732 results about "Power market" patented technology

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

The invention provides a semantic enhancement and dynamic completion method and system for a power market data graph. The method comprises the following steps: acquiring target entity information and external data of a power market data graph by responding to a data query instruction; converting external data into structured semantic tags, then traversing the topological structure, establishing a mapping relationship between the tags and entity attributes, and combining query instruction influence logic to calculate topological integrity indexes and mark missing entities or correlate to form a missing tag set; establishing attribute mapping based on a target entity and external data, generating a semantic vector fusing cross-domain features, and projecting the semantic vector to a topological space according to a missing mark set to form cross-domain feature topological nodes and connections; embedding a missing position to reconstruct a topological structure, and checking logic connectivity; the dynamic conduction chain is analyzed, and a feedback interface containing the semantic relation and the completion structure is generated to respond to query. According to the method, conduction logic closed-loop verification of the missing elements in the power market data graph is realized.

The invention discloses a cross-platform power transaction data interaction optimization method, and particularly relates to the technical field of power transaction data digital twinning. Simulation transaction data of market participants is generated based on an antagonistic neural network model; identifying a causal relationship among market variables from the simulation transaction data of the digital twin through a causal discovery algorithm, and constructing a causal graph; modeling spatial dependence and causal conduction paths among market participants by using a graph neural network model, and performing anti-factual reasoning in a single external impact scene in a digital twinborn body by intervening key variables in a causal graph; simulating dynamic game behaviors of market participants in an extreme weather parameter distribution scene; the power market failure assessment is carried out by combining the causal conduction path to obtain the power market failure assessment index, and early warning is carried out based on the power market failure assessment index, so that the adaptability and stability of the power market in extreme weather scenes are improved.

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

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

The invention relates to an electricity-hydrogen comprehensive energy system planning method considering multi-time scale uncertainty. The method comprises the following steps: establishing an electricity-hydrogen comprehensive energy system model; based on the electricity-hydrogen comprehensive energy system model, considering the uncertainty of wind and light output and load requirements, and constructing a planning mathematical model of the electricity-hydrogen comprehensive energy system by using a multi-time scale uncertainty modeling method fusing opportunity constraint and stochastic optimization; based on the electricity-hydrogen comprehensive energy system model, establishing an electricity market model as a lower-layer optimization model of a planning mathematical model; and solving the planning mathematical model by adopting a two-stage optimization method to obtain a planning scheme. Compared with the prior art, a multi-time-scale planning mathematical model of the electricity-hydrogen comprehensive energy system is established, and planning of the electricity-hydrogen comprehensive energy system is achieved.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

Provided is a day-ahead electricity price prediction method based on a parallel multi-dimensional attention mechanism, which method belongs to the technical field of day-ahead electricity price prediction in a power market. The maximum mutual information coefficient is used to select variables having a certain relevance with an electricity price, so as to assist in predicting future electricity price data; next, a decomposition algorithm is used to decompose an original electricity price signal; and then, electricity price sub-components obtained by means of decomposition and the electricity-price-related variables are inputted into an MBI-IPMDA-PBISA deep learning model, so as to predict a future electricity price. In this way, an MBI-IPMDA-PBISA deep learning model can fully learn a complex association relationship between electricity price sub-components and electricity-price-related variables and a future electricity price, the problem of error superimposition caused by separately predicting the electricity price sub-components and then superimposing same to obtain the future electricity price is also avoided, and the problem of the accuracy of existing attention mechanisms during prediction being low is solved, thereby improving the prediction accuracy.

The invention relates to an electricity market information extracting and pushing method and system based on multi-modal semantic fusion. The method comprises the steps that an electricity market field pre-training corpus is constructed; adopting a staged pre-training strategy; performing multi-modal coding on the input power market information data; a text confidence score and an image confidence score are generated, a cross-modal attention incidence matrix is established, and image-text fine-grained interaction features are captured and fused to generate unified semantic representation; a user model containing long-term stable preferences and short-term dynamic interests is established based on unified semantic representation generated through fusion, a sequence decreasing exploration strategy is adopted, high-correlation intelligence is pushed through known preferences, new domain content is randomly inserted to mine potential interests, and the weight of pushed content is adjusted in real time according to user feedback. The problems of heterogeneous sources, diversified formats, different standards, asymmetric information and the like of power market information data can be effectively solved, and accurate and real-time power market information pushing service is provided for users.

The invention provides a power market flexibility resource configuration method and device, and the method comprises the steps: obtaining the real-time measurement, weather forecast and equipment state data of a power grid node, and extracting the spatial-temporal characteristics of a power grid topology; calculating new energy output and equipment availability rate based on meteorological and equipment data; generating a dynamic adjustment margin matrix in combination with the spatial-temporal characteristics, the new energy output and the equipment availability rate; predicting a quantile map of the short-term flexibility demand; global optimization is decomposed into regional sub-problems and solved, power adjusting instructions of the power generation side and the load side are generated, and resource dynamic configuration is achieved. The method improves the evaluation precision and response speed, and guarantees the stability of the power grid.

The invention relates to the field of intelligent power grids, and discloses an electric power marketization operation optimization method and system based on user-side resource aggregation, and the method comprises the following steps: a), recognizing and classifying user-side resources, specifically including distributed power generation, energy storage equipment and adjustable loads, forming a user-side resource database, and facilitating the subsequent management and scheduling; b) formulating transaction rules of market participants, and determining participation conditions, rights and obligations of household users, small commercial users and large industrial users to ensure fair participation of various users in the market; and c) automatically executing the transaction through the smart contract. Through user side resource aggregation, the method can effectively integrate distributed power generation, an energy storage system and an adjustable load, optimizes supply and demand balance of power, and enables a user to adjust a power utilization strategy in different demand time periods through a real-time monitoring and dynamic pricing mechanism, thereby reducing resource waste and improving the overall efficiency of a power system.

The invention discloses a redundancy constraint identification and elimination method for large-scale power market clearing simulation, and the method comprises the steps: building a safety constraint unit combination and safety constraint economic dispatching optimization model in which a tracking unit, a new energy unit and an energy storage power station jointly participate in market clearing; factors such as unit self-constraint, new energy randomness and energy storage charge-discharge balance are comprehensively considered; according to a large number of line power flow constraints involved in the clearing process, the maximum possible transmission power of a power transmission line is calculated, the conversion relation between unit power and load requirements on the line is combined, and redundancy of safety constraints is rapidly recognized and screened through an analysis method, so that efficient elimination is achieved; and solving the node marginal electricity price based on the solved unit combination start-stop scheme result and the declared unit output plan. The method is more suitable for a large-scale market environment, can effectively reduce the consumption of computing resources, improves the real-time performance of market clearing, and optimizes the operation management of a power transmission network.

The invention discloses an energy storage income maximization method based on a strategy gradient algorithm, and the method comprises the following steps: collecting energy storage equipment state parameters, power market real-time price data and load prediction data, and carrying out the preprocessing; feature extraction is carried out, charging and discharging strategy parameters are initialized, and an initial charging and discharging strategy is generated; according to the initial charging and discharging strategy, generating a charging and discharging action of a cost cycle, executing the charging and discharging operation, and generating charging and discharging feedback information; a reward signal is calculated, and the reward signal, the energy storage state feature vector and the charging and discharging action are used for updating charging and discharging strategy parameters; performing feedback optimization on future cycle income by using a time sequence dependency relationship; and performing lower-layer autonomous strategy optimization and upper-layer equipment coordination, and generating a globally optimal cooperative charging and discharging strategy. The strategy gradient and graph optimization method is fused, energy storage cooperative control is achieved, and the method has the advantages of being high in income, high in adaptability and stable in strategy.

The invention discloses an energy storage system multi-scene optimization scheduling method and device, equipment, a medium and a product, and relates to the field of model optimization, and the method comprises the steps: obtaining an initial decision variable and a constraint term which are used for training a scheduling model; obtaining a prediction data set; based on the predicted photovoltaic output in the prediction data set, the first predicted electricity price set, the second predicted electricity price set, the initial decision variable and the constraint term, generating the predicted total income of the electric energy market and the auxiliary service market; on the basis of the predicted total income, updating the initial decision variable so as to optimize the scheduling model, and obtaining an optimized target scheduling model; based on the optimal decision variable of the target scheduling model, adjusting a charging and discharging control strategy of the energy storage system; according to the predicted electricity price and total income, the charging and discharging strategy of the energy storage system is dynamically adjusted, so that the energy storage system can participate in an electric energy market and an auxiliary service market at the same time, and the economic benefit of the whole electric power market is maximized.

The invention relates to the field of energy storage and power supply, in particular to an industrial and commercial energy storage and power supply system for a flexible distribution network, which comprises the following steps: acquiring multi-source data through an Internet of Things sensing device, grading according to load reliability, and configuring an independent power supply loop and a data acquisition unit; determining an energy storage access point by using algorithms such as deep learning and Kalman filtering, and dynamically adjusting the pre-storage capacity; a multi-mode interconnection topology is constructed through equipment such as a flexible intelligent switch, and energy storage cooperative scheduling is realized in combination with reinforcement learning and droop control; a power market is linked by means of a multi-time scale optimization model and a shadow price mechanism, and an economical and reliable scheduling scheme is generated; and finally, fault self-healing is realized through graded early warning and flexible equipment, so that the flexibility, reliability and economy of the distribution network are improved.

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 cooperation system and method between big data and a virtual power plant, the system comprises a plurality of power generation terminals, an energy storage terminal, a power utilization terminal, an energy management cloud platform, a power grid and a power market, the power generation terminals aggregate small solar energy and wind energy dispersed in different spaces to form a unified scheduling energy supply platform; according to the method, the multi-target optimization problem in the system is effectively solved by using the multi-target particle swarm optimization algorithm, and the targets of power fluctuation minimization and income maximization are achieved. According to the method, big data and a virtual power plant are used for cooperation, scheduling optimization is carried out through an improved multi-objective particle swarm optimization algorithm and an objective function with the maximum electric-interference double-optimal metric value, and under the conditions of meeting power balance constraints, energy storage capacity constraints and the like, the optimal scheduling strategy is searched, electric power fluctuation is balanced, and multiple objectives of income are achieved; and finally, outputting a cooperative scheduling scheme satisfying multi-objective optimization, and realizing power fluctuation minimization and income maximization.

The invention is applicable to the field of power demand analysis, and provides a user demand-oriented power transaction matching recommendation method and system. The method comprises the following steps of: acquiring power consumption data of a user, and generating a resonance group and a feature packet with similar power consumption modes and risk tolerance; generating a power utilization comprehensive bidding plan in combination with the resonance group characteristics and the real-time market data; performing transaction matching based on the bidding plan, preferentially selecting the power generation resource with the closest electrical distance, introducing a green energy premium coefficient, and outputting a transaction result; and performing investment portfolio optimization analysis according to a transaction result, and generating a customized power package containing traditional energy, new energy and stored energy in combination with user risk preference. Through multi-dimensional data integration and intelligent algorithm application, accurate matching of power generation resources and user demands is realized, power transaction efficiency and user satisfaction are improved, and new energy consumption and sustainable development of the power market are promoted.

A microgrid island division method based on a cumulative foreground theory and a transfer learning thought comprises the following steps: step 1, aiming at a day-ahead decision-making scene, taking income maximization in a scheduling period as a core target, integrating short-term renewable energy output prediction and electricity price fluctuation data, and constructing a capacity optimization configuration model; 2, constructing a double-layer model, constructing a transaction decision model by the upper-layer model by taking energy storage power station revenue maximization as a target, and determining a power station quotation strategy; the lower layer model takes the sum of the electric energy cost and the frequency modulation service cost as the target to construct a combined clearing model of the electric energy and the frequency modulation market; and 3, solving the double-layer model constructed in the step 2. According to the method, energy storage can participate in an electric energy and frequency modulation market through strategic quotation under a reasonable market mechanism, and an energy storage power station is stimulated to optimize resource allocation, so that the frequency modulation pressure of a power system is reduced, the energy storage economic benefit is improved, and the stability and resource utilization efficiency of the power market are further improved.

The invention discloses a virtual power plant regulation capability test method and system based on a deep belief network, and the method comprises the steps: deploying an intelligent terminal system at an aggregation resource side in a virtual power plant, and collecting resource operation data in real time; dynamically loading market rule parameters of the target transaction variety through a rule configuration engine; the method comprises the following steps: preprocessing historical scheduling data of a virtual power plant, collecting resource-level data and virtual power plant-level data, cleaning abnormal data, and interpolating and filling missing values; calculating an adjustment capability index based on the preprocessed data; inputting the indexes and the rule parameters into a pre-trained deep belief network DBN model, and outputting a virtual power plant overall regulation capability predicted value and a marginal contribution weight of each aggregation resource; generating a virtual power plant admission judgment report and a resource optimization suggestion according to a comparison result of the predicted value and the market rule parameter; according to the invention, differential evaluation of the virtual power plant regulation capability indexes is realized, and the access test requirements under diversified power market rules are met.

The invention relates to a multi-expert non-marketization power prediction method and device and a storage medium, and is applied to the technical field of power market transactions, and the method comprises the steps: carrying out the adaptive recognition of a working condition through a multi-expert architecture, and accurately capturing the boundary change direction caused by the sudden weather change, load fluctuation and other factors, it is ensured that the prediction curve is consistent with the real adjustment direction of the scheduling side, and the problem that an existing simplified extrapolation method easily causes prediction direction errors is solved; according to the method, only the physical quantity which can be obtained by the transaction subject is used as input, so that the model has higher applicability and stability in an actual prediction scene. Modeling is carried out on a multivariable coupling relation through a KAN coupling mapping model, it is ensured that the predicted non-marketization boundary and variables such as new energy output and provincial dispatching loads meet the real supply and demand balance logic, the prediction result is closer to the actual market condition, and the physical rationality of the prediction result is ensured.

The invention relates to the technical field of power system resource optimization scheduling, in particular to a deep scheduling method and system for deep peak regulation of a coal-fired unit, and the method comprises the steps: collecting multi-dimensional operation parameters of the unit, a deep peak regulation instruction and a carbon price signal of a carbon emission permit trading market, and achieving timestamp alignment; constructing a five-dimensional security constraint model associated with the economic cost boundary; taking peak regulation income maximization as a target, combining fire coal purchase cost, carbon transaction cost and equipment maintenance cost to construct a target function, adopting a dynamic planning algorithm to solve an economic dispatching scheme, and executing a cross-system cooperation strategy; and on the basis of LSTM-based equipment life prediction and digital twin simulation, triggering a cross-system correction strategy. According to the method, the commercial cooperation problem of economy, environmental protection and equipment reliability in deep peak regulation is solved, and the peak regulation income maximization of the coal-fired unit in an electricity market environment is realized.

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

The invention provides a power transaction agent system based on AI auxiliary decision making, the system can provide intelligent power transaction service based on an intelligent contract, the system is responsible for processing massive, high-frequency and multi-dimensional data, providing deep insight and accurate prediction, liberating traders from heavy data work, and improving the working efficiency of the traders. The method focuses on strategy formulation and risk control of transactions. The system can provide questioning and answering services for power transactions for users, and can perform power transaction prediction and visualization in each mode based on collected power transaction big data, so that power market intelligent analysis, retrieval and other services of traders, analysts and decision makers can be deeply endowed, multiple functions are integrated, and the user experience is improved. The system assists traders, users, managers and the like, realizes intelligent service and lightweight service of power transaction, reduces transaction cost, and improves market circulation efficiency and benefits of power transaction.

The invention discloses an electric power economic dispatching model solving method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: constructing a safety constraint mathematical model based on an overload generator tripping type electric power system safety and stability control device, and determining an original feasible region of an overload generator tripping type condition section according to the safety constraint mathematical model; reconstructing the original feasible region through the introduced cut plane to obtain a reconstructed feasible region; solving is carried out based on power monitoring and data acquisition SCADA real-time acquisition data, market clearing data and a reconstructed feasible region to generate a new cut plane through iteration, so that on the premise of guaranteeing precision, power market intra-day real-time rolling clearing can be rapidly solved, and the efficiency is improved. The method effectively guarantees the speed and precision requirements of power market construction for safety constraint economic dispatching, ensures that the market clearing result has the actual performability of a power grid, and especially solves the problem that the real-time clearing of the power market is difficult due to overload generator tripping type condition sections.

The invention relates to a power plant unit maintenance optimization method considering multiple uncertain factors of a power market, and belongs to the technical field of power system operation management. The method comprises the following steps: firstly, obtaining spot market electricity price and new energy output data in each time period of a historical year in a power plant; a random scene set considering spot market electricity price and new energy output is established based on historical data, time-varying features are described through data segmentation, state space division and a non-uniform Markov model, and the random scene set is generated by using a Monte Carlo method; and finally, taking maximization of the total expected income of the power plant as an objective function, constructing an optimization model in combination with preset constraint conditions of the unit, inputting the random scene set into the optimization model for solving, and outputting an optimized unit operation and maintenance strategy which comprises maintenance plans of wind power, photovoltaic and thermal power units and corresponding output plans. Multiple uncertain factors in the power market can be effectively handled, and the operation and maintenance benefits of a power plant are improved.

The invention discloses an electric power spot market clearing prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing an information storage knowledge base, storing multi-dimensional data of different regions in different historical time periods in the information storage knowledge base, calling first multi-dimensional data of a target region in a first time period from the information storage knowledge base, and storing the first multi-dimensional data in a second time period; determining first clearing data of the target area in the second time period according to the first multi-dimensional data and a prediction model; the prediction model is used for predicting clearing data of different areas in different time periods according to the multi-dimensional data; determining a power market supply-demand relationship of the target area in a second time period, and adjusting the first clearing data according to the power market supply-demand relationship to obtain second clearing data; and if the second clearing data meets the preset condition, submitting the second clearing data, and storing the second clearing data in the information storage knowledge base. According to the scheme, the accuracy of clearing data prediction is improved, and the adaptability of the electric power spot market is enhanced.