A method and system for reliability evaluation of a power generation system considering carbon emission constraints

Abstract

The application provides a kind of carbon emission constraint considered power generation system reliability evaluation method and system, comprising: obtaining the parameter in target power generation system, and setting system carbon emission total amount constraint;Establish unit commitment optimization model considering carbon emission constraint, under the constraint conditions such as satisfying unit operation constraint, power balance constraint, the start-stop state and output result of each period thermal power unit are solved;Based on the unit commitment optimization result, combined with the forced outage characteristics of thermal power unit and the uncertainty of new energy output, the system total available capacity probability model is constructed, and the system capacity probability distribution is obtained by using convolution method;By comparing between system total available capacity distribution and load, the system reliability index loss of load probability LOLP is calculated, and the quantitative evaluation of power generation system reliability under low-carbon dispatching condition is realized.The application can reflect the influence of carbon emission constraint on unit operation state and system capacity distribution.

Description

Technical Field

[0001] This invention belongs to the field of power system operation, and specifically relates to a method and system for reliability assessment of power generation systems that takes carbon emission constraints into account. Background Technology

[0002] With the rapid growth of new energy installed capacity, the operation mode of the power system is shifting from the traditional high-carbon centralized power supply mode to a low-carbon and clean power supply mode. Although the large-scale integration of new energy sources such as wind power and photovoltaics has reduced the carbon emission level of the system, the randomness and volatility of their output pose new challenges to the reliability of power supply. Against this backdrop, how to ensure reliable power supply while meeting carbon emission reduction requirements has become an important research direction in the field of power system operation and planning.

[0003] Reliability assessment of power generation systems is a crucial means of measuring system power supply capacity. It primarily uses analytical or simulation methods to calculate indicators such as the probability of load failure (LOLP) to evaluate the system's power supply adequacy under random fault conditions. Most existing reliability assessment methods for power generation systems are based on static unit conditions or employ dispatch results that do not consider carbon emission constraints. These methods ignore the dynamic limitations of carbon emission limits on the actual available capacity of units, leading to a physical disconnect between the assessment model and actual dispatch scenarios, and making it difficult to reflect the true impact of low-carbon operation strategies on system reliability.

[0004] In recent years, some studies have begun to introduce carbon emission constraints into production simulations to achieve low-carbon optimized scheduling by limiting the total carbon emissions of thermal power units or increasing carbon emission costs. However, most existing studies focus on the impact of carbon constraints on system economics, without further considering the impact of carbon constraints on the reliability of system power supply after changing the operating state of the units. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for reliability assessment of power generation systems that takes into account carbon emission constraints, so as to solve the problem that traditional reliability assessment methods do not take into account the changes in unit output and operating status under low-carbon dispatch conditions, and realize quantitative assessment of the power supply reliability of the system under carbon emission constraints.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A reliability assessment method for power generation systems considering carbon emission constraints includes the following steps: Step 1: Obtain the parameters of the target power generation system, including the operating parameters of thermal power units, load curve, installed capacity of new energy sources and predicted output curve, and set the upper limit of the total carbon emissions of the target power generation system; Step 2: Based on the obtained parameters and the upper limit of total carbon emissions, establish a unit combination optimization operation model that considers carbon emission constraints. By solving the unit combination optimization operation model, obtain the start-up and shutdown status and output results of thermal power units in each time period. Step 3: Based on the start-up and shutdown status and output results of thermal power units in each time period, and combined with their forced shutdown rate, construct a probability model of the available capacity of thermal power units, and establish a probability model of multi-state output of new energy. Then, obtain the probability distribution of the total available capacity of the system through the joint probability convolution method. Step 4: Compare the probability distribution of the total available capacity of the system with the load, calculate the probability of load failure (LOLP) of the system reliability index, and quantitatively assess the reliability of the power generation system under the condition of carbon emission constraints.

[0007] A further improvement of this invention lies in that, in step two, based on the acquired parameters, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of the thermal power units in each time period are obtained, including: A piecewise linearization method is adopted to introduce piecewise variables of unit output, transforming the nonlinear carbon emission characteristics of thermal power units into linear constraints. Furthermore, a total system carbon emission constraint is applied, allowing the carbon emission constraint to participate in the unit output optimization and start-up / shutdown decision-making process. The specific process is as follows: Carbon emissions from thermal power units typically exhibit a non-linear relationship with unit output, described using a quadratic function. (1) in E i ( P i ) for generator unit i carbon emissions, P i For the power output of the unit, a i , b i , c i Carbon emission coefficient; By employing a piecewise linearization method to process the carbon emission function, the unit output is divided into multiple linear intervals, and the unit output is then expressed as: (2) in This is the minimum output of the unit. u i,t This indicates the unit's start-up and shutdown status. For the first k Segment incremental output, K The number of segments; Accordingly, the unit's carbon emissions are expressed as follows: (3) in It is a generator unit i During the period t carbon emissions, This represents the carbon emissions corresponding to the unit's minimum output. For the first k Carbon emission increment coefficient for the segment; Furthermore, establish systemic carbon emission limits: (4) in T represents the upper limit of the total carbon emissions of the system, and T represents the total number of time periods included in the unit combination scheduling cycle.

[0008] A further improvement of this invention lies in that, in step three, based on the start-up and shutdown status and output results of the thermal power units in each time period, and combined with their forced outage rate, a probabilistic model of the available capacity of the thermal power units is constructed, including: The original system has n The thermal power units are 100 units, and the installed capacity of each thermal power unit is 100 units. , For the corresponding forced outage rate, the actual available capacity of the i-th thermal power unit is: The actual usable capacity of the nth thermal power unit is then... The total available capacity of thermal power units is obtained by convolving the actual available capacity of each thermal power unit. , To determine the on / off state of thermal power units using UC after considering carbon emission constraints, 1 represents on-time and 0 represents off-time. The specific probabilistic model for the available capacity of thermal power units is as follows: (5) (6) in" "Refers to convolution operation."

[0009] A further improvement of this invention lies in that, in step three, a multi-state output probability model of the new energy source is established, and then the probability distribution of the total available capacity of the system is obtained through a joint probability convolution method, including: A multi-state output probability model for new energy sources was established to characterize the uncertainty of new energy sources. The uncertainty of new energy sources was further considered in the reliability assessment stage of power generation systems under carbon emission constraints. The multi-state output probability model for new energy sources is expressed as follows: (7) in For new energy m The output value corresponding to each state. Then, represents the probability of each state occurring, and satisfies... ; Then, the probability distribution of the total available capacity of the system is obtained by using the joint probability convolution method between the probability model of available capacity of thermal power units and the probability model of multi-state output of new energy units. : (8).

[0010] A reliability assessment system for power generation systems considering carbon emission constraints, comprising: Parameter acquisition unit: Acquires parameters of the target power generation system, including thermal power unit operating parameters, load curve, new energy installed capacity and predicted output curve, and sets the upper limit of total carbon emissions of the target power generation system; The first model establishment and solution unit: Based on the obtained parameters and the upper limit of total carbon emissions, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of thermal power units in each time period are obtained. The second model establishment and solution unit: Based on the start-up and shutdown status and output results of thermal power units in each time period, and combined with their forced shutdown rate, a probability model of the available capacity of thermal power units is constructed, and a probability model of the output of new energy in multiple states is established. Then, the probability distribution of the total available capacity of the system is obtained by the joint probability convolution method. Comparison Unit: The probability distribution of the total available capacity of the system is compared with the load, the probability of load failure (LOLP) is calculated as the system reliability index, and the reliability of the power generation system under the condition of carbon emission constraints is quantitatively evaluated.

[0011] A further improvement of this invention lies in that, in the first model establishment and solution unit, based on the acquired parameters, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of thermal power units in each time period are obtained, including: A piecewise linearization method is adopted to introduce piecewise variables of unit output, transforming the nonlinear carbon emission characteristics of thermal power units into linear constraints. Furthermore, a total system carbon emission constraint is applied, allowing the carbon emission constraint to participate in the unit output optimization and start-up / shutdown decision-making process. The specific process is as follows: Carbon emissions from thermal power units typically exhibit a non-linear relationship with unit output, described using a quadratic function. (1) in E i ( P i ) for generator unit i carbon emissions, P i For the power output of the unit, a i, b i , c i Carbon emission coefficient; By employing a piecewise linearization method to process the carbon emission function, the unit output is divided into multiple linear intervals, and the unit output is then expressed as: (2) in This is the minimum output of the unit. u i,t This indicates the unit's start-up and shutdown status. For the first k Segment incremental output, K The number of segments; Accordingly, the unit's carbon emissions are expressed as follows: (3) in It is a generator unit i During the period t carbon emissions, This represents the carbon emissions corresponding to the unit's minimum output. For the first k Carbon emission increment coefficient for the segment; Furthermore, establish systemic carbon emission limits: (4) in T represents the upper limit of the total carbon emissions of the system, and T represents the total number of time periods included in the unit combination scheduling cycle.

[0012] A further improvement of this invention lies in that, in the second model establishment and solution unit, based on the start-up and shutdown status and output results of the thermal power units in each time period, and combined with their forced shutdown rate, a probabilistic model of the available capacity of the thermal power units is constructed, including: The original system has n The thermal power units are 100 units, and the installed capacity of each thermal power unit is 100 units. , For the corresponding forced outage rate, the actual available capacity of the i-th thermal power unit is: The actual usable capacity of the nth thermal power unit is then... The total available capacity of thermal power units is obtained by convolving the actual available capacity of each thermal power unit. , To determine the on / off state of thermal power units using UC after considering carbon emission constraints, 1 represents on-time and 0 represents off-time. The specific probabilistic model for the available capacity of thermal power units is as follows: (5) (6) in" "Refers to convolution operation."

[0013] A further improvement of this invention lies in that, in the second model establishment and solution unit, a multi-state output probability model of new energy is established, and then the probability distribution of the total available capacity of the system is obtained through a joint probability convolution method, including: A multi-state output probability model for new energy sources was established to characterize the uncertainty of new energy sources. The uncertainty of new energy sources was further considered in the reliability assessment stage of power generation systems under carbon emission constraints. The multi-state output probability model for new energy sources is expressed as follows: (7) in For new energy m The output value corresponding to each state. Then, represents the probability of each state occurring, and satisfies... ; Then, the probability distribution of the total available capacity of the system is obtained by using the joint probability convolution method between the probability model of available capacity of thermal power units and the probability model of multi-state output of new energy units. : (8).

[0014] An electronic device includes: a processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of the method for reliability assessment of a power generation system considering carbon emission constraints.

[0015] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for reliability assessment of a power generation system considering carbon emission constraints.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a method and system for reliability assessment of power generation systems considering carbon emission constraints. By introducing carbon emission constraints into the unit combination, the total carbon emission limit participates in the unit output optimization and start-up / shutdown decision-making process, thereby reflecting the changes in unit operating status under low-carbon operating conditions. Based on the unit combination results considering carbon constraints, a probabilistic model of system available capacity is constructed, enabling the reliability assessment to reflect the impact of carbon emission constraints on system capacity distribution. At the same time, considering the uncertainty of new energy output, the reliability of power generation systems under low-carbon dispatch conditions is quantitatively assessed, thereby improving the applicability and accuracy of power generation system reliability assessment. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is an overall flowchart of the method of the present invention.

[0019] Figure 2 A comparison chart showing the change in average LOLP with wind power installed capacity before and after considering carbon emission constraints in the system.

[0020] Figure 3 The curve shows the relationship between the average LOLP and the upper limit of total carbon emissions for a wind power installed capacity of 1600MW.

[0021] Figure 4 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0022] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0023] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0024] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0027] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] Example 1 like Figure 1 As shown, the present invention provides a reliability assessment method for power generation systems considering carbon emission constraints, comprising the following steps: Step 1: Obtain the operating parameters, load curve, new energy installed capacity and predicted output curve of the target power generation system's thermal power units, and set the upper limit of the system's total carbon emissions.

[0029] This embodiment uses an improved IEEE-RTS79 test system for verification. The system includes 26 conventional thermal power units and 2 wind farms, each with an installed capacity of 600MW, for a total installed wind power capacity of 1200MW. The thermal power units in the system range in capacity from 12MW to 400MW, with a total installed capacity of 3105MW. The forced outage rate for thermal power units is uniformly set to 0.004; the predicted output curves for wind farms are obtained based on historical wind power data; the system's total carbon emission constraints are set according to the system's operating scenario. When the total installed wind power capacity is 1200MW, the upper limit for the system's total carbon emissions is set at 20,000 tCO2.

[0030] Step 2: Based on the parameters obtained in Step 1, establish a unit combination optimization operation model that considers carbon emission constraints. By solving this model, obtain the start-up and shutdown status and output results of thermal power units in each time period.

[0031] A unit combination optimization model is constructed with the objective of minimizing the total system operating cost. The objective function includes the fuel cost and start-up / shutdown cost of the thermal power units. Both the fuel cost function and the carbon emission function are processed using a piecewise linearization method to transform the nonlinear optimization problem into a mixed-integer linear programming problem. Model constraints include upper and lower limits for unit output, ramp-up constraints, minimum start-up / shutdown time constraints, power balance constraints, reserve constraints, and total system carbon emission constraints. By introducing a carbon emission constraint—that the total carbon emissions of the thermal power units do not exceed an upper limit—carbon emission limits are incorporated into the unit output optimization and start-up / shutdown decision-making process, thereby reflecting changes in the unit's operating status under low-carbon operating conditions.

[0032] The established unit combination optimization model was solved using a 24-hour scheduling cycle to obtain the start-up and shutdown status and output results of thermal power units in each time period, providing basic data for subsequent system reliability assessment.

[0033] Step 3: Based on the start-up and shutdown status and output results of the thermal power units obtained in Step 2, construct a probability model of the available capacity of the thermal power units in combination with their forced outage rate, and establish a probability model of the output of new energy units in multiple states. Then, obtain the probability distribution of the total available capacity of the system through the joint probability convolution method.

[0034] In this embodiment, the available capacity probability model for thermal power units is jointly determined by the unit's operating status and the forced outage rate. For wind farms, a wind power probability distribution model is established based on the predicted output curve. Wind power output can be described using a Beta distribution, a normal distribution, or other probability distributions. A multi-state random output scenario and its corresponding probability are constructed using a discretization method, thus forming a multi-state wind power output model. Probabilistic convolution is performed on the multi-state output models of the two wind farms respectively to obtain the probability distribution of the total available wind power capacity. Then, combined with the available capacity probability model of thermal power units, the probability distribution of the total available system capacity is obtained through convolution operations.

[0035] Step 4: Compare the probability distribution of total available capacity of the system obtained in Step 3 with the load, calculate the system reliability index Loss of Load Probability (LOLP), and quantitatively assess the reliability of the power generation system under carbon emission constraints.

[0036] The average LOLP of the system during the scheduling cycle is obtained by summing the probabilities of the total available capacity of all systems being less than the load within 24 hours and then dividing by the duration of 24 hours. Additionally, changing the total installed wind power capacity allows for comparative experiments to observe changes in system reliability; for example, increasing the installed wind power capacity from 1200MW to 1600MW.

[0037] First, to verify the impact of carbon emission constraints on the reliability of power generation systems, Figure 2 The paper presents a comparison of the system's average LOLP with and without carbon emission constraints, showing the changes in wind power installed capacity. In this embodiment, the system's average LOLP generally increases after the introduction of carbon emission constraints, indicating that carbon emission constraints reduce the system's power supply reliability to some extent. This is because carbon emission constraints limit the output and operating status of some high-emission thermal power units, reducing the system's available capacity and thus increasing the probability of load shedding.

[0038] Furthermore, to analyze the impact of total carbon emission constraints on system reliability, the upper limit of total carbon emissions of the system is gradually reduced. Figure 3 The relationship between average LOLP and the upper limit of system carbon emissions is presented. Figure 3As can be seen in this embodiment, as the upper limit of system carbon emissions gradually decreases, the average LOLP of the system gradually increases, indicating that stricter carbon emission constraints will reduce the reliability of system power supply. The above results demonstrate that the method proposed in this invention can reflect the impact of carbon emission constraints on the reliability of power generation systems, achieving a quantitative assessment of the reliability of power generation systems under low-carbon operating conditions.

[0039] Example 2 like Figure 4 As shown, the present invention provides a reliability assessment system for power generation systems considering carbon emission constraints, comprising: Parameter acquisition unit: Acquires parameters of the target power generation system, including thermal power unit operating parameters, load curve, new energy installed capacity and predicted output curve, and sets the upper limit of total carbon emissions of the target power generation system; The first model establishment and solution unit: Based on the obtained parameters and the upper limit of total carbon emissions, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of thermal power units in each time period are obtained. The second model establishment and solution unit: Based on the start-up and shutdown status and output results of thermal power units in each time period, and combined with their forced shutdown rate, a probability model of the available capacity of thermal power units is constructed, and a probability model of the output of new energy in multiple states is established. Then, the probability distribution of the total available capacity of the system is obtained by the joint probability convolution method. Comparison Unit: The probability distribution of the total available capacity of the system is compared with the load, the probability of load failure (LOLP) is calculated as the system reliability index, and the reliability of the power generation system under the condition of carbon emission constraints is quantitatively evaluated.

[0040] In the first model establishment and solution unit of this embodiment, based on the acquired parameters, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of thermal power units in each time period are obtained, including: A piecewise linearization method is adopted to introduce piecewise variables of unit output, transforming the nonlinear carbon emission characteristics of thermal power units into linear constraints. Furthermore, a total system carbon emission constraint is applied, allowing the carbon emission constraint to participate in the unit output optimization and start-up / shutdown decision-making process. The specific process is as follows: Carbon emissions from thermal power units typically exhibit a non-linear relationship with unit output, described using a quadratic function. (1) in E i ( P i ) for generator unit i carbon emissions, P i For the power output of the unit,a i , b i , c i Carbon emission coefficient; By employing a piecewise linearization method to process the carbon emission function, the unit output is divided into multiple linear intervals, and the unit output is then expressed as: (2) in This is the minimum output of the unit. u i,t This indicates the unit's start-up and shutdown status. For the first k Segment incremental output, K The number of segments; Accordingly, the unit's carbon emissions are expressed as follows: (3) in It is a generator unit i During the period t carbon emissions, This represents the carbon emissions corresponding to the unit's minimum output. For the first k Carbon emission increment coefficient for the segment; Furthermore, establish systemic carbon emission limits: (4) in T represents the upper limit of the total carbon emissions of the system, and T represents the total number of time periods included in the unit combination scheduling cycle.

[0041] In the second model establishment and solution unit of this embodiment, a probabilistic model of the available capacity of thermal power units is constructed based on the start-up and shutdown status and output results of thermal power units in each time period, combined with their forced shutdown rate, including: The original system has n The thermal power units are 100 units, and the installed capacity of each thermal power unit is 100 units. , For the corresponding forced outage rate, the actual available capacity of the i-th thermal power unit is: The actual usable capacity of the nth thermal power unit is then... The total available capacity of thermal power units is obtained by convolving the actual available capacity of each thermal power unit. , To determine the on / off state of thermal power units using UC after considering carbon emission constraints, 1 represents on-time and 0 represents off-time. The specific probabilistic model for the available capacity of thermal power units is as follows: (5) (6) in" "Refers to convolution operation."

[0042] In the second model establishment and solution unit of this embodiment, a multi-state output probability model of new energy is established, and then the probability distribution of the total available capacity of the system is obtained through the joint probability convolution method, including: A multi-state output probability model for new energy sources was established to characterize the uncertainty of new energy sources. The uncertainty of new energy sources was further considered in the reliability assessment stage of power generation systems under carbon emission constraints. The multi-state output probability model for new energy sources is expressed as follows: (7) in For new energy m The output value corresponding to each state. Then, represents the probability of each state occurring, and satisfies... ; Then, the probability distribution of the total available capacity of the system is obtained by using the joint probability convolution method between the probability model of available capacity of thermal power units and the probability model of multi-state output of new energy units. : (8).

[0043] Example 3 The present invention provides an electronic device comprising: a processor and a memory coupled to the processor, the memory storing a computer program, wherein the computer program, when executed by the processor, implements the steps of the aforementioned method for reliability assessment of a power generation system considering carbon emission constraints.

[0044] The electronic device may also include one or more of the following: multimedia components, input / output (I / O) interfaces, and communication components.

[0045] The processor controls the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory stores various types of data to support the operation of the electronic device. This data may include, for example, instructions for any application or method operating on the electronic device, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia components may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio components are used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0046] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing a storage medium sharing method.

[0047] Example 4 The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for reliability assessment of a power generation system considering carbon emission constraints.

[0048] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0050] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0051] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0053] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A reliability assessment method for a power generation system considering carbon emission constraints, characterized in that, Includes the following steps: Step 1: Obtain the parameters of the target power generation system, including the operating parameters of thermal power units, load curve, installed capacity of new energy sources and predicted output curve, and set the upper limit of the total carbon emissions of the target power generation system; Step 2: Based on the obtained parameters and the upper limit of total carbon emissions, establish a unit combination optimization operation model that considers carbon emission constraints. By solving the unit combination optimization operation model, obtain the start-up and shutdown status and output results of thermal power units in each time period. Step 3: Based on the start-up and shutdown status and output results of thermal power units in each time period, and combined with their forced shutdown rate, construct a probability model of the available capacity of thermal power units, and establish a probability model of multi-state output of new energy. Then, obtain the probability distribution of the total available capacity of the system through the joint probability convolution method. Step 4: Compare the probability distribution of the total available capacity of the system with the load, calculate the probability of load failure (LOLP) of the system reliability index, and quantitatively assess the reliability of the power generation system under the condition of carbon emission constraints.

2. The reliability assessment method for a power generation system considering carbon emission constraints according to claim 1, characterized in that, In step two, based on the acquired parameters, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of thermal power units in each time period are obtained, including: A piecewise linearization method is adopted to introduce piecewise variables of unit output, transforming the nonlinear carbon emission characteristics of thermal power units into linear constraints. Furthermore, a total system carbon emission constraint is applied, allowing the carbon emission constraint to participate in the unit output optimization and start-up / shutdown decision-making process. The specific process is as follows: Carbon emissions from thermal power units typically exhibit a non-linear relationship with unit output, described using a quadratic function. (1) in E i ( P i ) for generator unit i carbon emissions, P i For the power output of the unit, a i , b i , c i Carbon emission coefficient; By employing a piecewise linearization method to process the carbon emission function, the unit output is divided into multiple linear intervals, and the unit output is then expressed as: (2) in This is the minimum output of the unit. u i,t This indicates the unit's start-up and shutdown status. For the first k Segment incremental output, K The number of segments; Accordingly, the unit's carbon emissions are expressed as follows: (3) in It is a generator unit i During the period t carbon emissions, This represents the carbon emissions corresponding to the unit's minimum output. For the first k Carbon emission increment coefficient for the segment; Furthermore, establish systemic carbon emission limits: (4) in T represents the upper limit of the total carbon emissions of the system, and T represents the total number of time periods included in the unit combination scheduling cycle.

3. The reliability assessment method for a power generation system considering carbon emission constraints according to claim 2, characterized in that, In step three, based on the start-up and shutdown status and output results of thermal power units in each time period, and combined with their forced outage rate, a probabilistic model of the available capacity of thermal power units is constructed, including: The original system has n The thermal power units are 100 units, and the installed capacity of each thermal power unit is 100 units. , For the corresponding forced outage rate, the actual available capacity of the i-th thermal power unit is: The actual usable capacity of the nth thermal power unit is then... The total available capacity of thermal power units is obtained by convolving the actual available capacity of each thermal power unit. , To determine the on / off state of thermal power units using UC after considering carbon emission constraints, 1 represents on-time and 0 represents off-time. The specific probabilistic model for the available capacity of thermal power units is as follows: (5) (6) in" "Refers to convolution operation." 4. The reliability assessment method for a power generation system considering carbon emission constraints according to claim 3, characterized in that, In step three, a multi-state output probability model for new energy sources is established, and then the probability distribution of the total available capacity of the system is obtained through a joint probability convolution method, including: A multi-state output probability model for new energy sources was established to characterize the uncertainty of new energy sources. The uncertainty of new energy sources was further considered in the reliability assessment stage of power generation systems under carbon emission constraints. The multi-state output probability model for new energy sources is expressed as follows: (7) in For new energy m The output value corresponding to each state. Then, represents the probability of each state occurring, and satisfies... ; Then, the probability distribution of the total available capacity of the system is obtained by using the joint probability convolution method between the probability model of available capacity of thermal power units and the probability model of multi-state output of new energy units. : (8)。 5. A reliability assessment system for a power generation system considering carbon emission constraints, characterized in that, include: Parameter acquisition unit: Acquires parameters of the target power generation system, including thermal power unit operating parameters, load curve, new energy installed capacity and predicted output curve, and sets the upper limit of total carbon emissions of the target power generation system; The first model establishment and solution unit: Based on the obtained parameters and the upper limit of total carbon emissions, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of thermal power units in each time period are obtained. The second model establishment and solution unit: Based on the start-up and shutdown status and output results of thermal power units in each time period, and combined with their forced shutdown rate, a probability model of the available capacity of thermal power units is constructed, and a probability model of the output of new energy in multiple states is established. Then, the probability distribution of the total available capacity of the system is obtained by the joint probability convolution method. Comparison Unit: The probability distribution of the total available capacity of the system is compared with the load, the probability of load failure (LOLP) is calculated as the system reliability index, and the reliability of the power generation system under the condition of carbon emission constraints is quantitatively evaluated.

6. The reliability assessment system for a power generation system considering carbon emission constraints according to claim 5, characterized in that, In the first model establishment and solution unit, based on the acquired parameters, a unit combination optimization operation model considering carbon emission constraints is established. By solving the unit combination optimization operation model, the start-up and shutdown status and output results of thermal power units in each time period are obtained, including: A piecewise linearization method is adopted to introduce piecewise variables of unit output, transforming the nonlinear carbon emission characteristics of thermal power units into linear constraints. Furthermore, a total system carbon emission constraint is applied, allowing the carbon emission constraint to participate in the unit output optimization and start-up / shutdown decision-making process. The specific process is as follows: Carbon emissions from thermal power units typically exhibit a non-linear relationship with unit output, described using a quadratic function. (1) in E i ( P i ) for generator unit i carbon emissions, P i For the power output of the unit, a i , b i , c i Carbon emission coefficient; By employing a piecewise linearization method to process the carbon emission function, the unit output is divided into multiple linear intervals, and the unit output is then expressed as: (2) in This is the minimum output of the unit. u i,t This indicates the unit's start-up and shutdown status. For the first k Segment incremental output, K The number of segments; Accordingly, the unit's carbon emissions are expressed as follows: (3) in It is a generator unit i During the period t carbon emissions, This represents the carbon emissions corresponding to the unit's minimum output. For the first k Carbon emission increment coefficient for the segment; Furthermore, establish systemic carbon emission limits: (4) in T represents the upper limit of the total carbon emissions of the system, and T represents the total number of time periods included in the unit combination scheduling cycle.

7. The reliability assessment system for a power generation system considering carbon emission constraints according to claim 6, characterized in that, In the second model establishment and solution unit, based on the start-up and shutdown status and output results of thermal power units in each time period, and combined with their forced outage rate, a probabilistic model of the available capacity of thermal power units is constructed, including: The original system has n The thermal power units are 100 units, and the installed capacity of each thermal power unit is 100 units. , For the corresponding forced outage rate, the actual available capacity of the i-th thermal power unit is: The actual usable capacity of the nth thermal power unit is then... The total available capacity of thermal power units is obtained by convolving the actual available capacity of each thermal power unit. , To determine the on / off state of thermal power units using UC after considering carbon emission constraints, 1 represents on-time and 0 represents off-time. The specific probabilistic model for the available capacity of thermal power units is as follows: (5) (6) in" "Refers to convolution operation." 8. The reliability assessment method for a power generation system considering carbon emission constraints according to claim 7, characterized in that, In the second model establishment and solution unit, a multi-state output probability model for new energy is established, and then the probability distribution of the total available capacity of the system is obtained through the joint probability convolution method, including: A multi-state output probability model for new energy sources was established to characterize the uncertainty of new energy sources. The uncertainty of new energy sources was further considered in the reliability assessment stage of power generation systems under carbon emission constraints. The multi-state output probability model for new energy sources is expressed as follows: (7) in For new energy m The output value corresponding to each state. Then, represents the probability of each state occurring, and satisfies... ; Then, the probability distribution of the total available capacity of the system is obtained by using the joint probability convolution method between the probability model of available capacity of thermal power units and the probability model of multi-state output of new energy units. : (8)。 9. An electronic device, characterized in that, include: A processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of a reliability assessment method for a power generation system considering carbon emission constraints as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a reliability assessment method for a power generation system considering carbon emission constraints, as described in any one of claims 1-4.

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