A method, system, device and storage medium for evaluating power generation capacity

Abstract

The present disclosure relates to the technical field of energy, and particularly relates to a power generation capacity evaluation method, system, device and storage medium, wherein the method comprises: obtaining first time-series operation data and second time-series operation data; based on a preset reliability index, constructing a first reliability optimization model and a second reliability optimization model through the first time-series operation data and the second time-series operation data respectively; solving the first reliability optimization model and the second reliability optimization model respectively to obtain a first target reliability and a second target reliability; calculating an equivalent load capacity according to the first target reliability; calculating an equivalent reliable capacity according to the second target reliability; and evaluating the power generation capacity according to the equivalent load capacity or the equivalent reliable capacity. The accuracy of evaluating the power generation capacity is improved.

Description

Technical Field

[0001] This disclosure relates to the field of energy technology, and in particular to a method, system, device and storage medium for assessing power generation capacity. Background Technology

[0002] In recent years, the construction of multi-energy complementary systems has made solid progress, effectively addressing the challenges posed by the intermittency and randomness of new energy output through the deep integration of traditional and new energy sources. To better quantify the supporting role of multi-energy complementary systems in ensuring energy supply security, it is necessary to scientifically assess the reliable power generation capacity of these systems.

[0003] However, the traditional method for assessing reliable power generation capacity based on Monte Carlo time-series production simulation is highly susceptible to the influence of initial values. Different initial values ​​result in different number of iterations, significantly different calculation times, and the final results may not be the same. Furthermore, the traditional calculation method yields a single numerical value, leading to a large deviation between the calculated reliable power generation capacity and the actual value. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a method, system, equipment, and storage medium for assessing power generation capacity.

[0005] This disclosure provides a method for assessing power generation capacity, including: Acquire the first time series runtime data and the second time series runtime data; Based on preset reliability indicators, a first reliability optimization model and a second reliability optimization model are constructed using the first time-series running data and the second time-series running data, respectively. Solve the first reliability optimization model and the second reliability optimization model respectively to obtain the first target reliability and the second target reliability; Calculate the equivalent load-carrying capacity based on the reliability of the first target; Calculate the equivalent reliable capacity based on the second target reliability; The power generation capacity is assessed based on the equivalent load capacity or the equivalent reliable capacity.

[0006] Furthermore, after acquiring the first time-series runtime data, the process includes: The load sequence in the first time-series running data is normalized to obtain the original peak load and the normalized load sequence.

[0007] Furthermore, the step of calculating the equivalent load-carrying capacity based on the first target reliability includes: Based on the first time-series operational data and the first target reliability, construct a first peak load optimization model and a second peak load optimization model; Solve the first peak load optimization model and the second peak load optimization model respectively to obtain the first peak load and the second peak load; The difference between the first peak load, the second peak load and the original peak load is calculated respectively to obtain the equivalent load-carrying capacity.

[0008] Furthermore, the step of calculating the equivalent reliable capacity based on the second target reliability includes: Based on the second time-series operational data and the second target reliability, a first reliable capacity optimization model and a second reliable capacity optimization model are constructed. Solve the first reliable capacity optimization model and the second reliable capacity optimization model respectively to obtain the first reliable capacity and the second reliable capacity; Based on the first reliable capacity and the second reliable capacity, an equivalent reliable capacity is obtained.

[0009] Furthermore, the assessment of power generation capacity based on the equivalent load-carrying capacity or the equivalent reliable capacity includes: The equivalent load-carrying capacity is calculated by summing it with the capacity of a conventional unit to obtain the power generation capacity; or, The power generation capacity is obtained by calculating the sum of the equivalent reliable capacity and the conventional unit capacity.

[0010] Furthermore, the reliability index settings include: Calculate the load loss at each moment; Reliability indicators are obtained by statistically analyzing load loss.

[0011] This disclosure also provides a power generation capacity assessment system, including: The acquisition module is used to acquire the first time series running data and the second time series running data; The construction module is used to construct a first reliability optimization model and a second reliability optimization model based on preset reliability indicators, using the first time-series running data and the second time-series running data respectively. The first calculation module is used to solve the first reliability optimization model and the second reliability optimization model respectively to obtain the first target reliability and the second target reliability. The second calculation module is used to calculate the equivalent load capacity based on the reliability of the first target. The third calculation module is used to calculate the equivalent reliable capacity based on the second target reliability. An evaluation module is used to evaluate power generation capacity based on the equivalent load capacity or the equivalent reliable capacity.

[0012] Furthermore, the evaluation module includes: The first evaluation submodule is used to calculate the sum of the equivalent load capacity and the conventional unit capacity to obtain the power generation capacity; or, The second evaluation submodule is used to calculate the sum of the equivalent reliable capacity and the conventional unit capacity to obtain the power generation capacity.

[0013] This disclosure also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the power generation capacity assessment method.

[0014] This disclosure also provides a computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of the power generation capacity assessment method.

[0015] The technical solution provided in this disclosure has the following advantages compared with the prior art: By acquiring first and second time-series operational data, and based on preset reliability indicators, a first reliability optimization model and a second reliability optimization model are constructed using the first and second time-series operational data, respectively. The first and second reliability optimization models are solved to obtain a first target reliability and a second target reliability. The equivalent load-carrying capacity is calculated based on the first time-series operational data and the first target reliability. The equivalent reliable capacity is calculated based on the second time-series operational data and the second target reliability. The power generation capacity is evaluated based on the equivalent load-carrying capacity or the equivalent reliable capacity. Solving the optimization model to calculate the reliable power generation capacity avoids the initial value dependence caused by the iterative process of traditional Monte Carlo time-series simulation, improving the stability of the results. From both the load side and the power generation side, upper and lower limits of the system's reliable power generation capacity under certain reliability conditions are provided, providing a quantitative basis for complementary strategy optimization. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the power generation capacity assessment method provided in the embodiments of this disclosure; Figure 2A schematic diagram of a power generation capacity assessment system provided in an embodiment of this disclosure; Figure 3 A schematic diagram of the evaluation module provided in an embodiment of this disclosure. Detailed Implementation

[0019] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0020] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0021] Figure 1 A schematic diagram of the power generation capacity assessment method provided in the embodiments of this disclosure; as shown Figure 1 As shown, a method for assessing power generation capacity includes: Step S1: Obtain the first time series running data and the second time series running data; In this embodiment, the first time-series operational data is used to characterize the system's operational status before the addition of new energy sources, and the second time-series operational data is used to characterize the system's operational status after the addition of new energy sources. The time-series operational data includes, but is not limited to: system load time-series data, conventional unit parameters and output data, new energy unit parameters and output data (such as wind power and photovoltaic), and optional energy storage or demand response data. This data can be obtained through dispatch automation systems, energy management systems (EMS), and historical operational databases; or, in planning and forecasting scenarios, generated through time-series simulation models built based on historical data and meteorological information. By acquiring the two types of time-series operational data before and after the addition of new energy sources, a comparable baseline operational status can be formed, providing a unified data foundation for subsequent evaluation of the impact of new energy sources on the system's power supply capacity under the same reliability conditions. This ensures that subsequent calculations and evaluations are based on real or repeatable time-series operational data, avoiding evaluation biases caused by analysis based solely on static capacity or single-point data, and improving the credibility and engineering feasibility of the evaluation results.

[0022] Step S2: Based on preset reliability indicators, construct a first reliability optimization model and a second reliability optimization model using first time-series running data and second time-series running data, respectively. In this embodiment, the probability of load loss (LOLP) is selected as the preset reliability index. A first reliability optimization model is constructed based on the first time-series operating data to describe the supply and demand balance of the system at each time before the addition of renewable energy. A second reliability optimization model is constructed based on the second time-series operating data to describe the supply and demand balance of the system at each time after the addition of renewable energy. The optimization model sets the load loss amount variable and 0-1 slack variables to characterize whether a load loss event occurs at each time. Operating constraints such as unit output limit, minimum output, and availability can be introduced as needed. By constructing an optimization model with reliability index as constraint or objective, the power supply capacity of the system under different operating states can be uniformly described at the mathematical level, avoiding the ideal premise that the system can fully meet the load. This transforms system reliability assessment from empirical judgment into a calculable and comparable optimization problem, laying the model foundation for the subsequent quantitative calculation of equivalent load capacity and equivalent reliable capacity.

[0023] Step S3: Solve the first reliability optimization model and the second reliability optimization model respectively to obtain the first target reliability and the second target reliability; In this embodiment, the first and second reliability optimization models are solved using strategies such as branch and bound, cutting plane, or heuristic algorithms to obtain the lowest load loss probability achievable by the system before and after the addition of new energy sources. These are used as the first target reliability and the second target reliability, respectively. The first target reliability provides a reliability constraint for evaluating power generation capacity from the perspective of equivalent load-carrying capacity, while the second target reliability provides a reliability constraint for evaluating power generation capacity from the perspective of equivalent reliable capacity.

[0024] Step S4: Calculate the equivalent load-carrying capacity based on the first target reliability. In this embodiment, under the condition of maintaining the first target reliability, and combined with the first time-series operational data, the peak load that the system can guarantee after the addition of new energy is calculated. Since the operating state of the system under a given reliability is not unique, the aforementioned peak load may vary within a range. By calculating the additional peak load capacity that the system can bear after the addition of new energy relative to before the addition, the equivalent load-carrying capacity of the new energy can be obtained. Peak load is a key indicator for measuring the system's power supply capacity. Determining the peak load that the system can bear under a given reliability condition can intuitively reflect the true power supply capacity boundary of the system. The equivalent load-carrying capacity characterizes the value of new energy from the load-side perspective, transforming the complex reliability problem into a comparison problem of peak load levels. This allows the improvement effect of new energy on the system's power supply capacity to be clearly quantified, reflecting that the addition of new energy enables the system to serve more loads without reducing reliability.

[0025] Step S5: Calculate the equivalent reliable capacity based on the second target reliability. In this embodiment, under the condition of maintaining the second target reliability, a system model after removing new energy sources is constructed by combining the second time-series operating data. The ideal conventional unit capacity that needs to be added to the system is then calculated, enabling the system to achieve the same reliability level as the system containing new energy sources, thereby obtaining the equivalent reliable capacity of the new energy sources. Since the operating state of the system under a given reliability is not unique, the aforementioned equivalent reliable capacity may vary within a range. Equivalent reliable capacity, measuring the contribution of new energy sources to system reliability from the perspective of the generation side, is a core indicator in capacity planning and capacity market pricing. It allows the reliability contribution of new energy sources to be expressed in a form that can replace the capacity of conventional units, facilitating a unified comparison with traditional power sources.

[0026] Step S6: Assess the power generation capacity based on the equivalent load capacity or equivalent reliable capacity.

[0027] In this embodiment, using equivalent indicators for power generation capacity assessment avoids relying solely on rough estimates based on installed capacity, thereby improving the reliability of the assessment results. This achieves a closed-loop application from reliability assessment to power generation capacity assessment.

[0028] In some possible implementations, after obtaining the first timing data, the following steps are included: The load sequence in the first time series operation data is normalized to obtain the original peak load and the normalized load sequence.

[0029] In this embodiment, after acquiring the first time-series operational data, the system load sequence is normalized. Specifically, using the maximum load value (i.e., the original peak load) within the corresponding time range of the first time-series operational data as a benchmark, the load value at each moment is proportionalized, so that the normalized load sequence value is limited to a preset range (e.g., 0 to 1). The normalization process can employ maximum value normalization, interval scaling, or other equivalent methods. The absolute values ​​of load vary significantly across different time periods and system scales. Directly using the original load values ​​for reliability calculations and optimization modeling can easily lead to inconsistent model parameter scales, affecting the stability and comparability of the optimization solution. Load normalization eliminates differences in load dimensions and scale, making the load change trend the primary analysis object. This helps improve the numerical stability of the load loss probability model and the peak load optimization model, while facilitating comparative analysis across different systems and time scales, thereby enhancing the versatility and applicability of the method of this invention.

[0030] In some possible implementations, the equivalent load-carrying capacity is calculated based on the first objective reliability, including: Based on the first time-series operational data and the first target reliability, construct the first peak load optimization model and the second peak load optimization model; Solve the first peak load optimization model and the second peak load optimization model respectively to obtain the first peak load and the second peak load; The difference between the first peak load, the second peak load and the original peak load is calculated respectively to obtain the equivalent load-carrying capacity.

[0031] In this embodiment, a peak load optimization model with peak load as the decision variable is constructed based on the first time-series operating data, the original peak load, the normalized load sequence, and the first target reliability. This model solves for the peak load that the system can bear under the constraint of a preset reliability index. Since the operating state of the system under a given reliability is not unique, the aforementioned peak load may vary within an interval, where the upper bound is the first peak load and the lower bound is the second peak load. Subsequently, the differences between the first peak load and the second peak load and the original peak load are calculated to obtain the equivalent load-carrying capacity of the new energy source on the load side.

[0032] In some possible implementations, the equivalent reliable capacity is calculated based on the second objective reliability, including: Based on the second time-series operational data and the second target reliability, construct the first reliable capacity optimization model and the second reliable capacity optimization model; Solve the first reliable capacity optimization model and the second reliable capacity optimization model respectively to obtain the first reliable capacity and the second reliable capacity; Based on the first reliable capacity and the second reliable capacity, an equivalent reliable capacity is obtained.

[0033] In this embodiment, the renewable energy output data is removed from the second time-series operational data to form the time-series operational data after removing renewable energy. Based on this, a system model satisfying the second target reliability condition is constructed, and the ideal conventional unit capacity required to be added to the system under the same reliability conditions is calculated. This capacity value is taken as the equivalent reliable capacity of the renewable energy. Since the system's operating state under a given reliability is not unique, the aforementioned ideal conventional unit capacity may vary within a range, where the lower bound is the first reliable capacity and the upper bound is the second reliable capacity. The core idea of ​​equivalent reliable capacity is to assess how much conventional power capacity renewable energy can replace at the reliability level. By removing renewable energy and compensating for capacity in reverse, an equivalent relationship between renewable energy and conventional units can be mathematically established, allowing the reliability contribution of renewable energy to be quantified in the form of capacity substitution. This facilitates a unified comparison with traditional power sources and is beneficial for capacity planning, capacity market design, and reserve capacity configuration.

[0034] In some possible ways, generating capacity is assessed based on equivalent load-carrying capacity or equivalent reliable capacity, including: The equivalent load-carrying capacity is calculated by summing it with the capacity of conventional units to obtain the power generation capacity. or, The equivalent reliable capacity is calculated as the sum of the capacity of the conventional units to obtain the power generation capacity.

[0035] In this embodiment, equivalent load capacity or equivalent reliable capacity is selected as the input indicator based on the application scenario. When using equivalent load capacity, it is superimposed on the total capacity of the normally operating conventional units in the system to obtain the system's power generation capacity from the load side perspective. When using equivalent reliable capacity, it is superimposed on the total capacity of the conventional units to obtain the system's power generation capacity from the generation side perspective. Equivalent load capacity and equivalent reliable capacity reflect the reliability contribution of new energy sources from different technical perspectives, respectively. By combining the calculation with the capacity of conventional units, the reliable power generation capacity of the entire multi-energy complementary system is obtained, improving the accuracy and reliability of the power generation capacity assessment results and making the assessment results more consistent with the actual system situation.

[0036] In some possible implementation methods, reliability metrics are set, including: Calculate the load loss at each moment; Reliability indicators are obtained by statistically analyzing load loss.

[0037] In this embodiment, the system's load loss is calculated at each moment, which is the difference between the system load and the available power generation output. When the power generation output is less than the load, the load loss is recorded; when the power generation output is greater than or equal to the load, the load loss is zero. By statistically analyzing load loss events at multiple moments or under multiple scenarios, the load loss probability is calculated as a reliability indicator. This gives the reliability assessment clear statistical significance, objectively reflects the power supply risk of the system under different operating conditions, and provides a unified reliability benchmark for calculating equivalent load capacity and equivalent reliable capacity.

[0038] Among some possible implementation methods, power generation capacity assessment based on equivalent load capacity includes: The equivalent load-carrying capacity of the new energy source is obtained by calculating the difference in load levels that the power generation system can supply before and after the addition of new energy sources under certain reliability conditions. This leads to the reliable power generation capacity of the multi-energy complementary system from the load-side perspective. In actual calculations, the load level that the system can supply is measured by the peak value of the load curve, and the reliability calculation index... Then, the load shedding probability is used. The power generation capacity assessment method based on equivalent load capacity follows these steps: Before the addition of new energy sources, the system is in constant motion. The original load Normalize the load, and denote the normalized load as... .

[0039] Solve for the load failure probability of the system before the addition of new energy sources. The solution can be obtained through the following optimization model:

[0040] In the formula, This indicates the time of conventional generating units (such as hydropower, thermal power, etc.) contribution; Indicates time The normalized load value; This is the original peak load of the system before the addition of new energy sources; It is a moment The amount of load loss; It is a 0-1 slack variable. Indicates time The actual power generation of the system equals the system load. Indicates time The actual power generation of the system is less than the system load; The number of time points to consider during the calculation.

[0041] Solve for the system's failure probability after adding new energy sources. The first peak load that can be supplied at that time :

[0042] In the formula, This indicates the time of conventional generating units (such as hydropower, thermal power, etc.) contribution; This indicates that the new energy unit is at any time contribution; It is a moment Loss of load; It is a 0-1 slack variable. Indicates time The actual power generation of the system equals the system load. Indicates time The actual power generation of the system is less than the system load; The number of time points to consider during the calculation.

[0043] The probability of load failure is calculated as follows: The upper limit of the load capacity of new energy vehicles:

[0044] Solve for the system's failure probability after adding new energy sources. The second peak load that can be supplied at that time :

[0045] In the formula, This indicates the time of conventional generating units (such as hydropower, thermal power, etc.) contribution; This indicates that the new energy unit is at any time contribution; It is a moment Loss of load; It is a 0-1 slack variable. Indicates time The actual power generation of the system equals the system load. Indicates time The actual power generation of the system is less than the system load; The number of time points to be considered in the calculation; .

[0046] The probability of load failure is calculated as follows: Lower limit of the load capacity of new energy sources:

[0047] The reliable power generation capacity of new energy sources can be obtained by following the above steps. Therefore, the reliable power generation capacity of the multi-energy complementary system at the load side angle is: , In the formula It is the capacity of conventional generating units that can operate normally in a multi-energy complementary system.

[0048] Among the possible implementation methods, power generation capacity assessment based on equivalent reliable capacity includes: By solving for the ideal conventional generating unit capacity that new energy can replace under certain reliability conditions, the equivalent reliable capacity of new energy is obtained, and thus the reliable power generation capacity of the multi-energy complementary system from the power generation side is obtained. In actual calculations, the ideal conventional generating unit capacity that new energy can replace is used... This indicates that reliability calculation indicators The probability of load shedding is still used. The power generation capacity assessment method based on equivalent reliable capacity follows these steps: Solve for the load failure probability of a multi-energy complementary system containing new energy sources. The solution can be obtained through the following optimization model:

[0049] In the formula, This indicates the time of conventional generating units (such as hydropower, thermal power, etc.) contribution; This indicates that the new energy unit is at any time contribution; Indicates time The system load value; It is a moment Loss of load; It is a 0-1 slack variable. Indicates time The actual power generation of the system equals the system load. Indicates time The actual power generation of the system is less than the system load; The number of time points to consider during the calculation.

[0050] Solve for the system's failure probability after removing new energy sources. The lower bound of the ideal conventional unit capacity that needs to be added, i.e., the first reliable capacity. :

[0051] In the formula: This indicates the time of conventional generating units (such as hydropower, thermal power, etc.) contribution; Indicates time The system load value; It is a moment Loss of load; It is a 0-1 slack variable. Indicates time The actual power generation of the system equals the system load. Indicates time The actual power generation of the system is less than the system load; The number of time points to consider during the calculation.

[0052] Solve for the system's failure probability after removing new energy sources. The upper bound of the ideal conventional unit capacity that needs to be added, i.e., the second reliable capacity. :

[0053] In the formula: This indicates the time of conventional generating units (such as hydropower, thermal power, etc.) contribution; Indicates time The system load value; It is a moment Loss of load; It is a 0-1 slack variable. Indicates time The actual power generation of the system equals the system load. Indicates time The actual power generation of the system is less than the system load; The number of time points to be considered in the calculation; .

[0054] The reliable power generation capacity of new energy sources can be obtained through the above steps. Therefore, the reliable power generation capacity of the multi-energy complementary system at the power generation side angle is In the formula It is the capacity of conventional generating units that can operate normally in a multi-energy complementary system.

[0055] Figure 2 This is a schematic diagram of a power generation capacity assessment system provided in an embodiment of this disclosure; as shown below. Figure 2 As shown, this disclosure also provides a power generation capacity assessment system, including: The acquisition module 21 is used to acquire the first time series running data and the second time series running data; The construction module 22 is used to construct a first reliability optimization model and a second reliability optimization model based on preset reliability indicators and using first time-series running data and second time-series running data, respectively. The first calculation module 23 is used to solve the first reliability optimization model and the second reliability optimization model respectively to obtain the first target reliability and the second target reliability. The second calculation module 24 is used to calculate the equivalent load capacity based on the first target reliability. The third calculation module 25 is used to calculate the equivalent reliable capacity based on the second target reliability. Evaluation module 26 is used to evaluate power generation capacity based on equivalent load capacity or equivalent reliable capacity.

[0056] Figure 3 A schematic diagram of the evaluation module provided in an embodiment of this disclosure; as shown Figure 3 As shown, in some possible implementations, the evaluation module includes: The first evaluation submodule 261 is used to calculate the sum of the equivalent load capacity and the conventional unit capacity to obtain the power generation capacity. or, The second evaluation submodule 262 is used to calculate the sum of the equivalent reliable capacity and the conventional unit capacity to obtain the power generation capacity.

[0057] This disclosure also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the power generation capacity assessment method.

[0058] This disclosure also provides a computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of the power generation capacity assessment method.

[0059] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0060] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing power generation capacity, characterized in that, include: Acquire the first time series runtime data and the second time series runtime data; Based on preset reliability indicators, a first reliability optimization model and a second reliability optimization model are constructed using the first time-series running data and the second time-series running data, respectively. Solve the first reliability optimization model and the second reliability optimization model respectively to obtain the first target reliability and the second target reliability; Calculate the equivalent load-carrying capacity based on the reliability of the first target; Calculate the equivalent reliable capacity based on the second target reliability; The power generation capacity is assessed based on the equivalent load capacity or the equivalent reliable capacity.

2. The power generation capacity assessment method according to claim 1, characterized in that, After obtaining the first time-series runtime data, the process includes: The load sequence in the first time-series running data is normalized to obtain the original peak load and the normalized load sequence.

3. The power generation capacity assessment method according to claim 2, characterized in that, The step of calculating the equivalent load-carrying capacity based on the first target reliability includes: Based on the first time-series operational data and the first target reliability, construct a first peak load optimization model and a second peak load optimization model; Solve the first peak load optimization model and the second peak load optimization model respectively to obtain the first peak load and the second peak load; The difference between the first peak load, the second peak load and the original peak load is calculated respectively to obtain the equivalent load-carrying capacity.

4. The power generation capacity assessment method according to claim 1, characterized in that, The calculation of equivalent reliable capacity based on the second target reliability includes: Based on the second time-series operational data and the second target reliability, a first reliable capacity optimization model and a second reliable capacity optimization model are constructed. Solve the first reliable capacity optimization model and the second reliable capacity optimization model respectively to obtain the first reliable capacity and the second reliable capacity; Based on the first reliable capacity and the second reliable capacity, an equivalent reliable capacity is obtained.

5. The power generation capacity assessment method according to claim 1, characterized in that, The assessment of power generation capacity based on the equivalent load capacity or the equivalent reliable capacity includes: The equivalent load-carrying capacity is calculated by summing it with the capacity of a conventional unit to obtain the power generation capacity; or, The power generation capacity is obtained by calculating the sum of the equivalent reliable capacity and the conventional unit capacity.

6. The power generation capacity assessment method according to any one of claims 1 to 5, characterized in that, The reliability index settings include: Calculate the load loss at each moment; Reliability indicators are obtained by statistically analyzing load loss.

7. A power generation capacity assessment system, characterized in that, include: The acquisition module is used to acquire the first time series running data and the second time series running data; The construction module is used to construct a first reliability optimization model and a second reliability optimization model based on preset reliability indicators, using the first time-series running data and the second time-series running data respectively. The first calculation module is used to solve the first reliability optimization model and the second reliability optimization model respectively to obtain the first target reliability and the second target reliability. The second calculation module is used to calculate the equivalent load capacity based on the reliability of the first target. The third calculation module is used to calculate the equivalent reliable capacity based on the second target reliability. An evaluation module is used to evaluate power generation capacity based on the equivalent load capacity or the equivalent reliable capacity.

8. The power generation capacity assessment system according to claim 7, characterized in that, The evaluation module includes: The first evaluation submodule is used to calculate the sum of the equivalent load capacity and the conventional unit capacity to obtain the power generation capacity; or, The second evaluation submodule is used to calculate the sum of the equivalent reliable capacity and the conventional unit capacity to obtain the power generation capacity.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the power generation capacity assessment method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the power generation capacity assessment method as described in any one of claims 1 to 6.

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