Periodic unit commitment optimization decision-making method and system considering multi-type unit constraints

Abstract

The invention discloses a weekly unit combination optimization decision-making method and system considering multi-type unit constraints, and the method comprises the steps: obtaining the basic information of multi-type units, and the basic information of the multi-type units comprises new energy statistical information, thermal power unit operation conditions, load prediction values, tie line curves and unit parameters; new energy available power is calculated according to the new energy statistical information; calculating final limits of multiple working conditions of the thermal power generating unit according to the operation working conditions of the thermal power generating unit; setting time windows of pumped storage power generation and water pumping according to the load predicted value, the tie line curve and unit parameters; constructing a weekly unit combination objective function and constraint conditions, and forming a weekly unit combination model by taking the new energy available electric power, the final limit of multiple working conditions of the thermal power generating unit and the time window of pumped storage power generation and water pumping as boundaries of the constraint conditions; and solving the weekly unit commitment model to obtain a weekly unit commitment optimization decision.

Description

Technical Field

[0001] The present invention belongs to the field of weekly unit combination, and in particular relates to a weekly unit combination optimization decision method and system considering constraints of multiple types of units. Background Art

[0002] Weekly unit scheduling is a core approach to minimizing power generation costs and balancing environmental goals by optimizing the start / stop status and output schedules of various generators one week in advance, while meeting future power load demands, spare capacity, and system safety constraints. This approach comprehensively considers unit characteristics (such as minimum start / stop times, ramp rates, and fuel costs), grid topology constraints, and the uncertainty of renewable energy output and load forecasts. Utilizing optimization algorithms such as mixed integer programming, it develops a dispatch plan that balances economic efficiency, reliability, and low carbon emissions. This ensures the safe and stable operation of the power system in a complex and fluctuating environment, while promoting efficient resource utilization and achieving emission reduction targets.

[0003] Currently, with the rapid construction of new power systems, uncertainty within the system is increasing. The accuracy of renewable energy forecasts is low, and directly using predicted output can lead to significant deviations in unit combinations. The power generation limits of thermal power units are affected by a variety of operating conditions, and traditional calculation methods are relatively rough, making it difficult to accurately determine the operating boundaries of thermal power plants. Due to the long time periods of weekly unit combinations, inaccuracies in the boundaries often lead to poor implementability of the calculation results. Furthermore, the diverse operating conditions of regulating power sources such as thermal power and pumped storage increase the difficulty of unit combinations. Summary of the Invention

[0004] The purpose of the present invention is to provide a weekly unit combination optimization decision method and system that takes into account the constraints of multiple types of units, so as to solve the problems of traditional weekly unit combination, such as large deviation in new energy prediction, inaccurate boundaries, and long calculation time for unit combination including pumped storage.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The weekly unit commitment optimization decision-making method considering multi-type unit constraints includes: Obtaining basic information of multiple types of units, including new energy statistical information, thermal power unit operating conditions, load forecast values, tie line curves, and unit parameters; Calculate available electricity from new energy sources based on new energy statistical information; Calculate the final quota of thermal power units under multiple operating conditions based on the operating conditions of the thermal power units; Set the time window for pumped storage power generation and pumping according to the load forecast value, tie line curve and unit parameters; The weekly unit combination objective function and constraints are constructed, and the weekly unit combination model is formed with the available power of renewable energy, the final limit of thermal power units under multiple operating conditions, and the time window of pumped storage power generation and pumping as the boundaries of the constraints; Solve the weekly unit combination model and obtain the weekly unit combination optimization decision.

[0006] Furthermore, the new energy statistical information includes historical new energy predicted output and actual output statistical data.

[0007] Furthermore, the calculation of the available power of new energy sources based on the new energy statistical information specifically includes: Based on the historical new energy forecast output and actual output statistics, the probability density of the new energy forecast volatility is calculated using the following formula:

[0008]

[0009] Where: is the new energy forecast volatility; It is the predicted output of new energy; It is the actual output of new energy; is the probability density of the new energy forecast volatility; yes Number of occurrences; is the total number of samples; According to the probability density of the new energy forecast volatility, the probability distribution function of the new energy forecast volatility is calculated. The calculation formula is as follows:

[0010] Where, is the probability distribution function of the new energy forecast volatility; Based on the probability distribution function of the new energy forecast volatility and the selected confidence level , determine the corresponding new energy forecast volatility ; Volatility prediction based on new energy The corresponding available output of new energy can be calculated by adding the predicted output of new energy. The calculation formula is as follows:

[0011] Where: It is the available output of new energy.

[0012] Furthermore, the calculation of the final quota of the thermal power unit under multiple operating conditions according to the operating conditions of the thermal power unit specifically includes: Confirm the status of the thermal power unit according to its operating conditions. If it is in standby or maintenance mode, the final limit of the thermal power unit is 0; If it is not in standby or maintenance condition, confirm whether the thermal power unit is in test condition. If so, the final limit of the thermal power unit is equal to the specific output under the test condition. If it is not in standby or maintenance or test condition, confirm whether the thermal power unit is in frequency regulation operation condition. If so, the final limit of the thermal power unit is equal to the frequency regulation limit; If the unit is not in standby or maintenance operation, test operation or frequency modulation operation, confirm whether it is in heating operation. If so, the unit's final limit is equal to the heating limit. If it is not in standby or maintenance conditions, test conditions, frequency regulation operation conditions and heating conditions, the final limit of the thermal power unit is equal to the rated limit.

[0013] Furthermore, the time window for pumped storage power generation and pumping is set according to the load forecast value, the tie line curve and the unit parameters, specifically including: According to the load forecast value and the tie line curve, the total net load curve of the power grid system is calculated, which is expressed as follows:

[0014] Where: is the net load of the power grid system during period t; is the total load of the power grid system during period t; is the total planned output of the tie lines of the power grid system during period t; The time window for power generation and pumping is determined based on the total net load curve of the power grid system. The power generation time window meets the following requirements:

[0015] The pumping time window meets the following requirements:

[0016] Where: 、 is the maximum and minimum value of the net load of the power grid system; 、 is the start and end power of the power generation time window; 、 is the start and end time of the power generation time window, 、 is the start and end power of the pumping time window; 、 is the start and end time of the pumping time window, 、 is the determination coefficient of the power generation and pumping time window.

[0017] Furthermore, the weekly unit combination objective function and constraint conditions are constructed to form a weekly unit combination model, specifically including: Construct the weekly unit combination objective function, which is expressed as follows:

[0018] Where T is the period of weekly unit combination calculation, is the number of thermal power units, is the number of pumped storage units, m is the serial number of the pumped storage unit, It is a pumped storage unit m exist t The operating cost of the moment, The output of thermal power unit i at time t is The cost of electricity generation, is the start-up and shutdown cost of thermal power unit i at time t, Whether the thermal power unit i changes from shutdown to startup at time t; The constraints include power grid system load balance constraints, thermal power unit output upper and lower limit constraints, thermal power unit start and stop constraints, pumped storage state constraints, and pumped storage output constraints.

[0019] Furthermore, the load balance constraint of the power grid system is expressed as:

[0020] Where: is the number of new energy units, 、 、 They represent the output of new energy, thermal power and pumped storage units at time t respectively. Available power from new energy sources Sure, is the total load of the power grid system during period t; is the total planned output of the tie lines of the power grid system during period t; The upper and lower output constraints of the thermal power unit are expressed as:

[0021] Where: 、 are the final upper and lower limits of thermal power unit i in period t, respectively; The start-stop constraint of the thermal power unit is expressed as:

[0022]

[0023]

[0024]

[0025] Where: 、 is the operating status of thermal power unit i in time period t and t-1, 1 means running, 0 means shutdown, Indicates the state change of the thermal power unit from startup to shutdown; The pumped storage state constraint is expressed as:

[0026] Where, 、 They are respectively the power generation and pumping operation states of the pumped storage unit. 、 is the start and end time of the power generation time window, 、 is the start and end time of the pumping time window; The pumped storage output constraint is expressed as:

[0027] Where, 、 are the power generation and pumping power of the pumped storage unit, 、 are the lower and upper limits of the power generation capacity of the pumped storage unit, 、 are the lower and upper limits of the pumping power of the pumped storage unit, Provide power for the operation of the pumped storage unit.

[0028] The weekly unit commitment optimization decision system considering multi-type unit constraints includes: Information acquisition module: used to obtain basic information of multiple types of units, including new energy statistical information, thermal power unit operating conditions, load forecast values, tie line curves and unit parameters; The first calculation module is used to calculate the available power of new energy according to the new energy statistical information; The second calculation module is used to calculate the final limit of multiple operating conditions of the thermal power unit according to the operating conditions of the thermal power unit; The third calculation module is used to set the time window for pumped storage power generation and pumping according to the load forecast value, the tie line curve and the unit parameters; Model construction module: used to construct the weekly unit combination objective function and constraints, and use the available power of renewable energy, the final limit of multiple operating conditions of thermal power units, and the time window of pumped storage power generation and pumping as the boundaries of the constraints to form a weekly unit combination model; Solution module: used to solve the weekly unit combination model and obtain the weekly unit combination optimization decision.

[0029] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the weekly unit combination optimization decision method considering constraints of multiple types of units are implemented.

[0030] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the weekly unit combination optimization decision method considering multi-type unit constraints.

[0031] Compared with the prior art, the present invention has the following beneficial technical effects: To address the problem of large deviations in renewable energy forecasts, which can affect unit commitment results, this paper proposes a method for rapidly accounting for renewable energy fluctuations in weekly unit commitments. Based on statistical analysis results, a distribution function for renewable energy fluctuations is established, and a confidence level is determined based on operational conditions. Ultimately, the available output of renewable energy when incorporated into unit commitments is calculated. This calculation better accounts for long-term renewable energy forecasts, resulting in a more realistic available output for incorporating renewable energy into unit commitments, providing more robust boundary conditions for unit commitment determination.

[0032] In response to the problems of multiple operating conditions of thermal power units and inaccurate traditional calculations, the present invention determines the operating conditions of unit maintenance, testing, frequency regulation, and heating, and gradually determines the final limit of the thermal power units according to priority, thereby determining a more prepared output range for the thermal power units participating in the unit combination.

[0033] Since pumped-storage units have two operating states, power generation and pumping, and the power generation output is both positive and negative, the calculation time of the unit combination becomes longer. The present invention combines the "peak shaving and valley filling" positioning of pumped storage and proposes a method for determining the time window of pumped-storage power generation and pumping, solidifies the time period of pumped-storage power generation and pumping, reduces the scale of unit combination optimization, and improves the efficiency of unit combination calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0035] Figure 1 Schematic diagram of the process of the present invention; Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] Example 1 Weekly unit commitment optimization decision-making method considering multi-type unit constraints, see Figure 1 ,include: Obtaining basic information of multiple types of units, including new energy statistical information, thermal power unit operating conditions, load forecast values, tie line curves, and unit parameters; Calculate available electricity from new energy sources based on new energy statistical information; Calculate the final quota of thermal power units under multiple operating conditions based on the operating conditions of the thermal power units; Set the time window for pumped storage power generation and pumping according to the load forecast value, tie line curve and unit parameters; The weekly unit combination objective function and constraints are constructed, and the weekly unit combination model is formed with the available power of renewable energy, the final limit of thermal power units under multiple operating conditions, and the time window of pumped storage power generation and pumping as the boundaries of the constraints; Solve the weekly unit combination model and obtain the weekly unit combination optimization decision.

[0039] The present invention takes into account the volatility distribution characteristics of new energy, proposes a method for incorporating the predicted output of new energy into unit combination, takes into account various operating conditions of thermal power units, determines the thermal power output limit under each operating condition according to priority, proposes a method for determining the power generation time window and pumping time window of pumped-storage units, and finally establishes a weekly unit combination model, which improves the accuracy of the model while improving the calculation efficiency of the unit combination.

[0040] Example 2 The weekly unit commitment optimization decision-making method considering multi-type unit constraints includes: 1. Obtain basic information on various types of units, including new energy statistics, thermal power unit operating conditions, load forecasts, tie line curves, unit parameters, and other basic information.

[0041] 2. Calculate the available electricity from renewable energy sources based on renewable energy statistical information.

[0042] (1) Based on historical statistics on predicted and actual new energy output, the probability density of the new energy forecast volatility is calculated using probability theory and mathematical statistics. The calculation formula is as follows.

[0043] (1) (2) Where: is the new energy forecast volatility, expressed in percentage (%); It is the predicted output of new energy; It is the actual output of new energy; is the probability of the new energy forecast volatility; yes Number of occurrences; is the total number of samples; (2) According to the probability density of the new energy forecast volatility, the probability distribution function of the new energy forecast volatility is calculated. The calculation formula is as follows.

[0044] (3) Where: is the probability distribution function of the new energy forecast volatility, expressed in percentage (%); (3) Select confidence level Then, according to the probability distribution function of the new energy forecast volatility, the corresponding new energy forecast volatility is determined. .

[0045] (4) Volatility prediction based on new energy The corresponding available output of new energy can be calculated by combining the predicted output of new energy with the following formula.

[0046] (4) Where: It is the predicted output of new energy; It is the available output of new energy.

[0047] 3. Calculate the final limits for a thermal power unit under multiple operating conditions. The final limits for a unit are affected by various operating conditions. Considering the priority of each operating condition, determine the final limits for the unit in the following steps.

[0048] (1) Confirm the status of the thermal power unit. If it is in standby or maintenance mode, the final limit of the thermal power unit is 0; (5) Where: 、 It is the upper limit and lower limit of the final output of the thermal power unit.

[0049] (2) If it is not in the above state, confirm whether the thermal power unit is in the test condition. If so, the final limit of the thermal power unit is equal to the specific output under the test condition.

[0050] (6) Where: It is the specific output of the thermal power unit under test conditions.

[0051] (3) If it is not the above status, confirm whether the thermal power unit is in frequency regulation operation condition. If so, the final limit of the thermal power unit is equal to the frequency regulation limit.

[0052] (7) (8) Where: It is the frequency regulation margin reserved for thermal power units; 、 It is the upper limit and lower limit of rated output of thermal power units.

[0053] (4) If it is not the above status, confirm whether the unit is in heating operation. If so, the unit's final limit is equal to the heating limit.

[0054] (9) (10) Where: 、 、 、 is the electric-thermal relationship constant of the thermal power unit, and H is the heating capacity of the thermal power unit.

[0055] (5) If none of the above conditions exist, the final limit of the thermal power unit is equal to the rated limit.

[0056] (11) (12) 5. Set time windows for pumped storage power generation and pumping.

[0057] (1) Calculate the net load curve According to the load forecast value and the tie line curve, the total net load curve of the power grid system is calculated. The power grid system refers to the entire power grid system involved in the weekly unit combination.

[0058] (13) Where: is the net load of the power grid system during period t; is the total load of the power grid system during period t; is the total planned output of the interconnection lines in the power grid system during period t.

[0059] (2) Confirm the power generation time window and pumping time window. Based on the total net load curve of the power grid system and taking into account the "peak shaving and valley filling" effect of pumped storage, determine the power generation and pumping time windows.

[0060] The power generation time window must meet the following requirements: (14) The pumping time window must meet the following requirements: (15) Where: 、 is the maximum and minimum value of the net load of the power grid system; 、 is the start and end power of the power generation time window; 、 is the start and end time of the power generation time window, 、 is the start and end power of the pumping time window; 、 is the start and end time of the pumping time window, 、 It is the determination coefficient of the power generation and pumping time window, which is set according to operational needs.

[0061] 6. Construct a weekly unit combination model. Construct the weekly unit combination objective function and constraints to form a weekly unit combination model.

[0062] (1) Objective function (16) Where T is the period of weekly unit combination calculation, is the number of thermal power units, is the number of pumped storage units, m is the serial number of the pumped storage unit, It is a pumped storage unit m exist tThe operating cost of the moment, The output of thermal power unit i at time t is The cost of electricity generation, is the start-up and shutdown cost of thermal power unit i at time t, Whether the thermal power unit i changes from shutdown to startup at time t.

[0063] (2) Constraints (a) Power grid system load balance constraints (17) Where: is the number of new energy units, 、 、 Time t represents the output of new energy, thermal power, and pumped storage units, respectively. From the above Sure.

[0064] (b) Upper and lower output limits of thermal power units (18) Where: 、 are the final upper and lower limits of unit i in period t determined above.

[0065] (c) Constraints on starting and stopping thermal power units (19) (20) (twenty one) (twenty two) Where: 、 is the operating status of thermal power unit i in time period t and t-1, 1 means running, 0 means shutdown, Indicates the status change of the thermal power unit from startup to shutdown.

[0066] (d) Pumped storage state constraints (twenty three) Where, 、 They are respectively the power generation and pumping operation status of the pumped storage unit.

[0067] (e) Pumped storage output constraints (twenty four) Where, 、 are the power generation and pumping power of the pumped storage unit, 、 are the lower and upper limits of the power generation capacity of the pumped storage unit, 、 are the lower and upper limits of the pumping power of the pumped storage unit, Provide power for the operation of the pumped storage unit.

[0068] 7. Solving the weekly unit combination model. The above weekly unit combination model belongs to a mixed integer programming model and can be solved using the corresponding mixed integer programming method.

[0069] The present invention mainly solves the following problems: First, consider the volatility distribution characteristics of new energy, establish a distribution function, consider the confidence level, incorporate the predicted reliable output of new energy into the unit combination, and improve the accuracy of the use of new energy prediction data.

[0070] The second is to consider the various operating conditions of thermal power units, determine the thermal power output limit under each operating condition according to priority, and improve the accuracy of the power generation boundary of thermal power units.

[0071] Third, heuristic methods are used to determine the power generation time window and pumping time window of the pumped-storage unit, reducing the optimization space for unit combination and improving the calculation efficiency of the weekly unit combination model.

[0072] Example 3 Weekly unit commitment optimization decision system considering multi-type unit constraints, see Figure 2 ,include: Information acquisition module: used to obtain basic information of multiple types of units, including new energy statistical information, thermal power unit operating conditions, load forecast values, tie line curves and unit parameters; The first calculation module is used to calculate the available power of new energy according to the new energy statistical information; The second calculation module is used to calculate the final limit of multiple operating conditions of the thermal power unit according to the operating conditions of the thermal power unit; The third calculation module is used to set the time window for pumped storage power generation and pumping according to the load forecast value, the tie line curve and the unit parameters; Model construction module: used to construct the weekly unit combination objective function and constraints, and use the available power of renewable energy, the final limit of multiple operating conditions of thermal power units, and the time window of pumped storage power generation and pumping as the boundaries of the constraints to form a weekly unit combination model; Solution module: used to solve the weekly unit combination model and obtain the weekly unit combination optimization decision.

[0073] Example 4 A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the weekly unit combination optimization decision method considering constraints of multiple types of units are implemented.

[0074] Example 5 A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the weekly unit combination optimization decision method considering multi-type unit constraints.

[0075] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take 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.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 processes in the flowcharts and / or block diagrams. 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.

[0077] 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.

[0078] 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.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A weekly unit commitment optimization decision-making method considering multi-type unit constraints is characterized by: include: Obtaining basic information of multiple types of units, including new energy statistical information, thermal power unit operating conditions, load forecast values, tie line curves, and unit parameters; Calculate available electricity from new energy sources based on new energy statistical information; Calculate the final quota of thermal power units under multiple operating conditions based on the operating conditions of the thermal power units; Set the time window for pumped storage power generation and pumping according to the load forecast value, tie line curve and unit parameters; The weekly unit combination objective function and constraints are constructed, and the weekly unit combination model is formed with the available power of renewable energy, the final limit of thermal power units under multiple operating conditions, and the time window of pumped storage power generation and pumping as the boundaries of the constraints; Solve the weekly unit combination model and obtain the weekly unit combination optimization decision.

2. The weekly unit commitment optimization decision-making method considering multi-type unit constraints according to claim 1 is characterized in that: The new energy statistical information includes historical new energy predicted output and actual output statistical data.

3. The weekly unit commitment optimization decision-making method considering multi-type unit constraints according to claim 2 is characterized in that: The calculation of the available power of new energy based on the new energy statistical information specifically includes: Based on the historical new energy forecast output and actual output statistics, the probability density of the new energy forecast volatility is calculated using the following formula: Where: is the new energy forecast volatility; It is the predicted output of new energy; It is the actual output of new energy; is the probability density of the new energy forecast volatility; yes Number of occurrences; is the total number of samples; According to the probability density of the new energy forecast volatility, the probability distribution function of the new energy forecast volatility is calculated. The calculation formula is as follows: Where, is the probability distribution function of the new energy forecast volatility; Based on the probability distribution function of the new energy forecast volatility and the selected confidence level , determine the corresponding new energy forecast volatility ; Volatility prediction based on new energy The corresponding available output of new energy can be calculated by adding the predicted output of new energy. The calculation formula is as follows: Where: It is the available output of new energy.

4. The weekly unit commitment optimization decision method considering multi-type unit constraints according to claim 1 is characterized in that: The calculation of the final quota of the thermal power unit under multiple operating conditions according to the operating conditions of the thermal power unit specifically includes: Confirm the status of the thermal power unit according to its operating conditions. If it is in standby or maintenance mode, the final limit of the thermal power unit is 0; If it is not in standby or maintenance condition, confirm whether the thermal power unit is in test condition. If so, the final limit of the thermal power unit is equal to the specific output under the test condition. If it is not in standby or maintenance or test condition, confirm whether the thermal power unit is in frequency regulation operation condition. If so, the final limit of the thermal power unit is equal to the frequency regulation limit; If the unit is not in standby or maintenance operation, test operation or frequency modulation operation, confirm whether it is in heating operation. If so, the unit's final limit is equal to the heating limit. If it is not in standby or maintenance conditions, test conditions, frequency regulation operation conditions and heating conditions, the final limit of the thermal power unit is equal to the rated limit.

5. The weekly unit commitment optimization decision-making method considering multi-type unit constraints according to claim 1 is characterized in that: The step of setting the time window for pumped storage power generation and pumping according to the load forecast value, the tie line curve and the unit parameters specifically includes: According to the load forecast value and the tie line curve, the total net load curve of the power grid system is calculated, which is expressed as follows: Where: is the net load of the power grid system during period t; is the total load of the power grid system during period t; is the total planned output of the tie lines of the power grid system during period t; The time window for power generation and pumping is determined based on the total net load curve of the power grid system. The power generation time window meets the following requirements: The pumping time window meets the following requirements: Where: 、 is the maximum and minimum value of the net load of the power grid system; 、 is the start and end power of the power generation time window; 、 is the start and end time of the power generation time window, 、 is the start and end power of the pumping time window; 、 is the start and end time of the pumping time window, 、 is the determination coefficient of the power generation and pumping time window.

6. The weekly unit commitment optimization decision method considering multi-type unit constraints according to claim 1 is characterized in that: The construction of the weekly unit combination objective function and constraint conditions to form a weekly unit combination model specifically includes: Construct the weekly unit combination objective function, which is expressed as follows: Where T is the period of weekly unit combination calculation, is the number of thermal power units, is the number of pumped storage units, m is the serial number of the pumped storage unit, It is a pumped storage unit m exist t The operating cost of the moment, The output of thermal power unit i at time t is The cost of electricity generation, is the start-up and shutdown cost of thermal power unit i at time t, Whether the thermal power unit i changes from shutdown to startup at time t; The constraints include power grid system load balance constraints, thermal power unit output upper and lower limit constraints, thermal power unit start and stop constraints, pumped storage state constraints, and pumped storage output constraints.

7. The weekly unit commitment optimization decision method considering multi-type unit constraints according to claim 6 is characterized in that: The load balance constraint of the power grid system is expressed as: Where: is the number of new energy units, 、 、 They represent the output of new energy, thermal power and pumped storage units at time t respectively. Available power from new energy sources Sure, is the total load of the power grid system during period t; is the total planned output of the tie lines of the power grid system during period t; The upper and lower output constraints of the thermal power unit are expressed as: Where: 、 are the final upper and lower limits of thermal power unit i in period t, respectively; The start-stop constraint of the thermal power unit is expressed as: Where: 、 is the operating status of thermal power unit i in time period t and t-1, 1 means running, 0 means shutdown, Indicates the state change of the thermal power unit from startup to shutdown; The pumped storage state constraint is expressed as: Where, 、 They are respectively the power generation and pumping operation states of the pumped storage unit. 、 is the start and end time of the power generation time window, 、 is the start and end time of the pumping time window; The pumped storage output constraint is expressed as: Where, 、 are the power generation and pumping power of the pumped storage unit, 、 are the lower and upper limits of the power generation capacity of the pumped storage unit, 、 are the lower and upper limits of the pumping power of the pumped storage unit, Provide power for the operation of the pumped storage unit.

8. A weekly unit commitment optimization decision system considering multi-type unit constraints, characterized by: include: Information acquisition module: used to obtain basic information of multiple types of units, including new energy statistical information, thermal power unit operating conditions, load forecast values, tie line curves and unit parameters; The first calculation module is used to calculate the available power of new energy according to the new energy statistical information; The second calculation module is used to calculate the final limit of multiple operating conditions of the thermal power unit according to the operating conditions of the thermal power unit; The third calculation module is used to set the time window for pumped storage power generation and pumping according to the load forecast value, the tie line curve and the unit parameters; Model construction module: used to construct the weekly unit combination objective function and constraints, and use the available power of renewable energy, the final limit of multiple operating conditions of thermal power units, and the time window of pumped storage power generation and pumping as the boundaries of the constraints to form a weekly unit combination model; Solution module: used to solve the weekly unit combination model and obtain the weekly unit combination optimization decision.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the weekly unit commitment optimization decision method considering multi-type unit constraints as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the weekly unit commitment optimization decision method considering multi-type unit constraints as described in any one of claims 1 to 7 are implemented.

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