462 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 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 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 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 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.

The invention provides an optimized scheduling method for an integrated energy system of an electrolytic aluminum park. The optimized scheduling method comprises the following steps: constructing a refined electrolytic aluminum load model, and constructing a demand response model and a carbon market model in which electrolytic aluminum load participates; an improved CSP-CCHP unit model is constructed, and an original self-contained power plant of the electrolytic aluminum park is replaced with the improved CSP-CCHP unit; an electrolytic aluminum park comprehensive energy system optimization scheduling model is constructed and solved, optimization scheduling results are obtained, and the optimization scheduling results comprise park operation total cost, park operation carbon emission, various unit output conditions and various load optimization scheduling results. Compared with a model which only considers the simple characteristic of the load in the prior art, the optimal scheduling method for the integrated energy system of the electrolytic aluminum park, provided by the invention, has the advantages that the model is more refined and practical, and accurate scheduling support is provided for participation of the load of the electrolytic aluminum park in the power market and demand response.

The invention discloses an energy storage multi-time scale coordinated optimization scheduling method and system based on model prediction control and a medium, and the method combines the dynamic characteristics of an energy storage system and the scheduling demand of multi-time scales, and carries out the coordination between short-term scheduling and long-term scheduling through model prediction control. Wherein the short-term scheduling is based on the real-time predicted system state and demand, and model prediction control is adopted for optimization so as to realize accurate control of the charging and discharging process of the energy storage equipment; for long-term scheduling, energy storage operation strategies from several hours to several days in the future are planned, so that the economic benefits of the system and the absorption capacity of renewable energy sources are maximized; and by establishing a space equation of the energy storage system and combining the predicted load, renewable energy power generation and market electricity price information, a multi-time-scale optimization framework is adopted to carry out joint scheduling. Experimental results show that the method can significantly improve the operation efficiency of the energy storage system, reduce the power grid load fluctuation, and reduce the energy storage cost and the power market risk at the same time.

The invention provides a self-adaptive electricity selling side spot auxiliary decision making method based on multi-model collaboration and risk dynamic grading, and is suitable for the technical field of electricity market transaction decision making. According to the method, historical and real-time market data are collected in real time, and the electricity price difference is predicted by using multiple prediction models. The mean square error of each model is calculated, the weight of the model is dynamically adjusted, and the prediction results of each model are integrated through a Stacking method, so that the prediction precision and robustness are remarkably improved. Based on the comprehensive prediction result and the real-time market index, the market risk is dynamically evaluated, and the arbitrage strategies are respectively formulated according to different risk levels: the aggressive arbitrage strategy is adopted under the low-risk condition, and the conservative strategy is adopted under the high-risk condition. According to the invention, through real-time transaction execution and closed-loop feedback, model parameters and strategy thresholds are continuously optimized, adaptive adjustment of spot transaction decisions is realized, and the profitability and risk control level of an electricity selling side are effectively improved.

The invention relates to an optimal control method and system for an energy storage system to participate in a medium-and-long-term and day-ahead electricity market, and a medium. The method comprises the steps of collecting historical load and historical spot transaction price of a user and local medium-and-long-term transaction time-of-use electricity price data; predicting the hourly load of the user in the future day and the local day-ahead power spot transaction price; constructing an energy storage model based on the energy storage physical property parameters; maximized energy storage system income is set as an optimization target, and constraint conditions are set according to battery capacity, efficiency, transformer capacity and the like; a genetic algorithm is adopted to obtain an energy storage system charging and discharging strategy, including energy storage charging and discharging power at each moment and a market participation mode, for 24 hours in the next day. The method can comprehensively consider the medium and long term transaction time-of-use electricity price, the day-ahead market price and the user load characteristics, and maximizes the economic benefit while meeting the electricity demand of the user.

The invention is applicable to the technical field of power transaction and energy management, and provides a power transaction aid decision management method, which comprises the following steps of: analyzing and predicting a power market environment through an energy large model according to power transaction environment information to obtain an energy prediction result, and performing decision management on the power market environment on the basis of the energy prediction result. The energy scheduling strategy is generated for the electricity market environment through the multi-target optimization model, and the electricity transaction in the electricity market environment is scheduled according to the energy scheduling strategy, so that the decision-making balance and comprehensiveness are improved, the method can better adapt to the complex scenes such as large-scale access of new energy and frequent change of electricity market rules, and the user experience is improved. And the market risk is reduced, so that the method has better adaptability and application prospects.

The invention belongs to the technical field of power systems, and particularly discloses a micro-grid electricity-carbon joint transaction method and system and a medium, and the method comprises the steps: constructing a regional interconnection micro-grid system model; constructing a local electricity-carbon combined market mechanism, carrying out order matching by adopting a multi-round bilateral auction mode, determining a transaction result in combination with an electricity price and carbon price combined bidding rule, and carrying out trend constraint verification; the method comprises the following steps: constructing a multi-agent Markov decision process model based on a micro-grid model and a market mechanism, and setting a state space, an action space and a multi-target reward function; and a multi-agent near-end strategy optimization algorithm MAPPO-TDSA fusing task decomposition and an attention mechanism is adopted to train each agent strategy network, and settlement with an external power market and a carbon trading market is carried out according to a micro-grid trading execution result and a power carbon quota income and expenditure condition. According to the method, energy-carbon collaborative transaction and intelligent regulation and control between regional micro-grids are realized, and the method has remarkable low-carbon property, autonomy and strategy optimization capability.

The invention discloses a large-scale public building demand response intelligent regulation and control method based on a multi-mode reinforcement learning agent system. According to the method, a multi-modal reinforcement learning agent system is constructed by introducing a multi-modal data processing and multi-agent cooperation mechanism; the multi-modal reinforcement learning agent system is deployed in the large-scale public building, and cooperative control of the multi-modal reinforcement learning agent system is realized through a distributed execution mechanism; and cooperatively solving the demand response double-layer regulation and control model of the large-scale public building in the electricity-carbon-green certificate market based on a multi-mode reinforcement learning agent system, thereby regulating and controlling key adjustable and controllable equipment, and realizing participation of the large-scale public building in demand response regulation and control of the electricity market, the carbon market and the green certificate market. The method has the advantages that through multi-modal data fusion, the operation and maintenance state sensing capability of the large-scale public building is improved, and the adaptability of the large-scale public building to a dynamic market and a complex environment is enhanced.

The invention provides a software and hardware integrated intelligent terminal power transaction system and method, relates to the technical field of power market transaction, and solves the technical problems of data potential safety hazards and insufficient real-time performance caused by dependence on an external platform in a power transaction process in the prior art. The system specifically comprises a hardware architecture and a software function module, the hardware architecture comprises a processor, a redundant power supply, a storage device, a network interface, a security encryption chip and a display interface; the software function module comprises a core function layer, an interface layer and a security layer; wherein the core function layer is used for being responsible for processing and controlling the whole flow of power transaction; the interface layer is used for carrying out standardized and ordered external data interaction; and the security layer is used for guaranteeing the security of the terminal from multiple dimensions of communication, transaction, data and operation and maintenance. The method is used for electricity market transaction.

The invention discloses an electricity market subject credit risk assessment method and system, and relates to the field of electricity market operation and risk management, and the method comprises the steps: constructing a first assessment system based on an SCP framework, and carrying out the corresponding data collection; determining an index weight based on the first evaluation system and the collected data; establishing a fuzzy comprehensive evaluation model based on the first evaluation system and the index weight; evaluating the evaluation object by using the fuzzy comprehensive evaluation model; the method is an effective tool for evaluating and managing the risk of a market subject, provides a decision basis for market participants, helps to realize effective configuration of resources, stable operation of the market and sustainable development of the new energy industry, promotes healthy development of the new energy industry, improves the operation efficiency of the market, and reduces systematic risks.

The invention relates to the technical field of system dynamics, and discloses an electricity certificate carbon market price analysis method and system based on system dynamics, and the method comprises the steps: S1, determining the main body of a green certificate transaction market and the main body of a carbon emission permit transaction market, and determining a renewable energy power generation mode; s2, constructing a TCC cost control mechanism; s3, TCC cost is calculated according to a TCC cost control mechanism; s4, establishing stock flow dynamic model frames of the green certificate market and the electricity market and the carbon emission permit trading market and the electricity market, and combining the two dynamic model frames to generate an electricity certificate carbon market stock flow dynamic model frame; and S5, analyzing the electricity evidence carbon market price. The system comprises an information input unit, a mechanism construction unit, a calculation and analysis unit and a system dynamics model framework construction unit. According to the method, the system dynamics and the method for controlling and optimizing the TCC cost are combined, and the analysis accuracy and reliability are improved.

The invention provides a node electricity price scene generation method and device based on space-time combination and residual diffusion, and relates to the technical field of electricity price scene generation. A historical node electricity price sequence data set is formed, then a space-time joint neural network model is input, space-time features of node electricity price data are extracted, and after a predicted node electricity price sample fusing time sequence dynamic and space topology information and a condition information matrix are generated, a residual error denoising diffusion model is input; the power price probability distribution is subjected to refined modeling through forward noise adding and reverse denoising processes, a node power price scene covering multi-dimensional uncertainty is generated, the space-time relevance and uncertainty of the node power price can be captured, the generated node power price scene is more real and closer to reality, and the node power price is more accurate. And a reliable decision basis is provided for power market participants to optimize a scheduling strategy.

The invention provides a day-ahead electricity market-oriented multi-virtual power plant transaction decision-making method and device, and relates to the technical field of virtual power plant optimization operation. The method comprises the following steps: establishing a power distribution network operator-virtual power plant master-slave game double-layer structure according to a power distribution network operator and a multi-virtual power plant system; constructing an objective function and constraint conditions of the power distribution network operator pricing game model; constructing an objective function and constraint conditions of the robust transaction model of the deterministic virtual power plant; the uncertainty of wind power, photovoltaic and controllable loads is considered, the deterministic virtual power plant robust transaction model is converted into an uncertainty virtual power plant robust transaction model, and then a distributed robust scheduling model is obtained; solving by adopting a dynamic Kriging meta-model and introducing a local search mechanism to obtain a transaction decision; wherein the column and constraint generation algorithm is adopted to solve the distributed robust scheduling model. The method can help the virtual power plant aggregator to significantly improve the earnings in the day-ahead transaction.

The invention discloses a power generation alliance electricity-carbon-green certificate market bidding decision-making method based on an MADDPG algorithm. The power generation alliance comprises a renewable energy unit and a conventional thermal power unit, and the market further comprises a traditional thermal power generation body and a wind and light new energy power generation body. The method comprises the following steps: constructing a quotation decision-making model of each market subject participating in the electricity-carbon-green certificate market based on the selling income of each market subject in the electricity-carbon-green certificate market, and taking the quotation decision-making model as a decision-making model; constructing an electricity market day-ahead clearing model, a carbon market clearing model and a green certificate market clearing model as coupling clearing models by taking minimum electricity purchase cost, carbon market welfare maximization and green certificate market welfare maximization of the electricity market as targets; constructing a double-layer coupling transaction model by taking the decision model as an upper-layer model and the coupling clearing model as a lower-layer model; and adopting an MADDPG algorithm to solve the double-layer coupling transaction model, and determining the middle bid price, the middle bid amount and the total income of each market subject in the electricity market, the carbon market and the green certificate market.

The invention discloses an information generation method, device and equipment and a storage medium, and the method comprises the steps: carrying out the image processing of historical and future market feature data of each time point of an electricity market, obtaining the market image data of each time point, carrying out the reconstruction processing of historical and future meteorological feature data of each time point, and obtaining the market image data of each time point; obtaining meteorological image data of each time point; performing dimension reduction processing on the market image data and the meteorological image data at each time point to obtain one-dimensional market vector data and one-dimensional meteorological vector data at each time point; fusing the two pieces of one-dimensional vector data to obtain target fusion data of each time point; inputting the target fusion data of each time point into a pre-trained prediction model to obtain a predicted electricity price of each time point on a prediction day, an output result of the prediction model being a weighted summation result of at least two sub-prediction models; and generating participation decision information for the participation end of the electricity market based on the predicted electricity price at each time point of the prediction day.

The invention relates to a multi-body robust optimization scheduling method based on a bidirectional master-slave game, and belongs to the field of comprehensive utilization of energy. A bidirectional master-slave game model is provided, an integrated energy system operator (IESO) is used as a leader to optimize energy pricing, an energy storage operator (ESO) is used as a secondary leader, a load aggregator (LA) is used as a follower, and each main body formulates an energy transaction strategy according to the own state and benefit target to optimize the internal running state of the system, so that various load requirements are met. Uncertain factors are added into a proposed bidirectional master-slave game model, the uncertainty of wind and light output and electricity price of an electricity market is considered, a safety constraint economic optimization model under the worst condition is established, and the problem is converted into an MILP formula by using a strong duality theory. And finally, solving the proposed bidirectional master-slave game model by adopting a golden section method, only exchanging limited energy quantity and price information during transaction, and protecting data information privacy of each subject.

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

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