92 results about "Clearing price" patented technology

The invention is suitable for the field of electric power system operation and market clearing, and provides an electric power spot market annual security constraint unit combination rapid clearing method and system, and the method comprises the following steps: receiving input data; constructing an annual SCUC optimization problem based on the input data; the annual SCUC optimization problem is solved by adopting a two-stage calculation process, in the first stage, an initial solution is calculated by simplifying network topology and reducing time resolution, and in the second stage, the annual optimization problem is decomposed into a plurality of subtasks for parallel rolling calculation; according to the method, annual market and system data are received, an annual SCUC problem is constructed, a special solving method is applied, the optimal or near-optimal power generation plan and market clearing price in the annual period are determined in an actual time frame, more effective long-term planning is facilitated, and the method is suitable for popularization and application. The market efficiency is improved, and reliable operation of a power system is supported.

The embodiment of the invention relates to an analog simulation method based on electric power spot transaction. The analog simulation method comprises the steps of obtaining static structure data such as power grid topology and unit parameters and dynamic time sequence data such as load and new energy prediction; performing preprocessing and multi-time scale division on the dynamic data; constructing a multi-agent model with differentiated characteristics based on the processed data; each agent generates and reports energy block information such as a time-sharing block, a continuous block, a curve block or a variable block according to a perceived market environment; constructing a market clearing model considering system power balance, network power flow, unit operation and energy block characteristic constraint based on all declarations, and solving to obtain clearing electricity price and electric quantity; a result is fed back to the intelligent agent, and the strategy is updated through deep reinforcement learning; and performing rolling co-simulation between the day-ahead market and the real-time market, wherein a day-ahead clearing result is used as a boundary condition of the real-time market. The authenticity, accuracy and practicability of market simulation are improved.

The invention provides a power grid dynamic management and control method, device and equipment based on a multi-market subject game, and relates to the technical field of power system management. The method comprises the steps that market demand data and price fluctuation data are acquired, a multi-agent mixed game model constructed in advance is acquired, and the multi-agent mixed game model comprises a bidding decision-making layer and a market clearing layer; in a bidding decision-making layer, each power generation enterprise selects a corresponding game mode according to the market demand data and the price fluctuation data, and formulates a power supply strategy by taking maximization of profit as a target; each thermal power enterprise formulates a power supply strategy according to the power market supply-demand ratio and the thermal power enterprise market share based on a multi-agent depth deterministic strategy gradient MADDPG algorithm; and in the market clearing layer, the independent system operator determines the planned power generation amount and the market clearing price of each power generation enterprise according to the load constraint condition and the unit constraint condition by taking the minimum power purchase cost as a target. The decision-making accuracy of the power generation enterprise can be improved.

1. Name of this design product: Graphical user interface for power grid peak-shaving monitoring, traceability and evidence storage for computers. 2. Purpose of this design product: used for running programs and display. 3. The key design point of this appearance design product lies in the interface content of the user interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Since there are no design points in the rear view, left view, right view, top view and bottom view, the rear view, left view, right view, top view and bottom view are omitted. 6. Purpose of the Graphical User Interface: This design product is based on blockchain and is used for power grid peak-shaving auxiliary services. It displays, monitors, and traces and stores historical data in real time to verify the peak-shaving market clearing situation. The interface displays the intraday peak-shaving market clearing curve, the day-ahead peak-shaving market clearing curve, settlement vouchers, clearing vouchers, clearing price, number of transactions, number of blocks, number of nodes, highest TPS, and other information for the purpose of tracing and storing peak-shaving auxiliary services. In the main view, in the three modules of Intraday Peak Shaving Market Clearing Curve, Day-Ahead Peak Shaving Market Clearing Curve, and Peak Shaving Market Clearing Price, click the drop-down box next to "Select Date" to display the clearing curve and clearing price information for a specific date; in the Settlement Voucher and Clearing Voucher modules, click the drop-down boxes next to "Market Type" and "Generation Time" to view the settlement vouchers and clearing vouchers that meet the conditions.

The invention provides a clearing price interval prediction method based on an LSTM network and a similar point matching algorithm, and the method comprises the following steps: carrying out the optimization of an independent variable feature set, taking a real-time clearing price as a target variable based on the historical data of an electricity market at an interval of 15 minutes, constructing a point prediction model based on the LSTM network, and carrying out the calculation of the point prediction model. On the basis of optimization of an independent variable feature set, a long-short-term memory neural network is adopted to construct a real-time clearing price point prediction model, high-precision point prediction, similar point matching and statistical feature extraction are realized, feature vectors of to-be-predicted time points are constructed to be used for matching similar time points in a historical data set, and a real-time clearing price point prediction model is obtained. And constructing upper and lower limits of the prediction interval. A real-time clearing price point prediction model is constructed based on an LSTM network, and an upper limit and a lower limit of a real-time clearing price prediction interval are formed in combination with a similar point matching algorithm and an interval feature statistical analysis method, so that uncertainty in an electricity market is quantified better, and more decision bases are provided for market participants.

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.

A wind storage joint optimization regulation and control method considering conditional value-at-risk comprises the following steps: acquiring prediction data of wind power plant output and day-ahead electric energy market clearing price in each transaction period, and generating a large number of uncertain scenes through a Monte Carlo simulation method; then scene reduction is carried out through a K-medoids clustering algorithm to generate a reasonable number of typical day-ahead spot electricity price and new energy output scenes, the two typical scenes are permutated and combined to obtain final scene data, and the final scene data is input into a pre-constructed wind storage joint participation day-ahead electricity market output regulation and control optimization model; taking the day-ahead expected net income maximization as the target, introducing CVaR to measure the income risk, setting a corresponding target function and constraint conditions, and solving to obtain an output regulation and control plan and a bidding decision of the wind power plant and the novel energy storage in each simulation transaction scene. According to the method, the complementary advantages of novel energy storage and new energy are utilized, the new energy output fluctuation and deviation punishment risks caused by prediction deviation are reduced, the market competitiveness of clean energy can be improved, and new energy consumption is promoted.

This invention relates to a coupled clearing method and apparatus for virtual power plants participating in the spot electricity market, applied in the field of electricity market technology. The method includes: calculating the High-Gross Price Ratio (HGRP) as the threshold for economic compensation of DRVPPs, compensating DRVPPs only when the node marginal price is higher than the HGRP, thus preventing the activation of non-economical DRVPPs during low-price periods, preventing low-cost generation resources from being squeezed out of the market, and ensuring the authenticity and effectiveness of the market clearing price signal; incorporating DRVPPs as a controllable and constrained demand-side adjustment variable into the clearing model, avoiding the failure to reflect the adjustment value due to its inclusion in load forecasting, and overcoming the problem of simply treating it as "negative load" and ignoring response constraints, thereby improving the rationality of the clearing results; and adopting a differentiated economic compensation mechanism based on the node marginal price, linking the adjustment revenue of DRVPPs to the marginal electricity cost of the node, achieving precise matching of DRVPP resources with the actual adjustment needs of the system.

This application discloses a method, apparatus, equipment, and medium for day-ahead nodal electricity price forecasting and correction based on price state grouping and gating correction, relating to the field of electricity price forecasting technology. The method includes: constructing a training sample window using historical market operation characteristic data; classifying price states according to historical clearing prices to generate price state labels; training a model within the training sample window to obtain a global forecasting model and a price state sub-forecasting model; performing offline initial forecasting of day-ahead nodal electricity prices using the global forecasting model, and correcting the initial price forecasting result by calling the corresponding sub-forecasting model according to the corresponding price state; if a forecast value meets a preset gating condition, further determining whether it meets a preset zero-price condition; if so, performing gating correction to obtain the final electricity price forecasting result. This application can improve the targeting and accuracy of day-ahead nodal electricity price forecasting under different price states, and enhance the ability to identify and correct special price states such as zero-price.

This invention proposes a day-ahead spot clearing price-load data repair method based on generative adversarial networks. A training dataset is established; K-means clustering is used to cluster the user electricity load dataset, classifying it into typical user groups; a deep convolutional generative adversarial network (DCGAN) model is established; the DCGAN model is trained using historical day-ahead spot market clearing price data and historical user electricity load data after classifying typical user groups, retaining the generator structure for each typical user group; a binary mask matrix is ​​introduced to represent the missing locations of newly collected user electricity load data; then, the newly collected day-ahead spot market clearing price dataset, typical user electricity load dataset, and target user electricity load data with missing data are input into the generator corresponding to the typical user group, outputting new generated data; the missing parts of the target user electricity load data are filled in using the new generated data samples.

The invention discloses a capacity market clearing and pricing method and device and a medium, which are applied to an electric power system, and the method comprises the steps: obtaining the new energy real-time data of a new energy station in the electric power system, inputting the new energy real-time data into a capacity contribution evaluation network model, and obtaining an equivalent reliable capacity; calculating a traditional unit capacity, an energy storage capacity and a demand side response capacity respectively; and respectively inputting the equivalent reliable capacity, the traditional unit capacity, the energy storage capacity and the demand side response capacity into a clearing model to obtain a bid-winning subject and a clearing price of the corresponding capacity. According to the method, the effective capacity of various resources can be accurately quantified, and a price signal capable of truly reflecting the capacity scarcity degree of the system is formed on the basis, so that the optimal power supply structure investment is effectively guided, and long-term reliable operation of the high-proportion new energy power system is guaranteed.

The invention discloses a bidding strategy generation method for an electricity-carbon combined market and related equipment, and the method comprises the steps: obtaining real-time market data and historical market data in a preset simulated market environment corresponding to a main intelligent agent and a plurality of other intelligent agents as competition objects, obtaining a power market predicted clearing electricity price and a carbon market predicted price based on historical market data through the prediction model; obtaining subject state information of a subject agent and behavior characteristics of other agents, and obtaining strategy elements based on the obtained data through a trained bidding strategy model; and obtaining a power market quotation strategy and a carbon market transaction strategy based on the strategy elements, and forming a bidding strategy of the power-carbon combined market. According to the method, the bidding strategy of the power generation manufacturer can be gradually optimized by integrating the market data and the competitive behavior characteristics, so that the income capability and the market competitiveness in the electricity-carbon combined market are improved.

The invention discloses a new energy electric power automatic transaction method, device, equipment and product. The method comprises the following steps: acquiring historical new energy output data, historical electric power spot market clearing price data, historical irradiance data and historical wind speed data; using the first neural network model to predict the current clearing price of the electric power spot market; using the second neural network model to predict the current new energy output; establishing a multi-objective function optimization model; and resolving the multi-objective function optimization model to obtain transaction declaration electric quantity, and automatically declaring the transaction declaration electric quantity in the electric power transaction. According to the invention, the method can determine the automatic declared transaction electric quantity in the power transaction through the prediction of the corresponding future condition based on the two neural network models, has the dynamic future prediction capability, can dynamically adjust the automatic declared transaction electric quantity in the power transaction, and effectively improves the transaction efficiency and benefits.

The invention relates to a regional peak regulation auxiliary service market pricing method and device and electronic equipment, and relates to the field of power system automation control, and the method comprises the steps: carrying out the peak regulation cost calculation of conventional hydropower in multiple resources, and obtaining the unit peak regulation cost of the conventional hydropower, determining a multivariate resource price limit according to the difference between the unit peak regulation cost and the multivariate peak regulation resource transaction mechanism; preferential market clearing is carried out on conventional hydropower with corresponding price limitation, grading price limitation processing is carried out on the price of the power generation right replacement transaction, and grading clearing processing is carried out on the clearing price of the power generation right replacement transaction exceeding the grading price limitation; determining a peak regulation compensation price adapted to the multi-element resources according to the clearing price, and settling a peak regulation auxiliary service cost based on the peak regulation compensation price; and the peak regulation auxiliary service cost is allocated to the target power generation enterprise and / or the target energy storage power station. According to the scheme, the peak regulation cost of the system can be reduced, the optimal configuration efficiency in the resource area is improved, and better connection with the provincial peak regulation market price is ensured.

This invention relates to a method for generating dynamic electricity prices and power flow diagrams for spot market transactions based on nodal price clearing results and adapted to different grid node distributions. The method first acquires grid node information, network model data, and Geo map data; then, it performs topology analysis based on the acquired data and system parameters, automatically generating a physical wiring distribution diagram based on the connection relationships of each node; it accesses the electricity spot market clearing results and calculates line loads according to set constraints; then, it automatically matches the clearing results with the node physical wiring diagrams; it renders nodal prices, power flow direction (p-direction), and line load information; finally, it generates a dynamic electricity price and power flow diagram, clearly displaying the nodal prices, branch power flow direction, and power flow load information of the grid to the grid dispatching agency; this greatly improves the work efficiency of dispatching personnel, enhances the power grid's reliability, and reduces the power control risks brought about by the electricity spot market.

The invention discloses a novel power system inertia auxiliary service acquisition method. The method comprises the following steps: step 1, carrying out day-ahead electric energy market pre-clearing; step 2, performing system frequency security check; step 3, calculating inertia vacancy required by the frequency safety of the system for a time period in which the frequency safety is not satisfied; step 4, performing optimization acquisition of inertia auxiliary service, and determining a conventional unit needing to be added and an inertia clearing price; step 5, carrying out day-ahead electric energy market formal clearing, and carrying out day-ahead electric energy market clearing optimization calculation under the determined startup mode; 6, based on the inertia clearing price, all the units providing the inertia are settled; the technical problem of obtaining inertia auxiliary service for maintaining safety and stability of a power grid under the condition of large disturbance is solved.

This invention discloses a method for analyzing key influencing parameters of the clearing price in the electricity spot market. Based on electricity spot market rules and nodal pricing models, it establishes the operating parameters affecting the clearing price and constructs an influencing factor system including unit cost, unit operation, system balance, and cross-sectional power flow. Then, it further constructs a full-time clearing simulation model of the electricity spot market considering carbon trading costs, establishing the objective function and constraints of the clearing model. Finally, based on the electricity spot market clearing simulation model, it compares the impact of each operating parameter on the market clearing price through sensitivity analysis and correlation diagram construction, thereby assessing the key parameters affecting the electricity spot market. This method not only systematically identifies the operating parameters affecting the clearing price in the electricity spot market but also allows for simple and efficient calculation and comparison of the impact of each parameter on the clearing price, thus clarifying the key parameters affecting the spot market and improving the decision-making efficiency of market participants.

The invention relates to a power plant real-time evaluation method and system based on an electric power spot market, and the system comprises a real-time evaluation module which carries out the real-time evaluation when an actual power generation load is consistent with a real-time market clearing load. The real-time evaluation module obtains the actual income of the current time period according to the information published by the power transaction center platform and the information of each generator set of the power plant, and performs evaluation according to the actual income of the current time period; the actual income of the current time period is equal to medium and long term decomposition electric quantity * medium and long term price + energy block electric quantity * energy block price + (on-grid electric quantity-medium and long term decomposition electric quantity-energy block electric quantity) * actual settlement price, and the actual settlement price is a calculated value obtained after the price limit rule is adopted for the real-time price; and when the actual power generation load is inconsistent with the real-time market clearing load, the real-time evaluation module obtains the deviation income of the current time period according to the information published by the power transaction center platform and the information of each generator set of the power plant, and performs evaluation according to the deviation income of the current time period.

The present disclosure relates to a power spot market clearing simulation method based on energy block transaction, comprising the following steps: obtaining the estimated bidding plan of each market participant; dividing the reported power of each market participant into several energy blocks; establishing a power-price pair model; establishing a day-ahead clearing model of the power spot market based on energy block transaction to obtain a day-ahead market clearing result; establishing a real-time clearing model of the power spot market based on energy block transaction and solving to obtain a real-time market clearing result, wherein the real-time market clearing result comprises real-time market clearing power and real-time market clearing price; and settling the income of each market participant according to the day-ahead market clearing result and the real-time market clearing result. The present disclosure considers the physical and economic constraints of multiple types of energy blocks in the day-ahead and real-time markets and the physical constraints of the power system, and can more accurately simulate the day-ahead-real-time two-stage clearing process of the power spot market based on energy block transaction in a market environment.

The invention belongs to the technical field of power spot market clearing, and provides a trans-provincial and trans-regional power spot market clearing optimization method, system and application, and the method comprises the steps: firstly constructing a double-layer optimization clearing model, enabling a lower-layer model to be a provincial-level power spot market pre-clearing model, independently optimizing a local unit combination strategy, and enabling a lower-layer model to be a provincial-level power spot market pre-clearing model; the upper-layer model is a regional electric power spot market coordination optimization model, and uniformly adjusts clearing price, trans-provincial transaction electric quantity and power generation resource distribution; the lower-layer model solves a unit commitment problem through a Q-Learning algorithm, the upper-layer model takes a unit commitment strategy output by a lower layer as an input parameter, and inter-provincial transaction optimization is solved by adopting a linear programming method; and the lower-layer model transmits the optimal unit combination strategy as a parameter to the upper-layer model to obtain the optimal clearing price, the unit operation strategy and the interconnection line power of the provincial power grid, and then the results are transmitted back to the lower-layer model for implementation.

The invention belongs to the technical field of power dispatching and operation, and particularly provides a negative electricity price scene-oriented source-grid-load-storage collaborative interaction and marginal cost optimization system and method, and the method comprises the steps: monitoring the state of a power market through a data collection module, predicting the supply and demand conditions of the system, and judging that there is a negative electricity price risk; calculating real-time marginal cost of the schedulable resources, wherein the real-time marginal cost comprises power generation resource marginal cost, energy storage resource marginal cost and load regulation marginal cost; establishing an optimization model by taking the minimization of the total marginal cost of the system as a target, and solving to obtain an optimal scheduling scheme of each resource; converting the optimal scheduling scheme into a specific control instruction, issuing the specific control instruction to each resource main body of a source, a network, a load and a storage, and executing power generation adjustment, charging and discharging or load interruption operation; and monitoring the execution effect of each resource instruction and the actual clearing electricity price of the market in real time, calculating the execution deviation, and carrying out rolling correction in the next optimization period. According to the invention, early warning of the negative electricity price risk can be realized, and global optimization scheduling is carried out on the source network load storage resources.

The invention relates to commercial data processing and management in the technical field of computers, in particular to an electricity market financial risk block chain monitoring method. The method comprises the steps of obtaining market clearing price, time-sharing matching, account position increment, margin occupation, cross-market linkage factors and supervision announcement metadata, performing naming specification, source signature binding and double-clock alignment, and minimizing fields to form verifiable input; constructing an index lattice based on a multi-dimensional base, assembling a scene template in combination with a sliding window sequence and policy key points, and completing fragment packaging and source pointer registration; fragment hash and time anchor serialization are executed on the chain, and the chain is solidified into a risk evidence chain through witness committee threshold check; and the contract engine generates a mark-leaving report and a visual board according to the threshold value, the change rate and the persistent trigger disposal script. According to the method, traceability and auditing links are enhanced, clock consistency and data aperture unification are optimized, the exposure range is reduced, and the early warning disposal collaboration and supervision adaptation degree is improved.

The invention relates to the technical field of electricity market electricity price prediction, and discloses a spot clearing electricity price prediction system fusing new energy output fluctuation characteristics, and the system comprises a fluctuation characteristic extraction module which carries out the variational mode decomposition of a new energy output prediction sequence, and generates fluctuation impact momentum and duration; the climbing scarcity calculation module is used for calculating a reference equivalent climbing scarcity in combination with the polymerization residual climbing capability and the fluctuation impact momentum; the hysteresis state correction module is used for correcting and generating a scarcity degree state index by using a state attenuation parameter based on fluctuation duration; the supply curve reconstruction module is used for generating a dynamic effective supply curve by carrying out linear deformation on the reference residual supply curve by utilizing the physical capacity cut-off quantity mapped by the index and a quotation slope correction coefficient; and the clearing electricity price solving module is used for outputting a predicted value based on the curve. The new energy fluctuation impact and hysteresis effect are effectively quantified, and the prediction precision is improved.

The invention discloses a virtual power plant adjustable capacity calculation method and system considering a sewage treatment plant. Acquiring power market side data, demand side response data and other source data, and preprocessing the data to construct a clearing price prediction model for predicting a power spot market clearing price and a demand side response market clearing price; establishing a multi-level market joint optimization scheduling model by taking minimization of the total cost of the electric power spot market and the electric power demand side response market as a target; solving the multi-level market joint optimization scheduling model under the production operation constraint condition of a sewage treatment plant; and calculating the adjustable capability of the virtual power plant according to the solved optimal lift pump power. By accurately predicting the market price and optimizing the lift pump scheduling, the sewage treatment plant provides reliable virtual power plant adjustable capability on the premise of guaranteeing the process safety, and the market income and scheduling efficiency are improved.

The application discloses a kind of key risk factor identification method, device, mobile terminal and storage medium, the method includes: according to spot market clearing price curve in historical statistical period, obtain multiple same segment curves and generate same segment curve set;Wherein, same segment curve is the same segment of spot market clearing price curve in historical statistical period;According to segmented bidirectional clamping model, the segmented curve shape similarity between each same segment curve in same segment curve set and reference price segmented curve is calculated respectively;According to segmented curve shape similarity, overall similarity is generated, and overall similarity is clustered and analyzed, to generate clustering analysis result;According to clustering analysis result, key risk factor is identified. The accuracy of identifying key risk factor is improved by the embodiment of the application, thereby the safety of power system is improved.

The invention is suitable for the technical field of power spot market clearing, and provides a power delivery in-province power spot market clearing method, and the method comprises the steps: carrying out the calculation of the delivery superposed power of a target unit in a current transaction period based on the obtained delivery power data and technical parameter data; generating a delivery newly-added curve segment based on the delivery superposed power and the acquired market price lower limit; performing data translation processing on each section of the original declaration curve data based on the outgoing superposed power, and performing data truncation processing on the declaration curve data after data translation processing to obtain an effective curve section after data truncation processing; performing data splicing on the delivery newly-added curve segment and the effective curve segment to generate a reconstructed declaration curve of the target unit; based on the reconstructed declaration curve, clearing the current transaction period in the power spot market in the province; the accuracy of clearing calculation input data is ensured, and the reliability of clearing price signals in the provincial spot market is improved.

The invention discloses a power peak-valley difference regulation and control method based on industrial user interaction. The method comprises the following steps: constructing a production-transaction joint optimization model by analyzing production-transaction characteristics of industrial users; establishing a market clearing model based on a peak regulation market mechanism; constructing an evolutionary game framework supporting multi-user interaction and transaction strategy learning; an industrial user market game income model and a dynamic learning strategy are constructed, an industrial user market transaction decision is solved through iteration, a user market bidding strategy is obtained, and a peak regulation market clearing price is determined; and realizing power peak-valley difference regulation and control based on user production and market clearing results. According to the method, the trading behavior characteristics of the industrial user peak regulation market are accurately described, the peak regulation market price evolution process is accurately described through the evolutionary game, reliable deduction of the peak regulation market evolution trend and power peak-valley difference regulation and control are achieved, and the method has great significance in efficient operation of a power system.

The invention discloses a partition capacity market clearing system considering novel resource participation. The system comprises a declaration module used for receiving and verifying capacity quotation data submitted by various market subjects which complete information registration in a capacity market; the trusted capacity calculation module is used for calculating the trusted capacity of different types of resources and calculating the trusted capacity of each partition based on the trusted capacity; the partition capacity demand calculation module is used for calculating the effective capacity demand of each partition in the future delivery year after the uncertainty of the new energy is considered based on the reliability evaluation result; the capacity adequacy evaluation module is used for evaluating the power generation capacity adequacy of the system according to the credible capacity and the capacity demand of the system; the reliability capacity clearing module is used for executing joint optimization clearing; the clearing result management module is used for outputting bid winning results and generating differentiated partition capacity clearing prices and duration premium signals; according to the invention, the toughness of an electric power system to deal with extreme conditions is improved.

The application is suitable for the technical field of power systems, and provides a multi-energy complementary clearing method and system for source-grid-load-storage cross-province mutual aid, which comprises the following steps: obtaining source-grid-load-storage data of each province in a cross-province interconnected power grid; constructing a clearing model with the minimum total operation cost of the cross-province interconnected power grid as an objective function based on the obtained source-grid-load-storage data; the constraint conditions include cross-province transmission line capacity constraints, each province power balance constraints, power supply side unit output constraints, energy storage operation constraints and multi-energy complementary coordination constraints; solving the clearing model to obtain the clearing output of each province power supply side unit, the power of cross-province transmission lines, the charging and discharging power of energy storage and the clearing price; generating a day-ahead market clearing plan of the cross-province interconnected power grid according to the solving result, and issuing the day-ahead market clearing plan to each province source-grid-load-storage subject for execution. The application constructs a cost model that accurately responds to the state of equipment and market environment, and can improve the collaborative efficiency of cross-province mutual aid, the new energy consumption level and the system operation safety.

Embodiments of the present application relate to a kind of simulation method based on power spot trading, comprising: obtaining grid topology, unit parameter and other static structure data and dynamic time series data such as load, new energy prediction;Dynamic data is preprocessed and divided into multiple time scales;Based on the data processed, construct a multi-agent model with different characteristics;Each agent generates and reports energy block information such as time block, continuous block, curve block or variable block according to the perceived market environment;Based on all reports, construct a market clearing model considering system power balance, network power flow, unit operation and energy block characteristic constraints and solve, obtain clearing price and power;The results are fed back to the agent, and the strategy is updated by deep reinforcement learning;Rolling collaborative simulation is carried out between day-ahead and real-time market, wherein the day-ahead clearing result is used as the boundary condition of real-time market.The present application improves the authenticity, accuracy and practicality of market simulation.