71 results about "Clearing price" patented technology

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 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 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.

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.

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 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 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.

The invention discloses a new energy prediction error-considered power auxiliary service providing method and system. According to the method, frequency modulation auxiliary service total cost is calculated based on clearing price and bid-winning capacity of bid-winning frequency modulation of a thermal power generating unit, and frequency modulation auxiliary service cost shared by a power generation side and a user side is calculated based on the frequency modulation auxiliary service total cost and on-grid electric quantity of the power generation side and the user side; based on the electric quantity correction coefficient, respectively calculating the apportioned peak regulation auxiliary service cost and the apportioned standby auxiliary service cost of the power generation side and the user side; and constructing a feature function based on a prediction error, quantifying an excess function according to the feature function, and solving the excess function by adopting a kernel method to form a new energy auxiliary service cost allocation scheme. According to the scheme of the invention, the fairness, accuracy and stability of an electric power auxiliary service cost allocation mechanism are improved.

The invention discloses an energy storage power station spot market automatic bidding system based on a large language model, and relates to the field of electricity market regulation, and the method comprises the steps: carrying out the analysis of a natural language instruction inputted by a user through a user instruction response module, and obtaining system parameters; historical clearing price sequences of different markets and the state of charge of a battery energy storage system are acquired through a data acquisition module; generating a bidding strategy through a bidding strategy generation module according to the system parameters, the historical clearing price sequence and the charge state of the battery energy storage system; verifying the technical feasibility of the bidding strategy through a bidding execution module, and submitting the bidding strategy to an energy market operator after the verification is passed; and recording an operation log in each operation cycle through a report generation module, and generating a natural language report based on the operation log. According to the invention, the automation degree and interpretability of the energy storage power station participating in electric power spot market bidding can be improved.

The invention discloses an electric power spot price difference prediction method and system coupled with meteorological change characteristics, and the method comprises the steps: obtaining spot clearing price data and market boundary data m days before a prediction day, and the meteorological historical data of each region in the same period; subtracting the acquired day-ahead clearing price from the real-time clearing price to obtain a price difference; respectively processing the meteorological data of each district and county published on the D-3 day, the D-2 day and the D-1 day, and generating comprehensive meteorological data of the whole province; calculating differences among the weather forecast data of continuous D days of D-3, D-2 and D-1 days, and constructing weather change characteristic indexes; constructing an XGBoost regression model, wherein input features comprise market boundary data, province-wide comprehensive weather forecast data and weather change feature data; and inputting the market boundary data, the weather forecast data and the weather change feature data of the prediction day D into the trained XGBoost model, and outputting a prediction result of the electric power spot price difference of the day.

A power auxiliary service providing method and system considering new energy prediction error. The method calculates the total cost of frequency modulation auxiliary service based on the clearing price and the winning capacity of the frequency modulation in the thermal power unit, calculates the allocated frequency modulation auxiliary service fee of the power generation side and the user side based on the total cost of the frequency modulation auxiliary service and the on-grid power of the power generation side and the user side, calculates the allocated peak shaving auxiliary service fee and the allocated standby auxiliary service fee of the power generation side and the user side based on the power correction coefficient, constructs a characteristic function based on the prediction error, quantifies the excess function according to the characteristic function, solves the excess function by using the kernel method, and forms a new energy auxiliary service fee allocation scheme. The scheme improves the fairness, accuracy and stability of the power auxiliary service fee allocation mechanism.

The invention discloses a power dispatching method and device based on a carbon reduction contribution degree and a dynamic game model, and the method comprises the steps: a first stage of a power dispatching step: in the game model, taking all producers as leaders, taking a virtual dispatching main body as a follower, carrying out the calculation to obtain an initial marginal clearing price, and taking the initial marginal clearing price as an initial marginal clearing price; taking the production and disappearing persons with matched power supply as dominant production and disappearing persons, and taking the production and disappearing persons without matched power supply as disadvantaged production and disappearing persons; in the second stage of the power dispatching step, in the game model, the dominant consumer is used as a leader, the disadvantaged consumer is used as a follower, and a secondary marginal clearing price is obtained through calculation; and calculating the profit of the producer and the consumer in the scheduling time period, and performing profit and line power loss distribution according to the carbon reduction contribution degree proportional relation between the producer and the consumer. By adopting the technical scheme, the leader and the follower in the game model are continuously replaced, so that the supply and demand relationship between the producer and the consumer is effectively adjusted along with different scheduling periods.

The invention discloses an independent energy storage participated spot electric energy and frequency modulation auxiliary service market joint transaction method and system, and the method comprises the steps: obtaining the quantity price declaration data of each operation main body, and determining the mode of each operation main body participating in a spot electric energy market and a frequency modulation auxiliary service market; taking the minimum total power generation cost of the system as a target, and constructing an independent energy storage participated spot electric energy market and frequency modulation auxiliary service market joint optimization target function; the independent energy storage charging and discharging characteristics are comprehensively considered to establish independent energy storage participated spot electric energy market and frequency modulation auxiliary service market joint optimization constraint conditions; market optimization clearing calculation is carried out, and the output plan and clearing price of each operation main body are published; the method can improve the market participation enthusiasm of the independent energy storage resources with the flexible adjustment effect.

The invention relates to the technical field of electric power system optimization operation, and discloses an electric power energy storage day-ahead scheduling method, which comprises the following steps: collecting electric power data used for inputting a mixed integer programming model, the electric power data comprising next day spot market clearing electricity price and historical load data; calculating the maximum demand constraint value of the next day according to the historical load data; constructing a mixed integer programming model; solving the constructed mixed integer programming model; if the solution is successful, generating a next-day scheduling plan and sending the next-day scheduling plan to the energy storage management module; if the solution fails, giving an alarm; and the energy storage management module executes the next-day scheduling plan, monitors the real-time demand in the execution process of the next-day scheduling plan on the running day, updates the input information of the mixed integer programming model by using the real-time demand, and compares the real-time demand with the real-time demand. According to the invention, the problem that efficient and accurate energy storage scheduling is difficult to realize in the prior art is solved.

The invention discloses a method for an independent energy storage power station to participate in spot-climbing market joint bidding clearing, and the method comprises the steps: building an economic optimization model with the minimum total electricity purchase cost of a dispatching center in a spot market and a climbing market as a first target; constructing a thermal power generating unit decision-making model by taking the maximum thermal power total income of the thermal power generating unit in the spot market and the climbing market as a second target; constructing an energy storage power station decision model by taking the maximum total energy storage income of the energy storage power station in the spot market and the climbing market as a third target; taking the economic optimization model as an upper-layer model, and taking the thermal power generating unit decision model and the energy storage power station decision model as lower-layer models to obtain a double-layer joint bidding clearing model; the double-layer joint bidding clearing model is solved, a joint clearing result is obtained, and the joint clearing result is used for indicating clearing electricity price and bid winning electric quantity of the spot market and the climbing market in each time period. According to the invention, the total electricity purchase cost of the system can be reduced and the income of each electricity seller can be optimized.