331 results about "Electricity price" patented technology

The invention relates to the technical field of power system optimization scheduling, and provides a multi-time-scale new energy-stored energy joint output optimization method and system based on spot price, so as to solve the problems of poor system operation stability and low new energy regulation and control precision in the prior art. The method comprises the following steps: detecting a temperature change rate of a composite phase change material in an energy charging and discharging process to determine a heat storage capability state of an energy storage unit, and generating a day-ahead joint scheduling plan considering operation cost and a new energy consumption rate in a coordinated optimization framework by combining day-ahead electricity price data; constructing an elastic matrix based on historical electricity consumption data, correcting the elastic matrix by using latest market information and a system state, and determining a load adjustment amount in combination with an intraday electricity price; and based on the real-time electricity price and the load adjustment amount, a model prediction control algorithm is adopted to calculate real-time output adjustment instructions of the new energy unit and the energy storage unit in a rolling manner, and the operation states of the two units are synchronously adjusted to realize collaborative optimization. The system operation stability and the new energy regulation and control precision are improved.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention discloses a transaction decision-making method and system in a market joint clearing environment, and relates to the technical field of electric power spot market transaction, and the method comprises the steps: S1, obtaining a predicted value of a node marginal electricity price, and constructing a quotation optimization model of each power generation main body; s2, designing an agent, and training the agent by taking the quotation optimization model as a reward function; s3, generating quotation data based on the trained agents, and constructing a joint clearing model of all power generation subjects; s4, calculating the actual value of the node marginal electricity price based on the joint clearing model, and calculating the deviation between the actual value and the predicted value; s5, judging whether the deviation exceeds a preset threshold value or not, if so, taking the actual value as a predicted value, and returning to the step S1 for closed-loop learning; and if not, taking the quotation data generated by each agent as a final quotation strategy. According to the invention, global optimization and intelligent collaboration of power generation subject transaction decisions can be realized in a complex market environment.

The invention discloses an electricity price execution and bill risk combined preposed intelligent early warning method, which relates to the technical field of electric power charging risk control, and comprises the following steps of: constructing rate digital twinning, managing atomic change according to a version directed acyclic graph, generating an explanation path of a trigger condition-calculation expression-bill line item, and mapping with a metering object, a customer object and a bill object; executability and traceability are realized. Based on layered samples and boundary samples, new and old rules are played back on double tracks of a sandbox, the bill difference, the uncertainty and the exposure degree are obtained, and differential hotspots are identified; generating a version binding scene pre-bill after a semantic structure, a coverage boundary, a performance budget and compliance consistent access control are passed, providing an alternative scene and collecting a receipt; gray level monitoring is carried out, rollback and secondary account checking are carried out according to the TariffID subgraph when triggering is carried out, and evidence fingerprints are formed; according to the scheme, aperture consistency and boundary coverage are improved, and accurate rollback and auditing verification are supported.

The invention discloses an optimal control method for metering a cold and heat storage air conditioning system in a laboratory, and relates to the technical field of optimal control, and the method comprises the following steps: predicting hourly cold load and heat load in a future preset period, and generating hourly load prediction data; obtaining time-of-use electricity price information, and generating electricity price time period division data; formulating an energy storage strategy based on hourly load prediction data and electricity price time division data; the method specifically comprises the steps that in the valley electricity price period, when it is predicted that a load demand exists in the future peak electricity price period, a cold and heat source module is controlled to operate, energy is stored in a cold and heat storage module till the rated capacity is reached or the valley period is ended, and the temperature and humidity in a laboratory are maintained within a first set range through a tail end processing module in the energy storage period; and formulating an energy release and conventional operation strategy based on hourly load prediction data, electricity price time division data and the real-time energy storage state of the cold storage and heat storage module. The method has the effect of improving the optimization control efficiency.

The invention discloses a multi-energy complementary optimal configuration method and system for a new energy base, and belongs to the technical field of power system planning. The method comprises the following steps: acquiring energy base system operation parameters, power generation equipment parameters, delivery channel capacity and power market electricity price data; constructing an optimal configuration model taking a receiving end time-of-use electricity price signal as input and aiming at stabilizing new energy output fluctuation and meeting delivery channel constraint; solving the model to obtain a thermal power, wind power, photovoltaic and energy storage capacity configuration scheme and a time sequence output plan; and determining a final optimal configuration scheme by analyzing the operation stability and delivery power volatility of the configuration scheme in different pricing modes. According to the method, the technical problem that the influence of the receiving end electricity price market is not fully considered during optimal configuration of the existing new energy base is solved, an optimal configuration scheme which meets the technical constraint and adapts to the market environment can be generated, and the operation economy and stability of the new energy base are remarkably improved.

The invention relates to the technical field of carbon neutralization, and discloses a critical electricity price calculation method based on green electricity energizing tempering coupling methanol preparation, and the method comprises the steps: building an ironmaking byproduct gas database and defining green hydrogen preparation parameters based on different ironmaking processes, and building a gas purification cost model based on the ironmaking byproduct gas database and the green hydrogen preparation parameters; constructing a ton methanol complete cost model of a tempering coupling methanol preparation path X; a reference process HCTM is selected, C (X) is made to be equal to C (HCTM), and an analytic expression of the critical electricity price is solved; the actual electricity price is compared with the critical electricity price, if the actual electricity price is compared with the critical electricity price, it is judged that the path X has economic advantages relative to the HCTM under the current year T and the carbon tax condition, and if not, it is judged that the path X does not have economic advantages relative to the HCTM. The investment risk of a green methanol project can be remarkably reduced.

The invention discloses an operation control method and device of an energy storage system based on demand defense. The method comprises the following steps: predicting and obtaining a load change curve, a photovoltaic output change curve, an electricity price change curve and a charge state change curve in a future preset time period based on a collected multi-source data set of a user side; predicting the electricity purchase cost of the user based on the electricity price change curve, the load change curve, the photovoltaic output change curve and the charge state change curve; predicting demand penalty cost based on the load change curve and the photovoltaic output change curve; constructing a target function at least based on the user electricity purchase cost and the demand penalty cost; and determining a day-ahead charging and discharging strategy based on the target function and a predetermined constraint condition, and controlling the operation of the target energy storage system based on the day-ahead charging and discharging strategy. According to the invention, the operation of the energy storage system can be reasonably and accurately controlled.

The invention belongs to the technical field of virtual power plants, and relates to a power spot market oriented virtual power plant resource dynamic aggregation method. The method comprises the following steps: extracting a distributed resource feature vector and mapping the distributed resource feature vector into a response type label; determining a price fluctuation period according to a spot price sequence, calculating a time sequence matching degree between each resource and a market rhythm by combining the feature vector, and constructing a comprehensive distance by fusing a clustering weight and a response type label; dividing the resource cluster into dynamic aggregation units by taking the variance of the minimum comprehensive distance as a target; a resource aggregation strategy is generated based on the aggregation unit, a control instruction is issued, and clustering weights and aggregation parameters are iteratively updated according to the clearing deviation rate fed back by market clearing. According to the method, the technical problems that the market dynamics is ignored, the clustering model is easy to merge heterogeneous resources and the closed-loop optimization is lacked in the existing static division are solved, and the effects of improving the market response capability of the virtual power plant, the control consistency of the aggregation unit and the adaptive optimization of continuous transactions are achieved.

The invention belongs to the field of electric power facility protection, and relates to an electric power facility external damage follow-up method and system, and the system comprises a follow-up process management module, an electric quantity loss calculation module, a block chain data analysis module, an electricity price loss calculation module, a simulation evaluation module and a typical case library module. The follow-up process management module manages whole-process information processing and follow-up work of the external breaking case according to a standardized process; the electric quantity loss calculation module analyzes the electric quantity loss of the external damage accident through a neural network algorithm and performs loss accounting; the block chain data analysis module carries out real-time evidence storage on external damage event data by utilizing a block chain technology; the electricity price loss calculation module calculates the electricity price loss of the user according to the electric quantity loss analysis result; the simulation evaluation module simulates an external damage accident scene and generates a standardized loss report; and the typical case library module is used for storing and managing typical external breaking cases. According to the invention, high efficiency, accuracy and credibility of external damage follow-up of electric power facilities can be ensured.

The invention discloses an electric power intra-day price prediction method based on dynamic holiday weight and multi-source fusion, and the method comprises the steps: obtaining multi-source historical data, and carrying out the time synchronization processing; holiday and festival time information is acquired, and a dynamic weight is generated based on the influence of holidays and festivals and upstream and downstream dates on the power load and the electricity price; constructing a multi-dimensional predictive factor matrix based on the multi-source historical data after time synchronization processing; and according to the multi-dimensional predictive factor matrix, on the basis of a collaborative optimization multi-model combination comprising a feedforward neural network model and a bagged regression tree model, intra-day joint prediction is executed, the intra-day joint prediction refers to a process of predicting the power load and the electricity price hourly, and an hourly prediction result of the power load and the electricity price is output. A dynamic holiday weight mechanism is introduced, a multi-source fused high-dimensional predictive factor matrix is constructed, and a multi-model combined predictive strategy of collaborative optimization of a feedforward neural network and a bagged regression tree is adopted, so that the precision and stability of intra-day electricity price and load prediction of the electricity market are improved.

The invention discloses an industrial and commercial time-of-use electricity price dynamic adjustment model considering an electric power spot market. The method comprises the steps of obtaining a historical load curve of a preset category; dividing the historical load curve into a first time period group based on a first preset quarter and a second time period group based on a second preset quarter based on a hierarchical clustering algorithm and by combining a load weighted Euclidean distance and a Ward connection method according to the historical load curve; obtaining a clustered load curve according to the first time period group and the second time period group; determining load characteristics according to the clustered load curve; adjusting a load elasticity coefficient according to the load characteristics and historical load data; constructing an optimized load model according to the load elastic coefficient; inputting the optimized load model into a single-target artificial hummingbird algorithm, and determining the electricity price of each time period; and determining a final electricity price scheme according to the electricity price of each time period. The invention solves the problem of insufficient seasonal load adaptability of the current power system.

The invention relates to the technical field of power regulation, in particular to a refrigeration control strategy optimization method based on cooling capacity storage and electricity price change, which comprises the following steps of: generating an energy efficiency parameter correction result by collecting a cooling capacity and refrigerant temperature difference, calculating a cooling capacity demand and a cooling capacity difference value and combining real-time electricity price and energy efficiency parameter normalization; the method comprises the steps of calculating a difference value between a cooling capacity demand and a refrigerating capacity, analyzing a power correction coefficient and an electricity price section, setting a cold storage instruction and a cold release instruction, adjusting a cold storage or cold release strategy in combination with an electricity price trend, generating an electricity price response control strategy set, and optimizing cold storage power adjustment, and comprises the steps of calculating a difference value between the cooling capacity demand and the refrigerating capacity, combining real-time electricity price and energy efficiency parameter normalization and fitting an energy efficiency parameter correction result; based on electricity price segmentation analysis, a proper cold storage or cold release instruction is set, the energy efficiency of equipment in a peak-valley electricity price interval is improved, the electricity price trend is analyzed to adjust a cold storage or cold release strategy, energy consumption waste is avoided, finally, the electric energy utilization efficiency is improved, the cost is reduced, and the flexibility and adaptability of a refrigeration trolley are enhanced.

The invention discloses a rural area new energy consumption-oriented user side time-of-use electricity price optimization method, which comprises the steps of determining an original daily net load curve according to user side load data and new energy output data of a rural area; clustering the original daily net load curve by using an improved K-means + + algorithm to obtain a typical daily net load curve; carrying out peak-valley period refined division based on the typical daily net load curve to obtain a peak-valley period division result; taking the minimum peak-valley difference rate of the power system in the rural area as a target, constructing a target function with the minimum peak-valley difference rate, and constructing a time-of-use electricity price optimization model in combination with the peak-valley time period division result; and solving the time-of-use electricity price optimization model by using an RODDPSO algorithm to obtain an optimal time-of-use electricity price scheme. Based on this, the method can effectively stimulate the user-side adjustable load to participate in demand response, guides the time-space transfer of the load, and smooths the rural power grid net load curve while improving the new energy consumption.

The application discloses a power market electricity price mechanism coordinated evolution implementation method, and particularly relates to the technical field of power market operation and regulation; based on a multi-dimensional power market scene set, evolution track characteristic parameter sets of the electricity price mechanism are extracted; a similarity clustering algorithm is used to divide the electricity price mechanism into coupled mechanism clusters, and a coupling degree coefficient matrix is constructed; a coordinated evolution function group is constructed based on the coupling degree to represent the linkage response relationship between the mechanisms; a disturbance factor is introduced to optimize the robustness of the model, forming a robust evolution model; the optimized model is applied to the current operating state of the target area, and the electricity price mechanism state prediction result of the next period is output; the electricity price tearing point and the mechanism conflict point in the prediction result are further identified, and the high-risk regulation interval is marked; the application can realize dynamic linkage modeling and stability early warning between multiple mechanisms, and improve the adaptability and regulation precision of the electricity price mechanism under high uncertainty scenarios.

The application discloses a kind of based on the dynamic display method and system of intelligent electric meter of electricity price signal, it is related to electric efficiency management technical field, comprising the following steps: S1, receiving and analyzing electricity price signal;S2, dynamically generating visual feature strategy;S3, generating scene energy-saving suggestion text;S4, control display screen carries out composite information rendering and display;S5, monitor condition changes and update display;The present application can quickly, clearly understand the electricity price state and know specific operation suggestion by dynamic, strong visual impact and direct text guidance, so as to effectively promote its adjustment power consumption behavior, improve demand side response speed and effect.

The invention provides a distributed power transaction abnormal electricity price identification method and device, and relates to the technical field of abnormal electricity price identification, and the method comprises the steps: obtaining distributed power transaction data, the distributed power transaction data comprises power transaction data of a plurality of nodes, and the power transaction data of the nodes comprises real-time electricity prices, real-time generated power and timestamps; preprocessing the distributed power transaction data; partitioning the preprocessed distributed power transaction data by using a preset clustering algorithm to obtain a plurality of electricity price partitions; and carrying out abnormal electricity price identification on all the electricity price partitions. The accuracy and the recognition efficiency of the abnormal electricity price recognition result can be improved.

This application relates to a control method, apparatus, and television device. The method includes: obtaining the current electricity price period based on peak-valley tiered electricity price data for the user's region, and obtaining the user's household electricity consumption data; determining whether preset energy-saving conditions are met based on the current electricity price period and household electricity consumption data; when the preset energy-saving conditions are met, generating an adjustment strategy based on the preset energy-saving conditions and the television device's equipment parameter data; and controlling the television device to execute the adjustment strategy, so that the television device can reduce power consumption while maintaining continuous playback of media content. Using this application, intelligent energy-saving control linked to peak-valley tiered electricity pricing can be achieved, balancing energy saving of the television device with the viewing experience.

The invention relates to an energy storage system capacity configuration method based on dual-mode prediction, and solves the problem of wind curtailment and electricity curtailment caused by insufficient energy storage residual capacity due to easy zeroing of the noon clearing electricity price compared with the prior art. The method comprises the following steps: obtaining historical transaction data and historical climate data of a regional electric power spot market; constructing an electricity market transaction distribution time period prediction model and training electricity price return-to-zero time period prediction; the electricity market transaction distribution time period prediction model trains the data prediction electricity price based on the predicted time period; acquiring real-time data and outputting a power market transaction distribution period prediction model result; and dynamically configuring the capacity of the energy storage system. The electricity price fluctuation condition under the current marketization transaction is considered, the BP neural network algorithm is combined to predict the electricity price return-to-zero interval in the noon power generation peak period, the all-day electricity price is predicted according to the result, the energy storage capacity is adjusted accordingly, and the method has the technical advantages of being scientific and reasonable, high in applicability, high in adjusting speed and the like.

This invention provides a method and system for economical operation scheduling of water source pumping stations based on time-of-use pricing. By constructing a two-layer architecture model for water lifting capacity configuration and start-up combination decision-making, and using unit start-up and shutdown limits, canal system safety operation, and ten-day water lifting targets as constraints, an optimization model is built and solved to minimize pumping station operating energy consumption and electricity costs, accurately generating start-up combination schemes for each time period within a ten-day period. This invention overcomes the scheduling limitations of discrete flow regulation for constant-speed pump units, comprehensively considering engineering safety, water supply safety, and operational economy. It constructs a scheduling methodology adapted to the dual constraints of poor unit flexibility and weak canal system regulation and storage, achieving economical operation based on time-of-use pricing while ensuring the dynamic optimization performance of the model, improving the robustness and intelligence of scheduling decisions, and filling the research gap in the optimized scheduling of pure constant-speed pump water source pumping stations.

This invention relates to the field of power system demand response technology, and provides a user demand response method based on master-slave game theory under cost control constraints. The method includes: establishing a demand response model using master-slave game theory, where electricity retailers design time-of-use pricing strategies, and electricity users respond based on a convex utility function; the utility function includes a quantification term for electricity usage satisfaction and a calculation term for electricity costs; the electricity retailers' revenue function includes four functions: user electricity costs, long-term market electricity purchase costs, current market electricity purchase costs, and conditional risk value, coupled with the characteristics of the electricity spot market and risk management requirements; proving the existence of a Nash equilibrium solution using backward induction; and optimizing the strategy based on a slime mold optimization algorithm. This invention provides important support for electricity retailers to formulate flexible pricing mechanisms and tap demand-side resources in terms of cost control for users with high response potential and optimization of peak-valley load differences.

The invention belongs to the technical field of data processing, and provides a power market price prediction method and system based on two-stage optimization, and the method comprises the steps: determining an initial generating capacity node value and an initial unit price node value of each generator set, and carrying out the interpolation refining operation of the initial generating capacity node value and the initial unit price node value, obtaining a refined generating capacity node value and a refined unit price node value; establishing an output interval prediction 0-1 planning model of the generator set; performing discrete optimization solution on the output interval prediction 0-1 planning model to obtain a predicted generating capacity interval of each generator set; according to the predicted generating capacity interval, the refined generating capacity node value and the refined unit price node value, establishing an average electricity price prediction quadratic programming model; and performing continuous optimization solution on the average electricity price prediction quadratic programming model to obtain an average electricity price prediction value. The scheme can adapt to market dynamic changes, and prediction precision, flexibility and reliability are effectively improved.

This invention belongs to the field of energy management technology for charging stations and discloses a joint scheduling method for energy management and electricity price response of charging stations with energy storage. The method acquires information on incoming power supply capacity, station basic load, energy storage unit operating status, charging terminal operating status, vehicle energy replenishment demand, and demand response, determining the demand response period and recovery period. Based on the station-level direct replenishment capacity without relying on energy storage units during the recovery period, it determines the maximum direct replenishment capacity, response-reserved replenishment energy, and response-transferred replenishment energy for each vehicle waiting to be charged. Furthermore, it determines the station-level minimum charging demand during the demand response period, the energy storage unit's recovery-reserved capacity, and the reduction boundaries on the vehicle side and energy storage side, generating a joint scheduling instruction, and replenishing the reduced replenishment energy during the recovery period. This method is applicable to demand response and energy replenishment scheduling of charging stations with energy storage.

This invention relates to a coupled clearing method and apparatus for virtual power plants participating in the spot electricity market, applied in the field of electricity market technology. The method includes: calculating the High-Gross Price Ratio (HGRP) as the threshold for economic compensation of DRVPPs, compensating DRVPPs only when the node marginal price is higher than the HGRP, thus preventing the activation of non-economical DRVPPs during low-price periods, preventing low-cost generation resources from being squeezed out of the market, and ensuring the authenticity and effectiveness of the market clearing price signal; incorporating DRVPPs as a controllable and constrained demand-side adjustment variable into the clearing model, avoiding the failure to reflect the adjustment value due to its inclusion in load forecasting, and overcoming the problem of simply treating it as "negative load" and ignoring response constraints, thereby improving the rationality of the clearing results; and adopting a differentiated economic compensation mechanism based on the node marginal price, linking the adjustment revenue of DRVPPs to the marginal electricity cost of the node, achieving precise matching of DRVPP resources with the actual adjustment needs of the system.

The invention discloses a local power grid optimal scheduling method based on double-cycle planning and double-mode control, and belongs to the technical field of power grid scheduling. Comprising the steps of generating a day-ahead plan according to prediction data, energy storage constraint data and time-of-use electricity price policy data, generating an intra-day rolling plan according to ultra-short-term prediction data, and correcting the day-ahead plan by using the intra-day rolling plan to obtain a double-cycle scheduling plan; when the local power grid executes the autonomous consumption mode, executing the double-cycle scheduling plan, and generating an autonomous consumption mode scheduling result; and when the local power grid executes the scheduling centralized control mode, generating a scheduling centralized control mode plan according to the scheduling instruction, executing the scheduling centralized control mode plan, and generating a scheduling result of the scheduling centralized control mode. According to the invention, through dual-cycle cooperative dual-mode adaptation, multi-target balance of high consumption of renewable energy sources, low power consumption cost and stable operation of the system is realized.

The present application relates to the technical field of power spot market transaction and thermal power unit operation optimization, in particular to a double-layer deduction method for thermal power unit group spot bidding strategy, which adopts a double-layer optimization model for solution, the upper layer is a similar layer of price unit value, the target is to minimize the weighted relative deviation of market simulation price and historical real price, and the upper and lower limits of segmented bidding and bidding quantity are set according to the capacity scale of the unit to set differentiated constraints; the lower layer is a market clearing layer, the target is to minimize the total system cost, and the system power balance, unit operation, new energy consumption, power transmission safety and other system operation and market clearing full-dimensional constraints are met, the optimal bidding strategy combination is obtained through the iterative interaction of bidding and price signals, the method adopts DE-PSO hybrid algorithm for solution, can accurately fit the real market price law, provides scientific bidding decision basis for power suppliers, and is suitable for different market operation scenes such as heating period and non-heating period, etc.

Embodiments relate to a virtual power plant system based on self-demand response. A system according to an embodiment relates to a virtual power plant system that analyzes in real time at least one of power demand data, power generation amount data, electricity price data, and distributed power information and provides the result of the analysis in order to enable an individual consumer to perform self-demand response (Self-DR) using a user terminal.

The invention discloses a power transaction prediction method, system and device based on similar day analysis, and a medium, mainly relates to the technical field of power transaction prediction, and is used for solving the problem that the comprehensive influence of various factors is not fully considered in the existing scheme. Comprising the following steps: calculating various types of dynamic weight vectors by utilizing a preset basic weight vector and a dynamic adjustment factor; obtaining current-day power associated data, and calculating the similarity between each historical day and the current day in the historical time period based on the current-day power associated data, the characteristic value, the power associated data in the historical time period and the dynamic weight vector; determining a plurality of historical days with the highest similarity as reference days; according to the real-time electricity price time-of-use curve of all the reference days, fitting the electricity price corresponding to each hourly moment of the current day, and outputting the confidence coefficient of each electricity price; and displaying the electricity price and the confidence coefficient on a preset auxiliary decision-making interface.

The invention discloses a multi-virtual power plant low-carbon retail pricing method based on a non-cooperative game, and belongs to the technical field of power system optimization and energy market transaction, and the method comprises the following steps: building a control model of a single virtual power plant; 2, correcting a dynamic carbon emission factor; 3, deducing the optimal retail electricity price of the plurality of virtual power plants; according to the invention, a multi-virtual power plant non-cooperative game pricing model based on dynamic carbon emission factor-economic benefit double chains is established, a value chain of low-carbon excitation and economic benefit is broken through, an accurate low-carbon benefit acquisition mode is created for the virtual power plant, the virtual power plant is promoted to preferentially dispatch renewable energy sources, and the economic benefit of the virtual power plant is improved. And the low-carbon operation level and the market competitiveness of the regional power system are effectively improved.

This invention proposes a day-ahead spot clearing price-load data repair method based on generative adversarial networks. A training dataset is established; K-means clustering is used to cluster the user electricity load dataset, classifying it into typical user groups; a deep convolutional generative adversarial network (DCGAN) model is established; the DCGAN model is trained using historical day-ahead spot market clearing price data and historical user electricity load data after classifying typical user groups, retaining the generator structure for each typical user group; a binary mask matrix is ​​introduced to represent the missing locations of newly collected user electricity load data; then, the newly collected day-ahead spot market clearing price dataset, typical user electricity load dataset, and target user electricity load data with missing data are input into the generator corresponding to the typical user group, outputting new generated data; the missing parts of the target user electricity load data are filled in using the new generated data samples.