request. , These are the estimated completion time under the current power allocation and the user's expected completion time, respectively. To match the elasticity coefficient of user demand Positively correlated service priority weights: requests with high demand elasticity receive higher priority weights. ; To and Negative correlation delay penalty coefficient; Dynamic security risk threshold; This represents the available capacity margin for the current node.

5. The intelligent power adjustment system for a rural network-friendly flexible charging pile according to claim 1, characterized in that, The objective function of the second layer optimization for: ， in, To optimize the total load of nodes within a time period The variance of the load is used to measure the smoothness of the load. Contribute to distributed new energy With total charging load The correlation coefficient of the curve is used to measure the level of local consumption. and These are the normalized weight coefficients, and The optimization variable is the adjustable power of each flexible charging pile, and the floating adjustment is based on the first-level optimization allocation result.

6. The intelligent power adjustment system for a rural network-friendly flexible charging pile according to claim 1, characterized in that, Also includes: The user interaction and incentive module, connected to the two-layer optimization decision module, is used to generate gradient charging strategy options with different adjustment ranges, expected delay times and corresponding incentive integrals based on the preliminary results of the second-layer optimization, and push them to the user client. The user-confirmed options are then fed back to the two-layer optimization decision module to correct the relevant parameters in the service satisfaction objective function and determine the final adjustment instructions.

7. A method for intelligent power adjustment of a rural-friendly flexible charging pile, using the intelligent power adjustment system for a rural-friendly flexible charging pile as described in any one of claims 1-6, characterized in that, Includes the following steps: S1: Through the multi-source state sensing module, charging demand data, rural power grid operation status data and new energy output prediction data are collected in real time; S2: Using the rural power grid status assessment and capacity margin calculation module, calculate the available capacity margin and dynamic security risk indicators of the current node based on the rural power grid operation status data; S3: Perform two-stage optimization decision-making through the aforementioned two-layer optimization decision-making module: S31: Perform the first layer of optimization, with the core goal of maximizing overall user satisfaction, and with the dynamic safety risk indicators and node capacity margin as constraints, perform the initial power allocation for charging demand. S32: Perform the second-level optimization, with the core objectives of smoothing the total load curve of nodes and improving the local consumption of new energy, establish a multi-objective optimization model, perform secondary coordinated adjustment on the initial allocation results, and generate the final adjustment command. S4: Through the instruction issuance and collaborative execution module, the final adjustment instruction is issued to the flexible power adjustment terminal of the relevant charging pile to perform flexible power adjustment.

8. The intelligent power adjustment method for a rural network-friendly flexible charging pile according to claim 7, characterized in that, In step S31, the calculation of overall user satisfaction incorporates a demand elasticity coefficient. This is used to quantify a user's tolerance for charging delay; for For users with low scores, the delay penalty term in the satisfaction function As a result, their charging needs are prioritized during the initial allocation.

9. The intelligent power adjustment method for a rural network-friendly flexible charging pile according to claim 7, characterized in that, In step S32, the improvement of local consumption of new energy is achieved by constructing a matching degree function between new energy output and charging load. Specifically, it is to maximize the Pearson correlation coefficient between the two in the same time series, and guide the adjustable charging load to the period when new energy is generated in large quantities.

10. The intelligent power adjustment method for a rural network-friendly flexible charging pile according to claim 7, characterized in that, After step S3 and before step S4, step S3a is also included: through the user interaction and incentive module, the secondary adjustment scheme is transformed into an incentive-based gradient option and pushed to the user; the satisfaction model parameters are adjusted according to user feedback; and the adjustment instruction is finally confirmed and fine-tuned.

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