A fast analysis method for energy storage capacity credit assessment

Abstract

The application belongs to the technical field of electrical engineering, and discloses a fast analysis method for energy storage capacity credit evaluation. The method integrates a single-layer optimization framework to eliminate iterative convergence, a fast reliability evaluation based on unit commitment and a scenario reduction algorithm to retain key statistical characteristics, and can efficiently evaluate energy storage capacity credit by considering detailed unit commitment models and multi-year renewable energy volatility. Example analysis of a typical provincial power system in China shows that, compared with traditional methods, the application can reduce the calculation time by 86% while maintaining 99.8% accuracy. The results show that the energy storage capacity credit is mainly related to the duration, the ratio of energy storage to renewable energy and the annual renewable energy change. The application provides an efficient tool for power system planners to evaluate the adequacy of energy storage capacity in power systems with high renewable energy penetration.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field related to electrical engineering, and more particularly, to a rapid analysis method for energy storage capacity credit evaluation. BACKGROUND

[0002] Energy storage is widely considered as a key solution to alleviate the volatility of renewable energy and improve system reliability, but its capacity credit is affected by various factors, including its own power, storage capacity, efficiency, and internal characteristics, as well as the penetration of renewable energy, load fluctuation, and flexibility of thermal power units. The existing energy storage capacity credit evaluation method has many limitations: excluding unit commitment constraints, assuming that energy storage scheduling is not limited, ignoring the operation restrictions caused by unit commitment; only emphasizing daily cycle operation, ignoring longer-term operation modes such as weekly or seasonal cycles; ignoring long-term volatility of renewable energy, such as seasonal fluctuations in wind speed or hydropower availability. These limitations make it difficult for existing methods to accurately and efficiently evaluate the capacity credit of energy storage, and cannot meet the needs of high renewable energy penetration power system planning and operation. SUMMARY

[0003] In view of the above defects or improvement needs of the prior art, the present application provides a rapid analysis method for energy storage capacity credit evaluation, aiming to solve the problems of complex calculation of energy storage capacity credit evaluation under high renewable energy penetration, ignoring unit commitment constraints and long-term renewable energy volatility, thereby providing an efficient and accurate energy storage capacity credit evaluation tool for renewable energy dominated power systems, supporting system planning and investment decision-making.

[0004] To achieve the above-mentioned purpose, according to one aspect of the present application, a rapid analysis method for energy storage capacity credit evaluation is provided, and the specific content is as follows:

[0005] Consider the scenario generation of multi-year weather conditions, mainly including generating multi-year operation scenarios based on weather and system data, extracting features related to reliability from each scenario to evaluate energy storage performance, and applying a hierarchical clustering method.

[0006] The comprehensive scenario is generated by sampling the hourly wind / solar power generation, thermal power unit availability, and actual load curve of each year. For renewable energy output, the capacity factor (CF) model is used to calculate the hourly wind / solar power for modeling:

[0007]

[0008]

[0009] wherein, are the available wind and photovoltaic power at time t, respectively; and t are the available wind and photovoltaic power at time t, respectively; and The corresponding capacity factor is derived from historical wind speed and solar radiation data; This refers to the rated power of the wind and solar turbine units.

[0010] For thermal power unit availability, Sequential Monte Carlo Simulation (SMCS) is used to generate a sequence of "operation-failure" states for the unit, with the state durations following an exponential distribution:

[0011]

[0012]

[0013] in, These represent the duration of operation and the duration of the fault, respectively. It is a uniform random variable; These are the failure rate and the repair rate, respectively.

[0014] Randomly sample renewable energy output, unit availability, and load demand to generate an annual operating scenario of 8760 hours.

[0015] Selection of features for energy storage capacity credit assessment. Maximum unsupplied power (MPNS) and maximum unsupplied power (MENS) are selected. MPNS represents the most severe instantaneous power shortage in a given scenario. Its calculation method is as follows:

[0016]

[0017]

[0018] in, Represents the total available power generation, including thermal power generation. and renewable energy generation renewable energy generation T represents a given time period. MENS quantifies the cumulative energy shortage under the worst-case scenario within a given time period, reflecting the long-term impact of power shortages. It is defined as:

[0019]

[0020] in This represents a predefined key time window, while It is the time step.

[0021] Scene reduction algorithm. K-means clustering is used in the MPNS-MENS feature space for each scene. This is represented in a two-dimensional feature space, defined by its corresponding MPNS and MENS values:

[0022]

[0023] Where N is the total number of generated scenes. The N scene sets are divided into K clusters using the K-means clustering algorithm. Within each cluster, a representative scene is selected according to a weighted scheme. :

[0024]

[0025] in It is a hierarchy The number of scenarios in It is the assigned weight. It is the closest centroid of the selected level. A representative scenario.

[0026] An ENS assessment model based on Fast Unit Combination (FUC) is used. The assessment of unsupplied power (ENS) is achieved by solving a power balance optimization problem, comprehensively considering the volatility of renewable energy, the dynamic characteristics of energy storage systems, and the adjustment flexibility of thermal power units. The system load reduction is calculated based on available generation capacity and energy storage dispatch, while the unsupplied power constraint can be obtained by accumulating the load reduction.

[0027]

[0028] in This represents load reduction.

[0029] The objective function of the optimization model is to minimize the total system cost, which includes the fuel cost of thermal power generation. Start-up costs and no-load cost as well as energy storage investment and operating costs There is also a load reduction penalty. Its formula is:

[0030]

[0031] The thermal power unit output model adopts the FUC model, which effectively captures ramp limits, start-up / shutdown dynamics, and minimum continuous operation / outage time. The corresponding constraints are:

[0032]

[0033]

[0034] in, It is a group j The total capacity of all units; Representing time respectively t unit jThe online capacity, startup capacity, and shutdown capacity are continuous variables limited by the total installed capacity of the units.

[0035] The power output of a thermal power unit group is limited by the following conditions:

[0036]

[0037]

[0038] in The available capacity limits due to forced shutdowns are defined. Additional constraints are not listed here.

[0039] Energy Storage Output Model. The Energy Storage System (ESS) model reflects actual charging / discharging behavior, energy balance, and system-level constraints. Charging and discharging power are limited by rated capacity.

[0040]

[0041] The net power output of an ESS is defined as follows:

[0042]

[0043] SOC is limited by maximum storage capacity:

[0044]

[0045] The dynamic changes in SOC are controlled by the following energy balance equation, which incorporates charging / discharging efficiency and self-discharge loss:

[0046]

[0047] To ensure operational feasibility during the planning period, constraints must be imposed on the initial and final SOCs:

[0048]

[0049] in, Representing time respectively t The charging and discharging power of the energy storage system; Indicates rated power capacity; This represents net power output, where a positive value indicates discharge. SOC is determined by... This indicates that it is subject to the maximum energy capacity. The limitations, and its evolution according to an energy balance equation, which includes charging efficiency Discharge efficiency and self-discharge rate In order to complete the entire planning cycle T To maintain feasibility, both the initial and terminal SOCs are fixed within the specified range. .

[0050] Power balance constraints and renewable energy output constraints. The system-wide power balance equations ensure that power generation, energy storage dispatch, and load adjustment meet supply and demand balance:

[0051]

[0052]

[0053] in It is time t Load factor at time, This is the peak load of the current scenario. yes t Total load demand at any given time.

[0054] The output of variable renewable energy sources (VRES) is limited by their installed capacity and time-varying capacity factor:

[0055]

[0056]

[0057] in and These represent the capacity factors for solar and wind power units, respectively, to ensure that the renewable energy generation curves match the actual situation.

[0058] Non-iterative ELCC evaluation model. Reliability constraints are explicitly incorporated into a single optimization model, eliminating the need for iteration. Adding an outer layer results in a two-layer optimization framework; the outer layer's optimization objective is to maximize the additional load. ELCC is defined as the maximum additional load that a system can support while ensuring that the expected power shortage (EENS) remains within the reliability target range. :

[0059]

[0060] Where M is a large weighting coefficient used to prioritize maximization .

[0061] The inner layer is based on the power balance constraint EENS assessment, which evaluates EENS by solving a power balance optimization problem that incorporates the volatility of renewable energy, energy storage dispatch, and the flexibility of thermal power units.

[0062] Replace the non-power supply constraint with:

[0063]

[0064] Total load demand in power balance constraints The calculation formula is replaced with: Other constraints remain unchanged;

[0065] Inner layer optimization ensures solutions under different operating conditions When load shedding occurs, system reliability is maintained.

[0066] The problem is reformulated as a single-layer optimization problem. Maximizing the ELCC (Elastic Compute Control) function is directly embedded into the objective function, thus reformulating the two-layer optimization framework into a single-layer optimization problem. A reward item is included. ,Will The maximization of M is integrated into the cost minimization objective, where M is a large weighting coefficient that prioritizes load increase while ensuring that reliability constraints are met.

[0067] The reformulated single-layer optimization problem is expressed as:

[0068]

[0069] This formulaic approach ensures that the optimization process minimizes system costs while maximizing system capacity. While maintaining the reliability constraints imposed by EENS limits, it facilitates higher levels of additional load. .

[0070] Scenario-based ELCC estimation with probability weights. The non-iterative ELCC model is extended to a scenario-based model, and the ELCC is calculated for each representative scenario. The final ELCC value of the entire system is obtained by probability-weighted summation:

[0071]

[0072] in Representative scenarios s The probability weights ensure that the uncertainty of changes in power generation and demand is accurately reflected.

[0073] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0074] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0075] The present invention also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the method described above.

[0076] In summary, compared with existing technologies, the rapid analysis method for energy storage capacity credit assessment provided by this invention integrates a non-iterative ELCC model, a rapid unit combination algorithm, and scenario reduction technology, achieving efficient assessment of energy storage capacity credit under high renewable energy penetration. This framework reduces computation time by 86% while maintaining 99.8% accuracy. It can capture unit combination constraints, multi-cycle energy storage cycles, and long-term renewable energy volatility, providing system planners with a practical tool to support energy storage capacity credit assessment in renewable energy-dominated power systems. Attached Figure Description

[0077] Figure 1 This is a flowchart illustrating the rapid analysis method for energy storage capacity credit assessment provided by the present invention.

[0078] Figure 2 The calculation time of the iterative method and the non-iterative method provided by this invention.

[0079] Figure 3 This invention provides the energy storage capacity credit using both iterative and non-iterative methods.

[0080] Figure 4 This invention provides capacity credits for energy storage with different configurations.

[0081] Figure 5 This invention provides capacity credit fluctuations for energy storage with different configurations under renewable energy uncertainty.

[0082] Figure 6 The CVaR energy storage capacity credit provided by this invention is lower than the expected energy storage capacity credit. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0084] This invention provides a rapid analytical model for credit assessment of energy storage capacity, the details of which are as follows:

[0085] Step 1: Consider the generation of scenarios based on multi-year meteorological conditions. This mainly includes generating multi-year operating scenarios based on meteorological and system data, extracting reliability-related features from each scenario to evaluate energy storage performance, and applying hierarchical clustering methods.

[0086] Step 1.1: The comprehensive scenario is generated by sampling hourly wind / solar power generation, thermal power unit availability, and actual load curves over many years. For renewable energy output, the capacity factor (CF) model is used to calculate hourly wind and solar power for modeling.

[0087]

[0088]

[0089] in, They are respectively t Available wind and solar power at all times; The corresponding capacity factor is derived from historical wind speed and solar radiation data; This refers to the rated power of the wind and solar turbine units.

[0090] For thermal power unit availability, Sequential Monte Carlo Simulation (SMCS) is used to generate a sequence of "operation-failure" states for the unit, with the state durations following an exponential distribution:

[0091]

[0092]

[0093] in, These represent the duration of operation and the duration of the fault, respectively. It is a uniform random variable; These are the failure rate and the repair rate, respectively.

[0094] By randomly sampling renewable energy output, thermal power unit availability, and load demand at each point in time, an 8760-hour annual operating scenario is generated.

[0095] Step 1.2: Selection of Energy Storage Capacity Credit Assessment Features. Select the Maximum Unsupplied Power (MPNS) and Maximum Unsupplied Power (MENS). MPNS represents the most severe instantaneous power shortage in a given scenario. Its calculation method is as follows:

[0096]

[0097]

[0098] in, It represents the total available power generation, including thermal power and renewable energy generation.

[0099] MENS quantifies the cumulative energy shortage under worst-case conditions over a given period, reflecting the long-term impact of power shortages. It is defined as:

[0100]

[0101] in This represents a predefined key time window, while It is the time step.

[0102] Step 1.3: Scene Reduction Algorithm. K-means clustering is used in the MPNS-MENS feature space for each scene. This is represented in a two-dimensional feature space, defined by its corresponding MPNS and MENS values:

[0103]

[0104] Where N is the total number of generated scenes. The N scene sets are divided into K clusters using the K-means clustering algorithm. Within each cluster, a representative scene is selected according to a weighted scheme. :

[0105]

[0106] in It is a hierarchy The number of scenarios in It is the assigned weight. It is the closest centroid of the selected level. A representative scenario.

[0107] Step 2: ENS Assessment Model Based on Fast Unit Combination (FUC). The assessment of unsupplied power (ENS) is achieved by solving a power balance optimization problem, comprehensively considering the volatility of renewable energy, the dynamic characteristics of energy storage systems, and the adjustment flexibility of thermal power units. The system load reduction is calculated based on available generation capacity and energy storage dispatch, while the unsupplied power constraint can be obtained by accumulating the load reduction.

[0108]

[0109] in This represents load reduction.

[0110] Step 2.1: The objective function of the optimization model is to minimize the total system cost, which includes the fuel cost of thermal power generation. Start-up costs and no-load cost as well as energy storage investment and operating costs There is also a load reduction penalty. Its formula is:

[0111]

[0112] Step 2.2: The thermal power unit output model adopted the FUC model, which effectively captures the ramp-up limitations, start-up / shutdown dynamics, and minimum continuous operation / outage time. The corresponding constraints are:

[0113]

[0114]

[0115] in, It is a generator set j The total capacity of all units; Representing time respectively t unit j The online capacity, startup capacity, and shutdown capacity are continuous variables limited by the total installed capacity of the units.

[0116] The power output of a thermal power unit group is limited by the following conditions:

[0117]

[0118]

[0119] in The available capacity limits due to forced shutdowns are defined. Additional constraints are not listed here.

[0120] Step 2.3: Energy Storage Output Model. The Energy Storage System (ESS) model reflects actual charging / discharging behavior, energy balance, and system-level constraints. Charging and discharging power are limited by rated capacity.

[0121]

[0122] The net power output of an ESS is defined as follows:

[0123]

[0124] SOC is limited by maximum storage capacity:

[0125]

[0126] The dynamic changes in SOC are controlled by the following energy balance equation, which incorporates charging / discharging efficiency and self-discharge loss:

[0127]

[0128] To ensure operational feasibility during the planning period, constraints must be imposed on the initial and final SOCs:

[0129]

[0130] in, Representing time respectively t The charging and discharging power of the energy storage system; Indicates rated power capacity; This represents net power output, where a positive value indicates discharge. SOC is determined by... This indicates that it is subject to the maximum energy capacity. The limitations, and its evolution according to an energy balance equation, which includes charging efficiency Discharge efficiency and self-discharge rate In order to complete the entire planning cycle T To maintain feasibility, both the initial and terminal SOCs are fixed within the specified range. .

[0131] Step 2.4: Power Balance Constraints and Renewable Energy Output Constraints. The system-wide power balance equations ensure that power generation, energy storage dispatch, and load adjustment meet supply and demand balance:

[0132]

[0133]

[0134] in It is time t Load factor at time, This is the peak load of the current scenario. yes t Total load demand at any given time.

[0135] The output of variable renewable energy sources (VRES) is limited by their installed capacity and time-varying capacity factor:

[0136]

[0137]

[0138] in and These represent the capacity factors for solar and wind power units, respectively, to ensure that the renewable energy generation curves match the actual situation.

[0139] Step 3: Non-iterative ELCC evaluation model. Reliability constraints are explicitly incorporated into a single optimization model, eliminating the need for iteration.

[0140] Step 3.1: Add an outer layer to obtain a two-layer optimization framework. The optimization objective of the outer layer is to maximize the additional load. ELCC is defined as the maximum additional load that a system can support while ensuring that the expected power shortage (EENS) remains within the reliability target range. :

[0141]

[0142] Where M is a large weighting coefficient used to prioritize maximization .

[0143] Step 3.2: The inner layer is based on the power balance constraint EENS evaluation. The EENS is evaluated by solving the power balance optimization problem, which incorporates the volatility of renewable energy, energy storage dispatch and the flexibility of thermal power units.

[0144] The EENS constraint replaces the non-power supply constraint with:

[0145]

[0146] Total load demand in power balance constraints The calculation formula is replaced with: Other constraints remain unchanged; inner-layer optimization ensures solutions under different operating conditions. When load shedding occurs, system reliability is maintained.

[0147] Step 3.3: Restate as a single-layer optimization problem. Maximize the ELCC directly into the objective function, reformulating the two-layer optimization framework as a single-layer optimization problem. Include the reward item. ,Will The maximization of M is integrated into the cost minimization objective, where M is a large weighting coefficient that prioritizes load increase while ensuring that reliability constraints are met.

[0148] The reformulated single-layer optimization problem is expressed as:

[0149]

[0150] This formulaic approach ensures that the optimization process minimizes system costs while maximizing system capacity. While maintaining the reliability constraints imposed by EENS limits, it facilitates higher levels of additional load. .

[0151] Step 3.4: Scenario-based ELCC estimation with probability weights. The non-iterative ELCC model is extended to a scenario-based model, and the ELCC is calculated for each representative scenario. The final ELCC value for the entire system is obtained by probability-weighted summation:

[0152]

[0153] in Representative scenarios s The probability weights ensure that the uncertainty of changes in power generation and demand is accurately reflected.

[0154] Example 1

[0155] This example analyzes a Chinese provincial power system using 2025 electricity demand and installed capacity data. The system comprises 50 dispatchable thermal power units, divided into three groups based on capacity: 13 units with a rated capacity of 1000MW, 19 units with a rated capacity of 600-1000MW, and 18 units with a rated capacity of 300-600MW. The system also includes 15GW of photovoltaic power, 17GW of wind power, 16.56GW of hydropower, and 1.55GW of energy storage, interconnected with adjacent provincial and regional power systems via 20.5GW of transmission lines. The system aims to meet a peak load of 51GW and an annual electricity consumption of 262TWh.

[0156] To quantify the computational efficiency of the proposed non-iterative ELCC method, benchmark simulations were conducted using energy storage with a duration of 2 hours and a renewable energy ratio of 0.1. Traditional iterative algorithms update the peak load PL using a hybrid binary partitioning method until convergence, while the non-iterative method calculates the ELCC in a single step. A comparison of the two methods is shown in Table 1.

[0157]

[0158] The non-iterative method reduced the average computation time per scene from 224.9 seconds to 32.1 seconds, a 7-fold speedup. The computation time distribution of the two methods is as follows: Figure 1 As shown, the capacity credit results are as follows: Figure 2 As shown, the method proposed in this invention is very stable, reduces the computational burden, and the ELCC results are very close to those of the non-iterative method, with a maximum deviation of only 0.2%.

[0159] Energy storage capacity credit is influenced by its duration, the ratio of energy storage to renewable energy, and the duration of annual renewable energy changes.

[0160] When the duration is fixed, increasing the power ratio of energy storage to renewable energy leads to a gradual decrease in capacity credit. The impact of different renewable energy ratios on energy storage capacity credit is as follows: Figure 3 As shown.

[0161] Storage configurations with shorter durations and lower renewable energy ratios show relatively smaller changes in capacity credit across different scenarios, while storage configurations with longer durations and higher renewable energy ratios exhibit larger changes in capacity credit. The impact of renewable energy fluctuations on the capacity credit of different storage configurations is as follows: Figure 4 As shown.

[0162] Conditional Value at Risk (CVaR) is used to measure the reliability of energy storage under extreme conditions. In CVaR-based assessments, energy storage capacity credit declines more severely for longer durations and higher renewable energy ratios. The reduction in CVaR energy storage capacity credit compared to expected energy storage capacity credit is shown as follows: Figure 5 As shown.

[0163] Example 2

[0164] The present invention also relates to an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0165] The electronic device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor performs various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0166] Example 3

[0167] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0168] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0169] Example 4

[0170] This invention provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the method described in the above embodiments of this invention.

[0171] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "again" in this invention are intended to illustrate the invention and are not intended to limit the invention.

[0172] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A rapid analysis method for credit assessment of energy storage capacity, characterized in that, Includes the following steps: Step 1: Sample renewable energy output, thermal power unit availability and load demand to generate annual operation scenarios. Extract reliability-related features from the annual operation scenarios: maximum power not supplied (MPNS) and maximum power not supplied (MENS). Apply hierarchical clustering in the MPNS-MENS feature space to extract representative scenarios. Step 2: Under the representative scenario, construct an evaluation model for the unsupplied power generation (ENS) based on the fast unit combination FUC; wherein, the model aims to minimize the total system cost, which includes the fuel cost of thermal power generation. Start-up costs Unloaded cost Energy storage investment and operating costs and load reduction penalties Its formula is: in For the collection of thermal power units, This refers to the output power of the thermal power unit. This refers to the starting capacity of thermal power units. For the online capacity of thermal power units, For the net power output of the energy storage system, For load reduction; The constraints of the model include: unsupplied power constraints, thermal power unit output constraints, thermal power unit power output constraints, energy storage output constraints, power balance constraints, and renewable energy output constraints. Step 3: Add an outer layer to the ENS evaluation model of unsupplied power based on fast unit combination FUC to obtain a two-layer optimization framework. The optimization objective of the outer layer is to maximize the additional load. Its formula is: Where M is the weighting coefficient; Based on the expected power shortage EENS constraint, the two-level optimization framework is reformulated as a single-level optimization problem, with the following formula: in To be included in the reward program; Therefore, the maximum additional load ELCC that the system can support while ensuring that EENS remains within the reliability target range is calculated using the following formula: in Representative scenarios s The probability weights.

2. The method as described in claim 1, characterized in that, Step 1: Sample renewable energy output, thermal power unit availability, and load demand to generate annual operating scenarios. Extract reliability-related features from these scenarios: Maximum Power Not Yet Powered (MPNS) and Maximum Power Not Yet Powered (MENS). Apply hierarchical clustering in the MPNS-MENS feature space to extract representative scenarios, including: Step 1.1: Model the output of renewable energy and calculate hourly wind and solar power: in, They are respectively t Available power of wind and solar power units at all times The corresponding capacity factor is derived from sampled historical wind speed and solar radiation data. Rated power for wind power and photovoltaic units; A "run-failure" state sequence is generated for the thermal power unit. The availability of the thermal power unit is the running time, and the state duration follows an exponential distribution. in, These represent the duration of operation and the duration of the fault, respectively. For uniform random variables, These are the failure rate and the repair rate, respectively. Sampling load demand at each time point Obtain the annual load demand; Step 1.2: Calculate MPNS: Represents the most severe instantaneous power shortage in a given scenario, and its calculation formula is as follows: in, Represents the total available power generation, including thermal power generation. and renewable energy generation renewable energy generation T is a given time period; MENS calculation: Used to quantify the cumulative energy shortage under worst-case conditions within a given time period. The formula for calculation is: in Represents a predefined key time window, It is the time step; Step 1.3: In the MPNS-MENS feature space, for each scene This is represented in a two-dimensional feature space, defined by its corresponding MPNS and MENS values: Where N is the total number of scenes generated; The K-means clustering algorithm is used to divide the N scene sets into K clusters. Within each cluster, a representative scene is selected according to a weighted scheme. : in It is a hierarchy The number of scenarios in It is the assigned weight. It is the closest centroid of the selected level. A representative scenario.

3. The method as described in claim 1, characterized in that, The constraints of the ENS evaluation model for unsupplied electricity based on fast unit combination FUC include: No power supply constraint: ,in This represents load reduction; State transition constraints for thermal power units: in, It is a thermal power unit j The total capacity of all thermal power units in the country; Representing time respectively t thermal power units j Online capacity, startup capacity, and shutdown capacity; Power output constraints of thermal power units: in It defines the available capacity limits due to forced shutdowns. and They are respectively The minimum and maximum values; Energy storage output constraints: ,in and Representing time respectively t The charging and discharging power of the energy storage system Rated power capacity; , ,in For SOC, For maximum storage capacity, This is the initial value of SOC; Power balance constraints: in It is time t Load factor at time, This is the peak load of the current scenario. yes t Total load demand at that time The output power of the photovoltaic unit. This refers to the output power of the wind turbine generator set; Renewable energy output constraints: in and These represent the rated capacities of photovoltaic and wind turbine units, respectively. and These represent the capacity factors for photovoltaic and wind turbine units, respectively.

4. The method as described in claim 1, characterized in that, The expected power shortage EENS constraint in step 3 includes: Replace the unsupplied power constraint in the unsupplied power constraint evaluation model based on fast unit combination FUC with... The total load demand in the power balance constraint The calculation formula is replaced with Other constraints remain unchanged; among them, For the target ENS, This represents peak load.

5. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

7. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 4.

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