A method and related device for cogeneration power plant capacity market bidding optimization

Abstract

This invention relates to the field of bidding technology in the capacity market for combined heat and power (CHP) power plants, and discloses an optimization method and related equipment for bidding in the capacity market for CHP power plants. The method includes: acquiring the correlation data of electrical load and heat load of each unit in a CHP power plant under target operating conditions; constructing a thermoelectric coupling relationship model for each unit through data processing; calculating the daily capacity charge of the CHP power plant by combining relevant pricing and time parameters in the capacity market, forming a capacity charge calculation model; constructing an optimization objective function for the bidding strategy based on the capacity charge calculation model, with maximizing the daily capacity charge of the power plant as the core objective; establishing a constraint model to limit the optimization process by combining the actual operating boundary conditions of the power plant and each unit; and solving the optimization objective function and constraint model using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each time period of the capacity market, forming the final bidding strategy. This invention significantly improves the scientific nature and accuracy of the strategy.

Description

Technical Field

[0001] This invention relates to the field of bidding technology in the capacity market of combined heat and power (CHP) power plants, specifically to an optimization method and related equipment for bidding in the capacity market of CHP power plants. Background Technology

[0002] Combined heat and power (CHP) plants, as important carriers of comprehensive energy utilization, possess the dual functions of power generation and heating. Their bidding strategies in the capacity market directly impact their economic benefits. The core objective of the capacity market is to ensure the long-term reliability of the power system. Power plants obtain corresponding capacity tariff revenue by bidding for available capacity in the capacity market. Currently, many existing CHP plant capacity market bidding strategies do not fully consider the impact of thermoelectric coupling characteristics on capacity tariffs, leading to a mismatch between the bid capacity and the actual revenue optimization needs, making it difficult to maximize capacity tariffs. Furthermore, they neglect key factors such as unit constraints and heating demand, rendering the bidding strategies insufficiently feasible and practical.

[0003] Therefore, there is an urgent need for a bidding strategy in the capacity market for cogeneration power plants that takes into account the thermo-electric coupling relationship, constraints, and the goal of maximizing profits, in order to address the shortcomings of existing technologies and enhance the competitiveness and economic benefits of power plants in the capacity market. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the purpose of this invention is to provide a method and related equipment for optimizing bidding in the capacity market of cogeneration power plants, so as to solve the technical problem of how to optimize bidding in the capacity market of cogeneration power plants.

[0005] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for optimizing bidding in the capacity market of cogeneration power plants, comprising: Acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under target operating conditions, and construct a thermoelectric coupling relationship model for each unit through data processing; Based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters in the capacity market, the daily capacity electricity fee of the cogeneration power plant is calculated, thus forming a capacity electricity fee calculation model. With the core objective of maximizing the daily capacity charge of power plants, an objective function for optimizing bidding strategies is constructed based on the capacity charge calculation model. Based on the actual operating boundary conditions of the power plant and its units, a constraint model is established to limit the optimization process; The optimization objective function and constraint model are solved by using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and to form the final bidding strategy.

[0006] Preferably, the target operating condition is the operating condition with maximum main steam flow, and the thermoelectric coupling relationship model is a linear function expression as follows:

[0007] in, For combined heat and power units in power plants i During the period t Electrical load under VWO conditions; This is the cogeneration unit at that time. i During the period t The heat load; and For combined heat and power units i Thermoelectric coupling coefficient under VWO conditions.

[0008] Preferred capacity charge calculation models include:

[0009] in, The daily capacity charge for a combined heat and power (CHP) power plant; It is a capacity-based electricity price; The total number of time periods; Time scale; This represents the total number of generating units in a combined heat and power (CHP) plant.

[0010] The preferred formula for optimizing the objective function is as follows:

[0011] in, The daily capacity charge for a combined heat and power (CHP) power plant; It is a capacity-based electricity price; The total number of time periods; Time scale; This represents the total number of generating units in a combined heat and power (CHP) plant.

[0012] Preferably, the constraint model includes:

[0013] in, For cogeneration power plants during the time period t Total external heat supply; For combined heat and power units i The lower limit of heating capacity; For combined heat and power units i The upper limit of heating capacity; For combined heat and power units i Minimum technical output; For combined heat and power units iThe upper limit of output.

[0014] Preferably, the preset optimization algorithm is a linear programming method, and the specific solution logic includes: .

[0015] The preferred formula for the final bidding strategy's bid capacity is as follows:

[0016] in, For cogeneration power plants t The bidding capacity results in the capacity market during the specified time period.

[0017] Secondly, the present invention also provides a combined heat and power (CHP) power plant capacity market bidding optimization system, comprising: The coupling modeling module is used to acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under the target operating conditions, and to construct a thermoelectric coupling relationship model of each unit after data processing. The electricity cost calculation module is used to calculate the daily capacity electricity cost of cogeneration power plants based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters in the capacity market, thus forming a capacity electricity cost calculation model. The objective construction module is used to construct an objective function for optimizing the bidding strategy based on the capacity charge calculation model, with the core objective of maximizing the daily capacity charge of the power plant. The constraint setting module is used to establish a constraint model based on the actual operating boundary conditions of the power plant and its units, thereby limiting the optimization process. The strategy solving module is used to solve the objective function and constraint model using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and form the final bidding strategy.

[0018] Thirdly, the present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cogeneration power plant capacity market bidding optimization method as described above.

[0019] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the bidding process in the capacity market of cogeneration power plants.

[0020] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides an optimization method for bidding in the capacity market of combined heat and power (CHP) power plants. By acquiring the correlation data between electrical and thermal loads under the target operating conditions of the units and constructing a thermoelectric coupling relationship model, it overcomes the limitations of traditional bidding methods that neglect the thermoelectric linkage characteristics and fail to fully utilize data. This method quantifies the coupling relationship, providing accurate data support for subsequent electricity cost calculation and strategy optimization, and effectively avoids bidding deviations caused by the disconnect between thermoelectric characteristics. Based on this coupling relationship model, and combining capacity market pricing and time parameters to calculate daily capacity electricity costs and form a calculation model, it can accurately adapt to market rules, ensuring the objectivity and accuracy of electricity cost accounting. This provides a reliable basis for clarifying revenue targets and solves the problems of poor parameter adaptability and large result deviations in traditional accounting methods. By constructing an optimization objective function with the core objective of maximizing daily capacity electricity revenue, the optimization direction of the bidding strategy is clarified, ensuring that strategy formulation always revolves around the core of revenue and avoiding revenue loss caused by aimless adjustments. Simultaneously, a constraint model is established by combining the actual operating boundary conditions of the power plant and units, achieving precise matching between the optimization process and actual operating capacity. This ensures that the bidding strategy pursues maximum revenue while remaining compliant and feasible, effectively avoiding the risk of strategies failing to be implemented or violating regulations due to detachment from actual operation. Using a pre-set optimization algorithm to solve the objective function and constraint model, the optimal bidding capacity for each time period can be efficiently output, forming a systematic final bidding strategy. Compared to traditional experience-based bidding methods, this significantly improves the scientific nature and accuracy of the strategy. It not only fully taps the power plant's capacity revenue potential and significantly increases daily capacity electricity revenue, but also adapts to changes in different time periods, market pricing, and unit operating states, enhancing the flexibility and adaptability of the bidding strategy. This helps power plants gain an advantage in capacity market competition and comprehensively improves the market operation efficiency and core competitiveness of power plants. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method for optimizing the market bidding for combined heat and power plant capacity in an embodiment of the present invention; Figure 2 This is a schematic diagram of the cogeneration power plant capacity market bidding optimization system in an embodiment of the present invention; In the diagram: 1. Coupled modeling module; 2. Electricity cost calculation module; 3. Target construction module; 4. Constraint setting module; 5. Strategy solving module. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] The purpose of this invention is to provide a method and related equipment for optimizing bidding in the market capacity of cogeneration power plants, so as to solve the technical problem of how to optimize bidding in the market capacity of cogeneration power plants.

[0025] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 See Figure 1 In one embodiment of the present invention, a method for optimizing bidding in the capacity market of combined heat and power (CHP) power plants is provided, comprising: Step 1: Obtain the correlation data of electrical load and heat load of each unit in the cogeneration power plant under the target operating conditions, and construct the thermoelectric coupling relationship model of each unit through data processing; Specifically, the target operating condition is the maximum main steam flow operating condition, and the thermoelectric coupling relationship model is a linear function expression as follows:

[0026] in, For combined heat and power units in power plants i During the period t Electrical load under VWO conditions; This is the cogeneration unit at that time. i During the period t The heat load; and For combined heat and power units i Thermoelectric coupling coefficient under VWO conditions.

[0027] In this embodiment, the combined heat and power unit i Thermoelectric coupling coefficient under VWO conditions and According to the combined heat and power unit of the power plant i Historical electrical load-heat load data under VWO conditions were obtained by fitting using the least squares method.

[0028] Step 2: Based on the thermoelectric coupling relationship model of each unit, combined with the relevant pricing and time parameters of the capacity market, calculate the daily capacity electricity fee of the cogeneration power plant to form a capacity electricity fee calculation model; Specifically, the capacity charge calculation model includes:

[0029] in, The daily capacity charge for a combined heat and power (CHP) power plant; It is a capacity-based electricity price; The total number of time periods; Time scale; This represents the total number of generating units in a combined heat and power (CHP) plant.

[0030] In this embodiment, the capacity electricity price of the combined heat and power plant The total number of time periods per day is determined by the provincial and municipal capacity pricing tables based on regional differences; the capacity charge settlement is divided into several time periods. T Typically 24, corresponding to the time scale. It takes 1 hour.

[0031] Step 3: With maximizing the daily capacity charge of the power plant as the core objective, construct the bidding strategy optimization objective function based on the capacity charge calculation model; Specifically, the formula for optimizing the objective function is as follows:

[0032] in, The daily capacity charge for a combined heat and power (CHP) power plant; It is a capacity-based electricity price; The total number of time periods; Time scale; This represents the total number of generating units in a combined heat and power (CHP) plant.

[0033] In this embodiment, the cogeneration unit in the objective function i During the period t heat load Decision variables for optimizing bidding strategies in the capacity market.

[0034] Step 4: Based on the actual operating boundary conditions of the power plant and each unit, establish a constraint model to limit the optimization process; Specifically, the constraint model includes:

[0035] in, For cogeneration power plants during the time period t Total external heat supply; For combined heat and power units i The lower limit of heating capacity; For combined heat and power units i The upper limit of heating capacity; For combined heat and power units i Minimum technical output; For combined heat and power units i The upper limit of output.

[0036] In this embodiment, the combined heat and power unit i Lower limit of heating capacity Upper limit of heating capacity Minimum technical output and output ceiling From the combined heat and power unit of the power plant i Refer to the thermodynamic characteristics book; for combined heat and power plants during certain periods t Total external heat supply It is obtained from the heat load demand curve of the regional heating network.

[0037] Step 5: Use a preset optimization algorithm to solve the objective function and constraint model to obtain the optimal bidding capacity of the power plant in each time period of the capacity market, and form the final bidding strategy.

[0038] Specifically, the preset optimization algorithm is a linear programming method, and the specific solution logic includes: .

[0039] Specifically, the formula for the final bidding strategy's bid capacity is as follows:

[0040] in, For cogeneration power plants t The bidding capacity results in the capacity market during the specified time period.

[0041] In this embodiment, the bidding strategy is implemented by sending the bidding capacity results of the cogeneration power plant in the capacity market during time period t to the power reporting platform.

[0042] In summary, this embodiment provides an optimized bidding method for the capacity market of combined heat and power (CHP) power plants. By acquiring the correlation data between electrical and thermal loads under the target operating conditions of the units and constructing a thermoelectric coupling relationship model, it overcomes the limitations of traditional bidding methods that neglect the thermoelectric linkage characteristics and fail to fully utilize data. This method quantifies the coupling relationship, providing accurate data support for subsequent electricity cost calculation and strategy optimization, and effectively avoids bidding deviations caused by the separation of thermoelectric characteristics. Based on this coupling relationship model, and combining capacity market pricing and time parameters to calculate daily capacity electricity costs and form a calculation model, it can accurately adapt to market rules, ensure the objectivity and accuracy of electricity cost accounting, provide a reliable basis for clarifying revenue targets, and solve the problems of poor parameter adaptability and large result deviations in traditional accounting methods. By constructing an optimization objective function with the core objective of maximizing daily capacity electricity revenue, the optimization direction of the bidding strategy is clarified, ensuring that strategy formulation always revolves around the core of revenue and avoiding revenue loss caused by aimless adjustments. Simultaneously, a constraint model is established by combining the actual operating boundary conditions of the power plant and units, achieving precise matching between the optimization process and actual operating capacity. This ensures that the bidding strategy pursues maximum revenue while remaining compliant and feasible, effectively avoiding the risk of strategies failing to be implemented or violating regulations due to detachment from actual operation. Using a pre-set optimization algorithm to solve the objective function and constraint model, the optimal bidding capacity for each time period can be efficiently output, forming a systematic final bidding strategy. Compared to traditional experience-based bidding methods, this significantly improves the scientific nature and accuracy of the strategy. It not only fully taps the power plant's capacity revenue potential and significantly increases daily capacity electricity revenue, but also adapts to changes in different time periods, market pricing, and unit operating states, enhancing the flexibility and adaptability of the bidding strategy. This helps power plants gain an advantage in capacity market competition and comprehensively improves the market operation efficiency and core competitiveness of power plants.

[0043] Example 2 according to Figure 2 As shown, this embodiment also provides a combined heat and power (CHP) power plant capacity market bidding optimization system, including: Coupled modeling module 1 is used to acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under the target operating conditions, and to construct a thermo-electric coupling relationship model of each unit after data processing. Electricity charge calculation module 2 is used to calculate the daily capacity electricity charge of cogeneration power plants based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters of the capacity market, and form a capacity electricity charge calculation model. Target construction module 3 is used to construct an objective function for optimizing the bidding strategy based on the capacity charge calculation model, with the core objective of maximizing the daily capacity charge of the power plant. The constraint setting module 4 is used to establish a constraint model based on the actual operating boundary conditions of the power plant and each unit, and to limit the optimization process. Strategy solving module 5 is used to solve the optimization objective function and constraint model using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and form the final bidding strategy.

[0044] Example 3 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a cogeneration power plant capacity market bidding optimization program.

[0045] When the processor executes the computer program, it implements the above-mentioned cogeneration power plant capacity market bidding optimization method, for example: Acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under target operating conditions, and construct a thermoelectric coupling relationship model for each unit through data processing; Based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters in the capacity market, the daily capacity electricity fee of the cogeneration power plant is calculated, thus forming a capacity electricity fee calculation model. With the core objective of maximizing the daily capacity charge of power plants, an objective function for optimizing bidding strategies is constructed based on the capacity charge calculation model. Based on the actual operating boundary conditions of the power plant and its units, a constraint model is established to limit the optimization process; The optimization objective function and constraint model are solved by using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and to form the final bidding strategy.

[0046] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example: Coupled modeling module 1 is used to acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under the target operating conditions, and to construct a thermo-electric coupling relationship model of each unit after data processing. Electricity charge calculation module 2 is used to calculate the daily capacity electricity charge of cogeneration power plants based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters of the capacity market, and form a capacity electricity charge calculation model. Target construction module 3 is used to construct an objective function for optimizing the bidding strategy based on the capacity charge calculation model, with the core objective of maximizing the daily capacity charge of the power plant. The constraint setting module 4 is used to establish a constraint model based on the actual operating boundary conditions of the power plant and each unit, and to limit the optimization process. Strategy solving module 5 is used to solve the optimization objective function and constraint model using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and form the final bidding strategy.

[0047] For example, the computer program can be divided into a coupled modeling module 1, an electricity cost calculation module 2, a target construction module 3, a constraint setting module 4, and a strategy solving module 5; The specific functions of each module are as follows: Coupled modeling module 1 is used to acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under the target operating conditions, and to construct a thermo-electric coupling relationship model of each unit after data processing. Electricity charge calculation module 2 is used to calculate the daily capacity electricity charge of cogeneration power plants based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters of the capacity market, and form a capacity electricity charge calculation model. Target construction module 3 is used to construct an objective function for optimizing the bidding strategy based on the capacity charge calculation model, with the core objective of maximizing the daily capacity charge of the power plant. The constraint setting module 4 is used to establish a constraint model based on the actual operating boundary conditions of the power plant and each unit, and to limit the optimization process. Strategy solving module 5 is used to solve the optimization objective function and constraint model using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and form the final bidding strategy.

[0048] The mobile terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The mobile terminal may include, but is not limited to, a processor and memory.

[0049] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the mobile terminal, connecting various parts of the mobile terminal via various interfaces and lines.

[0050] The memory can be used to store the computer program and / or module. The processor implements various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0051] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0052] Example 4 The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for optimizing the bidding process in the capacity market of a combined heat and power plant.

[0053] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0054] Based on this understanding, all or part of the processes in the above method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the above-described aggregated reinforcement learning resource scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.

[0055] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0056] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing market bidding for the capacity of combined heat and power (CHP) power plants, characterized in that, include: Acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under target operating conditions, and construct a thermoelectric coupling relationship model for each unit through data processing; Based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters in the capacity market, the daily capacity electricity fee of the cogeneration power plant is calculated, thus forming a capacity electricity fee calculation model. With the core objective of maximizing the daily capacity charge of power plants, an objective function for optimizing bidding strategies is constructed based on the capacity charge calculation model. Based on the actual operating boundary conditions of the power plant and its units, a constraint model is established to limit the optimization process; The optimization objective function and constraint model are solved by using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and to form the final bidding strategy.

2. The method for optimizing market bidding for combined heat and power (CHP) power plant capacity according to claim 1, characterized in that, The target operating condition is the operating condition with maximum main steam flow, and the thermoelectric coupling relationship model is a linear function expression as follows: in, For combined heat and power units in power plants i During the period t Electrical load under VWO conditions; This is the cogeneration unit at that time. i During the period t The heat load; and For combined heat and power units i Thermoelectric coupling coefficient under VWO conditions.

3. The method for optimizing market bidding for combined heat and power (CHP) power plant capacity according to claim 1, characterized in that, The capacity charge calculation model includes: in, The daily capacity charge for a combined heat and power (CHP) power plant; It is a capacity-based electricity price; The total number of time periods; Time scale; This represents the total number of generating units in a combined heat and power (CHP) plant.

4. The method for optimizing market bidding for combined heat and power (CHP) power plant capacity according to claim 1, characterized in that, The formula for the optimization objective function is as follows: in, The daily capacity charge for a combined heat and power (CHP) power plant; It is a capacity-based electricity price; The total number of time periods; Time scale; This represents the total number of generating units in a combined heat and power (CHP) plant.

5. The method for optimizing market bidding for combined heat and power (CHP) power plant capacity according to claim 1, characterized in that, The constraint model includes: in, For cogeneration power plants during the time period t Total external heat supply; For combined heat and power units i The lower limit of heating capacity; For combined heat and power units i The upper limit of heating capacity; For combined heat and power units i Minimum technical output; For combined heat and power units i The upper limit of output.

6. The method for optimizing market bidding for combined heat and power (CHP) power plant capacity according to claim 1, characterized in that, The preset optimization algorithm is a linear programming method, and the specific solution logic includes: 。 7. The method for optimizing market bidding for combined heat and power (CHP) power plant capacity according to claim 1, characterized in that, The formula for the bid capacity of the final bidding strategy is as follows: in, For cogeneration power plants t The bidding capacity results in the capacity market during the specified time period.

8. A system for optimizing market bidding for combined heat and power (CHP) power plant capacity, characterized in that, include: The coupling modeling module is used to acquire the correlation data of electrical load and heat load of each unit in a cogeneration power plant under the target operating conditions, and to construct a thermoelectric coupling relationship model of each unit after data processing. The electricity cost calculation module is used to calculate the daily capacity electricity cost of cogeneration power plants based on the thermoelectric coupling relationship model of each unit, combined with relevant pricing and time parameters in the capacity market, thus forming a capacity electricity cost calculation model. The objective construction module is used to construct an objective function for optimizing the bidding strategy based on the capacity charge calculation model, with the core objective of maximizing the daily capacity charge of the power plant. The constraint setting module is used to establish a constraint model based on the actual operating boundary conditions of the power plant and its units, thereby limiting the optimization process. The strategy solving module is used to solve the objective function and constraint model using a preset optimization algorithm to obtain the optimal bidding capacity of the power plant in each period of the capacity market, and form the final bidding strategy.

9. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the cogeneration power plant capacity market bidding optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the cogeneration power plant capacity market bidding optimization method as described in any one of claims 1-7.

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