176 results about "Market transaction" patented technology

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 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 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 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 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.

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 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 a hydrogen production and hydrogenation integrated station electric energy-peak regulation combined market transaction method considering hydrogen load demand response. The method comprises the following steps: 1) constructing a hydrogen production and hydrogenation integrated station day-ahead electric energy-peak regulation combined market transaction mechanism and framework; 2) constructing a cost model of the hydrogen production and hydrogenation integrated station for regulating and controlling multi-type hydrogen load demand response; 3) considering uncertainty factors such as hydrogenation load of the hydrogen fuel cell vehicle, and proposing a hydrogen production and hydrogenation integrated station market transaction model with maximum operation income as a target based on distribution robustness; 4) constructing a day-ahead electric energy-peak regulation combined market clearing model containing the hydrogen production and hydrogenation integrated station by taking social welfare maximization as a target; and 5) proposing a market transaction strategy solving method based on a differential privacy-consistent alternating direction multiplier method (DP-C-ADMM). According to the invention, a cooperative transaction mechanism in which the hydrogen production and hydrogenation integrated station participates in multiple markets is constructed, and a transaction model based on distributed robustness and a transaction strategy solving method considering privacy protection are proposing.

The invention provides an electric power spot market day-ahead clearing method and system based on flexible resource participation, and belongs to the technical field of electric power system operation and market transaction. Comprising the following steps: constructing a medium and long term transaction auxiliary decision-making model, and performing parameter optimization by adopting a gradient descent algorithm; according to a decision-making thought of the medium and long term transaction auxiliary decision-making model after parameter optimization, transaction variety weights are set, and transaction bins are formulated; optimizing the set weight by taking risk benefit balance as a decision-making target, and executing electric quantity distribution; and constructing an income risk quantification model, and adjusting the weights and transaction bins of different transaction varieties according to a quantification result to realize continuous optimization of the day-ahead clearing strategy of the electric power spot market. According to the method, the reasonability of power resource allocation can be remarkably improved, and strategic guidance is provided for day-ahead clearing of a power spot market.

The invention relates to the field of transaction electricity price prediction, in particular to an electricity spot market transaction electricity price prediction method. According to the invention, on the basis of the power capacity data of the power spot market, the historical accidental outage capacity of each unit time period in each historical day is obtained and the periodicity is analyzed, and the total estimated available capacity is determined by combining the environmental parameters of each unit time period of the prediction day and the total load analysis accidental outage capacity of the system. Calculating a spinning reserve rate and obtaining a corresponding initial electricity price in combination with the total system load of each unit time period of the prediction day, constructing a temperature trajectory vector of each unit time period of each historical day according to the sensible temperature of the same month over the years of the prediction day, and counting the electricity price change rates corresponding to different temperature trajectory vectors; according to the sensible temperature variable quantity of each unit time period of the prediction day, all the time periods to be corrected are screened, the initial electricity price is corrected according to the electricity price change rate corresponding to the temperature trajectory vector of each time period to be corrected, and the accuracy of electricity price prediction in a complex environment is improved.

This invention belongs to the technical field of pumped storage power station benefit analysis. It discloses a method for analyzing the economic benefits of pumped storage power stations based on diversified market transactions. The method includes: obtaining a structured market time series table, obtaining a detailed report on electricity revenue, obtaining an ancillary service revenue assessment report, obtaining a carbon market revenue report, obtaining a green value revenue analysis report, integrating the detailed report on electricity revenue, the ancillary service revenue assessment report, the carbon market revenue report, and the green value revenue analysis report, allocating the power station's public costs, obtaining a multi-market revenue and cost summary report, obtaining a net benefit sensitivity analysis report, and obtaining a comprehensive power station assessment and decision report, which are then output. Overall, this invention has significant advantages such as comprehensive benefit assessment, high accuracy in calculating net benefits from various markets, and reliable role in power station economic benefit analysis.

This application relates to the field of electricity market trading technology, and provides a collaborative trading decision-making method and system for electricity markets based on multi-time period machine learning, addressing the problem of low collaborative decision-making capability in cross-market electricity trading. The method includes: collecting heterogeneous data from the electricity spot, futures, and ancillary service markets across multiple time periods to construct a cross-market time-series knowledge graph that integrates temporal relationships; extracting historical price sequences and real-time supply and demand relationships from the graph, and then using a multi-scale machine learning model for feature analysis to obtain market state predictions; based on the prediction information, generating an initial trading decision scheme through multi-agent interactive negotiation, followed by cross-market consistency verification and rule compliance checks, and refining the scheme using a multi-round iterative optimization mechanism, ultimately outputting a unified trading strategy that satisfies multi-market constraints. This application improves the collaborative decision-making capability of cross-market electricity trading.

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

The invention relates to the technical field of electric power system load prediction, in particular to a virtual power plant load prediction method and system based on clustering and grey wolf optimization, and the method comprises the steps: collecting historical load data, building a load feature clustering model based on the historical load data through employing a K-means clustering algorithm, and carrying out the prediction of the load feature clustering model; determining an optimal clustering number k and carrying out clustering effect quantitative evaluation to generate a structured load data set; based on the structured load data set, constructing a corresponding BiLSTM neural network model for each cluster, and generating a plurality of neural network sub-models; based on the neural network sub-model, a key hyper-parameter combination of the neural network is dynamically adjusted by adopting WGO, iterative optimization is carried out by taking prediction error minimization as a target, and a hyper-parameter optimized load prediction model is output, so that the virtual power plant load prediction precision and robustness can be effectively improved; and accurate prior information support is provided for subsequent scheduling and market transaction.

This art asset value evaluation system using artificial intelligence may comprise: a memory for storing instructions; and a processor. When executed by the processor, the instructions can instruct the system to: collect, from a plurality of data sources, artwork-related data including artist information, artwork characteristic information and market transaction information; normalize the collected data to generate learning data; use the learning data to train the current value prediction model, a price change prediction model, and an investment value evaluation model; use the trained models to calculate the current market value, future price change prediction, and a long-term investment value of a specific artwork; and integrate the calculated results to generate a final evaluation result.

The invention discloses a hydrogen production and hydrogenation integrated station electric energy-peak regulation combined market transaction method considering hydrogen load demand response. The method comprises the following steps: 1) constructing a hydrogen production and hydrogenation integrated station day-ahead electric energy-peak regulation combined market transaction mechanism and framework; 2) constructing a cost model of the hydrogen production and hydrogenation integrated station for regulating and controlling multi-type hydrogen load demand response; 3) considering uncertainty factors such as hydrogenation load of the hydrogen fuel cell vehicle, and proposing a hydrogen production and hydrogenation integrated station market transaction model with maximum operation income as a target based on distribution robustness; 4) constructing a day-ahead electric energy-peak regulation combined market clearing model containing the hydrogen production and hydrogenation integrated station by taking social welfare maximization as a target; and 5) proposing a market transaction strategy solving method based on a differential privacy-consistent alternating direction multiplier method (DP-C-ADMM). According to the invention, a cooperative transaction mechanism in which the hydrogen production and hydrogenation integrated station participates in multiple markets is constructed, and a transaction model based on distributed robustness and a transaction strategy solving method considering privacy protection are proposing.

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

The invention proposes a new energy enterprise carbon asset financial accounting and value management system, and the system comprises a carbon asset data collection module, a carbon asset classification and modeling module, a carbon value evaluation module, a financial accounting and report module, a carbon market interface module, and a carbon value early warning and optimization module. All the modules form full-process closed-loop management from data acquisition, classification modeling and value evaluation to financial accounting, market transaction and risk early warning and optimization. According to the dynamic carbon value evaluation algorithm, through fusion of multi-dimensional dynamic data and the improved model, accurate, prospective and intelligent evaluation of the carbon asset value is realized, and the enterprise carbon asset management level is significantly improved. Accurate valuation data output by the algorithm provides support for a financial accounting and report module, ensures that enterprise carbon finance conforms to international and domestic accounting criteria, and assists enterprises in realizing value maximization and sustainable development of carbon assets in low-carbon economic transformation.

The application belongs to the technical field of power system operation and planning, and relates to a construction method of a distributed photovoltaic aggregator participating in a power market transaction architecture, comprising: 1, establishing an upper leader model with the goal of maximizing the distributed photovoltaic aggregator's income; 2, establishing a lower follower model with market transaction center social welfare maximization clearing as follower 1 and distribution system operator safety checking as follower 2; 3, solving a one master and multiple slave Stackelberg game model; the application provides a scientific and reasonable decision basis for the distributed photovoltaic aggregator participating in the power market transaction, helps to improve the income of the distributed photovoltaic aggregator, at the same time, can guarantee the safe and stable operation of the distribution system, and promotes the healthy and sustainable development of the power market.

The invention relates to the technical field of power systems and carbon trading, in particular to a zero-carbon park electricity-carbon joint distributed trading method based on carbon-green certificate market coupling. The method comprises the steps of constructing an objective function of a zero-carbon park electricity-carbon joint distributed transaction model based on carbon-green certificate market coupling, and constructing a carbon and green certificate market transaction model and a carbon-green certificate market coupling method of the zero-carbon park. The method comprises the following steps: constructing gas turbine, energy storage equipment, central air conditioning equipment, electric power balance and carbon emission balance constraints of a zero-carbon park electricity-carbon joint distributed transaction model based on carbon-green certificate market coupling, and carrying out distributed solution on the multi-park electricity-carbon joint transaction model by adopting an adaptive step length alternating direction multiplier algorithm; and obtaining an operation decision of the zero-carbon park system, and scheduling the zero-carbon park system. According to the zero-carbon park electricity-carbon joint distributed transaction method based on carbon-green certificate market coupling provided by the invention, collaborative optimization of electricity transaction and carbon emission right and green certificate matching is realized, the utilization efficiency of renewable energy sources in the park is remarkably improved, and the overall carbon emission level of the zero-carbon park is reduced.

The invention relates to the technical field of day-ahead electricity price prediction, in particular to a day-ahead electricity price prediction method, device and equipment based on a diffusion model and a computer readable storage medium, and the method comprises the steps: obtaining day-ahead market transaction data, real-time transaction data and auxiliary information in a fixed time period every day, and writing the data into a market information database; based on the market information database, aligning the market information database according to unified time granularity to obtain a multi-dimensional condition feature sequence; inputting the multi-dimensional condition feature sequence into the trained de-noising diffusion probability model, and generating an electricity price prediction sample set at discrete time points in the next day through reverse multi-step Markov de-noising sampling; and obtaining a statistical value of the electricity price prediction sample set at each time point, constructing a prediction curve, taking a preset quantile to obtain a probability interval, and writing a result into prediction data and an evaluation database. Probabilistic generation is carried out on the day-ahead electricity price by adopting a diffusion model, and the prediction precision in an extreme fluctuation scene is remarkably improved.

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method relates to the field of data processing, and comprises the following steps: when a computing power purchase request of a computing power purchase object is received, determining computing power resource demand information and a purchase strategy of the computing power purchase object from the computing power purchase request; screening computing power resource supply schemes provided by the plurality of computing power supply objects based on the computing power resource demand information to obtain a plurality of candidate supply schemes; scoring each candidate supply scheme based on a scoring rule matched with the purchase strategy to obtain a score value of each candidate supply scheme; and determining a target supply scheme from the plurality of candidate supply schemes based on the score value of each candidate supply scheme, and recommending the target supply scheme to the computing power purchase object. Through the method and the device, the problem of poor accuracy of recommending the computing power resources to the computing power purchaser in a computing power market transaction scene in related technologies is solved.

The invention provides a three-layer game-based clearing method for a multi-type energy storage participation frequency modulation market, and the method comprises the steps: abstracting a frequency modulation market transaction process into a multi-layer interaction framework through constructing a three-layer decision model; the first layer takes a system scheduling mechanism as a main body, minimizes the total cost of frequency modulation purchase, and introduces a performance penalty term to guide high-performance energy storage to preferentially win a bid; the second layer takes an energy storage aggregator as a main body to coordinate multi-type energy storage income maximization; and the third layer introduces a cooperative game among multiple types of energy storage units, and realizes fair distribution of income through asymmetric Nash bargaining. Energy storage technology differences are quantified through frequency modulation performance indexes, an improved distributed genetic joint quadratic programming algorithm is adopted for solving, and the optimal bid-winning capacity of each type of energy storage is output.

The present application relates to the technical field of power system energy scheduling, and discloses a virtual power plant electric energy frequency modulation method and system. In the joint clearing mode of the electric energy market and the frequency modulation auxiliary service market, the virtual power plant participates in the electric energy frequency modulation market transaction model, can reasonably allocate the bidding capacity in the two types of markets, establishes the basis for the optimal bidding scheme of the virtual power plant, and at the same time provides framework support for the virtual power plant to participate in multi-market coupling transaction. At the moment when wind and light are in full swing, the virtual power plant can obtain more bidding opportunities in the electric energy market with lower bidding; by aggregating high-quality frequency modulation resources such as energy storage, the virtual power plant can improve the comprehensive frequency modulation performance and the competitiveness in the frequency modulation market.

The invention relates to the technical field of electric power systems, and particularly discloses an electric vehicle cluster multi-market collaborative transaction decision-making method based on double-layer optimization, and the method comprises the steps: integrating dispersed EV charging behaviors into a virtual energy storage unit, and firstly building an energy state and a power boundary of a cluster; secondly, describing uncertainty caused by user behaviors through a probability model; and finally, deducing the auxiliary service capacity which can be provided by the cluster on the basis of the deterministic constraint. The step of constructing a double-layer collaborative optimization market transaction decision model comprises the substeps of constructing an upper-layer EV cluster participation multi-level market joint clearing model by taking minimization of system power consumption cost as a target function; constructing a lower-layer EVA multi-level market bidding decision-making model by taking comprehensive income maximization as an objective function; and solving the double-layer collaborative optimization market transaction decision model to obtain a decision scheme. The method has the advantages that the multi-market bidding decision can be optimized, the adjustable capability of the EV cluster is fully played, and the economic benefit is improved.

The invention belongs to a power market simulation method, and provides a power market transaction simulation drilling method and a related device for solving the problems that a practical operation training tool is lacked in a current power market construction process, and the adaptability with actual operation is insufficient in a market system design and rule optimization process. After a power market subject model, market load demand data and an adaptive environment are determined, power medium-and-long-term centralized bidding transaction simulation is performed according to the power market subject model and the adaptive environment by adopting a mode of cooperative interaction of a master station end and a substation end, and power medium-and-long-term rolling matchmaking transaction simulation is performed based on a marketization transaction rule. The method comprises the following steps: carrying out the whole-process closed-loop simulation of an electric power spot day-ahead market, calculating the comprehensive income score of a competitor according to a simulation result, and obtaining an electric power market transaction simulation drill result in combination with a model result and the comprehensive income score. The method is superior to the prior art in the aspects of simulation efficiency, operation stability and practical training.

The application discloses a multi-time scale power market coupling clearing method, and belongs to the field of power transaction. The method covers the difference settlement mechanism of electricity into the renewable energy long-term market transaction constraint, and reflects the dynamic execution process of the annual mechanism electricity through the monthly rolling update. The method can realize the time sequence coupling simulation among the annual, monthly and spot markets, accurately represents the transmission influence of the new energy difference settlement mechanism on the long-term transaction boundary, the spot market clearing result, the unit dispatching plan and the settlement result, thereby reasonably verifies the influence of different strategies on the clearing rationality and the renewable energy curtailment rate, reduces the renewable energy curtailment rate through reasonable strategies, improves the power system operation efficiency, improves the future renewable energy popularization and penetration rate, and provides technical support for the power market operation analysis, market rule checking and dispatching decision under the condition of high proportion of new energy.

The invention discloses a green electricity-green certificate-carbon collaborative transaction system and an implementation method thereof, and belongs to the technical field of electricity market transaction. The system comprises an inter-province / intra-province unified market balancing module, a green certificate market clearing module, a green certificate-carbon linkage deduction module and a data interaction and settlement module. Based on an inter-province / intra-province unified market equilibrium model, inter-province tie line power is incorporated into a unified optimization variable, and a physical network and multi-element market integrated decision is realized; establishing a green certificate market clearing mechanism based on the generalized Nash game, dynamically forming fair green certificate prices, and optimizing environmental value distribution; a green certificate-carbon market linkage deduction mechanism is designed, and linkage connection of green certificates and carbon quotas is achieved; and a transaction closed loop of electric energy, green certificate and carbon quota is completed through joint settlement. According to the method, the inter-provincial market barrier is effectively broken, and the problems that high-proportion new energy transmission end areas are limited in thermal power transmission economy, low in new energy consumption rate, uneven in environmental value distribution and the like are solved.