Energy storage system reliability evaluation and optimization method based on effective load bearing capacity

Abstract

The embodiment of the invention provides an energy storage system reliability evaluation and optimization method based on effective load bearing capacity, and the method comprises the steps: constructing a multi-scene evaluation framework for planning an energy storage system, and the multi-scene evaluation framework comprises a reliability evaluation model and an economic optimization model; adopting a reliability evaluation model, generating a new energy output sequence based on Monte Carlo simulation, and calculating a system reliability index; based on a system reliability index, calculating the capacity credibility of energy storage by adopting an effective load bearing capacity index; and an economic optimization model is adopted, capacity credibility is taken as input, energy storage configuration optimization is carried out in combination with investment cost and a capacity compensation income present value, and an optimal energy storage system planning scheme is output through full-scene traversal optimization under the constraint of system reliability. According to the technical scheme provided by the invention, multi-policy scene analysis can be supported, and a quantitative basis is provided for energy storage investment decision making and capacity compensation policy making.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and operation technology, and in particular relates to a method for reliability assessment and optimization of energy storage systems based on effective load carrying capacity. Background Technology

[0002] As the penetration rate of renewable energy in the power system continues to increase, the reliability and stability of the power system face severe challenges. Energy storage systems, as an important resource for flexibility regulation, can effectively smooth out the fluctuations in renewable energy and provide backup capacity, but the assessment of their capacity value and the quantification of capacity compensation policies have always been technical difficulties.

[0003] The existing methods for assessing energy storage capacity and the quantitative aspects of capacity compensation policies mainly suffer from the following problems:

[0004] Inaccurate capacity reliability assessment: Traditional methods often simply take the rated capacity of energy storage as the reliable capacity, ignoring the matching relationship between its operating characteristics and system requirements.

[0005] Incomplete economic analysis: Energy storage planning fails to comprehensively consider factors such as investment costs, capacity compensation, and user affordability, resulting in poor economic efficiency of configuration schemes.

[0006] Poor policy adaptability: The lack of sensitivity analysis on different capacity compensation policies (duration and amount) makes it impossible to provide quantitative basis for policy formulation.

[0007] Insufficient balance between techno-economic efficiency: While meeting reliability requirements, the relationship between energy storage duration, capacity size, and economic benefits has not been effectively balanced. Therefore, a systematic approach is urgently needed to accurately assess the reliable capacity of energy storage and guide optimal configuration. Summary of the Invention

[0008] The reliability assessment and optimization method for energy storage systems based on effective load carrying capacity provided in this application can solve key technical problems such as accurate assessment of energy storage system capacity value, economic optimization of configuration, and quantitative analysis of capacity compensation policies under the background of high proportion of renewable energy access.

[0009] In a first aspect, embodiments of this application provide a method for reliability assessment and optimization of energy storage systems based on effective load carrying capacity, including:

[0010] A multi-scenario evaluation framework for energy storage system planning is constructed, which includes a reliability evaluation model and an economic optimization model. The reliability evaluation model uses the expected system load failure as a reliability constraint, and the economic optimization model uses the minimization of the net cost of the energy storage system as the objective function.

[0011] A reliability assessment model was adopted to generate a new energy output sequence based on Monte Carlo simulation and to calculate the system reliability index.

[0012] Based on system reliability indicators, the capacity reliability of energy storage is calculated using the effective load carrying capacity indicator.

[0013] An economic optimization model is adopted, taking capacity reliability as input, and combining investment cost and present value of capacity compensation revenue to optimize energy storage configuration. Under system reliability constraints, the optimal energy storage system planning scheme is output through full scenario traversal optimization.

[0014] In one optional implementation, a reliability assessment model is used to generate a new energy output sequence based on Monte Carlo simulation and to calculate system reliability indicators, including:

[0015] The system generates new energy output sequences based on Markov chain Monte Carlo simulation, and calculates the expected system load loss and expected power shortage as indicators of system reliability.

[0016] In one optional implementation, a new energy output sequence is generated based on Markov chain Monte Carlo simulation, including:

[0017] Historical renewable energy output data is divided into multiple discrete states based on output magnitude;

[0018] Calculate the transition probabilities between discrete states and construct the state transition matrix;

[0019] State sequences are generated by random sampling based on the state transition matrix;

[0020] The state sequence is converted into specific new energy output values.

[0021] In one optional implementation, the capacity reliability of energy storage is calculated based on system reliability indicators and the effective load carrying capacity indicator, including:

[0022] The maximum effective load increment of the system under the same reliability level is searched using a binary search method, and the capacity reliability is determined by the ratio of the maximum effective load increment to the energy storage installed capacity.

[0023] In one alternative implementation, the net cost of the energy storage system is the difference between the investment cost of the energy storage system and the present value of the capacity compensation revenue.

[0024] In one alternative implementation, the investment cost of the energy storage system is calculated based on the rated power capacity and duration of energy storage, and the present value of capacity compensation income is calculated based on capacity reliability, rated power capacity of energy storage, preset capacity compensation rate, compensation period and discount rate.

[0025] In one optional implementation, the system reliability constraint is that the expected load loss after the addition of energy storage does not exceed a preset reliability threshold.

[0026] In one optional implementation, the optimal energy storage system planning scheme is output through full-scenario optimization, including:

[0027] Reliability assessment and economic calculation are performed on energy storage capacity within a preset range and under different combinations of durations. The configuration that meets the system reliability requirements and has the lowest net cost is selected as the optimal energy storage system planning scheme.

[0028] Secondly, embodiments of this application provide an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method provided in embodiments of this application.

[0029] Thirdly, embodiments of this application provide a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed in a computer, causes the computer to execute the method provided in embodiments of this application.

[0030] The technical solution provided in this application has the following beneficial effects:

[0031] This invention proposes a reliable capacity assessment and optimal configuration method for energy storage systems based on effective load carrying capacity. This method constructs a complete assessment framework including data preprocessing, renewable energy output sequence generation, reliability assessment, capacity credibility calculation, and optimal configuration search. By employing the Markov chain Monte Carlo method to generate renewable energy output sequences, combining this with a binary search algorithm to calculate effective load carrying capacity, and utilizing full-scenario traversal optimization to perform reliable capacity assessment and configuration optimization of the energy storage system, the optimal planning scheme for the energy storage system can be accurately obtained. In the assessment framework constructed in this invention, the goal is to minimize the net cost under the constraint of system reliability requirements. Net cost is defined as the difference between investment cost and the present value of capacity compensation revenue. It also considers key factors such as compensation period, compensation amount, and the time value of money, providing a scientific decision-making basis for energy storage investors to assess capacity value and investment returns.

[0032] The objective function of this invention considers the trade-off between energy storage capacity reliability and investment cost. By comparing the reliable capacity value provided by energy storage with the investment cost, it can provide quantitative suggestions for improving energy storage capacity compensation mechanisms and investment incentive policies. This invention accurately quantifies the capacity reliability of energy storage systems under different capacity configurations and durations through Monte Carlo simulation, establishing a correspondence between capacity reliability and duration. This provides a scientific basis for the reasonable assessment of energy storage capacity value in the electricity market environment and contributes to the healthy development of the energy storage industry. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the reliability assessment and optimization method for energy storage systems based on effective load carrying capacity provided in this application embodiment;

[0034] Figure 2 This is a graph showing the wind power, photovoltaic power output, and centrally dispatched load provided in an embodiment of this application;

[0035] Figure 3 This is a schematic diagram illustrating the comprehensive analysis of new energy penetration rate indicators provided in the embodiments of this application;

[0036] Figure 4 This is a schematic diagram comparing the optimal configuration results of energy storage systems under high, medium, and low penetration rate conditions provided in the embodiments of this application. Detailed Implementation

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

[0038] This application provides a method for reliability assessment and optimization of energy storage systems based on effective load carrying capacity. Figure 1 This is a flowchart illustrating the reliability assessment and optimization method for energy storage systems based on effective load carrying capacity provided in this application embodiment. The method can be executed by an energy storage system reliability assessment and optimization system based on effective load carrying capacity. The system can be implemented by software and / or hardware and can be configured in electronic devices such as computers.

[0039] like Figure 1 As shown, the technical solution provided in this application includes the following steps:

[0040] S110. Construct a multi-scenario evaluation framework for energy storage system planning. The multi-scenario evaluation framework includes a reliability evaluation model and an economic optimization model.

[0041] Among them, the reliability assessment model uses the expected system load loss as the reliability constraint, and the economic optimization model takes the minimization of the net cost of the energy storage system as the objective function.

[0042] The multi-scenario evaluation framework consists of two key models: a reliability assessment model and an economic optimization model. These two models, coupled through parameter transfer and constraints, form a closed-loop optimization system. The reliability assessment model is responsible for evaluating the system's reliability level from a technical perspective, while the economic optimization model optimizes the energy storage configuration from an economic perspective. The framework design considers the uncertainties of power system operation, covering various possible new energy output and load changes through multi-scenario simulations, ensuring the comprehensiveness and robustness of the evaluation results.

[0043] Reliability Assessment Model: This model uses the Loss of Load Expectation (LOLE) as the core reliability constraint. The LOLE index reflects the probability that the system load will exceed the available capacity within a given time period, and is a key indicator for measuring the reliability of a power system. Model inputs include available output sequences from conventional generating units, output sequences from new energy sources (such as wind and solar power), load sequences, and energy storage output sequences. Through Monte Carlo simulation, the model generates a large number of stochastic scenarios, calculates system reliability indices, and provides a technical foundation for subsequent economic optimization.

[0044] Economic Optimization Model: This model aims to minimize the net cost of the energy storage system. Net cost is defined as the difference between the energy storage investment cost and the present value of capacity compensation revenue, balancing initial investment with long-term returns. Model decision variables include installed energy storage capacity, storage duration, and capacity reliability, calculated using the effective load carrying capacity (ELCC) index. Constraints include investment cost limits, capacity compensation policy requirements (such as compensation period and amount), and system reliability requirements (such as the Loewe threshold). The model uses an optimization algorithm to find the economically optimal energy storage configuration while satisfying reliability constraints.

[0045] The reliability assessment model and the economic optimization model do not operate in isolation, but are tightly coupled through a capacity reliability index. The capacity reliability calculated by the reliability assessment model serves as an input parameter for the economic optimization model, ensuring that the economic optimization is based on accurate technical performance data. Simultaneously, the energy storage configuration scheme output by the economic optimization model is fed back into the reliability assessment model to re-verify the reliability index, forming an iterative optimization loop. This coupling mechanism guarantees that the planning scheme meets both system safety requirements and good economic efficiency.

[0046] The multi-scenario assessment framework is built upon probability theory and optimization theory. The reliability assessment model employs Monte Carlo simulation to handle randomness, while the economic optimization model uses mathematical programming methods to solve for the optimal solution. The framework design emphasizes the completeness of scenario coverage; for example, it uses the Markov Chain Monte Carlo (MCMC) method to generate new energy output sequences to simulate extreme situations that may occur but were not present in historical data. This not only improves the accuracy of the assessment but also enhances the adaptability of policy analysis.

[0047] S120. A reliability assessment model is adopted, and a new energy output sequence is generated based on Monte Carlo simulation, and the system reliability index is calculated.

[0048] In some embodiments, the calculation process of the reliability assessment model is as follows: First, based on historical data, time-series output scenarios for conventional units, wind power, and photovoltaics are generated, and a system operation simulation basis is constructed in combination with load data; then, the reliability index of the basic scenario is calculated through Monte Carlo simulation; next, an energy storage system is introduced to simulate the effect of its charging and discharging behavior on improving system reliability; finally, a bisection method is used to search for the load increment that the system can withstand while maintaining the same reliability level, and the capacity reliability is determined by the ratio of this load increment to the energy storage capacity, providing key technical parameter inputs for subsequent economic optimization.

[0049] The generation of new energy power output sequences using the MCMC method includes:

[0050] The output of new energy sources (wind power and solar power) is random and volatile, and directly using historical data cannot fully reflect all possible scenarios. To account for the uncertainty of new energy output in Monte Carlo simulations and thus more accurately calculate system reliability indicators and capacity credibility, the MCMC method is used to generate wind and solar power output sequences. The MCMC method can generate a large number of new random sequences with the same statistical and temporal characteristics based on historical data. These sequences can cover possible scenarios that did not appear in historical data.

[0051] In some embodiments, S120 includes the following sub-steps:

[0052] S121. Divide the historical new energy output data into multiple discrete states according to the output magnitude;

[0053] Historical wind power output and photovoltaic power output data are divided into several state intervals according to the output magnitude, and each state interval represents a certain range of output level.

[0054] S122. Calculate the transition probabilities between each discrete state and construct the state transition matrix;

[0055] By statistically analyzing the transition probabilities between states in historical data, a cumulative probability matrix is ​​formed as the state transition matrix. This matrix can accurately describe the temporal variation of new energy output.

[0056] S123. Generate state sequences by random sampling based on the state transition matrix;

[0057] Starting from the initial state, a state sequence is generated throughout the entire research period through random sampling and a state transition matrix. This process can simulate the temporal correlation characteristics of new energy output.

[0058] S124. Convert the state sequence into specific new energy output values;

[0059] The state sequence is converted into specific wind power and photovoltaic output values. Special processing is required for the diurnal characteristics of photovoltaic power generation: the increasing and decreasing law of output is considered during the daytime, and the output is forced to zero during the nighttime to ensure that the generated sequence conforms to the actual physical characteristics.

[0060] In some embodiments, the expected system load loss and expected power shortage are used as indicators of system reliability:

[0061] The Loss of Load Expectation (LOLE) is calculated as follows:

[0062] ;

[0063] In the formula, The expected value of the load loss; Total number of scenes; This is an indicator function; it returns 1 when the condition is met and 0 otherwise. Let be the total available system capacity (MW) for scenario s at time t. Let t be the total system load (MW) at time t, and T be the total number of hours in the study period.

[0064] The Expected Energy Exhaustion (EENS) is calculated as follows:

[0065] ;

[0066] In the formula, EENS represents the expected power deficiency.

[0067] The total available capacity of the system is calculated as follows:

[0068] ;

[0069] In the formula, This represents the available capacity for conventional generating units; Available capacity for new energy generating units; This represents the available energy storage capacity.

[0070] The available capacity of conventional generating units is calculated as follows:

[0071] ;

[0072] In the formula, This refers to the number of conventional generating units (units). This is the rated capacity of unit i; This represents the available state (0 or 1) of unit i in scenario s at time t.

[0073] The available capacity of new energy sources is calculated as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] In the formula, , These represent the available capacity (MW) of photovoltaic and wind turbine units at time t in scenario s; , These are the rated capacities (MW) of photovoltaic and wind turbine units, respectively. , , respectively, are the normalized output coefficients [0,1] of photovoltaic and wind turbine units in scenario s at time t.

[0078] The available energy storage capacity is calculated as follows:

[0079] ;

[0080] In the formula, The maximum charge / discharge power of the energy storage (MW); The available energy stored in scenario s at time t; For example, the time interval can be set to 1 hour.

[0081] S130. Based on system reliability indicators, the capacity reliability of energy storage is calculated using the effective load carrying capacity indicator.

[0082] Effective load capacity (ELCC) quantifies the actual supporting capacity of different resources (such as thermal power, energy storage, and new energy) at critical moments in the system, providing a scientific basis for capacity pricing and compensation, and achieving fair value measurement of various resources. Taking energy storage as an example, ELCC represents the maximum load increment that the system can withstand while maintaining unchanged reliability indicators after the addition of new energy storage resources. The advantage of this method is that it can accurately reflect the actual value of energy storage as a capacity resource, avoiding the limitations of traditional methods that simply assess based on rated capacity.

[0083] A binary search method is used to find the maximum effective load increment of the system under the same reliability level, and the capacity reliability is determined by the ratio of the maximum effective load increment to the energy storage installed capacity. Specifically, this includes:

[0084] Establishing the baseline reliability level: First, calculate the baseline reliability index of the system without added energy storage, especially the expected load shedding value (assumed to be LOLE). base This value serves as the benchmark for subsequent comparisons.

[0085] Load increment simulation test: After adding energy storage, the system load level is gradually increased, and the LOLE value under each load increment is calculated using a reliability assessment model. This process employs a bisection method for efficient search, ensuring that an accurate solution is found within a reasonable number of calculations.

[0086] Effective load increment determination: Adding energy storage improves system reliability and reduces the LOLE value. As the load increases, the LOLE also increases accordingly. (The last sentence appears to be incomplete and possibly refers to a different topic.) base When they are equal, the corresponding maximum load increment ΔL max This refers to the effective load carrying capacity of energy storage.

[0087] Capacity reliability calculation: Finally, the capacity reliability of energy storage is quantified using a formula:

[0088] In some embodiments, capacity confidence is calculated as follows:

[0089]

[0090] In the formula, CC represents the capacity confidence level. Maximum effective load increment (MW); Installed capacity (MW) for resources (wind power, photovoltaic, energy storage).

[0091] S140. An economic optimization model is adopted, with capacity reliability as input, and energy storage configuration is optimized by combining investment cost and present value of capacity compensation income. Under the system reliability constraint, the optimal energy storage system planning scheme is output through full scenario traversal optimization.

[0092] In some embodiments, the calculation process of the economic optimization model is as follows: under the premise of satisfying the system reliability constraints, different combinations of energy storage capacity and duration are traversed, the investment cost of each configuration and the present value of compensation income based on capacity credibility are calculated, the net cost is minimized as the objective function, the optimal energy storage configuration scheme is determined through full-scenario search, and the impact of different compensation policies on the optimal configuration and economy is analyzed to provide a quantitative basis for energy storage investment decisions.

[0093] In some embodiments, the objective function (minimizing net cost) is expressed as follows:

[0094] ;

[0095] In the formula, Net cost (ten thousand yuan), and optimization target; Energy storage investment cost (ten thousand yuan); This is the present value of capacity compensation revenue (in ten thousand yuan).

[0096] The investment cost is calculated as follows:

[0097] ;

[0098] In the formula, Rated energy storage capacity (MW); Energy storage duration (hours); The unit capacity investment cost (yuan / kWh) varies with the duration.

[0099] Calculation of the present value of capacity compensation revenue:

[0100] Capacity compensation revenue is calculated based on the reliable capacity of energy storage (capacity reliability × rated power capacity of energy storage) and the compensation policy (compensation period and annual compensation amount).

[0101] ;

[0102] In the formula, The capacity confidence level (a decimal between 0 and 1) is calculated using the ELCC index. Capacity compensation rate (RMB / kW / year); The discount rate (e.g., 7%). The compensation period (in years).

[0103] In some embodiments, the system reliability constraint is that the expected load loss after adding energy storage does not exceed a preset reliability threshold:

[0104] ;

[0105] In the formula, The system's LOLE (hours / year) after adding energy storage; For example, the system reliability requirement threshold (hours / year), ≤0.1 days / year, or 2.4 hours / year.

[0106] In some embodiments, the economic optimization model is also subject to cost constraints, including:

[0107] The cost constraints are as follows:

[0108] The annual compensation amount is limited by the user's affordability:

[0109] ;

[0110] ;

[0111] In the formula, The annual compensation amount is (ten thousand yuan). The capacity compensation control coefficient (yuan / kWh) is when This means that after the annual energy storage capacity compensation cost is passed on to the user side, the average electricity price cannot exceed 0.01 yuan / kWh. The physical meaning of this constraint is that when the energy storage capacity compensation cost is allocated to the total electricity consumption of the whole society, the increase in unit electricity cost must be controlled within a reasonable range to avoid excessively pushing up the end-user electricity price.

[0112] The present value of the total compensation amount shall not exceed a certain percentage of the initial investment:

[0113] ;

[0114] In the formula, This is the percentage (%) of total capacity compensation to initial investment. This constraint limits the ratio of total compensation to initial investment, preventing over-subsidization or insufficient investment recovery, and balances the interests of investors and the public.

[0115] Reliability assessment and economic calculation are performed on energy storage capacity within a preset range and under different combinations of durations. The configuration that meets the system reliability requirements and has the lowest net cost is selected as the optimal energy storage system planning scheme.

[0116] First, define the search space for energy storage configurations, such as the energy storage capacity range (50-900MW) and duration range (2-8 hours), and then combine each capacity-duration combination to form a configuration scenario to be evaluated.

[0117] For each configuration scenario, reliability and cost are evaluated sequentially.

[0118] Compare the net cost values ​​of all feasible configurations and select the configuration with the lowest net cost as the optimal solution. Simultaneously record key parameters such as capacity reliability, investment cost, and compensation income to provide a comprehensive basis for decision-making.

[0119] For example, in one embodiment, assuming a system with a total installed capacity of 3405MW, the load, wind power, and photovoltaic data used are obtained by proportionally scaling down the actual operating data of a power grid, wherein the annual peak load is 3000MW, and the wind power, photovoltaic output, and dispatch load curves are as follows: Figure 2 As shown.

[0120] New energy installed capacity penetration rate, power generation penetration rate, and monthly average power penetration rate under high penetration scenarios are indicators such as: Figure 3 As shown, three scenarios with different new energy penetration rates are set: low penetration (400MW wind power, 400MW photovoltaic), medium penetration (800MW wind power, 800MW photovoltaic), and high penetration (1300MW wind power, 1300MW photovoltaic). In all three scenarios, the installed capacity of thermal power and the system load remain unchanged.

[0121] Some system parameter settings are shown in Table 1:

[0122] Table 1 System Parameter Setting Table

[0123]

[0124] Based on historical data, scenarios for available power output sequences of conventional generating units, wind power output sequences, and photovoltaic power output sequences are simulated and generated. The reliability assessment model described above is used to calculate the capacity reliability of energy storage. Based on three scenarios of new energy penetration rates, and considering different capacity compensation policy intensities, the above economic optimization model is used to optimize the energy storage capacity configuration, and the impact of reliable energy storage capacity (ELCC) on economic efficiency is considered.

[0125] The solution was obtained using MATLAB R2022b. Table 2 shows the capacity compensation policy scheme, energy storage capacity scale, capacity reliability, and investment cost under the optimal scheme. Figure 4 shows the comparative analysis of the optimal configuration results of the energy storage system.

[0126] Table 2 Comparison of results at different permeability rates

[0127]

[0128] This embodiment studies the optimization of energy storage capacity configuration under different renewable energy penetration scenarios based on actual power grid operation data. The study employs Monte Carlo simulation and reliability assessment methods, comprehensively considering system reliability and economic constraints, and obtains the optimal energy storage configuration scheme through MATLAB solution.

[0129] The study set up three scenarios with low, medium, and high renewable energy penetration rates, with wind power and photovoltaic installed capacities of 400MW / 400MW, 800MW / 800MW, and 1300MW / 1300MW, respectively, while the thermal power installed capacity of 3405MW and the maximum system load of 3000MW remained constant. Optimization analysis was conducted through 500 simulated scenarios, within a range of energy storage capacity of 50-900MW and duration of 2-8 hours. The results show that:

[0130] As the penetration rate of new energy sources increases, the demand for energy storage configuration decreases. With wind and solar power installed capacity increasing from "400MW+400MW" to "1300MW+1300MW," the optimal energy storage capacity of the system significantly decreases from 400MW / 6h to 200MW / 2h. This indicates that a high proportion of new energy sources directly meets load demand to a certain extent, reducing reliance on energy storage.

[0131] The reliability of energy storage capacity decreases as penetration increases. At low and medium penetration rates, the reliability of energy storage capacity exceeds 84%, but it drops sharply to 67.1% in high penetration scenarios. This reflects the increased volatility of new energy output, which weakens the effectiveness of energy storage as a stable capacity resource.

[0132] The driving force behind investment is shifting. When renewable energy penetration is low, investment in energy storage primarily aims to provide large-scale capacity assurance, falling under the category of "infrastructure-type" investment. However, at high penetration rates, investment in energy storage focuses more on addressing specific reliability bottlenecks and short-term power support, representing a "system optimization-type" investment. Research suggests that differentiated compensation mechanisms should be adopted for regions at different stages of renewable energy development: Developing regions (low-to-medium penetration): Long-term, stable compensation policies are suitable to attract large-scale energy storage investment; Mature regions (high penetration): More robust and flexible short-term incentives or market mechanisms may be needed to quickly mobilize distributed, small-scale energy storage resources.

[0133] The system reliability meets the requirements in all scenarios. The expected loss load under all configurations is better than the reliability standard of 0.1 days / year. Notably, the highest reliability is achieved in high-penetration scenarios, indicating that the synergistic optimization of new energy and energy storage can effectively ensure system safety.

[0134] This application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any of the new energy-based power grid multi-resource coordinated control methods.

[0135] This application also proposes a computer storage medium storing a computer program, which, when executed by a processor, implements any one of the new energy-based power grid multi-resource coordinated control methods.

[0136] Computer storage media may be simply referred to as media. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Dual Data SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM). The various embodiments described in this specification are presented in a progressive manner, and similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, for embodiments of apparatus, devices, and non-volatile computer storage media, since they are substantially similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments.

[0137] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A method for reliability assessment and optimization of energy storage systems based on effective load carrying capacity, characterized in that, include: A multi-scenario evaluation framework for energy storage system planning is constructed, which includes a reliability evaluation model and an economic optimization model. The reliability assessment model uses the expected system load loss as a reliability constraint, and the economic optimization model uses the minimization of the net cost of the energy storage system as the objective function. Using the aforementioned reliability assessment model, a new energy output sequence is generated based on Monte Carlo simulation, and the system reliability index is calculated. Based on the system reliability index, the energy storage capacity reliability is calculated using the effective load carrying capacity index. Using the aforementioned economic optimization model, with the capacity credibility as input, and combining investment costs and the present value of capacity compensation revenue, energy storage configuration optimization is performed. Under system reliability constraints, optimization is carried out through full scenario traversal to output the optimal energy storage system planning scheme.

2. The method according to claim 1, characterized in that, Using the aforementioned reliability assessment model, a new energy output sequence is generated based on Monte Carlo simulation, and system reliability indices are calculated, including: The system generates a new energy output sequence based on Markov chain Monte Carlo simulation, and calculates the expected system load loss and expected power shortage as the system reliability indicators.

3. The method according to claim 2, characterized in that, New energy output sequences were generated based on Markov chain Monte Carlo simulations, including: Historical renewable energy output data is divided into multiple discrete states based on output magnitude; Calculate the transition probabilities between discrete states and construct the state transition matrix; A state sequence is generated by random sampling based on the state transition matrix. The state sequence is then converted into specific new energy output values.

4. The method according to claim 1, characterized in that, Based on the aforementioned system reliability indicators, the capacity reliability of energy storage is calculated using the effective load carrying capacity indicator, including: The maximum effective load increment of the system under the same reliability level is searched using a binary search method, and the capacity reliability is determined by the ratio of the maximum effective load increment to the energy storage installed capacity.

5. The method according to claim 1, characterized in that, The net cost of the energy storage system is the difference between the investment cost of the energy storage system and the present value of the capacity compensation revenue.

6. The method according to claim 5, characterized in that, The investment cost of the energy storage system is calculated based on the rated power capacity and duration of energy storage, and the present value of the capacity compensation income is calculated based on the capacity reliability, the rated power capacity of energy storage, the preset capacity compensation rate, the compensation period, and the discount rate.

7. The method according to claim 1, characterized in that, The system reliability constraint is that the expected load loss after adding energy storage to the system does not exceed a preset reliability threshold.

8. The method according to claim 1, characterized in that, Through full-scenario optimization, the optimal energy storage system planning scheme is output, including: Reliability assessment and economic calculation are performed on energy storage capacity within a preset range and under different combinations of durations. The configuration that meets the system reliability requirements and has the lowest net cost is selected as the optimal energy storage system planning scheme.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method of any one of claims 1-8.

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