343 results about "Market transaction" patented technology

The invention discloses a power transaction market risk dynamic assessment system, which comprises a multi-modal sensing module used for collecting physical equipment operation data, market transaction data and environmental parameters in real time, and performing space-time alignment processing through edge computing nodes to generate a fusion data stream; the self-adaptive evaluation engine is in data connection with the multi-modal sensing module and is used for analyzing multi-dimensional risk association in the fused data stream; the invention belongs to the technical field of electricity markets, and aims to solve the problems that in the prior art, historical data static analysis is relied on, spatial-temporal scales of multi-source data are not uniform, and data cannot be given in time. The technical effects are that a closed loop is formed through sensor data, a multi-mode sensing module (alignment processing), an adaptive evaluation engine (risk analysis), a digital twin system (simulation strategy) and a dynamic feedback module (optimization system), automatic calibration and learning are realized, and the risk prediction accuracy and the reliability of the scheme are improved.

The invention discloses a virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention discloses a listed company operation risk early warning method based on multi-source auditing and text semantic fusion, and relates to the technical field of auditing, and the method comprises the steps: S1, crawling and converging multi-source heterogeneous data of listed company financial newspapers, auditing suggestions, supervision announcements, inquiry letters, news public opinions and market transactions; according to the method, unstructured texts are subjected to cleaning, blocking and semantic vectorization processing, each text segment is embedded into a high-dimensional semantic space, a vector index is established, a bottom-layer knowledge base of an RAG framework is formed, in the stage, it is ensured that the data structure is uniform, the source is traceable, standardized input is provided for subsequent semantic retrieval and modeling, and the reliability of the system is improved. S2, a query expression is constructed based on a target company, a time window and a risk topic, dense semantic retrieval and sparse BM25 retrieval methods are comprehensively used, a time decay and source credibility weighting mechanism is introduced, and the problems that a traditional method is single in data dimension and information is split are solved.

The invention provides an urban virtual power plant operation method and device based on an intelligent agent architecture, relates to the field of artificial intelligence, and solves the problem that in the prior art, operation strategies related to a virtual power plant are mostly based on static rules and manually set optimization models. And rapid and efficient response is difficult to realize in a multi-participant, multi-constraint condition and dynamic electricity market environment. The method comprises the following steps: collecting multiple pieces of first data in an urban energy system in an urban virtual power plant; performing arrangement and task scheduling on the first data on the basis of data types required by the plurality of agents to execute tasks, determining the first data required to be processed by each agent, and calling the plurality of agents to process the first data required to be processed; and generating a regulation and control decision and a market transaction strategy of the urban virtual power plant based on the processing results output by the plurality of agents and the operation constraint conditions. The method is used in the operation process of the urban virtual power plant.

The invention belongs to the technical field of novel electric power system operation control and trust management, and particularly discloses a virtual power plant full-process trusted aggregation method and system based on a hierarchical trust chain. Differentiated hierarchical trust chain models of a data acquisition link, a scheduling control link and a market transaction link are constructed respectively; through real-time credibility evaluation and adaptive weight adjustment, credibility calculation of multi-agent collaborative decision is realized. According to the invention, a trust network formed by a plurality of efficient, safe and transparent virtual power plant trust chains is constructed, solid technical support is provided for fair competition, intelligent scheduling and reliable operation of a power market, and efficient interaction and stable operation of a power system are ensured.

The invention discloses a bidding and incentive combined strategy optimization method, system and device for a source-load double-side peak regulation auxiliary service market, and a storage medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: constructing a load aggregator and thermal power generating unit-oriented double-market joint optimization model according to parameter data of a peak regulation auxiliary service market and an incentive demand response market; the double-market joint optimization model is formalized into a Markov decision process; constructing a multi-task multi-agent reinforcement learning training framework; and solving the multi-task multi-agent reinforcement learning training framework through an asynchronous training multi-task multi-agent flexible action-evaluation algorithm to obtain a load aggregator bidding and incentive optimal joint strategy and an optimal bidding amount and quotation strategy of the thermal power generating unit. According to the method, game behaviors among multiple market participants and joint decision behaviors of demand-side market subjects in double markets are considered at the same time, and the method is widely applicable to multi-task agent collaborative optimization in the electricity market.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent pig raising and selling platform data analysis system integrated with a 4G camera, and aims to solve the problems of data semantic segmentation and space-time mismatching of an existing heterogeneous sensing system. The system comprises an intelligent sensing node deployed in a breeding place and a cloud data analysis platform. The intelligent sensing node integrates an individual identification unit, a multi-modal visual acquisition unit and an edge calculation unit, and realizes data source fusion and information extraction; the data analysis platform constructs individual digital twins, and performs multi-dimensional intelligent analysis through growth prediction, health anomaly detection, market transaction decision and other models. According to the scheme, accurate prediction and optimization decision making of the live pig individual full life cycle data are realized, and powerful support is provided for fine breeding management, health early warning and intelligent transaction.

The invention discloses a distributed adjustable resource layered dynamic aggregation method and system for a virtual power plant. The method comprises the steps of collecting operation data, environment data, user behavior data and market data of a distributed power supply, a flexible load and an energy storage facility, extracting core features, extracting feature parameters by adopting a quantification method, and constructing an adjustable resource feature database; constructing an operation capability index model, and obtaining a resource comprehensive operation capability value based on the index and the weight thereof; scheduling the resources in a classified, partitioned and graded manner; establishing a three-layer structure of a bottom-layer resource model, a middle-layer aggregation model and an upper-layer optimization model, and constructing a multi-objective optimization model; by executing a marketization transaction strategy, a power grid dispatching response strategy and an income distribution strategy, collaborative optimization operation of the virtual power plant, the power market and power grid dispatching is achieved, and finally multi-scene simulation verification and model parameter optimization are conducted. According to the scheme of the invention, efficient aggregation and collaborative optimization scheduling of adjustable resources are realized.

The invention discloses a park source network load storage multi-agent transaction mechanism in a multi-element market, and relates to the technical field of power market transaction mechanisms. The method comprises the following steps: determining a game relationship between a park source network load storage multi-agent and each benefit agent; depicting an electric energy market transaction framework, an auxiliary service market transaction framework and a multi-market collaborative transaction framework; based on medium and long term market transactions, spot market transactions and auxiliary service market transactions, a park source network load storage multi-subject transaction mechanism under multiple markets is constructed. According to the park source network load storage multi-agent transaction mechanism under the multi-element market provided by the invention, multiple agents are introduced to participate in multi-element market transaction and collaborative operation, so that diversified supply and consumption of electric power in the park are realized, and the competitiveness and flexibility of the electric power market are promoted; meanwhile, through information interaction and collaborative transaction, balance and optimization of power supply and demand in the park are realized, new energy is utilized to the greatest extent, and sustainability and stability of a power system are improved.

The invention discloses a hybrid sequential network power consumption prediction method for reinforcement learning dynamic calibration, and the method comprises the steps: collecting multivariable power consumption time sequence data, completing the data preprocessing through resampling, feature engineering, normalization and sliding window technologies, generating a supervised learning sample set, and dividing the supervised learning sample set into a training set, a verification set and a test set; constructing a hybrid prediction model comprising a dynamic capture module, a long-term dependence modeling module, a regression prediction module and a reinforcement learning dynamic fine tuning module; training and optimizing by adopting a staged training strategy to obtain a hybrid prediction model; multivariable power consumption time sequence data are collected in real time and preprocessed, the preprocessed data serve as input, real-time prediction of future total consumption is conducted through the mixed prediction model, and a final prediction result after dynamic fine adjustment is output. According to the method, accurate and efficient prediction of the power grid load can be realized, and a reliable technical solution can be provided for power system scheduling optimization, demand side management, market transaction and other scenes.

The invention relates to the technical field of power transaction, in particular to a virtual power plant multi-type power commodity joint transaction game method and system, and the method comprises the steps: constructing a multi-layer interaction framework between VPPO and VPP of multi-type power commodities; constructing a single VPP decision model by taking cost and utility minimization and market share maximization as targets; constructing a multi-virtual power plant operation optimization model based on different targets, and adopting distributed solution to obtain a local market transaction result; based on a multi-VPP operation optimization result and a local market transaction result, constructing a VPPO decision model by taking cost minimization as a target; the transaction process of the VPPO and the VPP is designed into a double-layer model, and the model is solved by adopting a data driving method of a DDPGAE algorithm to obtain a globally optimal power transaction strategy. According to the invention, the complexity problem of the virtual power plant in the joint transaction of multiple types of power commodities is effectively solved, the global optimal power transaction strategy is realized, and the market transaction efficiency and the resource configuration benefit are improved.

The invention relates to the technical field of data analysis, in particular to a data analysis method and system based on artificial intelligence, and the method comprises the following steps: collecting and arranging variable data of a financial market, the variable data comprising asset price, market transaction volume and policy change information, and obtaining a data integration result; and based on the data integration result, constructing a causal graph by using a Bayesian network, and determining a potential relationship between variables to obtain a causal graph construction result. According to the method, the key factors influencing the financial market variables can be identified and verified, so that the accuracy of model prediction is improved. The Bayesian network helps to construct an intuitive relation graph between variables, and helps to clarify complex interaction between the variables. The introduction of Do calculation further purifies the model, and miscellaneous factors are eliminated by accurately controlling variables, so that the estimation of the causal effect is ensured not to be interfered by external noise.

The invention provides a collaborative optimization scheduling strategy of an electric-thermal-hydrogen comprehensive energy system in a differentiated operation mode. The method comprises the following steps: firstly, establishing an electricity-hydrogen-heat comprehensive energy system, and respectively establishing WEHP module unified operation models of three electrolysis technologies, an HFC model considering thermoelectric coupling, an HSD model and a heat energy circulation system model; in an off-grid operation mode, supply and demand balance of electric power and heat energy is considered, and an objective function and corresponding constraints for minimizing the daily operation cost of the IES are designed. In a grid-connected operation mode, the IES participates in market transaction according to time-of-use electricity price and hydrogen energy transaction price, a target function considering electric power and hydrogen energy transaction cost is designed, and electric energy balance and tie line power constraint are established. And finally, converting the collaborative optimization scheduling model of the IES into a mixed integer linear model through a piecewise linearization method, establishing a collaborative optimization scheduling model in a differentiated operation mode, and solving the collaborative optimization scheduling model.

The invention discloses a photovoltaic power station medium and long term transaction declaration decision-making method and system, and belongs to the technical field of power transaction. According to the photovoltaic power station medium and long term transaction declaration decision-making method, weather condition data in a transaction period is predicted by constructing a weather prediction simulation model, an output feature extraction model, an index generation model, a declaration scene simulation model and an optimal output decision-making model, and typical features in photovoltaic output and market electricity price data are extracted; obtaining an output electricity price index combined sample; clustering processing is carried out, sampling is carried out, and photovoltaic output and electricity price scenes are obtained; and finally, obtaining the declaration decision information of the photovoltaic power station by taking the maximum income as the target, thereby fully considering the new energy power generation characteristics, forming a reasonably designed power transaction mode, and ensuring that the new energy fairly participates in the market transaction, so that the photovoltaic power station can avoid the risk and has sustainable income.

The invention relates to the technical field of power system operation, in particular to a risk monitoring method for power market transaction, which comprises the following steps: S1, acquiring power market transaction behavior data and cross-regional power transmission data in a target region in real time; s2, extracting abnormal transaction characteristic indexes based on the transaction behavior data; s3, calculating a fluctuation coefficient of each transmission path; s4, generating a comprehensive risk value matrix; s5, dynamically adjusting a risk judgment threshold according to the real-time transmission topology; and S6, when any element in the comprehensive risk value matrix exceeds the adjusted risk threshold value of the corresponding path, triggering a hierarchical control strategy. According to the method, the power transaction behavior characteristics and the cross-regional transmission fluctuation data are coupled in real time, a dynamic risk evaluation system is constructed, hierarchical management and control are intelligently triggered, and the accuracy of market abnormal transaction recognition and the timeliness of power grid operation risk prevention and control are remarkably improved.

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 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.

The invention discloses an energy storage system economy evaluation method and system and a storage medium, and belongs to the technical field of energy storage systems. Comprising the following steps: acquiring operation data, power market transaction data and equipment state data of an energy storage system in real time through a multi-source data acquisition module; a dynamic feature engineering module is used for screening key influence factors, and a comprehensive evaluation index system fusing market features, technical features and environment features is constructed; constructing an economy prediction model based on an LSTM neural network, inputting the data of the comprehensive evaluation index system into the economy prediction model, and outputting a multi-time scale economy evaluation result, including short-term income prediction, medium-term probabilistic income interval and long-term attenuation correction evaluation; and a revenue-risk balanced charging and discharging strategy is generated through a reinforcement learning algorithm, a strategy effect is rehearsed in a digital twin environment, and high-risk operation is dynamically adjusted. And the precision and adaptability of economic evaluation of the energy storage system can be remarkably improved.

The invention discloses an electric power spot market transaction decision-making method and system based on multi-factor dynamic analysis, and the method comprises the steps: building a power generation power prediction model based on historical power generation data, meteorological data and time information, predicting the power generation power of a future time period based on the power generation power prediction model, and carrying out the uncertainty analysis of the power generation power; establishing a market price prediction model based on the multi-source data, performing prediction based on the market price prediction model to obtain a market price in a future time period, and performing uncertainty analysis of the market price; building a transaction decision optimization model by taking profit maximization as a target and considering power generation cost, market risk and new energy consumption based on the power generation power and the market price of a future time period; and solving the transaction decision optimization model by adopting an improved genetic algorithm to obtain an optimal transaction decision scheme. Scientific, accurate and efficient transaction decision support is provided for the new energy power station in the electric power spot market, and the economic benefits and market competitiveness of the new energy power station are improved.

The invention discloses an electricity purchase strategy risk assessment method based on multiple market types, and belongs to the technical field of electricity market transaction risk management. Medium and long term, in-province spot goods and inter-province spot goods are brought into a unified risk assessment framework, and collaborative optimization of three markets is realized; three-dimensional risk measurement is introduced, risk identification is more comprehensive, and global optimum can be obtained between cost and risk based on combined risk measurement and scene optimization of CVaR; and through Hash calculation and block chain evidence storage, the integrity of the transaction data is protected, and the non-tampering and transparency of the transaction data are ensured. According to the electricity purchase strategy risk assessment method based on the multiple market types, quantitative risk assessment is provided for traders, the assessment provides more practical data support for transaction decisions, the traders are helped to better understand potential risks and opportunities, and therefore the transaction strategies of the traders are optimized. According to the method, the problem of risk dynamic balance under multi-market collaboration is solved, and auditable and traceable decision support is provided for power grid enterprises.

The invention provides an electricity market bidding strategy optimization method, system and device and a medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: acquiring public data of a power market, performing structured processing, extracting key information, and generating power market data; establishing a data optimization prediction model, and predicting the electricity price, the power demand load, the supply load, the enterprise power consumption cost and the power consumption at the future moment based on the power market data; receiving and configuring risk preference parameters input by a user, and generating an enterprise feature configuration file; sequentially calculating a market supply and demand influence factor, a real-time transaction price deviation degree, a reference income fluctuation value and a risk constraint term, and generating a final premium proportion based on a calculation result; on the basis of the final premium proportion, determining a bidding price at a future moment, constructing an objective function to optimize the bidding cost, solving an optimal premium proportion sequence and generating a bidding plan; and monitoring the income fluctuation rate in real time, triggering a feedback mechanism, and updating data.

The invention belongs to the field of electric power markets, and discloses a day-ahead electric energy-inertia combined market transaction method considering inertia safety, and the method comprises the steps: predicting the next-day load of a power grid through employing an improved random forest method, constructing a power balance constraint, carrying out the next-day wind and light output prediction through employing an ARIMA-LSTM method, and constructing a new energy unit output constraint; evaluating a minimum inertia demand based on a system frequency response model and establishing an inertia demand constraint; based on an Aumann-Shapley method, performing inertia support responsibility allocation according to marginal contribution inertia suppliers of various types of resources; and in combination with the above conditions, minimizing the total cost of the power generation side considering the electric energy transaction and the inertia service transaction is taken as a target function, the market model is solved, and a clearing result is determined. A power generation side unit is guided to reasonably make a next-day power generation plan through a marketization mechanism, electric energy supply is guaranteed, and related subjects are stimulated to provide inertia services.

The invention discloses a pumped storage power station marketization multi-variety collaborative transaction method and system. The method comprises the following steps: determining conditional risk value brought to market transaction by wind power output uncertainty; constructing a lower-layer spot market clearing model by taking minimum system operation cost as a first objective function under a first preset constraint condition according to the conditional value-at-risk; constructing an upper layer pumped storage operator optimization model by taking operation income maximization as a second objective function under a second preset constraint condition; and constructing a KKT condition of the lower-layer spot market clearing model, converting the lower-layer spot market clearing model into a constraint condition of the upper-layer pumped storage operator optimization model, and outputting a game equilibrium solution after solving by adopting Yalmip / Cplex. The decision-making advantage of pumped storage in the spot market can be effectively exerted, investment cost recovery and efficient operation are promoted, peak regulation and frequency modulation of a power grid are assisted, and the benefit win-win effect of a pumped storage power station and the power grid is achieved.

An electricity-carbon-green certificate market transaction method based on chaotic mapping considering carbon capturing, comprising: building a transaction strategy for describing the interaction of the market entities in a multi-element market, the multi-element market comprises an electricity market, a carbon market and a green certificate market; and building an upper-layer electricity-carbon-green certificate multi-market combined transaction model considering carbon emission settlement based on the transaction strategy. With the method proposed in the present invention, the model is built according to the energy transaction flow existing in among the transaction entities, efficient utilization of renewable energies and low carbon emissions can be realized, national requirement on low carbonization of market transaction can be satisfied, and a multi-element market transaction mechanism that complies with the dual-carbon target is provided for the existing energy transaction market.

The invention belongs to the technical field of market transaction risk prevention and control, and particularly relates to an abnormal bidding behavior recognition method and system for electricity market subject behavior characteristics, and the method comprises the steps: constructing a market subject abnormal bidding behavior characteristic set through employing a series of algorithms based on the historical operation data of all market subjects in an electricity market; acquiring market operation data of a to-be-detected market subject, and comparing the market operation data with the market subject abnormal behavior feature set; according to a comparison result, determining whether the to-be-detected market subject has an abnormal bidding behavior, and if so, determining the type of the abnormal bidding behavior; furthermore, the system warns related market subjects according to the determined types of the abnormal bidding behaviors, so that the purpose of identifying and correcting the abnormal bidding behaviors of the power market subjects is achieved, and fairness and orderliness of power market transactions are maintained.

The invention discloses a multi-modal data fusion-based chemical and plastic market condition analysis method, which belongs to the technical field of data analysis, breaks through the limitation of a single data type through the combination of market transaction data and text data, improves the semantic analysis precision of the text data by using an improved BERT model, and improves the semantic analysis accuracy of the text data. And the cross influence of the multi-dimensional structured data is quantized by calculating the structured data feature incidence matrix, and the text semantic vector and the structured data feature incidence matrix are fused to obtain a final fused feature vector to eliminate the limitation of a single mode, so that the prediction precision is improved, and the market information analysis accuracy is further improved.

The invention provides an automatic decision-making and real-time ordering execution system and method for electric power spot transaction, relates to the technical field of electric power market transaction, and solves the technical problems of slow transaction response and high operation risk caused by incapability of forming closed-loop automation due to separation of decision-making and execution links in the prior art. The system comprises a sensing layer, a decision algorithm layer and an execution layer, the sensing layer is used for acquiring multi-source data of an electric power spot market and preprocessing the multi-source data to obtain standard data; the decision algorithm layer is used for receiving standard data and carrying out analysis and prediction to obtain a prediction result; generating a transaction strategy by using an optimization algorithm based on the prediction result; the execution layer is used for receiving a transaction strategy; the transaction strategy is converted into a transaction instruction conforming to the transaction platform specification, and automatic ordering execution is carried out; and monitoring the execution state of the transaction instruction to obtain an instruction execution result. The method is used in the electric power spot transaction process.

The invention relates to an interval value carbon price prediction method based on multi-source data fusion and deep learning integration, and the method comprises the following steps: obtaining the actual historical transaction data of a carbon market, collecting the maximum price and the minimum price of the daily actual transaction of the carbon market, and determining an interval value carbon price sequence; key external driving factors influencing the carbon transaction price are screened through feature importance analysis, and data alignment is conducted on the key external driving factors of different market transaction days through a cubic spline interpolation method; cEEMDAN-VMD dual decomposition is adopted, and higher-precision multi-scale characterization of an original interval value carbon price sequence is achieved; and by taking the decomposition result and the determined external key driving factor as input and the carbon price prediction interval as output, constructing a carbon price interval prediction model, and finally outputting to obtain the carbon price prediction interval containing upper and lower boundary values. The method can effectively cope with the non-stationary and non-linear characteristics of the carbon valence sequence, and is good in expansibility and adaptability.

The invention discloses an electronic component supply chain intelligent management method and system, and relates to the technical field of intelligent supply chain management, and the method comprises the steps: collecting the real-time market transaction data of electronic components, and the real-time market transaction data comprises historical purchase data, historical inventory data, supplier delivery cycle data and component life cycle data; constructing a demand prediction model of the electronic component, and generating a demand prediction result; inputting the demand prediction result into a risk assessment model, and outputting a stockout risk index through a fuzzy comprehensive evaluation method; and according to the stockout risk index, carrying out risk grade division on the electronic component, generating a supplier purchase quota scheme, and realizing intelligent management of the electronic component supply chain. According to the invention, the prediction accuracy and anti-risk capability of the supply chain are effectively improved, scientific decision and intelligent management of electronic component purchase are realized, and the operation cost and supply interruption risk caused by stockout are reduced.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant day-ahead strategy optimization method in a spot mode, which comprises the following steps of: performing correlation analysis and feature extraction on Spearman time-phased medium and long-term markets, spot prices and market boundary information; a time-phased spot market price prediction model is formed, and time-phased spot market prices are predicted; adopting a bidding space linear regression method to carry out time-phased medium-and-long-term market transaction price prediction; a difference algorithm is adopted to establish a virtual power plant electric power spot market price declaration strategy optimization model; and generating a virtual power plant spot market declaration strategy. According to the method, the correlation between the market boundary factor-based time-sharing spot price and the medium and long term transaction price is analyzed, the market boundary data is utilized to predict the future time-sharing thermal power load rate mean value, and the future time-sharing thermal power load rate mean value is input into the pre-trained model, so that the accurate prediction of the time-sharing average price of the electric power spot market is realized.