New energy-storage system capacity confidence evaluation method considering multi-time scale

Abstract

This invention discloses a method for assessing the capacity confidence of a new energy-storage system considering multiple time scales. The technical solution adopted in this invention is as follows: using new energy output and load curves as inputs, a penalty function for compensation costs during peak net load periods is introduced, while simultaneously considering the operating losses of energy storage batteries. Multi-source collaborative operation optimization is performed with the goal of optimizing the overall system economy, thereby formulating an energy storage operation strategy. Then, a convolution method is used to calculate the system reliability index before and after adding evaluation resources, and a non-iterative interpolation algorithm is used to calculate the capacity confidence of the new energy-storage system at different time scales. This invention considers the operating losses of energy storage batteries and the compensation costs during peak net load periods, and formulates an energy storage operation strategy through multi-source collaborative optimization, realizing the capacity confidence assessment of new energy-storage systems at multiple time scales. This provides a reference for formulating operation strategies and capacity allocation optimization for new energy-storage systems in key scenarios affecting power supply security, thereby more effectively leveraging the power supply security support role of new energy systems.

Description

Technical Field

[0001] This invention pertains to evaluation methods in the field of power system planning, specifically involving a confidence assessment method for the capacity of new energy-storage systems that takes into account multiple time scales. Background Technology

[0002] Building a new power system with renewable energy as the mainstay is an important measure for constructing a new energy system. However, renewable energy is characterized by strong volatility and high uncertainty. In particular, under the influence of weather processes, such as "extreme heat and no wind" or "no solar power during the evening peak," renewable energy output and load output show a negative correlation, resulting in a low source-load matching degree. At the same time, as renewable energy begins to replace existing fossil fuel power on a large scale, the growth rate of coal-fired power generation capacity has declined significantly, posing a severe challenge to the power supply security of the power system.

[0003] On the other hand, the output of renewable energy is affected by seasonality and weather events, resulting in significant differences in output across different time periods. Therefore, it is necessary to study the capacity confidence of renewable energy in scenarios with shorter time scales to support system power balance across multiple time scales. Energy storage has the advantage of flexible charging and discharging, which can reduce the volatility and randomness of renewable energy output. By rationally arranging the charging and discharging time of the energy storage system, during periods of high system supply risk—i.e., critical scenarios affecting system power supply—discharging can improve the capacity confidence of renewable energy, thus serving as an effective measure to enhance the power supply capacity of renewable energy.

[0004] Therefore, researching a confidence assessment method for the capacity of new energy-storage systems that takes into account multiple time scales, analyzing the capacity value and power supply guarantee capability of new energy-storage systems, and providing a reference for the formulation of new energy-storage operation strategies and capacity allocation optimization are urgent technical problems to be solved. Summary of the Invention

[0005] The technical problem this invention aims to solve is to overcome the shortcomings of existing technologies and provide a method for assessing the capacity confidence of new energy-storage systems considering multiple time scales. This invention uses new energy output and load curves as inputs, introduces a net load peak-period compensation cost penalty function, and simultaneously considers the operating loss cost of energy storage batteries. It optimizes multi-source collaborative operation with the goal of achieving optimal overall system economics, thereby formulating an energy storage operation strategy. Then, it uses a convolution method to calculate the system reliability index before and after adding assessment resources, and uses a non-iterative interpolation algorithm to calculate the capacity confidence of the new energy-storage system at different time scales. This provides a basis for the regulation of energy storage systems in critical scenarios affecting power supply security, thereby improving the system's power supply security capability.

[0006] Therefore, the technical solution adopted by the present invention is as follows:

[0007] A method for assessing the capacity confidence of a new energy-storage system considering multiple time scales includes the following steps:

[0008] S1: Taking the new energy-storage system that needs to be connected to the grid as the resource to be evaluated, for each evaluation period corresponding to the evaluation time scale, the input data within each evaluation period is obtained; the input data includes the new energy time-series output data of the new energy-storage system, the load level of the grid, the original conventional unit installed capacity in the grid, and the operating parameters of the grid.

[0009] S2: For each time scale corresponding to the evaluation period, the input data within the evaluation period is used as the input of the multi-source collaborative optimization model. In the multi-source collaborative optimization model, the net load peak period compensation cost penalty function is introduced, and the energy storage battery operation loss cost is taken into account. The multi-source collaborative operation optimization is carried out with the goal of optimizing the overall economic efficiency of the system, thereby formulating the energy storage operation strategy and obtaining the energy storage time sequence output data.

[0010] S3: For each time scale corresponding to the evaluation period, based on the input data obtained in S1 and the energy storage time series output data obtained in S2, the convolution method is used to calculate the system reliability index before and after adding the resource to be evaluated.

[0011] S4: For each time scale corresponding to the assessment period, remove the resources to be assessed from the power grid, and then add different increases in the capacity of conventional generating units based on the original capacity of conventional generating units in the power grid. Recalculate the system reliability index corresponding to the different increases in the capacity of conventional generating units. Finally, calculate the confidence level of the new energy-storage system capacity within the assessment period using a non-iterative interpolation algorithm. After obtaining the confidence level of the new energy-storage system capacity within the corresponding assessment period for each time scale, the assessment results of the new energy power supply capacity under multiple time scales are formed.

[0012] Preferably, in step S2, a penalty function for compensation cost during peak load periods is introduced into the multi-source collaborative optimization model. Simultaneously, the operating loss cost of energy storage is considered, and the operating cost of conventional units is incorporated for collaborative optimization. The objective function is to minimize the sum of operating costs for all scenarios within the evaluation period. The formula for calculating the operating cost for any s-th scenario is:

[0013]

[0014]

[0015] In the formula, T is the scheduling period, and N is the time interval. g N represents the number of conventional generating units. esThe number of energy storage systems is represented by S, which is a set of key scenarios consisting of a typical day containing the first H hours of the net load continuity curve. These scenarios represent the scenarios with the highest risk to the power supply guarantee capability of the power system. Let be the operating cost of unit i at time t, whereby the operating cost includes fuel cost and start-up / shutdown cost; The penalty function representing the compensation cost for peak net load at time t; Let t be the operating loss cost of energy storage system i at time t, which is measured by the cost per kilowatt-hour, and its calculation formula is shown in equations (3) and (4):

[0016]

[0017]

[0018] In the formula, C op This represents the equivalent operating loss coefficient of the energy storage battery. and These are the energy storage charging power and discharging power, respectively, C w For the capacity price of energy storage batteries, E es To configure the capacity of the energy storage battery, D es N represents the depth of discharge of the energy storage battery. es For a discharge depth of D es The equivalent number of cycles at time t, where r1 is the interest rate coefficient;

[0019] Since the compensation cost penalty function is a piecewise function, the nonlinear function is linearized by introducing two 0-1 variables, thereby solving the optimization objective function through the optimization solver and obtaining the energy storage time-series output.

[0020] Preferably, in S1, the evaluation period is a variable time scale, including multiple time scales such as year, season, and key scenarios of 1 day to several days.

[0021] Preferably, in step S3, the system reliability index before and after adding the evaluation resources is calculated using the convolution method. The specific process is as follows:

[0022] First, based on the new energy time-series output data obtained in S1 and the energy storage time-series output data obtained in S2, convolution based on the time-series load curve is performed from the power generation side. That is, according to the outage capacity probability table COPT of the unit state model computer, the overall outage capacity probability table COPT of the conventional unit is calculated by convolution, thereby avoiding multi-level recursive calculations. The calculation method of the outage capacity probability table COPT is as follows:

[0023]

[0024]

[0025] In the formula, X represents a random variable representing a unit failure event. i Let i be a random variable representing a failure event occurring in unit i, where i = 1, 2, ..., N. g f(x) is the probability density function of the event, f i (x) is the probability density function of a failure event occurring in unit i, x∈X; given all N g The probability density function f(x) of each unit is used to generate the overall outage capacity probability table COPT of the units;

[0026] Then, the combined output of the new energy-storage system to be evaluated is subtracted from the pure load curve as a negative load, and the system reliability index R is calculated hourly based on the net load curve.

[0027] Preferably, the system reliability index R is the expected load shedding EENS.

[0028] Preferably, in step S4, the reliability indices before and after the access of the evaluation resource in the power grid are denoted as EENS0 and EENS1, respectively. After removing the evaluation resource from the power grid, different increases in the installed capacity of conventional generating units (ERG) are added to the original installed capacity G of the conventional generating units in the power grid, and the system reliability index EENS corresponding to each increase in installed capacity ΔG is recalculated. A series of scatter points showing the changes in the system reliability index EENS are obtained for the different increases in installed capacity ΔG. The increase in installed capacity ΔG and the system reliability index EENS are used as independent and dependent variables, respectively, and spline interpolation is used to fit ΔG and EENS to obtain a fitting function f0. Then, the value of the independent variable when the dependent variable equals EENS1 is determined based on the fitting function f0, and the determined value of the independent variable is used as the confidence capacity C. ECGC And based on confidence capacity C ECGC Calculate the capacity confidence level (CC) of a new energy-energy storage system. ECGC :

[0029] CC ECGC =C ECGC / G RS (7)

[0030] In the formula, G RS Resources to be evaluated.

[0031] The beneficial effects of this invention are as follows: This invention can evaluate the confidence assessment method of new energy-storage system capacity at multiple time scales, quantitatively assess the important role of new energy power supply capacity in key scenarios, and the proposed energy storage operation strategy that takes into account power supply guarantee in key scenarios can effectively improve the confidence of new energy capacity, that is, improve the power supply guarantee capability of the system. Attached Figure Description

[0032] Figure 1 This is a flowchart of the multi-timescale new energy-storage system capacity confidence assessment method of the present invention;

[0033] Figure 2 This is a flowchart of the non-iterative interpolation algorithm for calculating confidence capacity in an application example of the present invention;

[0034] Figure 3 This is a schematic diagram showing the change in charging and discharging power of a typical energy storage power station on a given day in an application example of this invention.

[0035] Figure 4 This is a schematic diagram illustrating the state of charge (SOC) change of a typical energy storage power station on a given day in an application example of this invention.

[0036] Figure 5 This is a schematic diagram showing the changes in combined wind and storage power output in an application example of the present invention. Detailed Implementation

[0037] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments.

[0038] In a preferred embodiment of the present invention, a method for assessing the capacity confidence of a new energy-storage system considering multiple time scales is provided, the method comprising the following steps:

[0039] S1: Using the new energy-storage system that needs to be connected to the grid as the resource to be evaluated, obtain the input data for each evaluation period corresponding to each evaluation time scale.

[0040] Note that in this step, the evaluation period is a variable time scale, which can be set according to the required evaluation needs, such as year / season / key scenario (days to several days). The input data acquired in each evaluation period includes the time-series output data of the new energy-storage system, the grid load level (time-series data), the original installed capacity of conventional generating units in the grid, and grid operating parameters (such as minimum start-up and shutdown time, ramp rate, and fuel cost coefficient). The acquired data can be input into the multi-source collaborative operation optimization model to solve for the optimal operation optimization model including energy storage that achieves the best overall system economy. The specific process for developing the multi-source collaborative optimization model is described in S2.

[0041] S2: For each time scale corresponding to the evaluation period, the input data within the evaluation period is used as the input of the multi-source collaborative optimization model. In the multi-source collaborative optimization model, the net load peak period compensation cost penalty function is introduced, and the energy storage battery operation loss cost is taken into account. The multi-source collaborative operation optimization is carried out with the goal of optimizing the overall economic efficiency of the system, thereby formulating the energy storage operation strategy and obtaining the energy storage time-series output data.

[0042] In this invention, the process of formulating the multi-source collaborative optimization model that takes into account power supply in key scenarios is as follows:

[0043] In this multi-source collaborative optimization model, the H hours preceding the net load duration curve (e.g., 480 hours) can be selected as the peak net load period. The typical day corresponding to the H hours preceding the net load duration curve is designated as the key scenario, representing the scenario with the highest risk to the power system's supply guarantee capability. A compensation cost penalty function for the peak net load period is introduced, while also considering energy storage operating losses and incorporating conventional unit operating costs for collaborative optimization. The optimization objective of this model is to minimize the overall system operating cost (including the compensation cost penalty function in the key scenario), i.e., to minimize the sum of operating costs for all scenarios within the evaluation period, thereby obtaining the time-series output of energy storage.

[0044] Since scenario s within the evaluation period includes critical scenarios (s∈S) and non-critical scenarios Therefore, the operating costs of the two scenarios need to be calculated separately. For any s-th scenario, the formula for calculating its operating cost is:

[0045]

[0046]

[0047] In the formula, T is the scheduling period, and N is the time interval. g N represents the number of conventional generating units. es The number of energy storage systems is represented by s, which represents the sth scenario. S is the set of key scenarios, consisting of the typical day where the first H hours of the net load continuity curve are located, i.e. the scenario with the highest risk to the power supply guarantee capability of the power system. Let t be the operating cost of unit i (including fuel cost and start-up and shutdown cost); The penalty function representing the compensation cost for peak net load at time t; Let t be the operating loss cost of energy storage system i at time t, which is measured by the cost per kilowatt-hour, and its calculation formula is shown in equations (3) and (4):

[0048]

[0049]

[0050] In the formula, C op This represents the equivalent operating loss coefficient of the energy storage battery. and These are the energy storage charging power and discharging power, respectively, C w For the capacity price of energy storage batteries, E es To configure the capacity of the energy storage battery, D es N represents the depth of discharge of the energy storage battery. es For a discharge depth of Des The equivalent number of cycles at time t, where r1 is the interest rate coefficient.

[0051] Furthermore, since the introduced compensation cost penalty function is a piecewise function, two 0-1 variables can be introduced to linearize the nonlinear function for easier solution. This allows the objective function of the multi-source collaborative optimization model to be solved by the optimization solver, and the energy storage time-series output to be obtained.

[0052] S3: For each time scale corresponding to the evaluation period, based on the input data obtained in S1 and the energy storage time-series output data obtained in S2, the convolution method is used to calculate the system reliability index before and after adding the resource to be evaluated.

[0053] In this invention, the system reliability index can be calculated using the convolution method. The specific process for calculating the system reliability index before and after adding evaluation resources using the convolution method is as follows:

[0054] First, based on the new energy time-series output data obtained in S1 and the energy storage time-series output data obtained in S2, convolution based on the time-series load curve is performed from the power generation side. That is, according to the capacity outage probability table (COPT) of the unit state model computer, the overall COPT of the conventional unit is calculated by convolution, thereby avoiding multi-level recursive calculations. The calculation method of COPY is as follows:

[0055]

[0056]

[0057] In the formula, X represents a random variable representing a unit failure event. i Let i be a random variable representing a failure event occurring in unit i, where i = 1, 2, ..., N. g f(x) is the probability density function of the event, f i (x) is the probability density function of a failure event occurring in unit i, x∈X; given all N g The probability density function f(x) of each unit generates the overall outage capacity probability table COPT for the entire unit.

[0058] Then, the combined output of the new energy-storage system to be evaluated is subtracted from the pure load curve as a negative load, and the system reliability index R is calculated time-by-time based on the net load curve. In the embodiments of the present invention, the system reliability index R is the expected load loss EENS.

[0059] S4: For each assessment period corresponding to each time scale, the resources to be assessed are removed from the power grid. Then, different increases in the installed capacity of conventional generating units are added to the original installed capacity of the grid, and the system reliability index corresponding to each increase in installed capacity is recalculated. Finally, the confidence level of the new energy-storage system capacity within the assessment period is calculated using a non-iterative interpolation algorithm. After obtaining the confidence level of the new energy-storage system capacity within the corresponding assessment period for each time scale, a multi-time-scale assessment result of the new energy power supply capacity can be formed to better support power balance.

[0060] Since the system's reliability index will be lower than the original system after adding the evaluation resources, assuming the reliability indices before and after adding the evaluation resources are EENS0 and EENS1 respectively, and the sum of the original conventional unit capacities in the system is G, after removing the resources to be evaluated from the grid, different increases in conventional unit capacity ΔG can be added to the original conventional unit capacity G in the grid. The system reliability index EENS corresponding to each increase in conventional unit capacity ΔG (total conventional unit capacity is G + ΔG) is recalculated. A series of scatter points showing the changes in the system reliability index EENS are obtained by using the increase in conventional unit capacity ΔG and the system reliability index EENS as independent and dependent variables respectively. A fitting function f0 is obtained by fitting ΔG and EENS using spline interpolation: EENS = f0(ΔG). Then, the value of the independent variable when the dependent variable equals EENS1 is determined based on the fitting function f0, expressed by the formula:

[0061]

[0062] in: This represents the inverse function of the fitted function f0. Based on the fitted function f0, the dependent variable value is set as EENS1, and the corresponding independent variable value is denoted as the confidence capacity C. ECGC .

[0063] Based on confidence capacity C ECGC The capacity confidence level (CC) of the new energy-energy storage system can then be calculated. ECGC :

[0064] CC ECGC =C ECGC / G RS (8)

[0065] In the formula, G represents the sum of the original conventional unit capacities in the system. RS For the resource to be evaluated (i.e., new energy-energy storage system); C ECGC and CC ECGC The confidence capacity and capacity confidence level are used to generate the overall COPT of the unit.

[0066] It is important to note that the assessment period in S1 is a variable time scale. Specific multi-scale assessments can be set as combinations of time scales such as year, season, or key scenario (typically one to several days, affected by extreme weather events). Therefore, for each time scale, the confidence capacity C for the corresponding assessment period can be obtained in the manner described above. ECGC and capacity confidence CC ECGC This allows for a more refined consideration of the impact of a high proportion of new energy access on the system's power balance, providing a basis for optimizing the ratio and operation strategy of new energy-energy storage systems, with the aim of better supporting power balance for new energy systems with strong volatility and high uncertainty.

[0067] To verify the effectiveness of the present invention, the evaluation method described in S1 to S4 above was implemented in the subsequent application examples using an IEEE-RTS79 system containing wind turbines and energy storage units. The specific steps will not be repeated, but the technical effects and implementation details are mainly given.

[0068] Application examples

[0069] In this case study, MATLAB software was used to develop the capacity confidence assessment method for new energy-storage systems, as shown in S1 to S4 of this invention, which considers multiple time scales. Further details will not be elaborated here. The following section primarily demonstrates the specific technical effects based on the case data. The multi-source collaborative optimization model in this method is solved using Gurobi, and the convolution method for calculating reliability indices and the non-iterative interpolation algorithm for calculating capacity confidence are implemented using a MATLAB program.

[0070] Operating environment:

[0071] AMD Ryzen 5 3400G CPU 3.70GHz, 16GB RAM, Microsoft Windows 10 x64

[0072] MATLAB 2020B, Gurobi 10.0.0

[0073] Implementation results:

[0074] This application example is based on the IEEE-RTS 79 standard test system, which includes 24 nodes and 38 lines, as well as 32 conventional turbine units. The peak load level is 2850MW, and a 60MW wind turbine unit is connected at node 1. The wind power output and load characteristic data are scaled down proportionally based on the wind power output and load levels of a province in East China in 2019. Furthermore, the energy storage system uses lithium iron phosphate batteries, with a construction scale of 12MW / 48MWh, SOC upper and lower limits of 0.9 and 0.1 respectively, and a charge / discharge efficiency of 90%. The key parameters of the conventional turbine units in the test system are shown in Table 1.

[0075] Table 1 Key parameters of the conventional unit in the test system

[0076]

[0077] Figure 1 This is a flowchart of a multi-timescale new energy-storage system capacity confidence assessment method, which includes steps such as data input, energy storage operation strategy formulation, calculation of the unit outage capacity probability table COPT and EENS reliability index, and calculation of confidence capacity using non-iterative spline interpolation.

[0078] Figure 2 The flowchart for calculating the confidence capacity using a non-iterative interpolation algorithm is presented. The confidence capacity of the new energy-storage system is quantitatively evaluated by changing the added capacity of conventional generating units, thereby calculating the capacity confidence of the new energy-storage system.

[0079] Table 2 presents the capacity confidence assessment results of new energy-storage systems across multiple time scales. It compares the differences in capacity confidence of new energy (and new energy-storage systems) at different time scales, such as annual, seasonal, and critical scenarios (typically one to several days, affected by extreme weather events). In Table 2, critical scenarios refer to typical daily scenarios distributed over the 480 hours before the peak net load, encompassing 62 typical days and 14 scenarios. The system reliability index EENS accounts for 84.97% of the annual EENS, reflecting the significant impact of critical scenarios on system reliability.

[0080] Table 2. Confidence Assessment Results of Capacity of New Energy-Storage Systems at Multiple Time Scales

[0081]

[0082] First, as shown in Table 2, the capacity confidence of the renewable energy system varies significantly across different seasons. Wind power has the highest capacity confidence due to higher output and lower load levels in winter, while it has the lowest in spring due to lower output levels. Second, energy storage primarily improves the capacity confidence of the wind power system in summer and autumn, while the capacity confidence remains unchanged during the non-net load peak periods in spring and winter. This is mainly because renewable energy accounts for a lower proportion of the system during these periods, and the net load peak periods are primarily driven by load. The proposed multi-source collaborative optimization model considers the operating loss cost of energy storage and the cost penalty function during the net load peak periods. During peak periods, energy storage increases the system capacity adequacy by discharging in key scenarios, thereby reducing the system reliability index EENS and improving the capacity confidence. During non-load peak periods, the operating loss of energy storage exceeds the system operating cost reduction, and the effect of energy storage discharging on system reliability improvement is not significant. Therefore, the energy storage system does not perform charging and discharging operations, thus having no impact on the capacity confidence of renewable energy.

[0083] Furthermore, the capacity confidence assessment results under the key scenario set have a smaller error compared to the whole year, and the capacity confidence of new energy capacity improved by energy storage under the key scenario can also achieve the same effect as the annual capacity confidence improvement, reflecting the important contribution of energy storage during periods of insufficient power supply capacity of the system and the effectiveness of the above-mentioned operation strategy.

[0084] Figure 3 This reflects the variation in charging and discharging power of the energy storage system on a peak day during the summer. Figure 4 This reflects the state of charge (SOC) changes of this typical daytime energy storage system. Figure 5 This reflects the changes in the combined output of wind and gas storage in this typical case. According to... Figures 3-5 It can be seen that the proposed multi-source collaborative optimization model enables energy storage to improve the system's power supply capacity by discharging when the system is short of power, thus improving the source-load synergy. Furthermore, the model allows for discharging when the load level is low, which has a smaller impact on the system's power supply capacity, thereby effectively improving the confidence level of new energy capacity and the system's power supply guarantee capability.

[0085] The present invention provides a method for assessing the capacity confidence of a renewable energy-storage system considering multiple time scales. Using renewable energy output and load level data as input, and combining conventional unit parameters, it formulates energy storage operation strategies through multi-source collaborative optimization. Furthermore, considering the differences in renewable energy output and load characteristics at different time scales, it calculates the capacity confidence of the renewable energy-storage system at multiple time scales using convolutional methods and non-iterative interpolation algorithms. Through this assessment method, the impact of a high proportion of renewable energy access on the system's power balance can be considered more precisely, providing a reference for the formulation of renewable energy-storage system operation strategies and capacity allocation optimization, thereby more effectively leveraging the power supply guarantee and support role of renewable energy systems.

[0086] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Rather, the invention encompasses any alternatives, equivalent methods, and solutions made within the scope of the claims as defined herein. Furthermore, to provide a better understanding of the invention, certain specific details are described below. However, those skilled in the art will fully understand the invention even without these detailed descriptions.

Claims

1. A method for assessing the capacity confidence of a new energy-storage system considering multiple time scales, characterized in that, Includes the following steps: S1: Taking the new energy-storage system that needs to be connected to the grid as the resource to be evaluated, for each evaluation period corresponding to the evaluation time scale, the input data within each evaluation period is obtained; the input data includes the new energy time-series output data of the new energy-storage system, the load level of the grid, the original conventional unit installed capacity in the grid, and the operating parameters of the grid. S2: For each time scale corresponding to the evaluation period, the input data within the evaluation period is used as the input of the multi-source collaborative optimization model. In the multi-source collaborative optimization model, the net load peak period compensation cost penalty function is introduced, and the energy storage battery operation loss cost is taken into account. The multi-source collaborative operation optimization is carried out with the goal of optimizing the overall economic efficiency of the system, thereby formulating the energy storage operation strategy and obtaining the energy storage time sequence output data. S3: For each time scale corresponding to the evaluation period, based on the input data obtained in S1 and the energy storage time series output data obtained in S2, the convolution method is used to calculate the system reliability index before and after adding the resource to be evaluated. S4: For each time scale corresponding to the assessment period, remove the resources to be assessed from the power grid, and then add different increases in the capacity of conventional generating units based on the original capacity of conventional generating units in the power grid. Recalculate the system reliability index corresponding to the different increases in the capacity of conventional generating units. Finally, calculate the confidence level of the new energy-storage system capacity within the assessment period using a non-iterative interpolation algorithm. After obtaining the confidence level of the new energy-storage system capacity within the corresponding assessment period for each time scale, the assessment results of the new energy power supply capacity under multiple time scales are formed.

2. The method for assessing the capacity confidence of new energy-storage systems considering multiple time scales as described in claim 1, characterized in that, In step S2, the multi-source collaborative optimization model introduces a penalty function for compensation cost during peak load periods, while also considering energy storage operation losses and incorporating conventional unit operation costs for collaborative optimization. The objective function is to minimize the sum of operating costs for all scenarios within the evaluation period. The formula for calculating the operating cost of any s-th scenario is as follows: In the formula, T is the scheduling period, and N is the time interval. g N represents the number of conventional generating units. es The number of energy storage systems is represented by S, which is a set of key scenarios consisting of a typical day containing the first H hours of the net load continuity curve. These scenarios represent the scenarios with the highest risk to the power supply guarantee capability of the power system. Let be the operating cost of unit i at time t, whereby the operating cost includes fuel cost and start-up / shutdown cost; The penalty function representing the compensation cost for peak net load at time t; Let t be the operating loss cost of energy storage system i at time t, which is measured by the cost per kilowatt-hour, and its calculation formula is shown in equations (3) and (4): In the formula, C op This represents the equivalent operating loss coefficient of the energy storage battery. and These are the energy storage charging power and discharging power, respectively, C w For the capacity price of energy storage batteries, E es To configure the capacity of the energy storage battery, D es N represents the depth of discharge of the energy storage battery. es For a discharge depth of D es The equivalent number of cycles at time t, where r1 is the interest rate coefficient; Since the compensation cost penalty function is a piecewise function, the nonlinear function is linearized by introducing two 0-1 variables, thereby solving the optimization objective function through the optimization solver and obtaining the energy storage time-series output.

3. The method for assessing the capacity confidence of a new energy-storage system considering multiple time scales as described in claim 1, characterized in that, In S1, the evaluation period is a variable time scale, including multiple time scales such as year, season, and key scenarios ranging from 1 day to several days.

4. The method for assessing the capacity confidence of a new energy-storage system considering multiple time scales as described in claim 2, characterized in that, In step S3, the system reliability index before and after adding evaluation resources is calculated using the convolution method. The specific process is as follows: First, based on the new energy time-series output data obtained in S1 and the energy storage time-series output data obtained in S2, convolution based on the time-series load curve is performed from the power generation side. That is, according to the outage capacity probability table COPT of the unit state model computer, the overall outage capacity probability table COPT of the conventional unit is calculated by convolution, thereby avoiding multi-level recursive calculations. The calculation method of the outage capacity probability table COPT is as follows: In the formula, X represents a random variable representing a unit failure event. i Let i be a random variable representing a failure event occurring in unit i, where i = 1, 2, ..., N. g f(x) is the probability density function of the event, f i (x) is the probability density function of a failure event occurring in unit i, x∈X; given all N g The probability density function f(x) of each unit is used to generate the overall outage capacity probability table COPT of the units; Then, the combined output of the new energy-storage system to be evaluated is subtracted from the pure load curve as a negative load, and the system reliability index R is calculated hourly based on the net load curve.

5. The method for assessing the capacity confidence of a new energy-storage system considering multiple time scales as described in claim 4, characterized in that, The system reliability index R is expressed as the expected load shedding EENS.

6. The method for assessing the capacity confidence of a new energy-storage system considering multiple time scales as described in claim 5, characterized in that, In step S4, the reliability indices before and after the grid access to the evaluation resource are denoted as EENS0 and EENS1, respectively. After removing the evaluation resource from the grid, different increases in conventional unit capacity ΔG are added to the original conventional unit capacity G in the grid, and the system reliability index EENS corresponding to each increase in conventional unit capacity ΔG is recalculated. A series of scatter points showing the changes in the system reliability index EENS are obtained for the different increases in conventional unit capacity ΔG. Using the increase in conventional unit capacity ΔG and the system reliability index EENS as independent and dependent variables, respectively, spline interpolation is used to fit ΔG and EENS to obtain the fitting function f0. Then, based on the fitting function f0, the independent variable value when the dependent variable equals EENS1 is determined, and the determined independent variable value is used as the confidence capacity C. ECGC And based on confidence capacity C ECGC Calculate the capacity confidence level (CC) of a new energy-energy storage system. ECGC : CC ECGC =C ECGC / G RS (7) In the formula, G RS Resources to be evaluated.

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