379 results about "Electricity price forecasting" patented technology

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

The invention discloses an optimized dispatching control method and system for transformer area energy storage, and relates to the technical field of energy storage dispatching, and the method comprises the following steps: integrating an intelligent load management system with an energy storage system, thereby monitoring the electrical load of a transformer area in real time, and feeding back the real-time data to the energy storage system; historical load data and electricity price data are collected, a prediction model is trained, future load and electricity price are predicted in real time, and prediction data are generated. And according to a prediction result, a preliminary energy storage system scheduling strategy is formulated, and a target function R is constructed, so that the target function R can dynamically adapt to changing load and electricity price conditions. According to the invention, through tight integration of the intelligent load management system and the energy storage system, real-time monitoring of the power grid load and efficient scheduling of the energy storage system are realized; in other words, the energy storage system can charge or discharge at the best time in combination with accurate load and electricity price prediction and scheduling strategy rules, power grid resources are utilized to the maximum extent, and unnecessary resource waste is avoided.

The invention relates to the technical field of uninterruptible power supplies, and provides a UPS peak shifting charging method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining electricity price historical data and load historical data in historical data of a power grid; performing model training on the electricity price historical data and the load historical data to obtain electricity price prediction data and load prediction data; determining a preset charging strategy of the UPS system based on the electricity price prediction data and the load prediction data; determining a real-time charging demand according to the obtained real-time battery pack capacity and the real-time load data of the power grid; the preset charging strategy is updated according to the real-time charging requirement, the target charging strategy is obtained, and the charging strategy of the UPS system is dynamically adjusted according to the real-time load condition of the power grid.

The invention provides a new energy station digital intelligent operation system, a control method, equipment and a medium, and relates to the technical field of new energy. According to the system, a marketization electricity price prediction result and a new energy output prediction result are output through an electricity price prediction module and a new energy output prediction module respectively, and an energy storage optimization scheduling module and a planning layout decision module work cooperatively. And combining the marketization electricity price prediction result and the new energy output prediction result to realize operation strategy optimization and capacity configuration decision of the new energy station, and solving and outputting a capacity configuration scheme corresponding to the new energy device and the energy storage device. According to the technical scheme, the multi-module collaborative new energy station digital intelligent operation system is constructed, so that full-link closed-loop optimization from prediction to scheduling to planning is realized, the adaptability of the system to power market fluctuation can be remarkably enhanced, and the operation income of the new energy station is ensured.

The invention provides a prediction method and device for operation parameters of a virtual power plant, a storage medium and a computer program product. Comprising the steps of performing data preprocessing on acquired influence factor data; using a time sequence decomposition algorithm to decompose the influence factor data into three-dimensional sequences including a trend component, a season component and a residual component, and segmenting each sequence into sequence blocks and mapping the sequence blocks into time sequence feature vectors; performing K-means clustering on word embedding used for pre-training the large language model, and selecting K clustering centers as semantic anchor points to be spliced with the time sequence feature vectors; finely adjusting the position embedding parameters of the large language model, the weight of the feedforward neural network in residual connection and the parameters of the normalization layer; and according to the semantic enhancement time sequence feature vector, predicting to obtain a trend component, a season component and a residual component of each dimension, and carrying out splicing and reverse normalization processing on the components to obtain a prediction result. According to the prediction method, the accuracy and real-time performance of virtual power plant load and electricity price prediction are improved.

The invention discloses an electricity price prediction large model construction method and system based on federated learning, and relates to the related technical field of electricity price prediction, and the method comprises the steps: executing the data collection of a time window in a preset time window, and building a window data set; executing sample feature extraction; performing multi-dimensional feature clustering by using the multi-dimensional feature identifier and configuring N clusters; performing cluster division based on double identification channels, and establishing an independent cluster and a combined cluster; independently training the decision tree by using the independent cluster, and executing weak classifier mapping training of the combined cluster; and establishing an electricity price prediction large model. The technical problems that in the prior art, power industry data are difficult to share, multi-source heterogeneous data cannot be integrated through federal learning, the electricity price prediction precision is low, and the prediction model generalization ability is poor are solved, and the technical effects that multi-mechanism data collaborative modeling is achieved, and the electricity price prediction model generalization ability and the electricity price prediction precision are enhanced are achieved.

The multi-energy complementary heat supply system comprises an external basic heat source, a middle-grade synergistic heat source, a high-grade energy storage heat source, an intelligent heating power mixing center and a central controller, and the intelligent heating power mixing center is used for heating heat source equipment of all levels and operation instructions of associated assemblies according to the operation instructions of the heat source equipment of all the levels and the operation instructions of the associated assemblies. Heat energy of different grades is mixed and allocated, and heat loads meeting the grade requirements of heat consumers are output to the heat consumers; the central controller is used for establishing a multi-target optimization model taking the minimum total operation cost of the system as a core target based on the electricity price prediction data and the different-grade thermal load prediction data, solving the multi-target optimization model and generating a system operation strategy under the condition that preset basic guarantee logic, economic operation logic and peak response and power grid interaction logic are met; and the generated system operation strategy is converted into an operation instruction for the heat source equipment of each level and the associated assembly, so that the reliability and the adaptability of operation control of the heat supply system are effectively improved.

The invention relates to the technical field of electric power market price prediction, in particular to an electric power spot price prediction method and system based on similar day probability correction, and the method comprises the following steps: S1, electric power data collection: obtaining historical market information of a day-ahead spot market and day-ahead electric power spot price data; s2, feature engineering construction: performing feature engineering construction on the collected power data; s3, data preprocessing: forming a data set; s4, performing model training: performing training on the LightGBM model; s5, similar day matching: screening k historical days with the highest similarity with the prediction day; s6, performing interval probability distribution modeling of the bidding space and the day-ahead electricity price: establishing a probability mapping matrix of the bidding space interval and the day-ahead electricity price interval; and S7, prediction result correction and final electricity price prediction: generating a final day-ahead spot electricity price prediction sequence. According to the invention, the method is closer to the actual market situation when the prediction logic is constructed, and the stability and adaptability of electricity price trend modeling are enhanced.

The invention discloses a photovoltaic energy storage cooperative control method and system, and the method comprises the steps: obtaining the real-time state data and environment information of distributed photovoltaic and energy storage equipment, and constructing a system network topology; constructing a hierarchical prediction model including photovoltaic power generation prediction, load demand prediction and electricity price prediction based on the system state data set; constructing a multi-objective function containing an economic benefit objective and a system stability objective based on a prediction result, and performing objective decomposition through recursive iteration; according to a local optimization target, generating a photovoltaic power adjustment strategy and an energy storage charging and discharging strategy by reducing a distributed maximum matching gap and reducing an order to break a distributed maximum independent set obstacle algorithm; and based on the distributed control strategy set, the system operation state is monitored in real time, and control parameters are dynamically adjusted by adopting an event triggering mechanism. According to the method, the problems of calculation complexity and communication dependence of traditional centralized control in a large-scale photovoltaic energy storage system are solved.

The invention provides a power distribution network multi-time scale typical scene generation method based on data driving. The method comprises the following steps: (1) generating sample data of regional load, new energy and historical electricity price by adopting a generative adversarial network through a data mining and feature extraction method; (2) generating medium and long term source charge electricity price prediction data; (3) scene clustering among different planning stages under a long time scale is carried out, and transition probability characteristics of different planning stages are determined; (4) denormalizing the source charge electricity price, matching a corresponding scene path probability, and generating a multi-stage scene tree; (5) performing scene clustering in a stage under a short time scale; and (6) combining the inter-stage typical scenes obtained in the step (3), and forming a multi-stage and multi-dimensional typical scene set by adopting a heuristic synchronous back substitution scene reduction method. According to the power distribution network multi-time-scale typical scene generation method, scene features among different planning stages can be analyzed more comprehensively, and a more complete scene is generated more comprehensively.

The invention provides a new energy station energy storage control system and method, and the system comprises a new energy output high-precision prediction module which is used for fusing local laser radar and numerical weather forecast data, and outputting an output predicted value; the SOC curve initialization module is used for initializing an energy storage SOC reference curve based on the output predicted value and the day-ahead market electricity price information; the real-time node electricity price prediction module is used for predicting a real-time electricity price trend; and the rolling optimization control module of the energy storage system is connected with other modules, and dynamically optimizes the charging and discharging instruction and the SOC state according to the output prediction value, the energy storage SOC reference curve and the electricity price prediction value of the real-time node electricity price prediction module. According to the invention, by fusing the multi-source prediction result and the real-time market information, on the premise of satisfying various operation and market constraint conditions, a highly adaptive energy storage dynamic optimization regulation and control mechanism is constructed, and key support is provided for economical operation of a new energy station in a spot market environment.

Provided is a day-ahead electricity price prediction method based on a parallel multi-dimensional attention mechanism, which method belongs to the technical field of day-ahead electricity price prediction in a power market. The maximum mutual information coefficient is used to select variables having a certain relevance with an electricity price, so as to assist in predicting future electricity price data; next, a decomposition algorithm is used to decompose an original electricity price signal; and then, electricity price sub-components obtained by means of decomposition and the electricity-price-related variables are inputted into an MBI-IPMDA-PBISA deep learning model, so as to predict a future electricity price. In this way, an MBI-IPMDA-PBISA deep learning model can fully learn a complex association relationship between electricity price sub-components and electricity-price-related variables and a future electricity price, the problem of error superimposition caused by separately predicting the electricity price sub-components and then superimposing same to obtain the future electricity price is also avoided, and the problem of the accuracy of existing attention mechanisms during prediction being low is solved, thereby improving the prediction accuracy.

The invention discloses a distributed photovoltaic power distribution network shared energy storage optimization method, and relates to the technical field of energy storage system optimization, and the method comprises the following steps: inputting the current electricity price of an electricity market into a preset electricity price prediction model, and obtaining a current rolling cycle electricity price prediction sequence, constructing a rolling optimization model based on the current rolling cycle electricity price prediction sequence, the energy storage system battery real-time charge state and the energy storage system operation constraint condition, taking the maximum energy storage system net income as an objective function, taking the battery life attenuation cost as a correction parameter, and obtaining an optimal charge and discharge power sequence, and controlling the charging and discharging actions of the energy storage system, monitoring an electricity price prediction deviation index in real time, optimizing and calibrating an electricity price prediction model, updating an optimal charging and discharging power sequence, adjusting the charging and discharging actions when the electricity price prediction deviation index is greater than a preset deviation threshold value, and repeating the steps at the beginning of a next rolling period, so that the benefits of the energy storage system are improved, and the battery loss is reduced.

The invention discloses a household electrical load optimization scheduling method and system based on an MAPPO algorithm. The method comprises the following steps: (1) collecting multi-source heterogeneous data from a user side and an external environment and carrying out preprocessing; (2) estimating the future equipment use probability of the user by collecting historical equipment use data of the user, and outputting an equipment behavior prediction vector; (3) acquiring real-time electricity price information, performing short-term electricity price trend prediction by using the time sequence prediction model based on a historical electricity price sequence, and outputting an electricity price prediction sequence; (4) generating a scheduling strategy by the MAPPO network based on the equipment behavior prediction vector and the electricity price prediction sequence, and outputting a corresponding scheduling result; and (5) sending a scheduling result to the home gateway, controlling the equipment, collecting an execution feedback result in real time, updating internal parameters of the MAPPO network, and realizing strategy iteration and adaptive adjustment of the MAPPO network.

The invention discloses an industrial park production plan decision-making method and system based on load prediction, and the method comprises the steps: constructing a day-ahead load prediction model and an intra-day load prediction model respectively, inputting meteorological data, holiday and festival data and historical load data in the industrial park input data into the day-ahead load prediction model for prediction to obtain day-ahead load prediction data, and inputting the day-ahead load prediction data into the intra-day load prediction model for prediction to obtain intra-day load prediction data; generating a day-ahead production plan decision through a production plan adjustment decision algorithm by using electricity price prediction data and day-ahead load prediction data in the input data of the industrial park, or wind-light power prediction data and day-ahead load prediction data in the input data of the industrial park; and generating an intra-day production plan decision through a production plan adjustment decision algorithm by using electricity price prediction data and intra-day load prediction data in the input data of the industrial park, or wind-light power prediction data and intra-day load prediction data in the input data of the industrial park.

The invention provides a new energy optimal power supply strategy method and system based on artificial intelligence, and relates to the technical field of new energy power supply strategies, and the method comprises the steps: constructing an electricity price prediction model, a weather prediction model and a human traffic-load prediction model; a dynamic scheduling optimization model is constructed according to the power supply cost, the power abandonment rate and the load deviation, and the power supply cost is calculated according to a prediction result of the electricity price prediction model; the electricity abandoning rate is determined according to a prediction result of the weather prediction model; the load deviation is calculated according to a prediction result of the pedestrian flow-load prediction model; and according to the dynamic scheduling optimization model, carrying out dynamic adjustment on economic, environmental protection and stability strategies of the power grid. The system comprises a data collection module, an electricity price prediction module, a weather prediction module, a people flow-load prediction module, a dynamic scheduling optimization module and a data output module. According to the invention, an efficient and intelligent solution can be provided for new energy optimal power supply strategy configuration.

The invention provides a new energy configuration, construction and energy storage combined operation optimization system and method, and relates to the technical field of new energy configuration and energy storage. According to electricity price prediction data, a single wind power station output prediction result, a single photovoltaic power station output prediction result, an AGC active power instruction and new energy real-time active power, overall income maximization is taken as a target function, new energy output constraint and energy storage operation cost are considered, and a new energy output prediction result is obtained according to a general principle of low-price charging and high-price discharging. And an energy storage charging and discharging strategy in a future period is given, the charging and discharging strategy is issued to the energy storage EMS, and automatic charging and discharging are controlled by the energy storage EMS. By predicting the node electricity price, the energy storage charging and discharging time is reasonably planned, the energy storage charging and discharging actions are automatically controlled, and the utilization rate of new energy distribution and storage is improved. By predicting the node electricity price, the energy storage charging and discharging time is reasonably planned, the energy storage charging and discharging actions are automatically controlled, and the utilization rate of new energy distribution and storage is improved.

The invention relates to the technical field of computers, and discloses an intelligent scheduling system and method for a high-efficiency charging platform, and the method comprises the steps: constructing a four-dimensional constraint model integrating the power grid load, the user demand, the equipment health degree and electricity price prediction, and employing a depth deterministic strategy gradient algorithm to drive a multi-target dynamic scheduling decision maker, the global optimization distribution of the charging resources in the space-time power dimension is realized, and strategy reconstruction is completed within 10s when a power grid emergency instruction or a device fault occurs. The system comprises a power grid sensing module, a user acquisition module, a health assessment module, an electricity price response module, a scheduling decision module, an instruction execution module and an emergency reconstruction module, and supports millisecond-level adaptive evolution. According to the method, the weighted reward function is constructed by quantifying the four indexes of power grid stability, user satisfaction, equipment loss and platform income, and the instruction verification and steady-state adaptive mechanism is combined, so that the user experience is synchronously improved, the service life of the equipment is prolonged, and the operation energy consumption is reduced on the premise of ensuring the safety.

The invention discloses an intelligent identification method and system for analyzing abnormal node electricity prices based on multi-dimensional backtracking. The method comprises the steps of collecting node real-time electricity prices, node synchronous real-time historical electricity prices and multi-dimensional data indexes influencing the node electricity prices in real time; the node electricity price and the multi-dimensional data are preprocessed, and a multi-dimensional real-time electricity price feature set is constructed. According to historical electricity price data, an electricity price prediction model of a BP nervous system introducing an adaptive fuzzy neural network is established, a multi-dimensional reference electricity price feature set is constructed, and an electricity price normal range is delimited. And comparing the multi-dimensional real-time electricity price feature set with the multi-dimensional reference electricity price feature set in real time, performing multi-dimensional backtracking analysis on the abnormal node electricity price, and analyzing the reason of the electricity price abnormality. And according to the backtracking analysis result, classification is carried out according to abnormal reasons, and an abnormal alarm is sent to technicians. According to the method, the abnormal electricity price and the reason thereof are accurately positioned, the problem response speed and the processing efficiency are improved, and powerful technical support is provided for stable operation of the electricity market.

The embodiment of the invention provides an energy storage charging and discharging strategy optimization method and device based on day-ahead and real-time electricity price fluctuation, and the method comprises the steps: obtaining a day-ahead electricity price prediction matrix which comprises the day-ahead electricity price prediction value of each time period in an energy storage system, and constructing a day-ahead income objective function through the day-ahead electricity price prediction matrix; generating a day-ahead charging and discharging declaration strategy for reflecting the state of the energy storage system in each time period by using the day-ahead income objective function; after entering the real-time market, acquiring a real-time predicted electricity price matrix containing the real-time predicted electricity value of each time period in the energy storage system, and constructing a real-time income objective function by using the real-time predicted electricity price matrix; a result of maximizing the revenue in the real-time market by considering the conditional value-at-risk, and establishing a final revenue model by taking the sum of the day-ahead revenue and the real-time revenue as a target; and performing optimal energy storage charging and discharging strategy optimization solution on the final income model to obtain an optimal energy storage charging and discharging strategy.

The invention relates to the technical field of electric power spot market real-time price prediction, and discloses an electric power spot market real-time price prediction method based on low-sample high-dimension data, and the method comprises the steps: extracting historical data from a database; carrying out feature extraction from historical data, and carrying out standardization processing on the extracted features; constructing a real-time electricity price prediction model by adopting a generative neural network model, and inputting the extracted features of the historical data into the real-time electricity price prediction model to obtain a first predicted electricity price; inputting the first predicted electricity price into a heuristic predictor for adjustment to obtain a second predicted electricity price; adjusting factors for adjusting the first predicted electricity price by the heuristic predictor comprise similarity, a trend factor, a seasonal factor and a special event adjusting factor, and fusing the adjusting factors to comprehensively adjust the first predicted electricity price; existing electricity price prediction cannot be reasonably quantified due to external factors in the early stage of development of an electric power spot market, so that the prediction result is inaccurate.

The present invention discloses a scheduling method for coupled operation of transactions and spot transactions of a multi-energy system, which solves the problem that the existing method cannot predict the trend of electricity prices in the future, and further cannot plan the power generation plan of the electricity spot market in advance when the uncertainty and intermittent fluctuations of renewable energy appear. The method comprises: obtaining the day-ahead spot transaction related data, loading the day-ahead spot transaction related data in real time, the electricity price prediction model outputs the electricity price prediction result and the transaction risk value corresponding to the electricity price prediction result, and the comprehensive optimization scheduling model calculates the predicted output value of the thermal power unit and the predicted output value of the new energy based on the electricity price prediction result and the transaction risk value; the present invention can effectively cope with the uncertainty and intermittent fluctuation of renewable energy by constructing the electricity price prediction model and the comprehensive optimization scheduling model for coordinated cooperation, and ensure that the multi-energy system can operate stably under different energy supply conditions.

The invention discloses a regional electricity price prediction method, device and system, and a storage medium. The method comprises the following steps: S1, constructing a regional electricity price prediction function; s2, extracting regional electricity price data features; and S3, according to the regional electricity price prediction function and the regional electricity price data features, regional electricity price prediction is carried out through an LSTM deep learning structure. By adopting the technical scheme of the invention, the accuracy and real-time performance of electricity price prediction can be remarkably improved, a scientific basis and decision support are provided for participants of the electricity market, and the healthy development and sustainable development goal of the electricity market is promoted to be realized.

The invention discloses a day-ahead electricity price prediction method based on a parallel multi-dimensional attention mechanism, and belongs to the technical field of electricity market day-ahead electricity price prediction. The method comprises the following steps: firstly, selecting a variable having a certain correlation with the electricity price by adopting a maximum mutual information coefficient to assist in predicting future electricity price data, and secondly, decomposing an original electricity price signal by adopting a decomposition algorithm; then, the electricity price sub-components and the electricity price related variables obtained through decomposition are input into an MBI-IPMDA-PBISA deep learning model, future electricity price prediction is carried out, and therefore the MBI-IPMDA-PBISA deep learning model can fully learn the complex incidence relation between the electricity price sub-components and the electricity price related variables and the future electricity price; the problem of error superposition caused by the fact that the electricity price sub-components are independently predicted and then superposed to obtain the future electricity price is avoided, the problem that an existing attention mechanism is not high in prediction precision is solved, and prediction precision is improved.

The invention discloses an electricity price prediction method and system considering electricity-carbon real-time coupling transaction, and belongs to the field of electricity price prediction, and the method comprises the following steps: S1, monitoring and recording the power consumption of each node in real time, and storing the power consumption data in a central database; s2, calculating the carbon emission of each node by using a preset carbon emission coefficient according to the power consumption data in the step S1, and tracking the carbon emission flow; s3, calculating the power utilization carbon emission intensity of each node in a specific time period based on the real-time monitored power consumption data in the step S1 and the tracked carbon emission flow data in the step S2; s4, on the basis of the collected carbon quota price data on the market, in combination with the real-time electricity utilization carbon emission intensity of the nodes, calculating the carbon price corresponding to electricity utilization of each unit, and obtaining the dynamic electricity utilization carbon price; and S5, electricity market transaction data and electricity demands of each node are collected in real time, the basic electricity price and the dynamic electricity utilization carbon price are embedded into the electricity price prediction model, and the coupled real-time electricity price is calculated. According to the electricity price prediction method and system considering the electricity-carbon real-time coupling transaction, the influence of the carbon emission cost on the electricity price can be reflected, more accurate price signals are provided for electricity users and market participants, and electricity transaction decisions and carbon emission management are optimized.

The invention provides an electricity price prediction method and system based on a multi-head attention mechanism and a convolutional network, and the method comprises the steps: obtaining model training data which is time series data; the model training data is input into an electricity price prediction algorithm model for training, the electricity price prediction algorithm model is of an encoder-decoder structure, and an encoder and a decoder are each composed of a network layer formed by connecting a multi-head attention mechanism and a time convolution network; and obtaining training results of electricity price prediction, and carrying out prediction value splicing to obtain corresponding electricity price prediction results in multiple time periods. According to the method, a series of preprocessing processes such as cleaning, feature extraction, dimensionality reduction and normalization are carried out on historical power related data, the obtained data are input into an electricity price prediction algorithm model based on a multi-attention mechanism and a time convolution network, and accurate electricity price prediction values in a plurality of time periods in the future can be calculated.

The invention relates to the technical field of electric power spot transaction, in particular to an electric power spot market day-ahead declaration optimization strategy generation method, device and equipment, and the method comprises the steps: carrying out the preprocessing of the obtained operation boundary data and historical transaction data of an electric power spot market of a target region, and obtaining the processed data; based on the processed data and historical electricity price prediction data, optimizing a pre-trained initial electricity price prediction model to obtain an electricity price prediction model; generating at least one power day-ahead declaration optimization strategy according to the obtained power demand information of the user and day-ahead electricity price prediction data obtained based on a power price prediction model; and selecting a target power day-ahead declaration optimization strategy from the at least one power day-ahead declaration optimization strategy based on an analysis result obtained by analyzing each power day-ahead declaration optimization strategy in the at least one power day-ahead declaration optimization strategy. The optimal declaration scheme is provided for the user on the premise that market rules and constraint conditions are met.

The invention discloses an electricity price prediction method and system based on multi-dimensional electric power data, and the method comprises the steps: obtaining the multi-dimensional electric power data of an electric power market, and the multi-dimensional electric power data comprise historical electricity price data, electric power load data, meteorological data and user behavior characteristics; based on the multi-dimensional power data, constructing an electricity price prediction model with a dynamic learning capability; and predicting the electricity price in the future time period through the electricity price prediction model, and generating an electricity price prediction result. As the model has dynamic learning ability, self-updating can be carried out according to real-time changing data, and the model is ensured to always adapt to fluctuation of markets and demands.

The invention discloses a new energy electricity price accurate prediction method and system based on virtual power plant aggregation regulation and control, and relates to the technical field of industrial data processing. The method comprises the steps: S1, carrying out the preprocessing of meteorological environment data, output equipment data and battery power grid operation data; s2, constructing a variation dynamics analysis and mutation depth discrimination mechanism based on the meteorological environment data, and entering an output prediction and optimization process and a scheduling load response process in a layered manner; s3, executing energy storage collaborative optimization scheduling and parameter correction according to a meteorological output regression prediction analysis result; and S4, performing electricity price time sequence multi-source driving analysis, and reconstructing an electricity price fluctuation response strategy. The problems that new energy output violently fluctuates within a minute level due to sudden weather change, and electricity price prediction lags due to the fact that an existing prediction model depends on too low weather data updating frequency and cannot capture the rapid change in time are solved.

The invention belongs to the technical field of electricity price prediction, and particularly discloses an electricity price prediction method and system based on dynamic subgraph learning, a terminal and a medium. Comprising the steps of collecting multi-source electricity market data such as load, weather and market transaction, performing normalization and missing value filling, and constructing a dynamic electricity price information graph; dynamic sub-graph division is executed based on the edge weight calculated in real time among the nodes, and a density peak value clustering method is adopted to determine the center of the sub-graph and periodically update the center of the sub-graph; extracting spatial features in the sub-graph through a graph convolutional network, generating a sub-graph embedded vector, and inputting the sub-graph embedded vector into a bidirectional recurrent neural network to obtain time sequence features; utilizing a multi-head attention mechanism to realize interactive fusion among different sub-graphs to obtain global feature representation; and predicting the future electricity price in combination with the global features and the historical electricity price sequence. And in the face of new energy output fluctuation, load sudden change or market mechanism adjustment and the like, the prediction flexibility and accuracy are improved.