Thermal power plant day-ahead transaction quotation scheme optimization method and device, equipment and medium

Abstract

The invention provides a thermal power plant day-ahead transaction quotation scheme optimization method and device, equipment and a medium, and belongs to the field of data index evaluation management. The method comprises the steps that a thermal power plant revenue function with revenue maximization as a target is constructed, factors such as medium and long term electric quantity and contract electricity price are considered, the optimal day-ahead bid winning electric quantity and real-time on-grid electric quantity are determined through optimization, and theoretical optimal revenue is achieved. After actual operation, the coverage rate of the actual income and the theoretical optimal income is calculated, and if the income coverage rate of the thermal power plant in the target time period is smaller than the preset income coverage rate, the thermal power plant can adjust the quotation scheme in the day-ahead transaction by improving electricity price prediction and the like so as to optimize the income. By optimizing the quotation scheme, the resource allocation efficiency, the market competitiveness, the risk resistance and the economic benefits of the thermal power plant are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of data index evaluation management, and specifically to a method for optimizing a day-ahead transaction quotation scheme of a thermal power plant, a device for optimizing a day-ahead transaction quotation scheme of a thermal power plant, an electronic device and a computer-readable storage medium. Background Art

[0002] In the current power market environment, enterprise operation evaluation is particularly important. As an important part of the national energy system, the healthy development of the power market is directly related to national energy security, economic development and social stability. Therefore, a scientific and reasonable evaluation of the operating conditions of power market enterprises will help government regulatory authorities better understand market dynamics and provide a basis for policy formulation. It will also help enterprises to discover their own shortcomings, optimize their business strategies and improve their market competitiveness.

[0003] For a long time, the main indicator used in the evaluation of power market enterprises in my country is utilization hours. Utilization hours refers to the ratio of the actual power generation of power generation equipment within a certain period of time to the maximum possible power generation of the equipment. It can reflect the production efficiency and market share of power generation enterprises to a certain extent.

[0004] However, with the deepening of electricity market reform, especially after entering the spot market, the revenue sources of power generation companies have become more diversified, resulting in the inability of traditional utilization hour evaluation indicators to evaluate the operating conditions of existing electricity market companies. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a method, device, equipment and medium for evaluating a day-ahead transaction quotation scheme of a thermal power plant to solve the above-mentioned problem.

[0006] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a method for optimizing a day-ahead transaction quotation scheme of a thermal power plant, comprising:

[0007] Taking the medium- and long-term electricity quantity in the target period, the medium- and long-term contract electricity price, the theoretical day-ahead bid electricity quantity, the day-ahead node electricity price, the theoretical real-time grid-connected electricity quantity, the real-time node electricity price and the theoretical operating cost as inputs, and maximizing the day-ahead electricity spot market revenue as the goal, the thermal power plant revenue objective function is constructed, and the constraints of the thermal power plant revenue objective function are determined;

[0008] The optimal solution is obtained for the revenue objective function of the thermal power plant, and the optimal theoretical day-ahead bid quantity and the optimal theoretical real-time grid-connected quantity in the target period that meet the constraint conditions of the revenue objective function of the thermal power plant are determined. Based on the revenue objective function of the thermal power plant, the theoretical optimal revenue of the thermal power plant in the target period corresponding to the optimal theoretical day-ahead bid quantity and the optimal theoretical real-time grid-connected quantity is determined.

[0009] Obtain the day-ahead bid electricity volume, real-time grid-connected electricity volume, medium- and long-term electricity volume, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period;

[0010] Based on the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period, the actual revenue of the thermal power plant during the target period is obtained;

[0011] Based on the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, the revenue coverage rate of the thermal power plant during the target period is obtained;

[0012] If the revenue coverage ratio of a thermal power plant in the target period is less than the preset revenue coverage ratio, the thermal power plant can adjust its quotation scheme for day-ahead transactions by improving electricity price forecasts, etc., to optimize revenue.

[0013] Optionally, the thermal power plant revenue objective function is:

[0014] max C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ; Among them, max C represents the maximum revenue of the electricity market, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, Q t,日前 represents the theoretical day-ahead bid electricity quantity in the target period, P t,日前 represents the day-ahead node electricity price during the target period, Q t,实时 represents the theoretical real-time online power consumption during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the theoretical operating cost during the target period.

[0015] Optionally, the constraints of the thermal power plant revenue objective function are:

[0016] Among them, Q t,中长期 Indicates the theoretical medium- and long-term electricity consumption during the target period, Q t,日前 The theoretical day-ahead bidding quantity in the target period, Q t,实时 Indicates the theoretical real-time online power consumption during the target period, Q t,max Indicates the maximum power generation during the target period.

[0017] Optionally, based on the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period, the actual revenue of the thermal power plant during the target period is obtained, including:

[0018] The following formula is used to calculate the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period to obtain the actual revenue of the thermal power plant during the target period;

[0019] C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ;

[0020] Among them, C represents the actual revenue of the thermal power plant during the target period, Q t,日前 represents the day-ahead bid electricity quantity during the target period, Q t,实时 Indicates the real-time Internet access power during the target period, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, P t,日前 represents the day-ahead node electricity price during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the operating cost during the target period.

[0021] Optionally, based on the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, the revenue coverage rate of the thermal power plant during the target period is obtained, including:

[0022] The following formula is used to calculate the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, and the revenue coverage rate of the thermal power plant during the target period is obtained;

[0023] Among them, δ represents the revenue coverage rate of the thermal power plant during the target period, C represents the actual revenue of the thermal power plant during the target period, and maxC represents the theoretical optimal revenue of the thermal power plant during the target period.

[0024] In a second aspect of an embodiment of the present invention, a device for optimizing a day-ahead transaction quotation scheme of a thermal power plant is provided, comprising:

[0025] The model building module is used to construct the thermal power plant's profit objective function with the medium- and long-term electricity volume in the target period, the medium- and long-term contract electricity price, the theoretical day-ahead bid electricity volume, the day-ahead node electricity price, the theoretical real-time grid-connected electricity volume, the real-time node electricity price and the theoretical operating cost as inputs, and the maximum electricity market profit as the goal, and determine the constraints of the thermal power plant's profit objective function;

[0026] The model solving module is used to optimize and solve the revenue objective function of the thermal power plant, determine the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power in the target period that meet the constraints of the revenue objective function of the thermal power plant, and determine the theoretical optimal revenue of the thermal power plant in the target period corresponding to the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power based on the revenue objective function of the thermal power plant;

[0027] The data acquisition module is used to obtain the day-ahead bid electricity quantity, real-time grid-connected electricity quantity, medium- and long-term electricity quantity, medium- and long-term contract electricity price, day-ahead node electricity price, real-time node electricity price and operating cost during the target period;

[0028] The revenue calculation module is used to obtain the actual revenue of the thermal power plant during the target period based on the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period;

[0029] A coverage rate calculation module is used to obtain the revenue coverage rate of the thermal power plant target period based on the actual revenue of the thermal power plant target period and the theoretical optimal revenue of the thermal power plant target period;

[0030] The parameter adjustment module is used to adjust the quotation scheme of its day-ahead transaction by improving the electricity price forecast and other methods to optimize the revenue if the revenue coverage ratio of the thermal power plant in the target period is less than the preset revenue coverage ratio.

[0031] Optionally, the revenue calculation module is specifically used for:

[0032] The following formula is used to calculate the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period to obtain the actual revenue of the thermal power plant during the target period;

[0033] C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ;

[0034] Among them, C represents the actual revenue of the thermal power plant during the target period, Q t,日前 represents the day-ahead bid electricity quantity during the target period, Q t,实时 Indicates the real-time Internet access power during the target period, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, P t,日前 represents the day-ahead node electricity price during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the operating cost during the target period.

[0035] Optionally, the coverage calculation module is specifically used for:

[0036] The following formula is used to calculate the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, and the revenue coverage rate of the thermal power plant during the target period is obtained;

[0037] Among them, δ represents the revenue coverage rate of the thermal power plant during the target period, C represents the actual revenue of the thermal power plant during the target period, and maxC represents the theoretical optimal revenue of the thermal power plant during the target period.

[0038] In a third aspect of the embodiments of the present invention, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the above-mentioned method for optimizing the day-ahead transaction quotation scheme of a thermal power plant is executed.

[0039] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, storing computer instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for optimizing the day-ahead transaction quotation scheme of a thermal power plant.

[0040] Beneficial effects of the present invention:

[0041] (1) Optimizing resource allocation: Through the method of the present invention, thermal power plants can more accurately predict and determine the amount of electricity that will be awarded in the power market, as well as the amount of electricity that will be connected to the grid in real time. This optimization helps thermal power plants to rationally arrange production plans based on market demand, reduce resource waste, and improve energy efficiency.

[0042] (2) Enhance market competitiveness: Through the dynamic quotation scheme provided by the present invention, thermal power plants can flexibly respond to changes in market supply and demand and participate in market transactions at more competitive prices, thereby increasing their market share.

[0043] (3) Reduce market risk: Market price fluctuation is one of the major risks faced by thermal power plants. The method of the present invention helps thermal power plants avoid price risks to a certain extent and stabilize operating income by optimizing the quotation scheme.

[0044] (4) Improving strategy adaptability: The method of the present invention can adjust the quotation scheme in real time according to market feedback, making it more in line with market dynamics and enhancing the flexibility and adaptability of the strategy.

[0045] (5) Improve revenue coverage: By continuously adjusting and optimizing the quotation plan, thermal power plants can ensure that the actual revenue is as close as possible to the theoretical optimal revenue, thereby improving the revenue level and enhancing the profitability of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0047] Figure 1 It is a flow chart of a method for evaluating a day-ahead transaction quotation scheme for a thermal power plant provided by an embodiment of the present invention;

[0048] Figure 2 It is a structural schematic diagram of a thermal power plant day-ahead transaction quotation scheme evaluation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0051] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0052] Embodiment 1

[0053] Please refer to Figure 1 , Figure 1 1 is a flow chart of a method for evaluating a day-ahead transaction quotation scheme for a thermal power plant provided by an embodiment of the present invention, the method comprising the following steps:

[0054] S100, taking the medium- and long-term electricity quantity in the target period, the medium- and long-term contract electricity price, the theoretical day-ahead bid electricity quantity, the day-ahead node electricity price, the theoretical real-time grid-connected electricity quantity, the real-time node electricity price and the theoretical operating cost as inputs, and maximizing the electricity market revenue as the goal, constructs the thermal power plant revenue objective function, and determines the constraints of the thermal power plant revenue objective function;

[0055] It can be understood that the medium- and long-term electricity volume and the medium- and long-term contract electricity price refer to the parameters known before the day-ahead declaration; the day-ahead node electricity price, the real-time node electricity price and the theoretical operating cost are the results of the electricity market clearing one day after the actual operation day; the theoretical day-ahead winning electricity volume, the theoretical real-time online electricity volume and the theoretical operating cost are the ideal results obtained by using the optimization method of the thermal power plant's day-ahead transaction quotation scheme when the day-ahead node electricity price and the real-time node electricity price are known one day after the actual operation day. It is the maximum profit that the thermal power plant can obtain under its ideal state.

[0056] Medium- and long-term electricity refers to the amount of electricity required based on a certain forecast or plan over a longer period of time (such as monthly, quarterly, annual or ten-day periods, etc.). This amount of electricity is based on the forecast of future electricity demand and takes into account factors such as possible load growth, seasonal changes, and economic development trends.

[0057] The medium- and long-term contract electricity price refers to the electricity price stipulated in the power supply contract signed between power generation enterprises and power users over a longer period of time (such as monthly, quarterly or annual). This price is usually determined based on market supply and demand forecasts, fuel costs, policy orientation and other factors.

[0058] The day-ahead bid electricity quantity and day-ahead node electricity price refer to the day-ahead electricity market transaction results obtained by the power dispatching agency before 17:30 on the day before the actual operation, based on the information reported by market members, the grid operation boundary conditions on the operation day, and the grid and unit operation constraints, using the long-term safety-constrained unit combination (SCUC) and safety-constrained economic dispatch (SCED) programs for optimization calculation and clearing. This quantity may differ from the planned quantity due to factors such as actual supply and demand conditions and equipment operation status.

[0059] The theoretical day-ahead winning electricity volume refers to the electricity volume determined by simulating the power market bidding process one day after the actual operation day using the day-ahead node electricity price in the clearing results. In the power market, power generation companies will quote based on the predicted demand and their own costs, and market operators will determine the winning electricity volume based on these quotes.

[0060] The day-ahead node electricity price is the maximum price that the system needs to pay in order to meet the additional unit of electricity demand at a specific node in the day-ahead market. This price reflects the tightest balance between electricity supply and demand at the node.

[0061] The theoretical real-time online power consumption refers to the power consumption actually supplied to the power grid by power generation enterprises one day after the actual operation day, using the real-time node electricity prices in the clearing results to simulate the power market bidding process.

[0062] Real-time online electricity volume refers to the ultra-short-term load forecast, new energy power generation forecast, intraday inter-provincial spot transaction results, intraday inter-provincial peak-shaving transaction results, the day-ahead quotation of each unit, unit operating parameters, line operating parameters, etc. as input information through the power dispatching agency, with the goal of minimizing the power generation cost of the entire network, considering the grid operation constraints such as backup demand and section limit and the unit operation constraints such as maximum and minimum output and climbing limit, and performing market rolling clearing calculations through the safety-constrained economic dispatch program (SCED), to form the next 15-minute power generation plan and real-time node electricity price for each unit.

[0063] The real-time node electricity price refers to the price that the system needs to pay in order to meet the additional unit of electricity demand at a specific node in the real-time electricity market. This price will change continuously based on real-time supply and demand, network conditions and other factors.

[0064] Theoretical operating costs refer to all costs that need to be paid to maintain the normal operation of the power system under ideal conditions. This includes fuel costs, operation and maintenance costs, depreciation, environmental protection costs, etc. Theoretical operating costs are a budget or planning concept used to guide the economic operation of power companies.

[0065] In one embodiment, the thermal power plant revenue objective function is:

[0066] max C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ; Among them, max C represents the maximum revenue of the electricity market, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, Q t，日前 represents the theoretical day-ahead bid electricity quantity in the target period, P t，日前 represents the day-ahead node electricity price during the target period, Q t,实时 represents the theoretical real-time online power consumption during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the theoretical operating cost during the target period.

[0067] In one embodiment, the constraints of the thermal power plant revenue objective function are:

[0068] Among them, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, Q t,日前 The theoretical day-ahead bidding quantity in the target period, Q t,实时 Indicates the theoretical real-time online power consumption during the target period, Q t,max Indicates the maximum power generation during the target period.

[0069] S200, optimizing and solving the revenue objective function of the thermal power plant, determining the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power in the target period that meet the constraint conditions of the revenue objective function of the thermal power plant, and determining the theoretical optimal revenue of the thermal power plant in the target period corresponding to the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power based on the revenue objective function of the thermal power plant;

[0070] Specifically, in the embodiment of the present invention, the objective function of the thermal power plant's revenue can also be optimized and solved by a target optimization algorithm.

[0071] Objective optimization algorithms are a class of mathematical methods used to find the best solution under given constraints in order to maximize or minimize one or more objective functions. These algorithms are widely used in fields such as engineering, economics, decision science, and machine learning.

[0072] The following is a detailed introduction to the multi-objective optimization algorithm:

[0073] Linear programming: Linear programming is a method in mathematical optimization, which is used to solve the optimal value (maximum or minimum value) of a linear objective function under given linear inequality or equality constraints. It is widely used in resource allocation, production planning, transportation problems and other fields.

[0074] Nonlinear programming: Nonlinear programming is to solve the optimization problem when at least one of the objective function or constraints is nonlinear. It is more complicated than linear programming because nonlinear problems usually have no general solution and need to be designed according to the specific problem.

[0075] Integer Programming: Integer programming is a special case of linear programming or nonlinear programming where the variables must be integers. In practical problems, integer programming is used in situations where the variables must be integers, such as project selection, equipment purchase, etc.

[0076] Dynamic programming: Dynamic programming is a method for solving complex problems in mathematics, management science, economics, bioinformatics, etc. It solves optimization problems by breaking them down into relatively simple sub-problems, and is particularly suitable for solving multi-stage decision-making process problems.

[0077] Genetic algorithm: Genetic algorithm is a heuristic search algorithm that simulates the principles of natural selection and genetics and optimizes problem solutions through selection, crossover and mutation operations. It is suitable for solving complex problems that are difficult to solve with traditional optimization methods.

[0078] Particle Swarm Optimization: Particle swarm optimization is an optimization tool based on swarm intelligence that simulates the social behavior of bird flocks or fish schools to find the optimal solution to the problem. Each "particle" represents a candidate solution in the problem space and adjusts its position by tracking its own historical best position and the best position of the entire group.

[0079] Simulated annealing: Simulated annealing is a general probabilistic algorithm used to find the global optimal solution to a given problem. The algorithm is inspired by the annealing process of solid materials and searches the solution space globally by slowly reducing the "temperature" (control parameter) to avoid falling into the local optimal solution.

[0080] Neural Network Optimization: Neural Network Optimization is the use of artificial neural networks to solve optimization problems. It usually involves training a neural network to predict or classify data and minimizing the prediction error by adjusting the network's weights and biases.

[0081] In order to understand how to use the target optimization algorithm to solve the objective function of the thermal power plant's revenue, the following example is used to illustrate the particle swarm optimization:

[0082] Step 1, initialize the particle swarm: randomly generate a certain number of particles, each particle represents a candidate solution in the problem space (i.e. the theoretical day-ahead bid power and the theoretical real-time online power). Each particle has its own position and speed. The position corresponds to the solution space of the objective function, and the speed determines the speed and direction of the particle's movement in the solution space.

[0083] Step 2, evaluate particle fitness: calculate the value of the objective function (i.e., the maximum profit of the electricity spot market on the day before) for each particle's position, and use this value as the particle's fitness. In optimization problems, it is usually desirable to minimize the objective function, so the lower the fitness, the better the solution.

[0084] Step 3, update the individual best and global best: each particle will remember its best position in history, and the entire particle group will share the global best position found so far.

[0085] Step 4, update particle speed and position: Update the speed and position of each particle based on the current speed, individual best position and global best position. The speed update formula usually contains inertia weight, cognitive component and social component, and the position update is based on the new speed.

[0086] Step 5, iterative optimization: Repeat steps 2 to 4 until the termination condition is met, such as reaching the maximum number of iterations, the change in the global best fitness is less than a certain threshold, or the accuracy requirement of the problem is met.

[0087] Step 6, output result: The final global optimal position is the approximate optimal solution of the objective function found by the algorithm.

[0088] For ease of understanding, the following is an example with reference to Table 1:

[0089] Table 1 Optimization parameters of the objective function of thermal power plant revenue

[0090]

[0091] According to Table 1 above, it can be seen that the result of optimizing the above 6 groups of bidding schemes is that the electricity market revenue corresponding to the 6th group of bidding schemes is the largest. Therefore, the day-ahead winning power (600MWh) and real-time online power (400MWh) corresponding to the 6th group of bidding schemes are the optimal theoretical day-ahead winning power and the optimal theoretical real-time online power in the target period respectively, and the corresponding electricity spot day-ahead market revenue (28400) is the theoretical optimal revenue of the thermal power plant in the target period.

[0092] S300, obtaining the day-ahead bid electricity quantity, real-time grid-connected electricity quantity, medium- and long-term electricity quantity, medium- and long-term contract electricity price, day-ahead node electricity price, real-time node electricity price and operation cost in the target period;

[0093] Day-ahead trading refers to a type of transaction in the spot electricity market, which is used to trade today's electricity one day in advance.

[0094] It should be noted that in the provinces and regions where the spot power trading market is open, the information on the unified dispatching load, new energy output, capacity of the units that must be turned on or off, and the interconnection line plan of each node is publicly disclosed through the power trading platforms of each province, and the information on the unified dispatching load, new energy output, capacity of the units that must be turned on or off, and the interconnection line plan are all time-sharing information, that is, the day is divided into multiple time points, that is, the trading platform announces the province's unified dispatching load, new energy output, capacity of the units that must be turned on or off, and interconnection line plan information at multiple time points of each node on the day, and the province's unified dispatching load, new energy output, capacity of the units that must be turned on or off, and the interconnection line plan information are different at each time point.

[0095] It should be noted that the day before the trading day is divided into multiple time periods in equal proportion, where the equal proportions include but are not limited to 24, 48 and 96. For example: 1440 minutes (24h) ÷ 24 = 60 minutes, that is, there are 24 time periods, 60 minutes as a unit, so; 1440 minutes (24h) ÷ 48 = 30 minutes, that is, there are 48 time periods, 30 minutes as a unit, so the time periods are 48; 1440 minutes (24h) ÷ 96 = 15 minutes, that is, there are 96 time periods, 15 minutes as a unit.

[0096] It should be noted that the winning electricity volume the day before is calculated through the relevant clearing information released by the trading center, and the clearing information includes but is not limited to: spot clearing electricity price, day-ahead node electricity price, real-time node electricity price, real-time transaction details of the unit the day before, actual power generation curve of the unit, day-ahead settlement time-sharing details, real-time settlement time-sharing details, contract settlement time-sharing details, day-ahead market clearing situation, real-time market clearing overview, 96-point actual power grid operation values, etc.

[0097] The day-ahead bid electricity refers to the unit combination status, power generation output plan and clearing price determined one day before the market operation date. This concept involves day-ahead transactions in the power market, where medium- and long-term contract curves and prices are usually fixed. Day-ahead transactions are conducted one day before the power market operation date and are intended to determine the power supply and price for the next day.

[0098] Real-time grid-connected electricity refers to the amount of electricity actually generated in the real-time electricity market. The real-time market is different from the day-ahead market in that it focuses more on the supply and demand of electricity on the actual day of operation. Real-time grid-connected electricity is usually related to the real-time operating status of the power system, including factors such as load forecasting and equipment operating status. This concept is reflected in the operation reserve, safety constraint unit combination and economic dispatch of the power system.

[0099] For ease of understanding, the following examples are provided in Table 2:

[0100] Table 2. Results of the previous transaction quotation clearing

[0101]

[0102] S400, based on the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period, obtain the actual revenue of the thermal power plant during the target period;

[0103] In one embodiment, step S400 specifically includes:

[0104] The following formula is used to calculate the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period to obtain the actual revenue of the thermal power plant during the target period;

[0105] C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ;

[0106] Among them, C represents the actual revenue of the thermal power plant during the target period, Q t,日前 represents the day-ahead bid electricity quantity during the target period, Q t,实时 Indicates the real-time Internet access power during the target period, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, P t,日前 represents the day-ahead node electricity price during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the operating cost during the target period.

[0107] For ease of understanding, the following is an example with actual data:

[0108] First, calculate the profit of the medium and long-term contracts: Q t,中长期 P t,中长期 =1000MWh 50 yuan / MWh=5000 yuan.

[0109] Then calculate the return of the day-ahead market: (Q t,日前 -Q t,中长期 )*P t,日前 =(1100MWh-1000MWh)*60 yuan / MWh=100MWh60 yuan / MWh=6000 yuan.

[0110] Then calculate the return of the real-time market part: (Q t,实时 -Q t,日前 )*P t,实时 =(1150MWh-1100MWh)*70 yuan / MWh=50MWh70 yuan / MWh=3500 yuan.

[0111] Finally calculate the running cost: Q t,实时 *C t =1150MWh30 yuan / MWh=34500 yuan.

[0112] Now we add these parts together and subtract the running costs to get the actual profit: C = 50,000 yuan + 6,000 yuan + 3,500 yuan - 34,500 yuan = 24,200 yuan.

[0113] S500, based on the actual income of the thermal power plant in the target period and the theoretical optimal income of the thermal power plant in the target period, the income coverage rate of the thermal power plant in the target period is obtained;

[0114] In one embodiment, the following formula is used to calculate the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period to obtain the revenue coverage rate of the thermal power plant during the target period;

[0115] Among them, δ represents the revenue coverage rate of the thermal power plant during the target period, C represents the actual revenue of the thermal power plant during the target period, and maxC represents the theoretical optimal revenue of the thermal power plant during the target period.

[0116] S600, if the revenue coverage ratio of the thermal power plant in the target period is less than the preset revenue coverage ratio, the thermal power plant may adjust its quotation scheme for day-ahead transactions by improving electricity price forecasts, etc., to optimize revenue.

[0117] Specifically, when the revenue coverage ratio of the thermal power plant in the target period is less than the preset revenue coverage ratio, it means that the clearing result released by the current power transaction is quite different from the bidding plan under the theoretical maximum profit, which just shows that the bidding plan in the day-ahead transaction is unreasonable. Therefore, we can adjust the bidding plan in the day-ahead transaction of the thermal power plant by improving the electricity price forecast and other methods to optimize the profit.

[0118] It is understandable that, as mentioned above, the day-ahead winning electricity quantity is calculated based on the bidding plan, and the bidding plan is obtained through a series of parameters such as the physical parameters of the unit, the day-ahead node electricity price and the real-time node electricity price, and of course other parameters. Therefore, we adjust the day-ahead node electricity price and the real-time node electricity price of the target period in the thermal power plant's revenue objective function, which is actually adjusting the bidding plan of the thermal power plant.

[0119] Table 3 Segmented quotation scheme

[0120]

[0121] In one or more of the above embodiments, the day-ahead bid electricity quantity in the target period is calculated by:

[0122] Specifically, when the price quoted by the thermal power plant to the power market is less than or equal to the day-ahead node electricity price P during the target period of the power market, t,日前The power market will allocate an output range to the power plant according to the quotation given by the thermal power plant to the power market. The maximum value of this range is taken as the winning output of the day before the target period. Then the winning output of the day before the target period is multiplied by the period (the unit needs to be converted into hours) to get the winning electricity volume of the day before the target period.

[0123] In one or more of the above embodiments, the real-time online power consumption in the target period is calculated in the following way:

[0124] Specifically, when the price quoted by the thermal power plant to the power market is less than or equal to the real-time node electricity price P of the target period of the power market, t,实时 The power market will allocate an output range to the power plant according to the quotation given by the thermal power plant to the power market. The maximum value of the range is taken as the real-time winning output of the target period, and then the real-time winning output of the target period is multiplied by the period (the unit needs to be converted into hours) to get the real-time winning electricity of the target period.

[0125] Beneficial effects of the present invention:

[0126] (1) Optimizing resource allocation: Through the method of the present invention, thermal power plants can more accurately predict and determine the amount of electricity that will be awarded in the power market, as well as the amount of electricity that will be connected to the grid in real time. This optimization helps thermal power plants to rationally arrange production plans based on market demand, reduce resource waste, and improve energy efficiency.

[0127] (2) Enhance market competitiveness: Through the dynamic quotation scheme provided by the present invention, thermal power plants can flexibly respond to changes in market supply and demand and participate in market transactions at more competitive prices, thereby increasing their market share.

[0128] (3) Reduce market risk: Market price fluctuation is one of the major risks faced by thermal power plants. The method of the present invention helps thermal power plants avoid price risks to a certain extent and stabilize operating income by optimizing the quotation scheme.

[0129] (4) Improving strategy adaptability: The method of the present invention can adjust the quotation scheme in real time according to market feedback, making it more in line with market dynamics and enhancing the flexibility and adaptability of the strategy.

[0130] (5) Improve revenue coverage: By continuously adjusting and optimizing the quotation plan, thermal power plants can ensure that the actual revenue is as close as possible to the theoretical optimal revenue, thereby improving the revenue level and enhancing the profitability of the enterprise.

[0131] Embodiment 2

[0132] Based on the same inventive concept, Figure 2 As shown, the embodiment of the present invention further provides a thermal power plant day-ahead transaction quotation scheme optimization device 200, comprising:

[0133] The model building module 210 is used to construct a thermal power plant profit objective function with the medium- and long-term electricity quantity, medium- and long-term contract electricity price, theoretical day-ahead bid electricity quantity, day-ahead node electricity price, theoretical real-time grid-connected electricity quantity, real-time node electricity price and theoretical operating cost in the target period as inputs and the maximum day-ahead electricity spot market profit as the goal, and determine the constraint conditions of the thermal power plant profit objective function;

[0134] The model solving module 220 is used to optimize and solve the revenue objective function of the thermal power plant, determine the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power in the target period that meet the constraint conditions of the revenue objective function of the thermal power plant, and determine the theoretical optimal revenue of the thermal power plant in the target period corresponding to the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power based on the revenue objective function of the thermal power plant;

[0135] The data acquisition module 230 is used to obtain the day-ahead bid electricity quantity, real-time grid-connected electricity quantity, medium- and long-term electricity quantity, medium- and long-term contract electricity price, day-ahead node electricity price, real-time node electricity price and operating cost in the target period;

[0136] The revenue calculation module 240 is used to obtain the actual revenue of the thermal power plant during the target period based on the day-ahead bid electricity volume, real-time grid-connected electricity volume, medium- and long-term electricity volume, medium- and long-term contract electricity price, day-ahead node electricity price, real-time node electricity price and operating cost during the target period;

[0137] A coverage rate calculation module 250 is used to obtain the revenue coverage rate of the thermal power plant target period based on the actual revenue of the thermal power plant target period and the theoretical optimal revenue of the thermal power plant target period;

[0138] The parameter adjustment module 260 is used to adjust the quotation scheme of its day-ahead transaction by improving the electricity price forecast, etc., to optimize the revenue if the revenue coverage ratio of the thermal power plant in the target period is less than the preset revenue coverage ratio.

[0139] Optionally, the revenue calculation module 240 is specifically used for:

[0140] The following formula is used to calculate the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period to obtain the actual revenue of the thermal power plant during the target period;

[0141] C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t;

[0142] Among them, C represents the actual revenue of the thermal power plant during the target period, Q t,日前 represents the day-ahead bid electricity quantity during the target period, Q t,实时 Indicates the real-time Internet access power during the target period, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, P t,日前 represents the day-ahead node electricity price during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the operating cost during the target period.

[0143] Optionally, the coverage calculation module 250 is specifically used for:

[0144] The following formula is used to calculate the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, and the revenue coverage rate of the thermal power plant during the target period is obtained;

[0145] Among them, δ represents the revenue coverage rate of the thermal power plant during the target period, C represents the actual revenue of the thermal power plant during the target period, and maxC represents the theoretical optimal revenue of the thermal power plant during the target period.

[0146] It should be understood that the device corresponds to the above-mentioned thermal power plant day-ahead transaction quotation scheme optimization method embodiment, and can execute each step involved in the above-mentioned method embodiment. The specific functions of the device can be found in the above description. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.

[0147] Embodiment 3

[0148] Based on the same inventive concept, an embodiment of the present invention also provides an electronic device, including: a processor and a memory, the memory storing machine-readable instructions executable by the processor, and the machine-readable instructions, when executed by the processor, execute the above-mentioned method for optimizing the day-ahead transaction quotation scheme of a thermal power plant.

[0149] In a typical configuration, an electronic device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0150] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0151] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0152] Embodiment 4

[0153] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer executes the above-mentioned method for optimizing the day-ahead transaction quotation scheme of a thermal power plant.

[0154] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0155] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0156] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0158] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0159] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0160] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0161] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for optimizing the day-ahead transaction quotation scheme of a thermal power plant, characterized in that: include: Taking the medium- and long-term electricity volume in the target period, long-term contract electricity price, theoretical day-ahead bid electricity volume, day-ahead node electricity price, theoretical real-time grid-connected electricity volume, real-time node electricity price and theoretical operating cost as inputs, and maximizing the electricity market revenue as the goal, the thermal power plant revenue objective function is constructed, and the constraints of the thermal power plant revenue objective function are determined; The optimal solution is obtained for the revenue objective function of the thermal power plant, and the optimal theoretical day-ahead bid quantity and the optimal theoretical real-time grid-connected quantity in the target period that meet the constraint conditions of the revenue objective function of the thermal power plant are determined. Based on the revenue objective function of the thermal power plant, the theoretical optimal revenue of the thermal power plant in the target period corresponding to the optimal theoretical day-ahead bid quantity and the optimal theoretical real-time grid-connected quantity is determined. Obtain the day-ahead bid electricity volume, real-time grid-connected electricity volume, medium- and long-term electricity volume, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period; Based on the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period, the actual revenue of the thermal power plant during the target period is obtained; Based on the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, the revenue coverage rate of the thermal power plant during the target period is obtained; If the revenue coverage ratio of the thermal power plant in the target period is less than the preset revenue coverage ratio, the theoretical day-ahead node marginal electricity price of the target period and the theoretical real-time node marginal electricity price of the target period in the revenue objective function of the thermal power plant shall be adjusted.

2. The method for optimizing the day-ahead transaction quotation scheme of a thermal power plant according to claim 1, characterized in that: The objective function of thermal power plant revenue is: max C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ;in, max C represents the maximum revenue in the electricity market, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, Q t日前 represents the theoretical day-ahead bid electricity quantity in the target period, P t日前 represents the day-ahead node electricity price during the target period, Q t,实时 represents the theoretical real-time online power consumption during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the theoretical operating cost during the target period.

3. The method for optimizing the day-ahead transaction quotation scheme of a thermal power plant according to claim 2, characterized in that: The constraints of the thermal power plant's revenue objective function are: Among them, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, Q t,日前 The theoretical day-ahead bidding quantity in the target period, Q t,实时 Indicates the theoretical real-time online power consumption during the target period, Q t,max Indicates the maximum power generation during the target period.

4. The method for optimizing the day-ahead transaction quotation scheme of a thermal power plant according to claim 1, characterized in that: Based on the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period, the actual revenue of the thermal power plant during the target period is obtained, including: The following formula is used to calculate the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period to obtain the actual revenue of the thermal power plant during the target period; C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ; Among them, C represents the actual revenue of the thermal power plant during the target period, Q t,日前 represents the day-ahead bid electricity quantity during the target period, Q t,实时 Indicates the real-time Internet access power during the target period, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, P t,日前 represents the day-ahead node electricity price during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the operating cost during the target period.

5. The method for optimizing the day-ahead transaction quotation scheme of a thermal power plant according to claim 1, characterized in that: Based on the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, the revenue coverage rate of the thermal power plant during the target period is obtained, including: The following formula is used to calculate the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, and the revenue coverage rate of the thermal power plant during the target period is obtained; Among them, δ represents the revenue coverage rate of the thermal power plant during the target period, C represents the actual revenue of the thermal power plant during the target period, and maxC represents the theoretical optimal revenue of the thermal power plant during the target period.

6. A device for optimizing the day-ahead transaction quotation scheme of a thermal power plant, characterized in that: include: The model building module is used to construct the thermal power plant's profit objective function with the medium- and long-term electricity volume in the target period, the medium- and long-term contract electricity price, the theoretical day-ahead bid electricity volume, the day-ahead node electricity price, the theoretical real-time grid-connected electricity volume, the real-time node electricity price and the theoretical operating cost as inputs, and the maximum day-ahead electricity spot market profit as the goal, and determine the constraints of the thermal power plant's profit objective function; The model solving module is used to optimize and solve the revenue objective function of the thermal power plant, determine the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power in the target period that meet the constraints of the revenue objective function of the thermal power plant, and determine the theoretical optimal revenue of the thermal power plant in the target period corresponding to the optimal theoretical day-ahead bid power and the optimal theoretical real-time grid-connected power based on the revenue objective function of the thermal power plant; The data acquisition module is used to obtain the day-ahead bid electricity quantity, real-time grid-connected electricity quantity, medium- and long-term electricity quantity, medium- and long-term contract electricity price, day-ahead node electricity price, real-time node electricity price and operating cost during the target period; The revenue calculation module is used to obtain the actual revenue of the thermal power plant during the target period based on the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period; A coverage rate calculation module is used to obtain the revenue coverage rate of the thermal power plant target period based on the actual revenue of the thermal power plant target period and the theoretical optimal revenue of the thermal power plant target period; The parameter adjustment module is used to adjust the theoretical day-ahead node marginal electricity price of the target period and the theoretical real-time node marginal electricity price of the target period in the thermal power plant's revenue objective function if the revenue coverage rate of the thermal power plant's target period is less than the preset revenue coverage rate.

7. The device for optimizing the day-ahead transaction quotation scheme of a thermal power plant according to claim 6, characterized in that: The revenue calculation module is specifically used for: The following formula is used to calculate the day-ahead bid electricity, real-time grid-connected electricity, medium- and long-term electricity, medium- and long-term contract electricity prices, day-ahead node electricity prices, real-time node electricity prices and operating costs during the target period to obtain the actual revenue of the thermal power plant during the target period; C=Q t,中长期 *P t,中长期 +(Q t,日前 -Q t,中长期 )*P t,日前 +(Q t,实时 -Q t,日前 )*P t,实时 -Q t,实时 *C t ; Among them, C represents the actual revenue of the thermal power plant during the target period, Q t,日前 represents the day-ahead bid electricity quantity during the target period, Q t,实时 Indicates the real-time Internet access power during the target period, Q t,中长期 Indicates the medium and long term electricity consumption during the target period, P t,中长期 represents the medium- and long-term contract electricity price during the target period, P t,日前 represents the day-ahead node electricity price during the target period, P t,实时 represents the real-time node electricity price during the target period, C t Represents the operating cost during the target period.

8. The device for optimizing the day-ahead transaction quotation scheme of a thermal power plant according to claim 6, characterized in that: The coverage calculation module is specifically used for: The following formula is used to calculate the actual revenue of the thermal power plant during the target period and the theoretical optimal revenue of the thermal power plant during the target period, and the revenue coverage rate of the thermal power plant during the target period is obtained; Among them, δ represents the revenue coverage rate of the thermal power plant during the target period, C represents the actual revenue of the thermal power plant during the target period, and maxC represents the theoretical optimal revenue of the thermal power plant during the target period.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method for optimizing the day-ahead transaction quotation scheme of a thermal power plant according to any one of claims 1 to 5 is executed.

10. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed on a computer, the computer is enabled to execute the method for optimizing the day-ahead transaction quotation scheme of a thermal power plant as described in any one of claims 1 to 5.

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