435 results about "Market transaction" patented technology

The invention provides an electric power spot market price prediction method and system based on artificial intelligence, and relates to the technical field of electric power price prediction, and the method comprises the steps: calculating an impact factor weight through employing a deep reinforcement learning optimization fuzzy reasoning system based on historical transaction data and external association data; performing wavelet transform decomposition on the data to obtain a feature fluctuation sequence, and performing weighted fusion to generate a market feature fusion sequence; the method comprises the following steps: constructing a bidirectional feedback neural network by using a graph attention network to extract market transaction features and generate feature correction parameters, generating layered early warning signals in combination with a cascade forest algorithm to construct a dynamic market behavior feature map, and inputting an optimized feature map into a prediction model group comprising a plurality of prediction sub-models, and realizing information sharing and complementary learning through a cross-modal feature interaction module, generating an initial prediction result, performing constraint correction according to an intelligent clearing rule, correcting a correction prediction result, and finally outputting a predicted price of the electric power spot market.

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 embodiment of the invention relates to the field of data processing, in particular to a multi-market transaction prediction method and system for a virtual power plant. The method comprises the steps of obtaining first predicted output data based on historical electricity prices, user load data and time-of-use electricity prices before a transaction, obtaining a first preset total energy demand through correction of a load fuzzy response model, and generating a day-ahead market bidding strategy and a first bidding electricity price; and on the same day of the transaction, acquiring second predicted output data based on the real-time data, receiving the first bidding result, correcting the first bidding result to obtain a second preset total energy demand, and generating a real-time market bidding strategy and a second bidding electricity price. And after the second bidding result is received, dynamically adjusting the real-time preset total energy demand, optimizing the power scheduling and charging and discharging strategy of the energy storage system, and balancing the energy reserve after the transaction. According to the method, the bounded rationality problem of the controllable load in the demand response process is solved, the user demand response result and the DER are effectively combined, and a solution is provided for the VPP to participate in the bidding plan of multiple types of power markets.

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 discloses a distributed energy scheduling management method and system for a virtual power plant, and relates to the technical field of distributed energy management, and the method comprises the steps: collecting electricity price information and operation information, carrying out the cleaning and preliminary processing of data, guaranteeing the data quality, and carrying out the data management; a machine learning algorithm is utilized to predict load information, electricity price and related energy price in a virtual power plant range, a hybrid optimization model is constructed and solved, optimal power generation, power utilization and energy storage operation plans for different power transaction markets are generated, efficient configuration of resources is realized, and the optimal power generation, power utilization and energy storage operation plans are obtained based on operation information data and prediction result data. According to the method, the operation information data and the prediction result data are acquired, the distributed energy units are recombined, an optimal resource combination for different market transactions is formed, a scheduling strategy for optimally configuring the resource combination is executed, and a real-time scheduling optimization strategy is generated according to the latest operation state information. And dynamic optimal configuration of the distributed energy unit is realized.

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 discloses a virtual power plant capacity optimization configuration method and system considering carbon tax, and belongs to the field of power markets. The invention discloses a virtual power plant capacity optimization configuration method considering carbon tax. The method comprises the following steps: analyzing virtual power plant capacity cost and operation income considering carbon tax and market transaction; constructing a virtual power plant internal resource capacity optimization configuration double-layer model based on the analysis of the virtual power plant capacity cost and the operation benefit; generating a spectral clustering typical scene based on an information gap decision theory considering source load uncertainty; and solving the virtual power plant internal resource capacity optimization configuration double-layer model by adopting a differential evolution algorithm to obtain a capacity optimization configuration scheme.

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 security industry system application change level quantitative evaluation method, which comprises the steps of reading original change application data, historical change records and market transaction data, and respectively generating a change feature vector, a historical influence vector and a market feature vector; performing time window segmentation and weight calculation on the market feature vector to generate a time sequence feature matrix; carrying out nonlinear transformation on the change feature vector and carrying out dependency analysis on the change feature vector and a historical influence vector to generate a coupling feature tensor; decomposing the coupling feature tensor and performing feature mapping with the time sequence feature matrix to generate an evaluation score; and performing clustering analysis based on the evaluation score and the historical influence vector, generating a level threshold vector, and further obtaining a level change result. According to the method, through dynamic fusion and adaptive optimization of the multi-dimensional data, accurate quantitative evaluation of the system change risk is realized, and the accuracy and reliability of evaluation are improved.

The invention belongs to a clearing method, and aims to solve the problem that market subjects are difficult to quote due to a concentrated bidding one-price quotation mode in an existing electricity market, relieve market risks caused by the fact that declarated prices obviously deviate from actual values, better exert a market regulation mechanism and promote market transaction. In order to prevent the declaration advantage of individual market subjects due to information asymmetry, the invention provides a clearing method and system based on interval quotation and a computer program product, buyers and sellers can declarate according to an interval quotation mode, and upper and lower limit values of quotation intervals are ranked; and forming a new purchaser quotation interval set and a new seller quotation interval set which are formed by sequentially adjacent upper and lower limit values, successively selecting a pair of corresponding purchase intervals to be cleared and sales intervals to be cleared according to the clearing mode, determining the corresponding purchaser and seller, and combining the uncleared amount of each party to obtain the corresponding purchaser quotation interval set and the seller quotation interval set. And determining the clearing amount and the clearing price corresponding to each party until clearing is completed.

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 relates to the technical field of power grids, and discloses a novel method and model for influencing power grid operation scheduling by participation of energy storage in electric power spot market transactions, and the method comprises the steps: carrying out the real-time monitoring of the state of an energy storage system, the real-time state mainly comprises the charging and discharging state, the capacity and the efficiency, and also comprises the charge state, the charging and discharging rate and the temperature of a battery; through reasonable charging and discharging of the energy storage system, energy can be utilized more efficiently, energy loss can be reduced, finally, the influence of the energy storage system participating in electric power spot market transaction on power grid operation scheduling is comprehensively evaluated, it is guaranteed that whether the participating influence is a positive influence or a negative influence can be obtained in time, and therefore support is provided for subsequent decision making; after novel energy storage is introduced into the market, the flexibility and response speed of the novel energy storage system are fully utilized by optimizing and adjusting a power grid operation strategy, and the application capability of the novel energy storage system is 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 provides a method and device for a virtual power plant to participate in market transaction, and the method comprises the steps: solving a target response amount by taking a minimum cost function as an optimization target after constructing the virtual power plant, determining a target excitation compensation price based on the target response amount and the minimum cost function value, and then taking a maximum preset benefit function as an optimization target, based on the target response amount and the target excitation compensation price, the target electricity purchase and sale amount and the target peak load shifting amount are solved, the electricity utilization cost of the virtual power plant is minimized, the economic benefit of the virtual power plant is maximized, the electricity utilization experience of a user is improved, and in addition, the target response amount and the target excitation compensation electricity price are obtained through the minimum electricity utilization cost. The enthusiasm of the user to change the power demand is improved, the power shortage condition is relieved, the abnormal power utilization condition caused by the power shortage is avoided, and the power utilization efficiency is improved.

The invention relates to the technical field of power system automation, and discloses a power market transaction strategy optimization method and system based on data driving. An inter-provincial electricity market transaction simulation environment containing multiple market members is constructed, a transaction declaration amount boundary is calculated based on a deep reinforcement learning-based transaction declaration auxiliary decision-making model according to the constructed inter-provincial electricity market transaction simulation environment, and model parameters are updated according to a transaction strategy benefit evaluation result. And according to the calculated inter-province power market transaction declaration amount boundary, constructing a corresponding inter-province-intra-province two-level market transaction optimization model, and according to the optimization result, obtaining the transaction declaration amount finally submitted by the target province in the inter-province power market, thereby realizing automatic calculation of the inter-province power market transaction declaration amount, saving human resources, and improving the efficiency. And the operation economy and cleanliness of the power grid of the target province are improved, so that the economic benefits of the target province participating in the inter-province power market are further improved.

The invention belongs to the technical field of energy management, and provides an integrated energy micro-grid energy management and transaction method under user demand response, which comprises the following steps of: 1, establishing an integrated energy micro-grid energy equipment model, a schedulable model of flexible loads such as an electric vehicle and a variable frequency air conditioner, and an operation optimization model of the schedulable model participating in market transaction; step 2, constructing a neural network required by MATD3; step 3, according to the model established in the step 1, utilizing an energy management center to interact with an interconnected micro-grid simulation environment, putting historical information obtained by interaction into an experience pool, and utilizing TD3 to realize optimization of a strategy network and an evaluation network; and step 4, controlling the micro-grid according to the optimized strategy network obtained in the step 3 so that the micro-grid can participate in the end-to-end market transaction. According to the method, nearby consumption of distributed energy is facilitated, redundant capacity and renewable energy of the comprehensive energy micro-grid are further utilized, and the economical efficiency of a micro-grid optimization scheduling strategy is taken into account.

The invention discloses an electric power spot market transaction strategy optimization method and system suitable for a combined distribution and storage main body, and belongs to the field of electrical engineering. According to the method, a quotation strategy optimization model of a joint distribution and storage main body in an electric power spot market is constructed based on a master-slave game architecture. The method comprises the following steps: firstly, respectively establishing transaction strategy optimization models of three subjects including a thermal power generating unit, a new energy power station and industrial and commercial users; secondly, an electric power spot market optimization clearing model is constructed; thirdly, the market optimization clearing model is equivalently converted into a constraint condition of a joint distribution and storage main body optimization problem through a KKT condition, so that a non-convex nonlinear double-layer game model is converted into a nonlinear single-layer optimization problem; finally, nonlinear terms in the optimization problem are linearized, and a mathematical model easy to solve is formed. Therefore, by constructing the transaction strategy model and performing numerical simulation, the optimal transaction strategy of the combined distribution and storage main body is finally output, the optimal transaction strategy can be used for guiding the spot market transaction decision, and the economic benefit is maximized.

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 a new energy power station spot transaction strategy simulation and market optimization operation method and system based on a master-slave game, and belongs to the field of electrical engineering. According to the method, a master-slave double-layer game architecture is adopted, and a spot market transaction strategy optimization model of each new energy power station is firstly established; secondly, constructing an electric power spot market optimization clearing model; thirdly, the market optimization clearing model is equivalently converted into a constraint condition of a new energy power station optimization problem through a KKT condition, so that a non-convex nonlinear double-layer optimization model is converted into a mathematical programming problem with equilibrium constraint of each new energy power station; and finally, the optimization model of each new energy power station is converted into a constraint condition through the KKT condition, and a ''equalization problem with equalization constraint'' is formed in parallel. Therefore, simulation of transaction strategies of a plurality of new energy power stations can be realized, market optimization clearing is synchronously realized, and the method is used for guiding core businesses such as clearing price prediction and market benefit evaluation of market operators.