742 results about "Electricity market" patented technology

The present invention relates to the technical field of electricity markets. Disclosed are a method for coordinated transmission and distribution dispatching of power grids in an electricity market environment, and a system. The method comprises: collecting transmission and distribution data of power grids, and performing economic dispatch modeling for a hybrid system incorporating hydropower and thermal power; performing linear processing on nonlinear terms in a model; and using Benders decomposition to accelerate the solution of the model, and optimizing coordinated transmission and distribution dispatching of the power grids. In the method of the present invention, by means of performing economic dispatch modeling for the hybrid system incorporating hydropower and thermal power, the power system's ability to integrate renewable energy is enhanced, improving the resource utilization efficiency; by means of converting complex nonlinear terms in an original model into linear expressions, the model is easier to solve, providing rapid response capabilities for power grid dispatching; and by using a Benders decomposition method to accelerate the solution, a problem is decomposed into a master problem and a sub-problem, which are solved independently, thus improving the overall solving efficiency, scalability and flexibility.

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 relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The invention relates to a source load storage control method for sustainable and stable output of new energy output power of an active power grid, and belongs to the technical field of new energy power systems. The method mainly comprises the following steps: based on ARIMA model prediction and load elastic response, guiding a user to optimize power consumption through real-time state perception, power prediction and time-of-use electricity price; a reference value is set and dynamically adjusted through multi-source data fusion, historical data and a scheduling instruction are integrated, and an output value of the source-storage combined system is corrected and output through an ARIMA model; a collaborative optimization mechanism is constructed, and load prediction, energy storage life and power grid interaction economy are optimized through multiple objective functions. Dynamic monitoring is realized through generalized power modeling and bidirectional flow discrimination, precision is improved by combining ARIMA prediction and rolling correction, and abandoned power is reduced. The load side actively participates in the electricity market, and the smooth curve relieves peak pressure; the intelligent charging and discharging strategy prolongs the energy storage life, optimizes economical efficiency and environmental protection, and provides a feasible technical path for a high-proportion new energy power grid.

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.

Provided in embodiments of the present disclosure are a method for scheduling workloads of a data center in an electricity market environment, and a device. In the embodiments of the present disclosure, a multi-stage random optimization solution that allows a data center to participate in a multi-level electricity market is provided. The types and data dependency relationship of workloads are taken into consideration, and a load scheduling model, in a time dimension, for the workloads is constructed; on the basis of electricity source information of the data center, a multi-stage electricity cost optimization model is constructed, so as to comprehensively consider the exogenic uncertainty of an electricity price and carbon emissions and the endogenic uncertainty of the workloads; on the basis of the multi-stage electricity cost optimization model, multi-stage optimization solving is performed on the load scheduling model, so as to obtain a load scheduling scheme and an electricity purchase strategy. Therefore, a more accurate and more reliable estimation is made concerning the electricity flexibility of the data center, such that the electricity cost of the data center can be reduced, support is provided for the reduction of carbon emissions, and the consumption of renewable energy sources is encouraged, thereby achieving the energy use flexibility of the data center in the multi-level electricity market environment.

The invention provides an electric power system transaction simulation method based on multi-agent agency, and belongs to the technical field of electric power system simulation, and the method comprises the steps: collecting the historical operation data of an electric power system, constructing a multi-agent system comprising a power generation agent, a flexible load agent and an electricity selling agent, and carrying out the Monte Carlo simulation and scene generation. According to the method, a deep reinforcement learning algorithm is adopted, a revenue function and a decision model of each agent are defined, market clearing calculation is executed, and transaction simulation is performed by constructing a verification scene library. The system can evaluate agent strategies, calculate market key indexes and feed market information back to agents, intelligence and dynamic optimization of electricity transaction decision are achieved, and the operation efficiency and transaction accuracy of the electricity market are effectively improved. The method provided by the invention solves the technical problem in the prior art that the interaction and learning mechanism between market participants are not fully considered, so that the simulation result and the actual market performance have large deviation.

The invention discloses a wind-light-water-storage combined body spot market operation optimization scheduling strategy, and belongs to the technical field of electric power system operation and control. Aiming at the problems of fluctuation of wind-solar power generation and uncertainty of prediction errors, the invention proposes that a wind-solar group is combined with cascade hydropower and an energy storage pump station thereof, and a wind-solar-water storage combination body is constructed to participate in a multi-time-scale power market; by constructing an optimal scheduling model considering the source-price uncertainty, the comprehensive benefit maximization of the complex in the day-ahead market, the real-time market and the medium-and-long-term contract decomposition is realized; an information gap decision theory and a conditional value-at-risk method are adopted to ensure that the joint profit can still be ensured when the wind and light prediction error is expanded; meanwhile, profits are distributed fairly through a Shapley method, and the benefits of all subjects are improved; and the market competitiveness and executive force of wind and light power generation are effectively improved, the absorption and utilization of new energy are promoted, the flexibility and toughness of the system are high, and powerful support is provided for constructing a green, low-carbon and efficient modern energy system.

The invention relates to the technical field of power system operation and control, in particular to a medium-and-long-term marketization operation bidding strategy for a wind-light-water-storage combination. Aiming at the problems of volatility, large prediction error, limited profit space and the like of wind and light power generation in the medium-and-long-term power market, the invention proposes that wind and light power generation and cascade hydropower are combined to participate in medium-and-long-term market bidding transaction together; wind and light power generation is encouraged to fully participate in the market by introducing a carbon transaction and green certificate transaction mechanism, and a double-layer optimization model is constructed by considering the influence of current market electricity price fluctuation; the upper layer is a combination quantity report and quotation decision-making model, and the lower layer is a market clearing model so as to realize combination market expected total revenue maximization and social welfare maximization; the strategy significantly improves the medium and long term contract competitiveness and profit space of the wind and light new energy, promotes the consumption of the new energy, and has good environmental benefits and economic benefits.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention relates to the technical field of electric power spot transaction, and discloses an electric power spot transaction optimization system. The system comprises a data acquisition module, a dynamic pricing module, a supply and demand prediction module, a transaction optimization model construction module, a real-time scheduling module and the like. The data acquisition module acquires market data; the dynamic pricing module generates a dynamic electricity price strategy based on a deep reinforcement learning algorithm and a mixed integer programming model; the supply and demand prediction module predicts supply and demand through a federated learning framework aggregation model; the transaction optimization model construction module constructs an objective function and constraint conditions by combining the results; and the real-time scheduling module uses an improved algorithm to solve and generate an optimal scheduling scheme. In addition, the system also has the functions of risk control, abnormal transaction detection, intelligent contract management, performance evaluation and the like. The system can optimize electric power spot transaction, improve transaction efficiency, guarantee power grid stability, prevent and control market risks, and promote healthy development of the electric power market.

The invention provides a virtual power plant power transaction method and device, and the method is applied to a server, and the method comprises the steps: executing a resource aggregation operation for an aggregable resource in a target region; executing a resource evaluation operation on the aggregable resources of each resource type to determine a corresponding evaluation result; constructing an optimal scheduling model corresponding to the virtual power plant based on the evaluation result; obtaining current electricity price curve data and power grid load prediction data of the electricity market in real time; substituting the current electricity price curve data into the first sub-objective function, and substituting the power grid load prediction data into the second sub-objective function so as to update the model parameters of the optimization scheduling model; and solving the updated optimal scheduling model by adopting an optimization algorithm to determine a power transaction strategy. Thus, the optimization scheduling model is established through resource aggregation and evaluation to determine the power transaction strategy, the efficiency and the income of the power transaction of the virtual power plant are improved, and the accuracy and the timeliness of strategy formulation are ensured by a real-time data acquisition and model parameter updating mechanism.

The embodiment of the invention relates to the field of data processing, in particular to an adaptive robust bidding method and system for a virtual power plant. The method comprises the steps that a load fuzzy response mechanism model is adopted, a load transfer parameter is generated according to time-of-use electricity price obtained in real time, and a bidding basis is obtained based on the load transfer parameter; obtaining historical transaction data of the first transaction day, and predicting first prediction bidding strategy data in the second transaction day based on the historical transaction data through the day-ahead market bidding strategy model; performing real-time bidding strategy calculation through a real-time market bidding strategy model based on the real-time transaction data of the day of the second transaction day to obtain second prediction bidding strategy data; and dynamically adjusting the real-time electricity price transaction information in each time period based on the second prediction bidding strategy data so as to realize a self-adaptive robust electric power bidding strategy. And through a load fuzzy response mechanism and an adaptive robust optimization model, bidding risks are reduced, and the economic benefits of the virtual power plant and the stability of the power market are improved.

The invention discloses an integrated energy system participation power-carbon market operation strategy considering a carbon market clearing mechanism, and aims to solve the problems that a current carbon market transaction mechanism is not clear and how to realize balanced income maximization of an integrated energy system in a power-carbon market. By establishing a double-layer optimization game model in which an integrated energy system participates in joint clearing of a power market and a carbon trading market, an upper layer constructs a bidding model by taking system operation cost minimization as a target, and quantifies a CCER voucher-carbon quota offset mechanism; the lower layer constructs a market clearing model by taking social welfare maximization as a target; according to the technical scheme, the method comprises the steps that an IES structure, an internal optimization scheduling model, a carbon quota distribution and CCER mechanism, an electric power and carbon market bidding model, a market clearing model and a double-layer optimization model are established; by adjusting the electricity-carbon collaborative transaction strategy, the effects of reducing the system operation cost, reducing the carbon emission and improving the social welfare are achieved, and an optimization strategy is provided for effective operation of the comprehensive energy system in the electricity-carbon market.

The invention provides a new energy green electricity transaction quantity reporting strategy algorithm based on deep learning. The new energy green electricity transaction quantity reporting strategy algorithm based on deep learning comprises the following steps: a, data collection: acquiring multi-source data from an electricity market, a weather forecast system, historical power generation data and a load prediction system, the data including historical electricity price, weather conditions, power generation power and power grid load data, and recording as X = {x1, x2,..., xn}, wherein xi is a time sequence data feature vector. According to the new energy green power transaction quantity reporting strategy algorithm based on deep learning, the problem of prediction errors caused by randomness and intermittency in new energy power generation is innovatively solved through a deep learning technology, and the prediction accuracy of the new energy green power transaction quantity reporting strategy algorithm based on deep learning is improved. Power generation fluctuation and weather change features are extracted through a multilayer convolutional neural network, and extreme weather events are automatically identified in combination with an attention mechanism, so that the algorithm can more accurately capture market fluctuation.

To provide a wholesale electricity trading system that sets a trading schedule for a next day in advance based on an electricity market price for the next day announced by a wholesale electricity exchange, and always realizes advantageous operation with minimal effort.SOLUTION: In a wholesale electricity trading system 1000 including a control unit (control computer) 1050 connected to an Internet environment (Internet cloud) 3000 via LTE communication or the like, and a server 1010 provided in a cloud and connected to the control unit via the Internet environment, the control unit acquires electricity market price data EPD for a next day announced by the Japan Electric Power Exchange 2000, sets a schedule for purchasing electricity when an electricity market price is lower than a prescribed first reference value PT1 and discharging electricity when the electricity market price is higher than a prescribed second reference value PT2, and charges and stores the purchased electricity in a storage battery 1030. The PT1 and PT2 have a relation of PT1<PT2 or PT1≤PT2.SELECTED DRAWING: Figure 1

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.

Present invention relates to a method of balancing electric power systems using multilevel optimization, wherein the method employs steps which shall primarily result in channeling power flows by clearing electricity markets, i.e., matching bids, close to real time. The method comprises following step of using a robust bi-level formulation, along with using a semídefinite-programming relaxation of the polynomial optimization problem to capture transmission constraints at the transmission-system operator (market operator) level and using extended formulation of the market-participant level to optimize run-time of the operation to effectively convexify the market-participant level.

The invention relates to a clustering-based industry subdivision electricity sale quantity prediction method and device and a storage medium, which are applied to the technical field of electricity market transactions, and comprise the steps of obtaining representation vectors in a historical preset period of time of an electricity user, so that the electricity consumption behavior of the user can be represented more accurately, the accuracy of a prediction model is improved, and the user experience is improved. Meanwhile, vectorization representation is carried out on the attributes of the users, and the attributes are spliced with power consumption behavior representation vectors, so that more comprehensive user features can be provided through the integration, and the generalization ability of the model is enhanced; according to the method, high-dimensional original time sequence data is converted into low-dimensional feature vectors, so that the calculation burden of a clustering algorithm is reduced, the clustering efficiency is also improved, users are divided into different class clusters, modeling prediction is carried out for the electrical load of each group of users, clearer model interpretation can be provided, and the user experience is improved. And the electricity utilization characteristics of different user groups can be understood conveniently.

The invention discloses a collaborative optimization decision-making method for an active power distribution network including production and consumer renting shared energy storage, and belongs to the technical field of power distribution network dispatching. Comprising the following steps: constructing a multi-agent collaborative transaction architecture containing energy storage shared by diasters; based on the multi-agent collaborative transaction architecture, through a multi-agent two-stage collaborative pricing strategy, the bargaining ability of a shared energy storage operator and a producer and a consumer is quantified; taking social benefit maximization and cooperative benefit distribution maximization as targets, and based on preset constraint conditions, constructing a multi-agent collaborative optimization model based on improved Nash bargaining; and optimizing the multi-agent collaborative optimization model in combination with bargaining capabilities of a shared energy storage operator and a producer and a consumer to obtain an optimal energy transaction volume and an optimal energy transaction price. The DDS-production and consumer-SESO collaborative decision-making framework provided by the invention can realize multi-agent cooperation and win-win, and reduce the scheduling difficulty of the power distribution network; the problems of non-uniform income distribution and high optimization scheduling difficulty in the existing electricity market transaction are solved.

The invention relates to the technical field of power systems, provides an electricity price and energy storage joint optimization method and system in an electricity market environment, and aims to solve the problem that in the prior art, deep cooperation between electricity price and energy storage and a market mechanism is difficult to achieve. The method comprises the steps of obtaining actual and predicted load data of a power grid, and generating load prediction error distribution of a power market; according to load prediction error distribution, actual and predicted load data of the power grid and available rotation kinetic energy of the composite flywheel array, active power compensation is carried out on power shortage of the power grid; acquiring a real-time electricity price signal of each transaction period in the power grid, and generating a dynamic charging and discharging threshold value; generating a dynamic charging and discharging strategy of the energy storage equipment based on the dynamic charging and discharging threshold value; and based on the compensated power and a dynamic charging and discharging strategy, suppressing the electricity price fluctuation of the power vacancy generation node. According to the invention, suppression of instantaneous electricity price suddenly rising and market fluctuation closed-loop control are realized, and the problems of physical response delay and market strategy separation are synchronously solved.

The invention discloses a distributed virtual power plant game optimization scheduling method, and belongs to the field of optimization scheduling. According to the method, aiming at the characteristics that the distributed power supplies are distributed dispersedly and have randomness and volatility when the large-scale distributed power supplies are accessed to the network, multi-virtual power plant coordinated scheduling under the non-cooperative game is reasonably carried out, the economic benefit of multi-virtual power plant operation is improved, and energy utilization is fully optimized; considering the influence of the renewable energy prediction error along with the time scale, and establishing a multi-virtual power plant multi-time scale optimization scheduling model based on distributed model prediction control; taking a single virtual power plant as a whole to participate in the electricity market, and establishing a multi-virtual power plant non-cooperative game model; the two-way auction mechanism is applied to the electricity market, and the two-way auction process is analyzed through a non-cooperative game model; by considering distributed virtual power plant optimization scheduling under the competition relationship, the operation economy and transaction electric quantity are optimized, and flexible interaction and reasonable electric energy distribution between virtual power plants are realized.

The invention discloses a distributed photovoltaic collaborative optimization investment decision-making method and system for a power distribution network, and the method comprises the steps: predicting power utilization modes and power utilization demands in a target investment region at a plurality of times, and generating a power utilization demand analysis result of the target investment region; calculating the installation cost of each photovoltaic planning layout configuration scheme, and generating layout cost evaluation data of each photovoltaic planning layout configuration scheme; predicting the power generation capacity of each photovoltaic planning layout configuration scheme, evaluating the influence of photovoltaic power generation on a surrounding ecological system, and generating environmental influence evaluation information of each photovoltaic planning layout configuration scheme; and calculating the operation cost, evaluating the investment income and fund recovery cycle of each photovoltaic configuration scheme in combination with the electricity market price and the government subsidy information, and generating investment decision data of the target investment area. The method optimizes the operation and energy structure of a power grid, enables the planning of a photovoltaic system to meet the actual demands, guarantees the environmental sustainability of a project, provides all-around investment decision support for a decision maker, and enhances the economic and environmental benefits of the project.

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 hydropower station experience electric quantity distribution method in an electric power market environment, particularly relates to the field of electric power optimization, and is used for solving the problems of accuracy and adaptability of electric quantity distribution of a hydropower station in a complex market environment. A multi-dimensional feature mapping matrix and a dynamic credibility evaluation system are constructed, and accurate quantitative analysis of historical prediction results is realized; based on the evaluation result, the short-term abnormal signal is accurately corrected by adopting a classification identification method, so that the prediction result can reflect the actual dynamic change in time; furthermore, by combining a nonlinear multi-objective optimization model, three core objectives of economic benefit maximization, risk minimization and ecological flow balance are organically fused, and an electric quantity distribution scheme with high economic benefit, low operation risk and ecological protection requirements is generated through a complex mathematical algorithm.

The invention discloses an offshore wind power configuration energy storage participation spot electricity market decision-making model and decision-making method, and the model comprises a data collection module, a virtual environment module, an algorithm model module, and a test updating module. The data acquisition module is used for collecting basic operation information of the offshore wind power configuration water storage and energy storage system and scheduling information provided by a power grid; the virtual environment module is used for constructing an operation environment of the offshore wind power configuration energy storage system according to short-term power dispatching requirements of a power grid department and forming a virtual environment; the algorithm model module performs mathematical modeling on the offshore wind power configuration energy storage system according to the basic operation information, and constructs a deep double-Q network model based on a system model for training an intelligent agent to make a decision and participate in short-term spot electricity market transaction; and the test updating module is used for evaluating the training effect of the current deep double-Q network model and performing parameter updating until the decision model output accords with an expected set evaluation standard. The model and the method improve the operation efficiency and benefits of the system.

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 provides a market operation simulation transaction system for electricity transaction. The system comprises a data acquisition subsystem, a data acquisition subsystem, a market behavior prediction and simulation subsystem, an anomaly detection subsystem, an emergency response subsystem and a system optimization subsystem. The invention provides a brand new electricity market simulation system, which can significantly improve the accuracy of market behavior prediction and the capability of coping with abnormal events. Therefore, optimization of market operation is facilitated, risks are reduced, and confidence of market participants and stability of the whole market can be improved.

The invention provides an electricity market, carbon market and green certificate market integrated transaction evaluation method, and relates to the technical field of electricity market transaction evaluation. The method comprises the following steps: constructing an electricity-certificate-carbon integrated transaction operation mechanism according to operation conditions of an electricity market, a carbon market and a green certificate market; according to the clearing models of the electricity market, the carbon market and the green certificate market, screening out influence factors of each electricity-certificate-carbon market and coupling factors of interaction influence among the markets, and qualitatively describing the influence of the change of market operation parameters on the integrated transaction; according to an economics principle, a function relationship between factors is constructed, a model is established from a macroscopic level, the influence of the change of single and multiple market operation parameters on integrated transaction is calculated, and a reference is provided for setting policy parameters of an electricity market, a carbon market and a green certificate market. According to the method, quantitative analysis research on the electricity-certificate-carbon combined market is realized. And a reference is provided for formulating an electricity-certificate-carbon combined market policy goal.

The invention provides a multi-mode hybrid expert model electricity transaction data processing method and system, a storage medium and electronic equipment. The method comprises the steps of collecting multi-source heterogeneous data in an electricity market environment; extracting multi-modal features of the multi-source heterogeneous data, and performing dynamic weighted fusion to obtain fusion features, wherein the multi-modal features comprise time sequence features, meteorological features, image features, text features and market features; different expert models including a load prediction model, a price prediction model, a risk assessment model and a strategy optimization model are allocated for processing and analysis results are obtained according to the fusion features; and generating output data in combination with the analysis result, wherein the output data comprises a service instruction and an analysis report. According to the method, the power transaction prediction precision is remarkably improved, the reasoning delay is greatly reduced, the dynamic adaptive capacity and a closed-loop self-evolution mechanism are achieved, the decision interpretability and the system robustness are enhanced, and the strict requirements of the power market for high precision, real-time performance and stability can be met.