A method and system for comprehensive coordination and optimization of electric and thermal scheduling

Abstract

The present invention provides an electric and thermal integrated coordinated optimization scheduling method, comprising: screening and processing pre-acquired error probability distribution data and calculating scenario probabilities; inputting the screened data into a pre-constructed multi-scenario unit combination model for solution based on the scenario probabilities to determine the start and stop conditions of the units; solving a pre-constructed scheduling plan model based on the determined start and stop conditions of the units and wind power prediction scenarios to obtain a scheduling strategy; wherein the multi-scenario unit combination model determines the start and stop conditions of each unit with the goal of minimizing the total power generation cost of the system while considering the scenario type and the probability of occurrence of each type of scenario; the scheduling plan model calculates the total power generation cost of the system based on the minimum and maximum output of the thermal power unit under each wind power prediction scenario; through the multi-scenario unit combination model and the scheduling plan model, the rationality and accuracy of the thermal power unit limit setting are improved, leaving a more scientific margin for further optimization in subsequent scheduling.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching plan, and relates to an electric and thermal comprehensive coordinated optimization dispatching method and system. Background Art

[0002] In some regions, where resources such as wind and coal are abundant, the majority of thermal power units in the power supply are used for heating. This significantly reduces the output adjustment range during the heating season. Furthermore, during the winter heating season, heat load demand is high. Due to the "heat-based electricity" principle, thermal power units rarely participate in peak regulation, resulting in a decrease in the system's peak-shaving capacity. Meanwhile, in winter, wind energy resources are abundant, and its output exhibits an anti-peaking characteristic. These two factors combine to create high wind power generation during off-peak periods, while the grid's peak-shaving capacity is insufficient. This limits wind power grid-connected capacity and leads to wind curtailment. Traditional dispatching and operating methods cannot meet the requirements of optimizing economic benefits and reducing pollution emissions. There is an urgent need to conduct in-depth research on the operating characteristics of cogeneration units, fully consider dispatching and operating under the conditions of large-scale wind power integration, improve wind power absorption capacity and the energy efficiency of thermal power units, balance heating demand with grid security, and enhance system peak-shaving capacity and the level of integrated power-heat dispatching.

[0003] For a long time, the fundamental principle for developing cogeneration has been to determine electricity production based on heat. This requires that the capacity and type of auxiliary furnaces and steam turbine generator sets be selected based on the heating load in the design of thermal power plants. This ensures the continuous and stable operation of the heating system while using electricity as a byproduct of the heating process. This has resulted in excess power generation during off-peak periods and insufficient power generation during peak periods, hindering the resolution of the peak-to-valley difference in the grid's load. The power limits for cogeneration units are currently set monthly in many provinces. However, due to significant variations in the actual external heating loads provided by each unit, the actual heat-to-electricity ratio and cogeneration efficiency of the units fall short of national regulations. This squeezes out the generation capacity of large-capacity, high-efficiency units during off-peak periods, impacting the grid's safe operation and overall energy conservation and environmental protection. Summary of the Invention

[0004] To address the existing problem of high wind power generation during off-peak periods and insufficient peak-shaving capacity of the power grid, which limits wind power grid-connected capacity and causes wind power abandonment, the present invention provides a method for comprehensive coordinated optimization and dispatching of electricity and heat, specifically including:

[0005] Screening and processing the pre-acquired error probability distribution data and calculating the scene probability;

[0006] Based on the scenario probabilities, the filtered data is input into a pre-built multi-scenario unit combination model for solution to determine the start and shutdown status of the units;

[0007] Based on the determined start-up and shutdown conditions of the units and the wind power forecast scenario, solving a pre-built scheduling model to obtain a scheduling strategy;

[0008] The multi-scenario unit combination model determines the start and stop conditions of each unit with the goal of minimizing the total power generation cost of the system while considering the scenario type and the probability of occurrence of each type of scenario;

[0009] The scheduling model calculates the total power generation cost of the system based on the minimum and maximum output of the thermal power units under various wind power forecast scenarios.

[0010] Preferably, the construction of the multi-scenario unit combination model includes:

[0011] Considering the scenario types and the probability of occurrence of each scenario type, the objective function is constructed with the goal of minimizing the total power generation cost of the system;

[0012] In each scenario, constraints are constructed based on unit characteristic constraints, network security constraints, and power balance constraints.

[0013] Preferably, the calculation formula of the objective function of the multi-scenario unit combination model is as follows:

[0014]

[0015] Where F is the total power generation cost of the system, including unit startup cost and coal consumption cost, I is the number of thermal power units, T is the total number of dispatching periods, SC i，t is the startup cost of the i-th unit in the t-th period, u i，t is the operating status of the i-th unit in the t-th period, 1 is on, 0 is off, N s is the number of scenes, p s is the probability of scene s, u i，t-1 is the operating status of the i-th unit in the t-1 period, 1 is on, is the coal consumption cost of the i-th unit in the s-th scenario and the t-th period.

[0016] Preferably, the coal consumption cost of the i-th unit in the s-th scenario and the t-th period is The calculation formula is as follows:

[0017]

[0018] Where, is the power value of the i-th unit in the t-th period and the s-th scenario, a i 、b i 、c i is the coal consumption cost coefficient of unit i.

[0019] Preferably, the scheduling plan model includes:

[0020] Based on the selected maximum wind power forecast scenario, the minimum output of the thermal power unit is determined, and the total power generation cost of the system is calculated;

[0021] Based on the selected minimum wind power forecast scenario, the maximum output of the thermal power unit is determined, and the total power generation cost of the system is calculated;

[0022] Based on the selected normal wind power forecast scenario, the normal output of the thermal power unit is determined, and the total power generation cost of the system is calculated.

[0023] Preferably, the dispatch planning model for the maximum wind power forecast scenario includes:

[0024] The goal is to achieve the minimum output of thermal power units;

[0025] The constraints are the upper and lower limits of electrothermal coupling output, power and electricity balance, unit output constraints, ramp constraints and grid security.

[0026] Preferably, the calculation formula of the objective function of the minimum output of the thermal power unit is as follows:

[0027]

[0028] Where F is the total power generation cost of the system, FC i，t represents the coal consumption cost of the i-th thermal power unit in the t-th period, QF′ i，t is the wind power abandoned by wind farm j in period t when the maximum wind power forecast scenario is selected, γ is the penalty factor, C k，t is the peak-shaving power of peak-shaving thermal power unit k in period t, η k，t is the peak-shaving quotation of peak-shaving thermal power unit k in period t, I, J, and K are the number of thermal power units, wind farms, and peak-shaving units, respectively, and T is the operating period.

[0029] Preferably, the dispatch planning model for the minimum wind power forecast scenario includes:

[0030] The goal is to maximize the output of the thermal power unit; the constraints are the upper and lower limits of the electric and thermal coupling output, power balance, unit output constraints, climbing constraints and grid security.

[0031] Preferably, the calculation formula of the objective function of the maximum output of the thermal power unit is as follows:

[0032]

[0033] Where, QF″ i，t It is the wind power abandoned by wind farm j in period t when the minimum wind power forecast scenario is selected.

[0034] Preferably, the dispatch planning model based on a normal wind power forecast scenario includes:

[0035] Based on the start-up and shutdown conditions of the units and the normal wind power forecast scenario, the objective function of the normal output of the thermal power units is constructed;

[0036] Determining constraints for the objective function of the normal output;

[0037] The constraints include: electrothermal coupling constraints, output upper and lower limit constraints, power balance constraints, unit output constraints, ramp constraints, and grid safety constraints.

[0038] Preferably, the calculation formula of the objective function of the normal output of the thermal power unit is as follows:

[0039]

[0040] Where FC′ i，t represents the coal consumption cost of the i-th thermal power unit (including thermal power units) in the t-th period under the normal scenario, I is the total number of thermal power units, and QF″′i,t is the wind power curtailment of wind farm j in the t-th period when the normal scenario of wind power forecast is selected.

[0041] Preferably, the scenario probability is calculated as follows:

[0042] Based on the historical statistical prediction value and prediction error, the wind power output value at time t is calculated and predicted, and based on the predicted wind power output value and the actual wind power variation range, multiple predicted wind power states at time t are obtained;

[0043] Inputting multiple predicted wind power states at time t and predicted wind power states at time t-1 into a state transition matrix to obtain the probability of transitioning from the predicted wind power state at time t-1 to each predicted wind power state at time t within a preset confidence level, and arranging the predicted wind power states at time t from largest to smallest according to the probability;

[0044] Based on the calculation of the probability of transferring from the predicted wind power state at time t-1 to the predicted wind power state at time t within a preset confidence level, the state transfer matrix is ​​used to calculate the wind power state scenarios and scenario probabilities for multiple time periods.

[0045] Based on the same concept, the present invention provides an electric and thermal comprehensive coordinated optimization scheduling system, including: a processing module, a startup status determination module and a solution module;

[0046] The processing module is used to screen and process the pre-acquired error probability distribution data and calculate the scene probability;

[0047] The startup status determination module is used to input the filtered data into a pre-built multi-scenario unit combination model based on the scenario probability to solve and determine the startup and shutdown status of the unit;

[0048] The solving module is used to solve a pre-built scheduling plan model based on the start-up and shutdown conditions of the determined units and the wind power forecast scenario to obtain a scheduling strategy;

[0049] The multi-scenario unit combination model determines the start and stop conditions of each unit with the goal of minimizing the total power generation cost of the system while considering the scenario type and the probability of occurrence of each type of scenario;

[0050] The scheduling model calculates the total power generation cost of the system based on the minimum and maximum output of the thermal power units under various wind power forecast scenarios.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention provides an electric-thermal integrated coordinated optimization scheduling method comprising: screening and processing data of error probability distribution acquired in advance, and calculating scenario probabilities; inputting the screened data into a pre-built multi-scenario unit combination model based on the scenario probabilities for solution to determine the start-up and shutdown conditions of the units; solving a pre-built scheduling plan model based on the determined start-up and shutdown conditions of the units and wind power prediction scenarios to obtain a scheduling strategy; wherein, the multi-scenario unit combination model determines the start-up and shutdown conditions of each unit with the goal of minimizing the total power generation cost of the system under the condition of considering the scenario type and the probability of occurrence of each type of scenario; the scheduling plan model calculates the total power generation cost of the system based on the minimum and maximum output of the thermal power unit under each wind power prediction scenario, and improves the rationality and accuracy of the thermal power unit limit setting through the multi-scenario unit combination model and the scheduling plan model, expands the space for the combined heat and power units to participate in peak regulation, and at the same time leaves a more scientific margin for further optimization in subsequent scheduling;

[0053] 2. The present invention provides a method and system for comprehensive coordinated optimization and scheduling of electric and thermal power, which determines the unit combination through probabilistic scenario generation and screening, while taking into account requirements such as peak-shaving auxiliary services, wind curtailment penalties, and coupling characteristics of thermal power units. It provides a power generation plan for thermal power units and an operating range at the same time, which is beneficial for planners to flexibly control thermal power units, improve system flexibility and peak-shaving capabilities, help to suppress fluctuations in new energy, and improve the power grid's ability to absorb new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of the method provided by the present invention;

[0055] Figure 2 A diagram showing the electrical and thermal coupling relationship of a thermoelectric generator set according to an embodiment of the present invention;

[0056] Figure 3A schematic flow chart of a method for comprehensive coordinated optimization of electricity and heat scheduling provided by an embodiment of the present invention;

[0057] Figure 4 This is a system structure diagram provided by the present invention. DETAILED DESCRIPTION

[0058] The embodiments of the present invention will be further described with reference to the accompanying drawings.

[0059] Example 1:

[0060] The present invention provides a method for comprehensive coordination and optimization of electric and thermal dispatching. Figure 1 The method flow chart is introduced, which specifically includes:

[0061] Step 1: Filter and process the pre-acquired error probability distribution data and calculate the scene probability;

[0062] Step 2: Based on the scenario probabilities, the filtered data is input into a pre-built multi-scenario unit combination model for solution to determine the start and shutdown status of the units;

[0063] Step 3: Based on the determined start-up and shutdown conditions of the units and the wind power forecast scenario, solving the pre-built scheduling model to obtain a scheduling strategy;

[0064] Step 1: Filtering and processing the pre-acquired error probability distribution data and calculating the scene probability, specifically includes:

[0065] A wind power scenario generation method was developed. The wind power forecast and wind power forecast error probability distribution were obtained, and a set of 1,000 wind power scenarios was generated using Monte Carlo simulation.

[0066] Wind power scenario selection. Using the state transfer matrix and synchronous back-substitution reduction method, 1000 sets of scenarios are reduced and recommended to obtain N wind power scenarios and the probability P of each scenario. s (s=1,2,……,N).

[0067] Scene tree generation process:

[0068] (1) Calculate the possible wind power output at time t. The predicted wind power output at time t is P t，wind , considering the historical statistical prediction error (with prediction value and prediction error distribution function), it is believed that the actual wind power variation range is P t，wind ×(1±3σ), σ is the standard deviation of the prediction error. According to the Monte Carlo simulation, multiple possible wind power states w are obtained. i (i=1, 2, 3...).

[0069] (2) According to the state transfer matrix, we can get the value of t-1Transfer to w t Probability of possible wind power status at any moment Determine the corresponding state w in descending order according to the probability i ∈w t , until it transfers to w t The probability is greater than a certain confidence level.

[0070] (3) Based on the wind power output status at time t, repeat (1) to (2) to obtain the wind power status at time t+1.

[0071] (4) Repeat (3) to obtain wind power status scenarios in multiple time periods. The probability of each scenario is

[0072] (5) Using the synchronous back-substitution reduction method, the scenario reduction screening is completed based on the minimum probability distance.

[0073] Step 2: Based on the scenario probabilities, the filtered data is input into a pre-built multi-scenario unit combination model to solve and determine the start and stop conditions of the units, specifically including:

[0074] Establish a multi-scenario unit combination model to determine the start and stop of the unit, combined with Figure 3 A flow chart of an electric-heat comprehensive coordinated optimization scheduling method is introduced.

[0075] Determine the objective function, which is as follows:

[0076]

[0077] Where F is the total power generation cost of the system, including unit startup cost and coal consumption cost, I is the number of thermal power units, T is the total number of dispatching periods, SC i，t is the startup cost of the i-th unit in the t-th period, u i，t is the operating status of the i-th unit in the t-th period, 1 is on, 0 is off, N s is the number of scenes, p s is the probability of scene s.

[0078] The coal consumption cost of the i-th unit in the s-th scenario and the t-th period is:

[0079]

[0080] In the formula represents the coal consumption cost in the tth period under the sth scenario, is the power value of the i-th unit in the t-th period and the s-th scenario, a i 、b i 、c i is the coal consumption cost coefficient of unit i.

[0081] Identify constraints

[0082] Constraints include unit characteristics, network security, and power balance, which need to be met in each scenario.

[0083] Model solution

[0084] After obtaining the occurrence probability of each scenario of the system, the above model is transformed into a definite unit combination model, and the above model is solved using mixed integer programming to obtain a definite unit combination.

[0085] Step 3: Based on the determined start-up and shutdown conditions of the units and the wind power forecast scenario, solve the pre-built scheduling model to obtain the scheduling strategy, which specifically includes:

[0086] Select the maximum wind power forecast scenario and determine the minimum output of the thermal power unit.

[0087] In order to solve the minimum output of the thermal power unit, the scenario with the largest wind power forecast is selected to establish a scheduling model.

[0088] Determine the objective function

[0089]

[0090] Where: F is the total power generation cost of the system, including the coal consumption cost of thermal power units, wind curtailment cost, and peak load regulation cost. i，t represents the coal consumption cost of the ith thermal power unit in the tth period, and the calculation method is the same as formula (2), QF′ i，t is the wind power abandoned by wind farm j in period t when the maximum wind power forecast scenario is selected, γ is the penalty factor, C k，t is the peak-shaving power of peak-shaving thermal power unit k in period t, η k，t is the peak-shaving quotation of peak-shaving thermal power unit k in period t, I, J, and K are the number of thermal power units, wind farms, and peak-shaving units, respectively, and T is the operating period.

[0091] Identify constraints

[0092] These include power balance, unit output constraints, ramp constraints, and grid security constraints. Thermal power units also need to consider electrical-thermal coupling constraints, as follows:

[0093] Combine Figure 2 The thermal power unit electric-thermal coupling relationship diagram is introduced. The conventional output upper limit of the thermal power unit i can be determined by the heat load and output lower limit

[0094]

[0095]

[0096] Where a 1i 、b 1i 、a 2i 、b 2i represents the coupling parameter, H i，t It represents the heating demand of thermal power unit i in period t, which can be obtained in advance.

[0097] A scheduling model is established to solve the output of the thermal power unit, which is the minimum output of the thermal power unit.

[0098] Select the minimum wind power forecast scenario to determine the maximum output of the thermal power unit.

[0099] In order to solve the maximum output of the thermal power unit, the scenario with the minimum wind power prediction is selected, and the objective function is adjusted as follows:

[0100]

[0101] The constraints are the same as those in step 4. The scheduling model is established and the output of the thermal power unit is obtained, which is the maximum output of the thermal power unit, QF″ i，t It is the wind power abandoned by wind farm j in period t when the minimum wind power forecast scenario is selected.

[0102] Select normal wind power forecast data to determine the reference output of the thermal power unit.

[0103] In order to solve the output curve of thermal power unit, normal wind power forecast data is selected, and the objective function

[0104] Where: FC′ i，t represents the coal consumption cost of the i-th thermal power unit (including thermal power units) in the t-th period under normal circumstances, I is the total number of thermal power units, QF″′ i，t It is the wind power abandoned by wind farm j in period t when the normal wind power forecast scenario is selected.

[0105] The constraints include power balance, unit output constraints, ramp constraints, grid safety constraints, etc. The thermal power unit also needs to consider the electric-thermal coupling constraints, establish a scheduling plan model, and solve the output of the thermal power unit, which is the reference output of the thermal power unit for reference by dispatchers.

[0106] Example 2:

[0107] Based on the same inventive concept, the present invention also provides an electric and thermal comprehensive coordinated optimization scheduling system, combined with Figure 4 The system structure diagram is introduced, which specifically includes: processing module, power-on status determination module and solution module;

[0108] The processing module is used to screen and process the pre-acquired error probability distribution data and calculate the scene probability;

[0109] The startup status determination module is used to input the filtered data into a pre-built multi-scenario unit combination model based on the scenario probability to solve and determine the startup and shutdown status of the unit;

[0110] The solving module is used to solve a pre-built scheduling plan model based on the start-up and shutdown conditions of the determined units and the wind power forecast scenario to obtain a scheduling strategy;

[0111] The multi-scenario unit combination model determines the start and stop conditions of each unit with the goal of minimizing the total power generation cost of the system while considering the scenario type and the probability of occurrence of each type of scenario;

[0112] The scheduling model calculates the total power generation cost of the system based on the minimum and maximum output of the thermal power units under various wind power forecast scenarios.

[0113] The startup condition determination module includes: a combination model target submodule and a combination model constraint submodule;

[0114] The combined model target submodule is used to consider the scenario type and the probability of occurrence of each scenario type, and to construct an objective function with the goal of minimizing the total power generation cost of the system;

[0115] The combined model constraint submodule is used to construct constraint conditions based on unit characteristic constraints, network security constraints and power balance constraints in each scenario.

[0116] The solution module includes: a maximum scene submodule, a minimum scene submodule and a normal scene submodule;

[0117] The maximum scenario submodule is used to determine the minimum output of the thermal power unit based on the selected maximum wind power forecast scenario and calculate the total power generation cost of the system;

[0118] The minimum scenario submodule is used to determine the maximum output of the thermal power unit based on the selected minimum wind power forecast scenario and calculate the total power generation cost of the system;

[0119] The normal scenario submodule is used to determine the normal output of the thermal power generation unit based on the selected normal wind power prediction scenario and calculate the total power generation cost of the system.

[0120] The maximum scenario submodule includes: a minimum output target unit and a minimum output constraint unit;

[0121] The minimum output target unit is used to target the minimum output of the thermal power unit;

[0122] The minimum output constraint unit is used to use the upper and lower limits of the electrothermal coupling output, electric power balance, unit output constraint, climbing constraint and grid safety as constraint conditions.

[0123] The minimum scenario submodule includes: a maximum output target unit and a maximum output constraint unit; the maximum output target unit is used to take the maximum output of the thermal power unit as the target;

[0124] The maximum output constraint unit is used to constrain the upper and lower limits of the electrothermal coupling output, power and electricity balance, unit output constraint, climbing constraint and power grid safety as constraint conditions.

[0125] The normal scenario submodule includes: a normal output unit and a normal output constraint unit;

[0126] The normal output unit is used to construct an objective function for the normal output of the thermal power unit based on the start-up and shutdown conditions of the unit and the normal scenario of wind power prediction;

[0127] The normal output constraint unit is used to determine constraint conditions for the objective function of the normal output;

[0128] The constraints include: electrothermal coupling constraints, output upper and lower limit constraints, power balance constraints, unit output constraints, ramp constraints, and grid safety constraints.

[0129] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can 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 can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0130] 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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.

[0131] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0133] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for comprehensive coordination and optimization of electric and thermal dispatching, characterized in that: include: Screening and processing the pre-acquired error probability distribution data and calculating the scene probability; Based on the scenario probabilities, the filtered data is input into a pre-built multi-scenario unit combination model for solution to determine the start and shutdown status of the units; Based on the determined start-up and shutdown conditions of the units and the wind power forecast scenario, solving a pre-built scheduling model to obtain a scheduling strategy; The multi-scenario unit combination model determines the start and stop conditions of each unit with the goal of minimizing the total power generation cost of the system while considering the scenario type and the probability of occurrence of each type of scenario; The scheduling model calculates the total power generation cost of the system based on the minimum and maximum output of the thermal power units under various wind power forecast scenarios; The scenario probability is calculated as follows: Based on the historical statistical prediction value and prediction error, the wind power output value at time t is calculated and predicted, and based on the predicted wind power output value and the actual wind power variation range, multiple predicted wind power states at time t are obtained; Inputting multiple predicted wind power states at time t and predicted wind power states at time t-1 into a state transition matrix to obtain the probability of transitioning from the predicted wind power state at time t-1 to each predicted wind power state at time t within a preset confidence level, and arranging the predicted wind power states at time t from largest to smallest according to the probability; Based on the calculation of the probability of the wind power state predicted at time t-1 being transferred to the wind power state predicted at time t within a preset confidence level, the wind power state scenarios and scenario probabilities of multiple time periods are calculated using the state transition matrix; The calculation formula of the objective function of the multi-scenario unit combination model is as follows: Where F is the total power generation cost of the system, including unit startup cost and coal consumption cost, I is the number of thermal power units, T is the total number of dispatching periods, SC i,t is the startup cost of the i-th unit in the t-th period, u i,t is the operating status of the i-th unit in the t-th period, 1 is on, 0 is off, N s is the number of scenes, p s is the probability of scene s, u i,t-1 is the operating status of the i-th unit in the t-1 period, 1 is on, is the coal consumption cost of the i-th unit in the s-th scenario and the t-th period.

2. The method according to claim 1, wherein The construction of the multi-scenario unit combination model includes: Considering the scenario types and the probability of occurrence of each scenario type, the objective function is constructed with the goal of minimizing the total power generation cost of the system; In each scenario, constraints are constructed based on unit characteristic constraints, network security constraints, and power balance constraints.

3. The method according to claim 1, wherein The coal consumption cost of the i-th unit in the s-th scenario and the t-th period is The calculation formula is as follows: Where, is the power value of the i-th unit in the t-th period and the s-th scenario, a i 、b i 、c i is the coal consumption cost coefficient of unit i.

4. The method according to claim 1, wherein The scheduling model includes: Based on the selected maximum wind power forecast scenario, the minimum output of the thermal power unit is determined, and the total power generation cost of the system is calculated; Based on the selected minimum wind power forecast scenario, the maximum output of the thermal power unit is determined, and the total power generation cost of the system is calculated; Based on the selected normal wind power forecast scenario, the normal output of the thermal power unit is determined, and the total power generation cost of the system is calculated.

5. The method according to claim 4, wherein The dispatch planning model for the maximum wind power forecast scenario includes: The goal is to achieve the minimum output of thermal power units; The constraints are the upper and lower limits of electrothermal coupling output, power and electricity balance, unit output constraints, ramp constraints and grid security.

6. The method according to claim 5, wherein The calculation formula of the objective function of the minimum output of the thermal power unit is as follows: Where F is the total power generation cost of the system, FC i,t represents the coal consumption cost of the i-th thermal power unit in the t-th period, QF′ i,t is the wind power abandoned by wind farm j in period t when the maximum wind power forecast scenario is selected, γ is the penalty factor, C k,t is the peak-shaving power of peak-shaving thermal power unit k in period t, η k,t is the peak-shaving quotation of peak-shaving thermal power unit k in period t, I, J, and K are the number of thermal power units, wind farms, and peak-shaving units, respectively, and T is the operating period.

7. The method according to claim 4, wherein The dispatch planning model for the minimum scenario of wind power forecast includes: The goal is to maximize the output of the thermal power unit; the constraints are the upper and lower limits of the electric and thermal coupling output, power balance, unit output constraints, climbing constraints and grid security.

8. The method according to claim 7, wherein The calculation formula of the objective function of the maximum output of the thermal power unit is as follows: Where, QF″ i,t It is the wind power abandoned by wind farm j in period t when the minimum wind power forecast scenario is selected.

9. The method according to claim 4, wherein: The dispatch planning model for normal wind power forecast scenarios includes: Based on the start-up and shutdown conditions of the units and the normal wind power forecast scenario, the objective function of the normal output of the thermal power units is constructed; Determining constraints for the objective function of the normal output; The constraints include: electrothermal coupling constraints, output upper and lower limit constraints, power balance constraints, unit output constraints, ramp constraints, and grid safety constraints.

10. The method according to claim 9, wherein The calculation formula of the objective function of the normal output of the thermal power unit is as follows: Where FC′ i,t represents the coal consumption cost of the i-th thermal power unit (including thermal power units) in the t-th period under normal circumstances, I is the total number of thermal power units, QF″′ i,t It is the wind power abandoned by wind farm j in period t when the normal wind power forecast scenario is selected.

11. An electric and heat comprehensive coordinated optimization scheduling system, characterized in that: include: Processing module, power-on status determination module and solution module; The processing module is used to screen and process the pre-acquired error probability distribution data and calculate the scene probability; The startup status determination module is used to input the filtered data into a pre-built multi-scenario unit combination model based on the scenario probability to solve and determine the startup and shutdown status of the unit; The solving module is used to solve a pre-built scheduling plan model based on the start-up and shutdown conditions of the determined units and the wind power forecast scenario to obtain a scheduling strategy; The multi-scenario unit combination model determines the start and stop conditions of each unit with the goal of minimizing the total power generation cost of the system while considering the scenario type and the probability of occurrence of each type of scenario; The scheduling model calculates the total power generation cost of the system based on the minimum and maximum output of the thermal power units under various wind power forecast scenarios.

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