A source-load interaction method based on power utilization information collection system

Abstract

The application discloses a source-load interaction method based on an electricity utilization information collection system, and relates to the field of power distribution network operation and optimization. The method comprises four steps: S1, electricity utilization data collection, dynamically acquiring user electricity utilization, distributed energy output and power grid state data through intelligent equipment to ensure real-time accuracy; S2, load characteristic analysis, constructing a quantitative model to analyze user load elasticity coefficient, adjustable potential and other characteristics, combining with distributed energy fluctuation to establish a supply and demand balance evaluation system to clearly define peak and valley periods and gaps; S3, interaction strategy generation, generating dynamic strategies such as electricity price incentive, load adjustment and energy scheduling based on the analysis results and issuing; S4, strategy execution and feedback, real-time monitoring of execution effect, and optimization of strategies through a closed-loop mechanism. The application solves the problems of static, single mode and low efficiency of traditional methods, improves the power supply and demand balance capability, renewable energy consumption rate and strategy accuracy, and supports low-carbon and intelligent operation of the power distribution network.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network operation and optimization, and particularly relates to a source-load interaction method based on a power consumption information collection system. BACKGROUND

[0002] The existing source-load interaction method has defects of staticity, single mode and low efficiency:

[0003] The staticity defect, the traditional method adopts a fixed strategy (such as a single electricity price, a fixed load adjustment instruction), which cannot be dynamically adjusted according to the real-time operation state of the power grid (such as frequency fluctuation, voltage deviation) and the fluctuation of distributed energy output (such as photovoltaic output changes with light, wind power output changes with wind speed), resulting in poor strategy adaptability and difficulty in coping with complex power grid conditions.

[0004] The single mode defect, the existing technology relies on a single data dimension (such as only considering user load or only focusing on energy output), lacks comprehensive analysis of user load characteristics (elastic coefficient, adjustable potential, power consumption habit) and distributed energy fluctuation, resulting in lack of comprehensive data support for strategy generation and limited peak load shifting effect.

[0005] The low efficiency defect, due to the lack of closed-loop feedback mechanism, the traditional method cannot optimize subsequent strategies according to the strategy execution effect, resulting in low user response rate, high renewable energy curtailment rate and difficulty in improving power grid operation efficiency.

[0006] Therefore, a source-load interaction method based on a power consumption information collection system is proposed. SUMMARY

[0007] In view of the defects of the existing source-load interaction method, the present application aims to build a dynamic knowledge graph, conduct multi-modal demand analysis, implement intelligent matching optimization and closed-loop iteration mechanism, form a complete technical solution of power consumption data collection-load characteristic analysis-interaction strategy generation-strategy execution and feedback, realize comprehensive control of user load characteristics and distributed energy fluctuation, accurately balance power supply and demand, improve renewable energy consumption rate and strategy adaptability, and provide a systematic solution for low-carbon and intelligent operation of power distribution networks.

[0008] In order to achieve the above purpose, the summary of the present application adopts the following technical solution:

[0009] A source-load interaction method based on a power consumption information collection system, comprising the following steps:

[0010] S1. Power consumption data collection, real-time acquisition of user power consumption data, collection of distributed energy output data and power grid operation state information; the data collection frequency is dynamically adjusted according to the demand to ensure the real-time and accuracy of the data;

[0011] S2. Load characteristic analysis, based on the collected power consumption data to construct quantitative model analysis of user load characteristics, at the same time, combined with the output fluctuation of distributed energy, the balance evaluation system of power supply and demand is established, and finally the peak and valley period of power grid and the supply and demand gap state are determined;

[0012] S3. Interactive strategy generation, according to the load characteristic analysis results and the operation demand of power grid, the dynamic source load interaction strategy is generated, and the dynamic source load interaction strategy generated is issued to the user terminal and distributed energy control system through the power consumption information acquisition system;

[0013] S4. Strategy execution and feedback, real-time monitoring of strategy execution effect, collection of user response data and power grid operation state, dynamic adjustment of interaction strategy; Through the feedback mechanism, the accuracy and effectiveness of the subsequent strategy are optimized.

[0014] Further, the power consumption data is obtained by real-time monitoring of the operation data of the user side and the power grid side through intelligent electric meters, sensors and other devices. The power consumption data includes but is not limited to: user power load curve, real-time output data of distributed energy, power grid frequency, voltage and other operating parameters; and the data is transmitted to the central processing system through the communication network.

[0015] Further, the S1 includes:

[0016] S111. Selection and deployment of collection equipment:

[0017] User side: select intelligent electric meter with DL / T645-2007 protocol, support voltage, current, active power, reactive power measurement, configure independent sampling channel for each phase, ensure data accuracy in three-phase unbalanced scene;

[0018] Distributed energy side: photovoltaic inverter needs to integrate RS485 interface, real-time output active power, daily power generation; wind turbine controller needs to collect wind speed, rotating speed and actual output data, sampling frequency is synchronized with intelligent electric meter;

[0019] Power grid side: install wireless temperature measurement sensor on 10kV line tower, configure synchronous phasor measurement unit (PMU) at the outlet of substation, collect power grid frequency, voltage and phase angle data; During the whole process, the sampling frequency of power grid side is 256 points / second;

[0020] S112. Dynamic acquisition frequency control:

[0021] Basic frequency, under normal working condition, the acquisition frequency is set to 5 minutes / time, which meets the normal monitoring demand;

[0022] High-frequency triggering condition, when the grid frequency deviation is more than ± 0.2 Hz, the voltage fluctuation is more than ± 5%, or the distributed energy output fluctuation rate is more than 10%, automatically switch to 1 minute / second high-frequency acquisition, and continue for 30 minutes after the parameters return to normal;

[0023] Custom scheduling, set the acquisition plan of special period through the central system, and forcibly enable 2-minute / second acquisition;

[0024] S113. Data transmission and preprocessing:

[0025] Transmission link, dual-link architecture with optical fiber as the main link and 4G / 5G as the backup link, optical fiber transmission delay ≤50ms, 4G / 5G backup link switches within 5 seconds after the main link is interrupted, and data is transmitted using AES-128 encryption algorithm;

[0026] Preprocessing rules:

[0027] Mark data with voltage > 420V or < 180V and current > 120A as invalid values;

[0028] For single-point missing values, use the weighted average of the previous and subsequent 10-minute data to fill in, with a weight coefficient of 0.3 for the previous 5 minutes and a weight coefficient of 0.7 for the subsequent 5 minutes.

[0029] Standardize the original data into JSON format, including device ID, acquisition timestamp, parameter name and value.

[0030] Further, the user load characteristics include the elasticity coefficient, adjustable potential, and electricity usage habits of the load.

[0031] Further, the user load characteristic quantification modeling method includes:

[0032] S21. User load characteristic quantification modeling, based on the collected electricity data, the response characteristics of the user load to the electricity price, the adjustable space and the electricity usage rules are analyzed by constructing a quantification model;

[0033] S22. Distributed energy output fluctuation analysis, extract the distributed energy output data and corresponding meteorological data collected in S1, convert the data to 1-minute granularity, and eliminate data with zero or excessive rated values caused by device failure; Then, calculate the fluctuation index, determine the daily output rule of the distributed energy through curve fitting, and identify the output peak and trough periods;

[0034] Calculate the fluctuation index:

[0035] Standard deviation : ;

[0036] for the minute output, for daily average output, daily minute number, = 1440;

[0037] coefficient of variation : ;

[0038] photovoltaic , wind power , and marked as fluctuation anomaly if exceeding;

[0039] ramp rate : , for the minute output;

[0040] photovoltaic maximum ramp rate ≤ 15% / 15min of rated capacity, wind power photovoltaic maximum ramp rate ≤ 20% / 15min;

[0041] curve fitting: photovoltaic quadratic function fitting daily curve, wind power based on wind speed-power curve conversion wind power output, clear output peak stage;

[0042] quadratic function fitting daily curve is expressed as:

[0043] ;

[0044] for daily output plan; for the number of hours; for the quadratic term coefficient of quadratic function, is the key constant that determines the concave-convex of distributed energy daily output curve; for the linear term coefficient of quadratic function, is the constant that affects the overall tilt trend of distributed energy daily output curve; for the constant of quadratic function, represents the basic output value of distributed energy;

[0045] wind speed-power curve is expressed as:

[0046] ;

[0047] represents the corresponding relationship function between the output power of wind power generation equipment (wind turbine) and the wind speed ; for the rated power of wind turbine, that is, the maximum power that the wind turbine can continuously and stably output under design conditions (usually rated wind speed and other working conditions);

[0048] S23. The power supply and demand balance evaluation system is constructed, the user load and the distributed energy data are integrated, the net load is calculated, the peak and valley periods are divided, and the supply and demand gap level is quantified to provide a clear target for the peak shaving / filling valley strategy.

[0049] Further, the step of analyzing the response characteristics of the user load to the electricity price, the adjustable space, and the electricity consumption law based on the collected electricity consumption data by constructing a quantitative model includes:

[0050] S211. Calculate the load elasticity coefficient :

[0051] Extract samples containing electricity price adjustment events from the historical data collected in S1. The historical data covers at least 5 or more electricity price adjustment events, and each historical data covers 7 days of 15-minute granularity load records before and after the adjustment; remove outliers in the data, and fill in missing values using the 3-point weighted average method, with the weight of the first point being 0.2, the weight of the second point being 0.1, the weight of the last point being 0.4, and the weight of the second last point being 0.3;

[0052] Calculate the load elasticity coefficient using the elasticity coefficient formula , the formula is:

[0053] ;

[0054] is the load change amount, is the baseline load, is the electricity price change amount, is the baseline electricity price;

[0055] Calculate the average elasticity coefficient by user type (residential, commercial, industrial), the results need to meet: residential users , commercial users , industrial users ; verify the significance of the results by t-test, and remove abnormal samples deviating from the mean value of the group by 3 times the standard deviation;

[0056] S212. Evaluate the adjustable potential;

[0057] Load classification, based on the physical characteristics and operation rules of electrical equipment, combined with user equipment account and load curve cross verification collected in S1, the load is divided into three categories:

[0058] Rigid load, adjustment weight equal to 0, verification standard is load curve fluctuation ≤5% / 24 hours;

[0059] Transferable load, equipment with time flexibility, adjustment weight equal to 0.8, verification standard is that the operation period can be delayed by ≥4 hours without affecting the use effect;

[0060] The load can be reduced, the power can be temporarily reduced, the device is adjusted, the weight is equal to 0.6, and the verification standard is that the power can be reduced by 30%-50% and the single reduction time is less than or equal to 2 hours;

[0061] Potential calculation, total adjustable potential Calculation formula:

[0062] ;

[0063] The rated power sum of the first class load; The adjustment weight of the first class of reducible load, which reflects the contribution coefficient of this class of load during adjustment; Indicates the number of classifications of reducible load;

[0064] At the same time, combined with the user compliance rate, the actual invocable capacity is calculated : ;

[0065] And through the K-means algorithm, the transferable load curve is clustered, the peak electricity consumption period and the valley available period are identified, and the transfer rate is calculated , and ≥50%.

[0066] ;

[0067] S213. Analyze the electricity consumption habit, group the data of the past 3 months according to weekdays / weekends and seasons, eliminate abnormal values and complete missing data, extract load peak value, time period and other characteristics, divide electricity consumption mode through clustering, and generate an image report for each user, including typical daily load curve, peak period, and device type proportion.

[0068] Further, the step of integrating user load and distributed energy data, calculating net load and dividing peak and valley periods, and quantifying the supply-demand gap level comprises:

[0069] S231. Calculate the net load : , is the total load of the user, is the total output of the distributed energy, and the curve is drawn according to 15-minute granularity;

[0070] S232. Divide the peak and valley periods, define the peak period based on the mean value and standard deviation of the net load in the past 30 days: , lasting ≥30 minutes; the valley period is: , lasting ≥30 minutes;

[0071] S233. Evaluate the supply-demand gap, in the supply-demand gap evaluation;

[0072] Total power supply of power grid :

[0073] ;

[0074] Power supply of traditional power sources (such as thermal power, hydropower, nuclear power, etc. conventional power generation forms);

[0075] Supply-demand gap :

[0076] ;

[0077] Classified by gap ratio:

[0078] Severe power shortage: ;

[0079] General power shortage: ;

[0080] Surplus: ).

[0081] Further, the source-load interaction strategy includes a price incentive scheme, a load adjustment instruction scheme, a distributed energy dispatching scheme, etc.

[0082] Further, the price incentive scheme is based on the user load elasticity coefficient and the power grid supply-demand state, and by dynamically adjusting the electricity price or providing demand response compensation, the user is encouraged to reduce the load in the peak period and increase the load in the off-peak period, so as to smooth the peak-valley difference of the power grid; the design steps of the price incentive scheme include:

[0083] Based on the load elasticity coefficient calculated in S2, the greater the absolute value of the elasticity coefficient, the more significant the influence of the electricity price adjustment on the load; combined with the real-time supply-demand ratio : , wherein is the net load, is the available power supply capacity, is greater, indicating that the supply and demand are more tense, and the electricity price needs to be increased to suppress the load;

[0084] Dynamic electricity price calculation formula:

[0085] Peak period electricity price , , wherein is the benchmark electricity price, is the electricity price adjustment coefficient (value 0.3-0.5), is the absolute value of the elasticity coefficient;

[0086] Valley period electricity price , , encourage users to increase electricity consumption by reducing prices;

[0087] Demand response compensation:

[0088] For users who actively participate in load transfer / cut, compensation is given according to the amount of cut: , wherein is the actual reduced load, is the compensation standard; the compensation amount increases with the increase of the supply-demand gap level;

[0089] The scheme is issued, and the real-time electricity price and compensation policy are pushed through the smart meter or user APP, and the peak valley period adjustment is announced in advance 1 hour.

[0090] Further, the adjustable load instruction scheme sends precise control instructions according to its adjustment potential and grid demand to realize the time transfer or power cut of the load; the adjustable load instruction generation method comprises:

[0091] Instruction object selection:

[0092] From the S2 adjustable potential evaluation result, select users / devices with high adjustment weight and historical response rate≥80%; preferentially select devices with little impact on user experience;

[0093] Specific instruction content:

[0094] Transferable load, instruct time-flexible devices to transfer time period, and provide supporting subsidies for incentives;

[0095] Cuttable load, send limited power adjustment range instructions or operation time length instructions to devices that can temporarily reduce power, while limiting the power of a single air conditioner to no more than 70% of the rated value;

[0096] Instruction execution amount calculation:

[0097] Single adjustment total target : ;

[0098] Safety factor, ; Supply-demand gap:

[0099] Allocate adjustment amount according to the proportion of user adjustable capacity :

[0100] ;

[0101] The adjustable capacity of the first user;

[0102] Instruction issuing channel: through the remote control interface of the power utilization information collection system, the instruction is issued in the form of encrypted message, ensuring that the instruction transmission delay is less than or equal to 10 seconds.

[0103] Further, the distributed energy scheduling scheme is used to coordinate the output of the distributed energy, preferentially consume renewable energy, reduce curtailment of wind and light, and at the same time, suppress the influence of output fluctuation of the distributed energy on the power grid. The steps for formulating the distributed energy scheduling scheme include:

[0104] Output plan formulation:

[0105] Based on the S2 distributed energy output curve prediction, the intraday output plan is formulated The planned output of photovoltaic needs to match the change of light intensity, and the planned output of wind power needs to refer to the wind speed forecast;

[0106] The planned output needs to meet the power grid constraints: , The power grid receiving capacity, and the ramp rate within 15 minutes is less than or equal to 10% of the rated capacity.

[0107] Real-time scheduling strategy:

[0108] Consumption priority, when , full consumption without adjustment; the actual output of photovoltaic / wind power;

[0109] When the actual output of photovoltaic / wind power , the local energy storage system is started to charge: If the energy storage is full, the output is limited according to the principle of wind power priority over photovoltaic; is the charging power of the local energy storage system;

[0110] Coordination with load regulation:

[0111] When the output of the distributed energy suddenly decreases, the emergency load reduction instruction that can reduce the load is immediately triggered to fill the output gap; when the output of the distributed energy suddenly increases, the valley period electricity price incentive is started to attract users to increase electricity consumption to consume the surplus;

[0112] Scheduling instruction issuing: through the RS485 interface, the output limiting instruction is sent to the photovoltaic inverter and the fan controller, and the precision control is within ±5% of the rated capacity.

[0113] Further, the strategy execution and feedback method:

[0114] S41. Real-time monitoring of the effect of the strategy execution, through the integration of the power utilization information collection system and the SCADA system data, the monitoring picture is updated every 5 seconds, and the user response and the power grid state are mainly tracked;

[0115] In terms of user response, the electricity price incentive response rate is calculated:

[0116] ;

[0117] During the calculation process, the transferable load is required to be ≥70%, the reducible load is required to be ≥80%, and the user APP satisfaction score is collected;

[0118] In terms of power grid state, the frequency, voltage and net load curve are monitored, and the peak-valley difference change after the execution of the strategy is calculated :

[0119] ;

[0120] Before the execution of the strategy, the maximum load of the power grid peak period (or the peak value in the peak-valley difference calculation) represents the high peak pressure of the power grid before the execution; After the execution of the strategy, the maximum load of the power grid peak period (or the new peak value) reflects the reduction effect of the high peak load after demand response;

[0121] When the instruction execution rate is <60% or the frequency deviation exceeds ±0.3Hz, an audible and light alarm is triggered to notify the dispatch personnel to intervene;

[0122] S42. Feedback data collection and analysis, feedback data collection covers user side, energy side and power grid side: the user side records the actual load curve through the smart meter , calculates the adjustment amount : , is the baseline load curve; the energy side records the deviation between the actual output and the planned output of the distributed energy : ; , the actual output of photovoltaic / wind power is , the frequency fluctuation and voltage fluctuation of the power grid side are collected; in the analysis, the strategy benefit index is calculated : , is the duration, is effective; the elasticity coefficient deviation is evaluated , is the actual elasticity coefficient calculated after the execution of the strategy, is the predicted elasticity coefficient; The model needs to be corrected; the response rate difference is counted according to the user type, and the high-response and low-response user groups are identified;

[0123] S43. Dynamic adjustment and optimization of strategy, based on the feedback analysis results, the strategy parameters: the price adjustment coefficient is dynamically optimized The adjustment formula is:

[0124] ;

[0125] The updated electricity price adjustment coefficient (such as the peak-valley electricity price surcharge coefficient) affects the intensity of electricity price incentives on the user side; The previous electricity price adjustment coefficient is the base value for the adjustment;

[0126] If the actual elasticity coefficient is lower than the model prediction, then increase. Command allocation is tilted towards high-response users, reducing quotas for low-response users; the output of distributed energy plans is based on... Corrections; In long-term iterations, the user resilience coefficient and adjustable potential model are updated weekly, and the strategy effectiveness is reviewed monthly, retaining the strategy benefit index. The superior strategy trains a strategy selection model using the Q-learning algorithm, automatically matching the optimal strategy with the grid state; grants green electricity certification and additional discounts to high-response users, and increases compensation standards for low-response industrial users, continuously improving strategy adaptability.

[0127] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are as follows:

[0128] This invention achieves systematic optimization of source-load interaction in the distribution network through an integrated technical solution encompassing electricity data acquisition, load characteristic analysis, interactive strategy generation, strategy execution, and feedback. The comprehensive beneficial effects are as follows:

[0129] The overall solution, through its dynamic, multi-dimensional, and closed-loop design, completely breaks through the limitations of traditional methods. Dynamic data acquisition ensures the real-time nature and accuracy of the data, providing a reliable foundation for subsequent analysis; load characteristic analysis, by quantifying user load characteristics and distributed energy volatility, constructs a comprehensive supply-demand balance assessment system, providing precise basis for strategy generation; interactive strategy generation combines user and energy characteristics to generate personalized, multi-type collaborative strategies, achieving precise source-load matching; strategy execution and feedback, through real-time monitoring and iterative optimization, continuously improve the effectiveness of the strategies.

[0130] In summary, this solution significantly improves the power grid's supply and demand balancing capabilities. By accurately identifying peak and off-peak periods and supply-demand gaps, and combining dynamic pricing, load regulation, and distributed energy dispatch, it effectively smooths out peak-valley differences and reduces grid operation pressure. It also substantially increases the renewable energy absorption rate, reduces wind and solar curtailment, and promotes the low-carbon transformation of the distribution network. Furthermore, it enhances the accuracy of strategies and user responsiveness by improving user participation and ensuring efficient strategy implementation through personalized strategies and feedback optimization. Finally, it ensures the safe and economical operation of the power grid by mitigating operational risks, reducing operating costs, and improving power supply reliability through real-time monitoring and alarm mechanisms.

[0131] The application provides a systematic solution for low-carbon and intelligent operation of a power distribution network, and comprehensively improves the economy, safety and sustainability of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0132] Figure 1 A method flowchart of a source-load interaction method based on a power utilization information collection system. DETAILED DESCRIPTION

[0133] In order to make the purpose, technical scheme and advantages of the application more clear, the application will be further described in detail below with examples. It should be understood that the specific examples described herein are only used to explain the application and not to limit the application.

[0134] As shown in Figure 1 a source-load interaction method based on a power utilization information collection system,

[0135] A source-load interaction method based on a power utilization information collection system, comprising the following steps:

[0136] S1. Power utilization data collection, real-time acquisition of user power utilization data (voltage, current, power, power utilization amount, etc.), and collection of distributed energy (such as photovoltaic, wind power) output data and power grid operation state information (such as frequency, voltage fluctuation); the data collection frequency is dynamically adjusted according to the demand to ensure the real-time and accuracy of the data;

[0137] The power utilization data is obtained by real-time monitoring of the operation data of the user side and the power grid side through intelligent electric meters, sensors and other devices. The power utilization data includes but is not limited to: user power utilization load curve, real-time output data of distributed energy, power grid frequency, voltage and other operation parameters; and the data is transmitted to the central processing system through the communication network to provide a basis for subsequent analysis;

[0138] S111. Selection and deployment of collection equipment:

[0139] User side: select an intelligent electric meter with DL / T645-2007 protocol, support voltage (0-400V), current (0-100A), active power (accuracy 0.2 level), reactive power (accuracy 0.5 level) measurement, configure independent sampling channels for each phase to ensure the accuracy of the data in the three-phase unbalanced scene;

[0140] Distributed energy side: photovoltaic inverter needs to integrate RS485 interface, real-time output active power (resolution 0.1kW), daily power generation (resolution 0.01kWh); wind turbine controller needs to collect wind speed (0-30m / s), rotating speed (0-1500r / min) and actual output data, and the sampling frequency is synchronized with the intelligent electric meter.

[0141] Grid side: Install wireless temperature measurement sensor on 10kV line tower (temperature measurement range -40℃~+125℃, accuracy ±1℃), configure synchronous phasor measurement unit (PMU) at the outlet of the substation, collect grid frequency (50Hz±0.5Hz, measurement accuracy ±0.001Hz), voltage (10kV±10%, accuracy 0.2 level) and phase angle data, grid side sampling frequency is 256 points / second;

[0142] S112. Dynamic acquisition of frequency control:

[0143] Basic frequency, normal working condition (grid frequency 50±0.1Hz, voltage deviation ≤±2%), acquisition frequency is set to 5 minutes / time, which meets the needs of regular monitoring;

[0144] High frequency trigger condition: when the grid frequency deviation is more than ±0.2Hz, the voltage fluctuation is more than ±5%, or the distributed energy output fluctuation rate (change amount within 1 minute / rated capacity) is more than 10%, automatically switch to 1 minute / minute high frequency acquisition, continue for 30 minutes after the parameters return to normal;

[0145] Custom scheduling: set the acquisition plan of special period (such as holidays, peak electricity consumption) through the central system, for example, 14:00-16:00 (air conditioning load peak) in summer Forced to start 2 minutes / minute acquisition.

[0146] S113. Data transmission and preprocessing:

[0147] Transmission link, dual link architecture with optical fiber as the main and 4G / 5G as the backup, optical fiber transmission delay ≤50ms, 4G / 5G backup link switches within 5 seconds after the main link is interrupted, data is transmitted using AES-128 encryption algorithm;

[0148] Preprocessing rules:

[0149] Outlier rejection: data with voltage >420V or <180V, current >120A are marked as invalid values;

[0150] Data completion: for single point missing values, use the weighted average of the previous and next 10 minutes data to fill (weight coefficient: 0.3 for the first 5 minutes, 0.7 for the last 5 minutes);

[0151] Format conversion: standardize the original data to JSON format, including device ID, acquisition timestamp (accurate to milliseconds), parameter name and value.

[0152] S2. Load characteristic analysis, based on the collected electricity consumption data, construct a quantitative model to analyze the load characteristics of users, at the same time, combined with the output fluctuation of distributed energy, establish a power supply and demand balance evaluation system, and finally determine the power grid peak and valley period and the supply and demand gap state;

[0153] S21. User load characteristic quantification modeling, based on the collected electricity data, the response characteristics of the user load to the electricity price, the adjustable space and the electricity consumption law are analyzed by constructing a quantitative model to provide user-side basis for strategy generation; specifically including:

[0154] S211. Calculate the load elasticity coefficient :

[0155] Extract samples containing electricity price adjustment events from the historical data collected in S1, the historical data covers at least 5 times of electricity price adjustment events, and the historical data covers 7 days of 15-minute granularity load records before and after each adjustment; remove outliers in the data (such as sudden load drop / surge caused by power failure, equipment failure), fill in missing values using the weighted average method of the previous and next 3 points, the weight of the first point is 0.2, the weight of the previous 2 points is 0.1, the weight of the last point is 0.4, and the weight of the last 2 points is 0.3;

[0156] Calculate the load elasticity coefficient using the elasticity coefficient formula , the formula is:

[0157] ;

[0158] is the load change amount, is the baseline load, is the electricity price change amount, is the baseline electricity price;

[0159] Calculate the average elasticity coefficient by user type (residential, commercial, industrial), the results need to meet: residential users , commercial users , and industrial users ; verify the significance of the results (confidence ≥ 95%) through t-test, and remove abnormal samples deviating from the mean value of the group by 3 times the standard deviation;

[0160] S212. Evaluate the adjustable potential:

[0161] Load classification, based on the physical characteristics and operation law of electricity equipment, combined with user equipment account and load curve collected in S1 cross verification, the load is divided into three categories:

[0162] Rigid load, such as medical monitors, refrigerators and other devices that require 24-hour continuous power supply, the adjustment weight is equal to 0, and the verification standard is that the load curve fluctuation is ≤5% / 24 hours;

[0163] Transferable load, such as electric vehicle charging piles, water storage type electric water heaters and other devices with time flexibility, the adjustment weight is equal to 0.8, and the verification standard is that the operation period can be delayed ≥4 hours and does not affect the use effect;

[0164] The load that can be reduced, such as air conditioner, non-core lighting, etc. The device that can temporarily reduce power adjusts the weight equal to 0.6, and the verification standard is that the power can be reduced by 30%-50% and the single reduction time is ≤2 hours;

[0165] Potential calculation, total adjustable potential calculation formula:

[0166] ;

[0167] The rated power sum of the first class load; The adjustment weight of the first class of reducible load, which reflects the contribution coefficient of this class of load when adjusting; Indicates the number of classifications of reducible load;

[0168] At the same time, combined with the user compliance rate, the actual callable capacity is calculated Actual callable capacity : ;

[0169] And through the K-means algorithm, the transferable load curve is clustered, the peak electricity consumption period and the valley available period are identified, and the transfer rate is calculated , which requires ≥50%.

[0170] ;

[0171] S213. Analyze the electricity consumption habit, group the data of the last 3 months according to the workday / weekend season, eliminate abnormal values and complete the missing data, extract the load peak value, time period and other characteristics, and divide the electricity consumption mode through clustering,

[0172] Generate a portrait report for each user containing the proportion of device types in the peak period of the typical daily load curve;

[0173] S22. Analysis of distributed energy output fluctuation, extract the distributed energy output data (photovoltaic 1 minute / point, wind power 10 seconds / point) and corresponding meteorological data (light intensity, wind speed) collected in S1, and convert the data to 1 minute granularity. Eliminate data with zero or overrated values caused by device failure; then, calculate the fluctuation index, determine the daily output law of distributed energy through curve fitting, and identify the output peak and valley periods;

[0174] Calculate the fluctuation index:

[0175] Standard deviation :

[0176] The rated power sum of the first Minute output, For average daily output, Daily minutes =1440;

[0177] coefficient of variation : ,

[0178] Photovoltaics Wind power If the value exceeds this range, it will be marked as an abnormal fluctuation.

[0179] Slope rate : , For the first Minute output;

[0180] The maximum ramp rate for photovoltaic power is ≤ 15% / 15min of the rated capacity, and the maximum ramp rate for wind power photovoltaic power is ≤ 20% / 15min.

[0181] Curve fitting: Photovoltaics use quadratic functions to fit intraday curves, while wind power uses wind speed-power curves to convert wind power output and identify peak output phases;

[0182] The intraday curve fitted by the quadratic function is expressed as:

[0183] ;

[0184] for Daily effort plan; In hours; The coefficient of the quadratic term in the quadratic function is a key constant that determines the concavity and convexity of the daily output curve of distributed energy. Let be the coefficient of the first term of the quadratic function, which is a constant that affects the overall tilt trend of the daily output curve of distributed energy; The constant is a quadratic function, representing the base output value of distributed energy resources;

[0185] The wind speed-power curve is represented as follows:

[0186] ;

[0187] This indicates the output power of the wind power generation equipment and the wind speed. The correspondence function; This is the rated power of the fan, which is the maximum power that the fan can continuously and stably output under design conditions;

[0188] S23. Construction of a power grid supply and demand balance assessment system, integrating user load and distributed energy data, calculating net load and dividing peak and valley periods, quantifying the supply and demand gap level, and providing clear objectives for peak-shaving / valley-filling strategies:

[0189] First, calculate the net load. : , Total user load For the total output of distributed energy resources, plot the curve at a 15-minute granularity.

[0190] Secondly, peak and valley periods are divided based on the average net load over the past 30 days. and standard deviation The peak period is defined as: The duration is ≥30 minutes; the valley period is: Continuing for ≥30 minutes;

[0191] Finally, the supply and demand gap is being assessed; the supply and demand gap assessment is in progress.

[0192] Total power supply of the power grid :

[0193] ;

[0194] The power supply capacity for traditional power sources (such as conventional power generation methods like thermal power, hydropower, and nuclear power);

[0195] supply and demand gap :

[0196] ;

[0197] Classification by gap percentage:

[0198] Severe power shortage: ;

[0199] General power shortage: ;

[0200] surplus: .

[0201] S3. Interactive Strategy Generation: Based on the load characteristic analysis results of S2 (user resilience coefficient, adjustable potential, peak and valley periods, supply and demand gap, etc.) and the real-time operation status of the power grid, dynamic source-load interaction strategies are generated, including three categories: electricity price incentive schemes, adjustable load adjustment command schemes, and distributed energy dispatch schemes. These strategies are distributed to user terminals and distributed energy control systems through the electricity consumption information collection system, aiming to balance power grid supply and demand, optimize operating efficiency, and prioritize the consumption of renewable energy to reduce wind and solar curtailment. The generation of strategies must take into account the user's sensitivity to electricity prices, the adjustable capacity of the load, and the output characteristics of distributed energy to ensure relevance and effectiveness.

[0202] Electricity price incentives are based on user load elasticity coefficients and grid supply and demand conditions. They incentivize users to reduce load during peak hours and increase load during off-peak hours by dynamically adjusting electricity prices or providing demand response compensation, thereby mitigating peak-to-valley differences in the grid. The design steps for an electricity price incentive scheme include:

[0203] The load elasticity coefficient calculated based on S2 is higher when the absolute value of the elasticity coefficient is larger (e.g., for residential users). The more significant the impact of electricity price adjustments on load (∈[−0.5,−0.2]), the greater the impact; combined with the real-time supply-demand ratio... : ,in Net load, For available power supply capacity, The larger the value, the tighter the supply and demand, requiring higher electricity prices to curb load.

[0204] Dynamic electricity price calculation formula:

[0205] Peak hour electricity price , ,in The benchmark electricity price, This is an adjustment coefficient (with a value of 0.3-0.5). It is the absolute value of the elastic coefficient;

[0206] Off-peak electricity price , Incentivize users to increase electricity consumption by lowering prices;

[0207] Demand response compensation:

[0208] Users who actively participate in load shifting / reduction will be compensated based on the amount of load reduction: ,in To actually reduce the load, The compensation standard; the compensation amount increases as the level of the supply-demand gap increases (in cases of severe power shortages). (Up 50%)

[0209] Once the plan is issued, real-time electricity prices and compensation policies will be pushed through smart meters or user apps, with peak-valley period adjustments announced one hour in advance.

[0210] Adjustable load (transferable, reduceable) command scheme, based on its adjustment potential and grid demand, sends precise control commands to achieve time-based load transfer or power reduction; adjustable load command generation methods include:

[0211] Instruction object filtering:

[0212] From the S2 adjustability potential assessment results, select users / equipment with high adjustment weights (load transferable, load reductionable) and historical response rates ≥80%; prioritize equipment with minimal impact on user experience (such as electric vehicle charging stations and air conditioners).

[0213] Specific instructions:

[0214] The load can be transferred, and instructions can be given to devices with time flexibility (electric vehicle users, storage water heaters) to specify the transfer period (e.g., from the peak period of 18:00-20:00 to the off-peak period of 23:00-6:00 the next day), and a subsidy incentive (e.g., 0.1 yuan / kWh) can be provided.

[0215] It can reduce load by sending power adjustment range instructions or running time instructions (≤2 hours per instance) to equipment that can temporarily reduce power. For example, it can raise the air conditioner temperature from 26℃ to 28℃, with a compensation of 0.5 yuan per unit, while limiting the power of a single air conditioner to no more than 70% of its rated value.

[0216] Instruction execution quantity calculation:

[0217] Single adjustment total target : ;

[0218] For safety reasons, ; This is a supply-demand gap;

[0219] Allocate adjustment amounts according to the user's adjustable capacity ratio. :

[0220] ;

[0221] For the first Adjustable capacity per user;

[0222] Command issuance channel: Commands are issued in encrypted message form through the remote control interface of the electricity information collection system to ensure that the transmission delay is ≤10 seconds.

[0223] The distributed energy dispatch is used to coordinate the output of distributed energy, prioritize the absorption of renewable energy, reduce wind and solar curtailment, and mitigate the impact of output fluctuations on the power grid. The steps for formulating a distributed energy dispatch scheme include:

[0224] Output plan formulation:

[0225] Based on the output curve prediction of S2 distributed energy, formulate daily output plan. The planned output of photovoltaic power needs to be matched with changes in sunlight intensity, and the planned output of wind power needs to take wind speed forecasts into account.

[0226] Planned power output must meet grid constraints: , The grid capacity is the maximum capacity that can be received, and the ramp rate within 15 minutes is ≤10% of the rated capacity.

[0227] Real-time scheduling strategy:

[0228] Priority should be given to disposal, when At that time, the full amount was consumed without adjustment; To actually contribute to photovoltaic / wind power;

[0229] Power curtailment control, when At that time, the local energy storage system will be activated for charging: If the energy storage is at full capacity, the output will be limited according to the principle of prioritizing wind power over solar power. Charging power for the local energy storage system;

[0230] Coordinate with load regulation:

[0231] When the output of distributed energy suddenly drops (more than 20% of rated capacity within 15 minutes), an emergency load reduction command is immediately triggered to fill the output gap.

[0232] When the output of distributed energy sources increases sharply, off-peak electricity pricing incentives will be activated to attract users to increase their electricity consumption surplus.

[0233] Dispatch command issuance: Output limit commands are sent to photovoltaic inverters and wind turbine controllers via RS485 interface, with accuracy controlled within ±5% of rated capacity.

[0234] S4. Strategy Execution and Feedback: Monitor the effectiveness of strategy execution in real time, collect user response data and power grid operation status, and dynamically adjust interactive strategies; optimize the accuracy and effectiveness of subsequent strategies through feedback mechanisms.

[0235] S41. Real-time monitoring of strategy execution effect: By integrating data from the electricity information collection system and the SCADA system, the monitoring screen is updated every 5 seconds, focusing on tracking user response and power grid status;

[0236] Regarding user response, the electricity price incentive response rate is calculated as follows:

[0237] ;

[0238] During the calculation process, it is required that the transferable load is ≥70% and the load that can be reduced is ≥80%, and user APP satisfaction ratings are collected;

[0239] Regarding the power grid status, monitor the frequency, voltage, and net load curves, and calculate the peak-to-valley difference changes after the strategy is implemented. :

[0240] ;

[0241] The maximum load during peak hours of the power grid before the strategy is implemented represents the peak pressure on the power grid before implementation. This represents the maximum load on the power grid during peak hours after the strategy is implemented, reflecting the effect of reducing peak load after demand response.

[0242] When the command execution rate is less than 60% or the frequency deviation exceeds ±0.3Hz, an audible and visual alarm is triggered to notify dispatchers to intervene.

[0243] S42. Feedback data acquisition and analysis, covering user side, energy side, and grid side: User side records actual load curves through smart meters. Calculate the adjustment amount : , This serves as the baseline load curve; the energy side records the deviation between the actual and planned output of distributed energy resources. : ; In order to formulate a daily work plan, The analysis includes the actual output of photovoltaic / wind power; frequency and voltage fluctuations are collected on the grid side; and the strategy benefit index is calculated during the analysis. : , For duration, Effective; assessing the deviation of the elasticity coefficient , This refers to the actual elasticity coefficient calculated after the strategy is executed. To predict the elasticity coefficient; The model needs to be revised regularly; differences in response rates should be statistically analyzed by user type to identify high-response and low-response user groups;

[0244] S43. Dynamic adjustment and optimization of strategies: Dynamically optimize strategy parameters based on feedback analysis results: electricity price adjustment coefficient. The adjustment formula is:

[0245] ;

[0246] The updated electricity price adjustment coefficient affects the intensity of electricity price incentives on the user side; The previous electricity price adjustment coefficient is the base value for the adjustment;

[0247] If the actual elasticity coefficient is lower than the model prediction, then increase. Command allocation is tilted towards high-response users, reducing quotas for low-response users; distributed energy plan output is based on... Corrections; In long-term iterations, the user resilience coefficient and adjustable potential model are updated weekly, and the strategy effectiveness is reviewed monthly, retaining the strategy benefit index. The superior strategy trains a strategy selection model using the Q-learning algorithm, automatically matching the optimal strategy with the grid state; grants green electricity certification and additional discounts to high-response users, and increases compensation standards for low-response industrial users, continuously improving strategy adaptability.

[0248] The above description is a preferred embodiment of the invention and is not intended to limit the scope of the invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A source-load interaction method based on an electricity consumption information collection system, characterized in that, Includes the following steps: S1. Electricity consumption data acquisition: Real-time acquisition of users' electricity consumption data, while also collecting output data of distributed energy sources and grid operation status information; the data acquisition frequency is dynamically adjusted according to demand to ensure the real-time performance and accuracy of the data; S2. Load characteristic analysis: Based on the collected electricity consumption data, a quantitative model is built to analyze the user load characteristics. At the same time, combined with the output fluctuation of distributed energy resources, a power grid supply and demand balance assessment system is established to ultimately clarify the peak and valley periods of the power grid and the state of supply and demand gap. S3. Interactive strategy generation: Based on the load characteristic analysis results and power grid operation requirements, a dynamic source-load interaction strategy is generated. The generated dynamic source-load interaction strategy is then distributed to the user terminal and the distributed energy control system through the electricity information collection system. S4. Strategy execution and feedback: Real-time monitoring of strategy execution effects, collection of user response data and power grid operation status, and dynamic adjustment of interactive strategies; The accuracy and effectiveness of subsequent strategies are optimized through feedback mechanisms; The user load characteristic quantitative modeling method includes: S21. Quantitative modeling of user load characteristics: Based on the collected electricity consumption data, a quantitative model is constructed to analyze the response characteristics, adjustability, and electricity consumption patterns of user load to electricity prices. S22. Distributed energy output volatility analysis: Extract the distributed energy output data and corresponding meteorological data collected in S1, convert the data to a uniform 1-minute granularity, and remove data with zero values ​​or exceeding rated values ​​caused by equipment failure; then, calculate the volatility index, and clarify the intraday output pattern of distributed energy through curve fitting, and identify the peak and trough periods of output. S23. The power grid supply and demand balance assessment system is constructed by integrating user load and distributed energy data, calculating net load and dividing peak and valley periods, quantifying the supply and demand gap level, and providing clear objectives for peak shaving / valley filling strategies. The steps of analyzing the response characteristics, adjustability, and electricity consumption patterns of user load to electricity prices by constructing a quantitative model based on collected electricity consumption data include: S211. Calculate the load elasticity coefficient : Samples containing electricity price adjustment events were extracted from historical data collected by S1. The historical data must cover at least 5 electricity price adjustment events, and each historical data must cover 15-minute granular load records for 7 days before and after each adjustment. Outliers in the data were removed, and missing values ​​were filled using a weighted average of the first and last 3 points. In the weighted average of the first and last 3 points, the weight of the first point was 0.2, the weight of the first 2 points was 0.1, the weight of the last point was 0.4, and the weight of the last 2 points was 0.

3. Calculate the load elasticity coefficient using the elasticity coefficient formula The formula is: ； This represents the load change. As the baseline load, For changes in electricity prices, The benchmark electricity price; S212. Assess the adjustability potential; Load classification, based on the physical characteristics and operating patterns of electrical equipment, and combined with cross-validation of user equipment ledgers and load curves collected by S1, divides the load into three categories: For rigid loads, the adjustment weight is equal to 0, and the verification standard is that the load curve fluctuation is ≤5% / 24 hours. The equipment is load-transferable and has time flexibility. The adjustment weight is 0.

8. The verification standard is that the running time can be delayed by ≥4 hours without affecting the performance. Equipment that can reduce load and temporarily lower power has an adjustment weight of 0.6, and the verification standard is that the power can be reduced by 30%-50% and the duration of a single reduction is ≤2 hours; Potential calculation, total adjustable potential Calculation formula: ； For the first The sum of the rated power of the loads of this type; For the first The adjustment weight of a type of load can be reduced, reflecting the contribution coefficient of that type of load during adjustment; Indicates the number of categories from which load can be reduced; S213. Analyze electricity usage habits, group data from three consecutive months by weekday / weekend and season, remove outliers and fill in missing data, extract features such as load peak and time period, and divide electricity usage patterns by clustering to generate a profile report for each user that includes typical daily load curves, peak hours, and the proportion of equipment types.

2. The source-load interaction method based on an electricity consumption information collection system according to claim 1, characterized in that, The electricity consumption data is obtained by real-time monitoring of the user side and the power grid side through devices such as smart meters and sensors. The electricity consumption data includes, but is not limited to: user electricity load curves, real-time output data of distributed energy sources, power grid frequency, and voltage operating parameters; and the data is transmitted to the central processing system through a communication network.

3. The source-load interaction method based on an electricity consumption information collection system according to claim 1, characterized in that, S1 includes: S111. Selection and Deployment of Data Acquisition Equipment: On the user side: Smart meters with DL / T645-2007 protocol are selected, which support the measurement of voltage, current, active power and reactive power. Each phase is equipped with an independent sampling channel to ensure the accuracy of data in three-phase unbalanced scenarios. On the distributed energy side: photovoltaic inverters need to integrate RS485 interfaces to output active power and daily power generation in real time; wind turbine controllers need to collect wind speed, rotational speed and actual output data, and the sampling frequency needs to be synchronized with the smart meter. On the grid side: Wireless temperature sensors are installed on 10kV line towers, and synchronous phasor measurement units (PMUs) are configured at the substation outlets to collect grid frequency, voltage, and phase angle data; throughout the process, the grid-side sampling frequency is 256 points / second. S112. Dynamic acquisition frequency control: The basic frequency, under normal operating conditions, is set to 5 minutes / time, which meets the needs of routine monitoring. High-frequency triggering conditions: When the grid frequency deviation exceeds ±0.2Hz, the voltage fluctuation exceeds ±5%, or the output fluctuation rate of distributed energy exceeds 10%, the system will automatically switch to high-frequency acquisition every minute and continue until the parameters return to normal for 30 minutes. Custom scheduling allows for setting specific time periods for data collection through the central system, forcing data collection every 2 minutes. S113. Data Transmission and Preprocessing: The transmission link adopts a dual-link architecture with fiber optic as the primary link and 4G / 5G as backup. The fiber optic transmission latency is ≤50ms, and the 4G / 5G backup link switches within 5 seconds after the primary link is interrupted. Data is transmitted using the AES-128 encryption algorithm. Preprocessing rules: Data with voltage > 420V or < 180V and current > 120A are marked as invalid values. For single missing values, the weighted average of the data from the preceding and following 10 minutes is used to fill the missing values, with a weighting factor of 0.3 for the first 5 minutes and 0.7 for the following 5 minutes. The raw data is standardized into JSON format, which includes device ID, collection timestamp, parameter name and value.

4. The source-load interaction method based on an electricity consumption information collection system according to claim 1, characterized in that, The user load characteristics mentioned include the load resilience coefficient, adjustability potential, and electricity consumption habits.

5. The source-load interaction method based on an electricity consumption information collection system according to claim 1, characterized in that, The steps of integrating user load and distributed energy data, calculating net load and dividing peak and valley periods to quantify the supply-demand gap include: S231. Calculate Net Load : , Total user load For the total output of distributed energy resources, plot the curve at a 15-minute granularity. S232. Divide peak and valley periods based on the average net load over the past 30 days. and standard deviation The peak period is defined as: The duration is ≥30 minutes; the valley period is: Continuing for ≥30 minutes; S233. Assessing the supply and demand gap; the supply and demand gap assessment is in progress. Total power supply of the power grid : ； The power supply capacity of a traditional power source; supply and demand gap : ； Classification by gap percentage: Severe power shortage: ; General power shortage: ; surplus: .

6. The source-load interaction method based on an electricity consumption information collection system according to claim 1, characterized in that, The dynamic source-load interaction strategy includes three categories: electricity price incentive scheme, adjustable load instruction scheme, and distributed energy dispatch scheme.

7. A source-load interaction method based on an electricity consumption information collection system according to claim 6, characterized in that, The electricity price incentive scheme is based on the user load elasticity coefficient and the power grid supply and demand status. It incentivizes users to reduce load during peak hours and increase load during off-peak hours by dynamically adjusting electricity prices or providing demand response compensation, thereby smoothing out the peak-valley difference in the power grid. The design steps for an electricity price incentive scheme include: The load resilience coefficient is calculated based on S2; the larger the absolute value of the resilience coefficient, the more significant the impact of electricity price adjustments on the load. This is combined with the real-time supply-demand ratio. : ,in Net load, For available power supply capacity, The larger the value, the tighter the supply and demand, requiring higher electricity prices to curb load. Dynamic electricity price calculation formula: Peak hour electricity price , ,in The benchmark electricity price, This is the electricity price adjustment coefficient. It is the absolute value of the elastic coefficient; Off-peak electricity price , Incentivize users to increase electricity consumption by lowering prices; Demand response compensation: Users who actively participate in load shifting / reduction will be compensated based on the amount of load reduction. : ,in To actually reduce the load, The compensation standard is as follows; the compensation amount increases as the level of the supply-demand gap increases. Once the plan is issued, real-time electricity prices and compensation policies will be pushed through smart meters or user apps, with peak-valley period adjustments announced one hour in advance.

8. A source-load interaction method based on an electricity consumption information collection system according to claim 6, characterized in that, The adjustable load command scheme, based on its adjustment potential and grid demand, sends control commands to achieve time-based load shifting or power reduction; the adjustable load command generation method includes: Instruction object filtering: From the S2 adjustability potential assessment results, select users / devices with high adjustment weights and historical response rates ≥80%; prioritize devices with minimal impact on user experience. Specific instructions: Transferable loads are instructed to specify the transfer period to equipment with time flexibility, and are accompanied by subsidies and incentives. It can reduce load and send instructions to devices that can temporarily reduce power to limit the power adjustment range or the operating time, while limiting the power of a single air conditioner to no more than 70% of its rated value. Instruction execution quantity calculation: Single adjustment total target : ; For safety reasons, ; This is a supply-demand gap; Allocate adjustment amounts according to the user's adjustable capacity ratio. : ； For the first Adjustable capacity per user; Command issuance channel: Commands are issued in encrypted message form through the remote control interface of the electricity information collection system to ensure that the transmission delay is ≤10 seconds.

9. A source-load interaction method based on an electricity consumption information collection system according to claim 6, characterized in that, The distributed energy dispatch scheme is used to coordinate the output of distributed energy, prioritize the absorption of renewable energy, reduce wind and solar curtailment, and mitigate the impact of output fluctuations on the power grid. The steps for formulating the distributed energy dispatch scheme include: Output plan formulation: Based on the output curve prediction of S2 distributed energy, formulate daily output plan. The planned output of photovoltaic power needs to be matched with changes in sunlight intensity, and the planned output of wind power needs to take wind speed forecasts into account. Planned power output must meet grid constraints: , The grid's capacity must be within acceptable limits, and the ramp rate must be ≤10% of the rated capacity within 15 minutes. Real-time scheduling strategy: Priority should be given to disposal, when At that time, the full amount was consumed without adjustment; To actually contribute to photovoltaic / wind power; Curtailment control, when the actual output of photovoltaic / wind power At that time, the local energy storage system will be activated for charging: If the energy storage is at full capacity, the output will be limited according to the principle of prioritizing wind power over solar power. Charging power for the local energy storage system; Coordinate with load regulation: When the output of distributed energy resources drops sharply, an emergency load reduction command is immediately triggered to fill the output gap; when the output of distributed energy resources increases sharply, off-peak electricity price incentives are activated to attract users to increase electricity consumption surplus. Dispatch command issuance: Output limit commands are sent to photovoltaic inverters and wind turbine controllers via RS485 interface, with accuracy controlled within ±5% of rated capacity.

10. A source-load interaction method based on an electricity consumption information collection system according to claim 1, characterized in that, The strategy execution and feedback method is as follows: S41. Real-time monitoring of strategy execution effect: By integrating data from the electricity information collection system and the SCADA system, the monitoring screen is updated every 5 seconds, focusing on tracking user response and power grid status; Regarding user response, the electricity price incentive response rate is calculated as follows: ； During the calculation process, it is required that the transferable load is ≥70% and the load that can be reduced is ≥80%, and user APP satisfaction ratings are collected; Regarding the power grid status, monitor the frequency, voltage, and net load curves, and calculate the peak-to-valley difference changes after the strategy is implemented. : ； The maximum load during peak hours of the power grid before the strategy is implemented represents the peak pressure on the power grid before implementation. This represents the maximum load on the power grid during peak hours after the strategy is implemented, reflecting the effect of reducing peak load after demand response. When the command execution rate is less than 60% or the frequency deviation exceeds ±0.3Hz, an audible and visual alarm is triggered to notify dispatchers to intervene. S42. Feedback data acquisition and analysis, covering user side, energy side, and grid side: User side records actual load curves through smart meters. Calculate the adjustment amount : , This serves as the baseline load curve; the energy side records the deviation between the actual and planned output of distributed energy resources. : ; In order to formulate a daily work plan, The analysis includes the actual output of photovoltaic / wind power; frequency and voltage fluctuations are collected on the grid side; and the strategy benefit index is calculated during the analysis. : , For duration, Effective; assessing the deviation of the elasticity coefficient , This refers to the actual elasticity coefficient calculated after the strategy is executed. To predict the elasticity coefficient; The model needs to be revised regularly; differences in response rates should be statistically analyzed by user type to identify high-response and low-response user groups; S43. Dynamic adjustment and optimization of strategies: Dynamically optimize strategy parameters based on feedback analysis results: electricity price adjustment coefficient. The adjustment formula is: ； The updated electricity price adjustment coefficient affects the intensity of electricity price incentives on the user side; The previous electricity price adjustment coefficient is the base value for the adjustment; If the actual elasticity coefficient is lower than the model prediction, then increase. Instruction allocation is tilted towards high-response users, while the quota for low-response users is reduced; Distributed energy plan output based on Corrections; In long-term iterations, the user resilience coefficient and adjustable potential model are updated weekly, and the strategy effectiveness is reviewed monthly, retaining the strategy benefit index. The superior strategy trains a strategy selection model using the Q-learning algorithm, automatically matching the optimal strategy with the grid state; it grants green electricity certification and additional discounts to high-response users, and increases compensation standards for low-response industrial users, continuously improving strategy adaptability.

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