Power distribution network reliability rapid evaluation method and system considering energy storage performance degradation

Abstract

The invention provides a power distribution network reliability rapid evaluation method and system considering energy storage performance degradation, and the method comprises the steps: building an energy storage performance degradation model considering the temperature, SOC range and time influence, and the energy storage performance degradation model comprises a battery cell capacity attenuation model and a battery power attenuation model; on the basis of the established model, the duration of each state of the equipment is described, so that a reliability evaluation model considering energy storage performance degradation in a fault scene is obtained, and the reliability evaluation model comprises an established power distribution network optimization scheduling model; the power distribution network optimization scheduling model is corrected, and energy storage performance attenuation in a normal scene is calculated based on the corrected scheduling model; and calculating a reliability index of the power distribution network based on the energy storage performance attenuation, wherein the reliability index is used for rapidly evaluating the reliability of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network reliability assessment, and in particular relates to a method and system for rapid assessment of distribution network reliability taking into account energy storage performance degradation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] To address climate change and the energy crisis, the global use of renewable energy is rapidly increasing. Due to the randomness and volatility of renewable energy, the deployment of energy storage (ES) has become a key means of addressing this challenge. Energy storage, due to its rapid response, strong adaptability, and high controllability, has become a hot topic of research. The rational allocation of energy storage can effectively regulate flexible resources and improve the reliability of distribution networks. Therefore, energy storage is seen as an effective way to accommodate renewable energy in distribution networks.

[0004] While energy storage systems can enhance distribution network reliability, aging and failure of their internal batteries can lead to a decline in energy storage performance, thereby weakening their contribution to distribution network reliability. Therefore, it is necessary to analyze the impact of energy storage system performance degradation on distribution network reliability. Furthermore, accurate distribution network reliability assessments and the development of energy storage scheduling plans must be based on detailed power flow analysis and load shedding assessments. Distribution network reliability assessments that take energy storage performance into account are computationally intensive, so improving assessment efficiency through improved models and algorithms is a core research issue.

[0005] Numerous studies have been conducted on the reliability of energy storage systems. Most common studies simplify energy storage systems into systems with available and unavailable states, or further consider different values ​​of state of charge (SOC), without fully considering the variability of energy storage system performance. Because energy storage is a highly time-dependent resource, its charge and discharge scheduling capabilities are affected by previous operating conditions. Some studies measure battery reliability by measuring capacity decay. These studies have shown that factors influencing battery degradation primarily include ambient temperature, age, state of charge, and depth of charge / discharge. The main approaches for constructing capacity decay models for energy storage batteries include empirical models and electrochemical mechanism models. Electrochemical mechanism models are used to analyze battery aging mechanisms and operating conditions in detail, but they struggle to effectively integrate internal electrochemical changes with the charge and discharge processes. Furthermore, these models are often limited to analyzing battery aging under specific experimental conditions. Empirical models, on the other hand, are often tailored to specific application scenarios and lithium battery types, using function fitting to analyze the impact of parameters such as ambient temperature, depth of charge / discharge, and SOC on battery capacity decay. Some existing literature uses exponential and polynomial functions to fit battery life degradation curves, while others propose a battery module reliability assessment method based on the health status of individual cells. However, these methods fail to incorporate distribution network operation strategies and cannot reflect the actual performance changes of energy storage systems within distribution networks. Overall, existing empirical models cannot fully reflect the interaction between energy storage performance degradation and distribution network scheduling decisions.

[0006] In addition, extensive research has been conducted on reliability assessment algorithms that take energy storage into account. Traditional Monte Carlo (MC) simulation is the most classic method, and many studies have proposed new improvement directions and models based on it. Current research focuses on improving algorithm efficiency. Some studies have proposed pseudo-sequential Monte Carlo simulation strategies and adaptive sampling methods to improve computational efficiency. In addition, a cross-entropy-based importance sampling method has been proposed to enhance computational performance, and an evaluation method has been accelerated by replacing optimal power flow calculations with Lagrange multiplier functions. Some literature has also proposed two criteria for identifying independent faults in distribution networks and an analytical method based on high-order fault state elimination to achieve rapid evaluation.

[0007] Machine learning techniques have also been applied to distribution network reliability assessment. However, these accelerated algorithms are often limited by the strong temporal nature of energy storage systems, making them difficult to apply directly to the reliability assessment of distribution networks containing energy storage. Some literature attempts to reduce the dimensionality of the problem by establishing probabilistic ES models, but more impactful results could be obtained by calculating the charge and discharge power of energy storage systems. Similarly, some literature proposes probabilistic multi-safety models for energy storage systems, but these also rely on sufficient data support. Therefore, existing distribution network reliability assessment research does not fully consider the impact of energy storage performance changes over time on the assessment results.

[0008] Although there have been many advances in the research of energy storage systems and their impact on distribution network reliability, there are still some unresolved issues. These include: Since energy storage is located within the distribution network, the dispatching decisions of the distribution network include energy storage dispatching decisions. However, after the energy storage performance decays, its performance, such as changes in charging and discharging power and rated capacity upper and lower limits, will affect the dispatching decisions of the distribution network.

[0009] (1) Energy storage performance degradation is affected by multiple factors such as energy throughput, temperature, and SOC range. It is a serious nonlinear problem in distribution network scheduling. How to incorporate the nonlinear decision-making quantity of energy storage performance degradation into distribution network scheduling decisions is a problem that has not yet been fully solved.

[0010] (2) The performance degradation of energy storage systems is strongly affected by time series, but the reliability assessment cycle is long. Long-term optimal power flow calculations will significantly increase the computational burden. Therefore, how to reasonably simplify the model while ensuring computational efficiency to accurately capture the dynamic changes in energy storage performance remains an urgent problem to be solved. Summary of the Invention

[0011] To overcome the above-mentioned deficiencies in the prior art, the present invention provides a method for rapid reliability assessment of a distribution network taking into account degradation of energy storage performance, thereby overcoming the deficiencies in reliability assessment of a distribution network containing energy storage.

[0012] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: First, a method for rapidly assessing distribution network reliability taking into account energy storage performance degradation is disclosed, comprising: Establish an energy storage performance degradation model that considers the effects of temperature, SOC range, and time. The energy storage performance degradation model includes a battery cell capacity degradation model and a battery power degradation model. Based on the established model, the duration of each state of the equipment is described, thereby obtaining a reliability assessment model that takes into account the degradation of energy storage performance under fault scenarios. The reliability assessment model includes the established distribution network optimization scheduling model; The distribution network optimization dispatch model is modified, and the energy storage performance degradation under normal scenarios is calculated based on the modified dispatch model; The reliability index of the distribution network is calculated based on the energy storage performance attenuation, and the reliability index is used to quickly evaluate the reliability of the distribution network.

[0013] As a further technical solution, the method further includes the steps of linearizing the established distribution network optimization dispatching model; After linearization processing, the optimization scheduling problem corresponding to the above distribution network optimization scheduling model is transformed into a mixed integer second-order cone programming problem, which can solve the scenario.

[0014] As a further technical solution, the distribution network optimization scheduling model taking into account the energy storage performance degradation in the fault scenario includes: The construction goal is to minimize the total cost of the system during the scheduling period; The corresponding constraints include: power flow constraints, energy storage operation constraints, safety constraints, load reduction constraints and new energy DG constraints.

[0015] As a further technical solution, the energy storage performance degradation under normal scenarios is calculated, specifically including: Under normal operating scenarios, by clustering temperature and DG output, we determine the temperatures and DG outputs that affect energy storage performance degradation in typical scenarios and calculate the energy storage performance degradation for these scenarios. Then, based on the cluster center of DG output and temperature in each scenario, we determine the energy storage performance degradation for that scenario.

[0016] As a further technical solution, the optimized scheduling model also includes recording the cumulative energy throughput of the energy storage in each scheduling cycle after solving the model, obtaining the cumulative energy throughput from the initial moment of the energy storage to the current cycle, and calculating the current energy storage performance through the energy storage performance attenuation model for the optimized scheduling of the distribution network in the next scheduling cycle.

[0017] Secondly, a distribution network reliability rapid assessment system taking into account energy storage performance degradation is disclosed, including: The energy storage performance degradation model construction module is configured to: establish an energy storage performance degradation model that considers the effects of temperature, SOC range, and time. The energy storage performance degradation model includes a battery cell capacity degradation model and a battery power degradation model; a reliability assessment model module configured to: describe the duration of each state of the device based on the established model, thereby obtaining a reliability assessment model that takes into account energy storage performance degradation under fault scenarios, wherein the reliability assessment model includes the established distribution network optimization scheduling model; The energy storage performance degradation calculation module under normal scenarios is configured to: modify the distribution network optimization scheduling model, and calculate the energy storage performance degradation under normal scenarios based on the modified scheduling model; The evaluation module is configured to calculate a reliability index of the distribution network based on the energy storage performance attenuation, wherein the reliability index is used to quickly evaluate the reliability of the distribution network.

[0018] One or more of the above technical solutions have the following beneficial effects: In order to quantify the energy storage performance degradation in the distribution network, the present invention constructs an energy storage performance degradation model. The model covers two aspects: capacity degradation and power degradation, and can be solved jointly with the distribution network optimization scheduling. Secondly, in order to improve the efficiency of solving the energy storage performance degradation and load loss under various operating scenarios, this paper establishes a rapid reliability assessment framework. The framework includes the refined solution of fault scenario indicators and the rapid quantification of energy storage performance degradation under normal scenarios. By incorporating the energy storage operation and maintenance costs into the optimized scheduling of normal scenarios, the overcharging and over-discharging behavior of energy storage is reduced, thereby reducing the energy throughput, slowing down the energy storage degradation rate, and further providing more potential to meet the power supply needs of fault scenarios.

[0019] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 Schematic diagram of the distribution network optimization scheduling mechanism taking into account energy storage performance degradation according to an embodiment of the present invention; Figure 2 Schematic diagram of distribution network reliability assessment taking into account energy storage performance degradation according to an embodiment of the present invention; Figure 3 This is a statistical diagram of sequential sampling fault orders according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the improved IEEE33-node distribution network structure; Figure 5 This is a schematic diagram of the energy storage scheduling results for two consecutive days; Figure 6 This is a schematic diagram comparing energy storage charging and discharging plans; Figure 7 Schematic diagram of the annual average failure rate of node load. DETAILED DESCRIPTION

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0023] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0024] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0025] Example 1 This embodiment discloses a method for rapid reliability assessment of a distribution network taking into account energy storage performance degradation, including: Step 1: Establish a semi-empirical model of ES performance degradation; Clustering the source-load and temperature scenarios yields multiple cluster centers (typical scenarios). The performance attenuation of energy storage under typical scenarios is calculated, and the performance attenuation of energy storage under each scenario is quantified using the calculation amount of the typical scenarios.

[0026] Step 2: Establish a reliability assessment framework that takes into account the degradation of energy storage performance, and establish a distribution network reliability assessment process that takes into account the degradation of energy storage performance.

[0027] In step 1, the energy storage system is related to the performance of the battery cells and their series and parallel configurations. Considering the role of advanced BMS systems, the performance of the battery cells is consistent with the overall performance distribution of the energy storage system. The performance distribution of the battery cells can be used to describe the performance distribution of the energy storage system. Battery cell performance degradation consists of two components: capacity degradation and power degradation.

[0028] (1-1) Battery cell capacity attenuation model 1) Calendar aging Calendar aging refers to the natural aging of batteries during storage. During the battery's life cycle, the capacity loss caused by calendar aging is related to the battery's time, temperature, and state of charge. The main aging mechanism is chemical reaction, and the Arrhenius law is used to explain the effect of thermal stress on performance. Equation (1) is the general form of battery capacity degradation over time, using the modified Arrhenius law.

[0029] (1) Where, is the per-unit value of capacity attenuation caused by calendar aging, where for Ambient temperature at the moment (in K), is the pre-exponential factor, and the reference state of charge range of the energy storage related, is the activation energy, is the Boltzmann constant, is the time degradation function of the battery. The selected aging shape function can be , and take When it is 1, the calendar capacity aging is linearly related to time.

[0030] The above formula (1) is used to calculate the capacity and charge-discharge performance degradation in daily energy storage scheduling. This part is calendar aging and is affected by temperature and time.

[0031] Cycle aging refers to the capacity loss caused by energy storage during the charge and discharge process. The power law equation is used to describe the relationship between battery capacity loss and energy throughput, as shown in Equation (2).

[0032] (2) (3) Where, is the per-unit value of energy storage capacity loss due to cycle aging, 、 Reference range during the cycle related, is the cumulative energy throughput of the energy storage, is the nominal voltage of the battery, is the initial rated capacity of the energy storage, is the temperature influence factor, for Ambient temperature at all times, is the ambient reference temperature, is the activation energy of cyclic aging.

[0033] Formula (2) is used to calculate the capacity and charge-discharge performance degradation in daily energy storage scheduling. This part is cycle aging, which is related to the number of charge and discharge times and temperature.

[0034] Combining cycle aging and calendar aging, the rated capacity value of energy storage at each moment is obtained, as shown in formula (4): (4) Where, is the initial rated capacity of the energy storage.

[0035] Formula (4) includes two parts: calendar aging and cycle aging. It is used here to calculate the remaining capacity of energy storage during scheduling, and it will also be used to calculate the charge and discharge performance later.

[0036] (1-2) Battery power attenuation model The charge and discharge power of a battery is related to its internal resistance. As the battery ages, its polarization internal resistance does not change significantly, while its ohmic internal resistance increases, causing the battery's power to decay. ) is often calculated through capacity attenuation rate, and related research has verified that The relationship between the internal resistance of the battery.

[0037] (5) (6) Where, and Represents batteries exist Remaining capacity and ohmic internal resistance at the moment; It's a battery capacity at the end of life; and It is a battery cell The initial capacity and ohmic internal resistance; is the ohmic internal resistance and Linear fit coefficients for numerical relationships.

[0038] The ohmic internal resistance of the energy storage battery is calculated using the above formulas (5) and (6), and the degradation of the charge and discharge performance is subsequently calculated using the ohmic internal resistance.

[0039] During the charging process, the battery can reach its maximum power when the voltage reaches its maximum value, so it can be considered that the maximum power of a battery cell is inversely proportional to its internal resistance. Power output capability .

[0040] (7) Where: For battery cells The initial maximum output power.

[0041] The above formula (7) is used to calculate the power output capacity (charging and discharging performance) of energy storage scheduling. for t Time battery monomer i The maximum output power determines the maximum performance of energy storage power output.

[0042] Step 2: A distribution network reliability assessment framework considering energy storage performance degradation.

[0043] In order to consider the timing when generating scenarios, Sequential Monte Carlo is used to describe the duration of each state of the device, thereby obtaining the operating scenario. The sub-technical solution of this embodiment uses a step-one model to model the energy storage of the distribution network, and the remaining devices are modeled using a two-state model sampling. The state duration sampling is shown in Equation (8): (8) Where, , is the failure rate and repair rate of the equipment, To generate pseudo-random numbers that follow a uniform distribution between (0, 1). After simulating the operation scenarios, the optimization scheduling model is used to calculate the energy storage performance degradation and reliability indicators for each scenario.

[0044] Through the above state duration sampling, the operation scenarios of all devices can be obtained, which are represented by variables 0 and 1, and 1 is used to fill all TTF samples.

[0045] (2-1) Reliability assessment model considering energy storage performance degradation under fault scenarios (1) Distribution network optimization dispatch model considering energy storage performance degradation under fault scenarios In the fault scenario, the goal of the optimization scheduling model is to minimize the total cost of the system within the scheduling period, as shown in formula (13). In the objective function, is the operating cost of the energy storage system, where It is the absolute value of the energy storage charging and discharging power. Indicates the load shedding cost. In normal scenarios, the load shedding cost item is not considered. Punishment for abandoning new energy.

[0046] (9) Where, is the duration of the scheduling cycle; 、 、 They are energy storage, nodes, The number of .

[0047] The objective function is mainly subject to the following constraints 1) Power flow constraint: Based on the large resistance-to-reactance ratio of the distribution network, the branch power flow method (Distflow) is used to describe the power flow of the distribution network. (10) Where, and Represents nodes The upstream and downstream node sets, and Branch The first section of active power and reactive power, when a fault occurs and causes line failure, the line The active and reactive power can be set to 0. and Node Active power and reactive power injection values, 、 For nodes The connected generator and DG output active power, For the node The connected generator outputs reactive power, and is 0 if there is no connection; For branch Impedance; For flow through branch The square of the current amplitude; For nodes The square of the voltage amplitude at .

[0048] 2) Energy storage operation constraints (11) (12) (13) (14) (15) Where, and for Variable, indicating the state of energy storage charging and discharging, Indicates energy storage Always charging; For energy storage Maximum power transmission capability at any moment; is the charging and discharging efficiency of energy storage, For energy storage The state of charge at the moment, and The upper and lower limits of the state of charge of the energy storage respectively; and Indicates the energy storage at the beginning and end of the dispatch cycle When the energy storage performance decays to the set threshold, replace the energy storage and and Updated to initial ratings.

[0049] 3) Security constraints (16) (17) Where, For branch The upper limit of current, and Represents nodes respectively The upper and lower voltage limits.

[0050] 4) Load reduction constraints (18) Where, for variable, express Time Node The load at. Representative Node The load at is not removed. Representative Node The load at is removed.

[0051] 5) New Energy DG Constraints (19) Where, and They are the pre-arranged active output and actual active output of DG respectively.

[0052] After solving the above optimization scheduling model in each scheduling cycle, the cumulative energy throughput of the energy storage in that cycle is recorded to obtain the cumulative energy throughput from the initial time of the energy storage to the current cycle. The current energy storage performance is calculated through the energy storage performance attenuation model and used for the distribution network optimization scheduling in the next scheduling cycle. The obtained distribution network optimization scheduling mechanism taking into account the energy storage performance attenuation is shown in the figure below. Figure 1 shown.

[0053] It should be noted that the performance solved in the previous cycle is used for this cycle. In the optimization scheduling problem, a scheduling cycle is 24 hours. The optimization scheduling model is also modeled based on a scheduling cycle, and the solution is obtained through the scheduling quantity within a cycle.

[0054] (2-2) Model linearization In the above optimization scheduling model, equations (1), (2), and (7) are serious nonlinear problems and cannot be solved by a simple second-order cone relaxation method. Taylor formula is used to expand their first-order linearization, as shown in equations (20), (21), and (22): (20) (twenty one) (twenty two) Where, 、 Energy storage In the Scheduling cycle Cyclic aging capacity attenuation and energy throughput at each moment, For energy storage In the Scheduling cycle The pre-exponential factor at time. 、 Batteries In the Scheduling cycle Rated capacity and rated charge and discharge power at the moment, For the The rated capacity at the initial moment of a scheduling cycle is given by The scheduling results of the cycle are obtained.

[0055] The inequality regarding the energy storage charging / discharging power constraint can be linearized using the Big M method as follows: (twenty three) Where, is a sufficiently large value. After linearization, the above optimization scheduling problem is transformed into a mixed integer second-order cone programming problem, which can solve the scenario.

[0056] In this embodiment, the two solutions are solutions to the same scheduling problem. The solution to the scheduling problem can obtain the energy storage performance, and then the energy storage performance of this cycle will affect the distribution network scheduling in the next scheduling cycle.

[0057] The above content of this example is equivalent to establishing a mixed-integer second-order cone programming model, which can be used to continuously solve the energy storage performance within the scheduling cycle. However, the distribution network reliability assessment requires large-scale periodic sampling. For example, sampling for 500 years, that is, 500*365 scheduling cycles. Using a computer to perform a scheduling solution takes 30-50 seconds. Solving so many scheduling cycles is very time-consuming, so the subsequent steps need to reduce the number of scheduling solutions.

[0058] (2-3) Rapid quantification steps for energy storage performance degradation under normal scenarios; Energy storage performance degradation is highly temporal. Sequential sampling simulations generate a large number of scenarios with normal performance. The energy storage performance degradation in these scenarios affects the energy storage's ability to supply interrupted loads in fault scenarios. Furthermore, solving the energy storage performance degradation problem in these normal scenarios is a large problem, making it difficult to solve using optimization scheduling models. To address the computational scale issue and incorporate energy storage performance degradation in normal scenarios into reliability analysis, a method for estimating energy storage performance based on scenario clustering and reduction is proposed.

[0059] In normal scenarios, there is no load shedding, so the load shedding cost does not need to be considered in the objective function. However, performance degradation in normal scenarios affects the energy storage's potential to restore power in fault scenarios. Therefore, in normal scenarios, it is necessary to consider the energy storage operation and maintenance costs, balance the contradiction between energy storage performance degradation and the distribution network's new energy consumption, and slow down energy storage performance degradation in normal scenarios to maximize the energy storage's potential to restore power in fault scenarios. The objective function was redesigned as shown in (24)-(26).

[0060] (twenty four) (25) (26) Where, for The segmentation mark of the moment is a 0-1 variable. When the discharge depth is at 1 if segmented, 0 otherwise; and is a piecewise linear constant, reflecting the impact of different charge and discharge depths on battery operation and maintenance costs; D is the number of linear segments; Characterize the impact of the daily average battery temperature on operation and maintenance costs.

[0061] Formulas (27)-(30) determine the number of linear segments in (25). The remaining constraints are the same as those in Section 3 (except the load shedding constraint).

[0062] (27) (28) (29) (30) Where, and for The upper and lower limits of the discharge depth. hour, ,otherwise .

[0063] The above constraints are segmented quantitative constraints on the energy storage charge and discharge depth. These constraints are related to the operation and maintenance costs in the objective function. By involving these constraints, the overcharge and over-discharge behavior of energy storage can be reduced and the service life cycle of energy storage can be improved.

[0064] By limiting the depth of energy storage charge and discharge, energy throughput can be reduced in normal scenarios, thereby extending storage lifespan. However, due to the large number of normal scenarios, it is impossible to determine the degree of energy storage performance degradation for all of them. It is necessary to extract key characteristics of energy storage performance degradation and use these characteristics to analyze the degree of performance degradation. Considering that the primary function of energy storage is peak and valley shifting, energy storage charge and discharge schedules are primarily influenced by renewable energy output, load demand, and temperature, providing a basis for extracting characteristics of energy storage performance degradation.

[0065] The ES optimization scheduling model and energy storage degradation model can be used to extract characteristic quantities of ES performance degradation. Energy storage performance degradation can be described by variables such as installed renewable energy capacity, load, temperature, the current cumulative throughput of energy storage, and the performance status at the previous moment. Although energy storage performance is time-varying, performance differences within consecutive scheduling cycles are minimal, and energy storage performance can be considered to be generally consistent across consecutive scheduling cycles.

[0066] Part of the above-mentioned characteristic quantities comes from the energy storage performance degradation modeling formula, in which the energy storage performance degradation is affected by these factors. The other part comes from the scheduling problem modeling. When the scheduling problem is modeled, these influences will affect the energy storage charging and discharging, and thus affect the energy storage performance degradation.

[0067] Therefore, when the load is determined, the performance degradation is related to the temperature and renewable energy output scenarios. By clustering the renewable energy processing and temperature scenarios, the temperature and renewable energy output under typical scenarios are obtained. The energy storage performance degradation under these scenarios is obtained through optimized scheduling calculations, which can realize the quantification of energy storage performance degradation.

[0068] Steps (2-4) Distribution network reliability assessment process taking into account energy storage performance degradation like Figure 2 As shown in the figure, under normal operation, by clustering temperature and DG output, we determine the temperature and DG output that affect energy storage performance degradation in typical scenarios, and calculate the energy storage performance degradation for these typical scenarios. Then, according to the time-series scenario sequence, based on the cluster center of DG output and temperature in each scenario, we determine the energy storage performance degradation for that scenario. It is worth noting that after clustering, when performing optimized scheduling calculations for these scenarios, the energy storage performance is reset to its initial performance state, resulting in errors.

[0069] This example uses the kmeans clustering method to cluster the temperature and new energy daily scenarios. A 50-year sampling scenario test is simulated through sequential sampling, such as Figure 3 As shown, 99% of the operating scenarios are normal, with a minority representing low-level fault scenarios. Calculating energy storage performance degradation for clustered typical scenarios effectively reduces computational complexity and provides a more accurate energy storage performance profile for sequentially simulated fault scenarios.

[0070] The distribution network reliability assessment process that takes into account energy storage performance degradation is divided into three steps. The first step is to use the sequential Monte Carlo sampling method to simulate the operation scenarios of the distribution network; the second step is to cluster the normal scenarios in the operation scenarios and calculate the energy storage performance degradation for the typical scenarios obtained by clustering; the third step is to calculate the reliability indicators of the distribution network through various scenarios.

[0071] The specific reliability evaluation algorithm process is shown in Algorithm 1.

[0072]

[0073] The reliability assessment of the distribution network taking into account the degradation of energy storage performance mentioned above requires obtaining the distribution network architecture, load, ambient temperature, and the predicted value of the distribution network's new energy output; The processing steps are mainly "running scenario simulation, running scenario performance solution and reliability calculation, reliability index statistics and output", which are simulated by computers (because the problem scale is very large) Calculation is achieved through computers; After calculation, the energy storage performance, replacement times, and distribution network reliability evaluation indicators are obtained; A brief explanation of the evaluation: Reliability assessment is carried out through "running scenario simulation, running scenario performance solution and reliability calculation, reliability index statistics and output". Here is an example to describe it. The probability of heads and tails of a coin tossing is 1 / 2. This is done by a person tossing it hundreds of thousands of times (this corresponds to running scenario sampling simulation), recording the heads and tails each time (this corresponds to running scenario reliability calculation), and finally counting the number of heads and tails that appear (this corresponds to reliability index statistics).

[0074] Case analysis: The improved IEEE33 bus system is used in the case study to verify the reliability evaluation method. Figure 4 As shown, the DESs (buses 16, 21, and 32) and wind turbines (WT, buses 16, 21, and 32) are used. The energy storage system has a rated capacity of 0.4234 MWh and an initial rated power of 0.3175 MW. The energy storage system is assumed to consist of three battery modules, each with 14 battery packs connected in parallel, and each battery pack consists of 14 batteries connected in series. Detailed parameters of the energy storage system and batteries are shown in Table 1. The case analysis was compiled in the MATLAB R2020b environment, modeled using the YALMIP toolbox, and solved using the GUROBI solver. All analyses were performed on a laptop (Intel Core i5-13th Gen 3.0 GHz, 16 GB RAM).

[0075] Table 1 Energy storage model parameters

[0076] Verification of the rapid quantification model of energy storage performance degradation: A scenario was established to verify the importance and correctness of the quantification of energy storage performance degradation. Based on IEEE 33 nodes, wind turbines and energy storage were connected at 16 nodes. It was assumed that the energy storage performance was in the initial state, and the same set of temperatures were set. The scenario was analyzed using the method designed in Section 4. First, in order to verify the rationality of the quantitative results of energy storage performance degradation, a normal scenario was set for two consecutive days. The temperature, DG output and other parameters were set to be consistent for the two days, and a scheduling simulation was performed for the two-day scenario. Taking into account the impact of energy storage performance degradation, the arrangement of energy storage charging and discharging plans in the same scenario was affected to a certain extent, but from a shorter time scale, the performance degradation of energy storage within the set two days was very small. In addition, the energy storage achieved the absorption of DG in a short period of time, which had little effect on the scheduling results, such as Figure 5 As shown in the figure, it can be roughly assumed that energy storage performance degradation under the same scenario is only related to ambient temperature and DG output, thus verifying the correctness of the energy storage performance degradation quantification method. To verify the necessity of the energy storage performance quantification method, the above example was modified to increase the number of simulation days to 50 consecutive days. The temperature curves of the 50-day continuous scenario were clustered into three typical temperature curves, and the energy storage performance after 50 days was calculated. The optimized scheduling model was used for comparison, and an accuracy analysis was performed to explain the errors.

[0077] Table 2 Comparison of energy storage performance quantification methods

[0078] As can be seen, the proposed method exhibits some error in quantifying energy storage performance degradation, with a maximum capacity error of 1.86% and a maximum charge / discharge power error of 2.3%. This error stems from the typical clustering scenario and initial energy storage performance, and is within a reasonable acceptable range. Furthermore, the proposed quantification method is highly efficient. Compared to solving 50 scenarios using continuous optimization scheduling, the proposed method saves nearly 90% of the time required to quantify energy storage performance degradation. This efficiency will be even higher when addressing a wider range of scenarios.

[0079] Energy storage life analysis In order to study the optimization effect of energy storage life in normal scenarios, this paper optimizes the charge and discharge plan of the energy storage system on a typical day (24 hours) under the conditions of considering and not considering battery life loss. The typical day temperature provided by the existing technology is used, and the optimization results are as follows: Figure 6 shown.

[0080] Compared to the case without considering battery operation and maintenance costs, taking battery operation and maintenance costs into account controls the energy storage's charge and discharge power at each moment within a smaller range. This avoids frequent charging and discharging, as well as deep charging and discharging, and reduces the energy storage's energy throughput, significantly slowing the energy storage degradation process. This is even more pronounced when comparing the number of energy storage replacements below.

[0081] Comparison of energy storage performance attenuation To demonstrate the impact of energy storage performance degradation on distribution network reliability, the following four simulation cases were conducted over a 500-year period. The results are the average of multiple calculations and analyses.

[0082] Case 1: Ignoring energy storage performance degradation, using two-state expansion to model the reliability of the energy storage system Case 2: Ignore the energy storage performance degradation in normal scenarios, take into account the energy storage performance degradation in fault scenarios, and do not consider the energy storage operation and maintenance costs. The energy storage performance is reduced to Replace energy storage Case 3: Using the method in Section 3 to consider the energy storage performance degradation in all scenarios, without considering the energy storage operation and maintenance costs, the energy storage performance is reduced to Replace energy storage Case 4: Using the full-scenario energy storage performance degradation considered in Section 3, and considering the energy storage operation and maintenance costs, the energy storage performance is reduced to Replace the energy storage when needed.

[0083] The reliability evaluation indicators of the distribution network under the three cases are shown in the following table.

[0084] Table 3 Comparison of reliability index and calculation time calculated by different methods

[0085] Table 3 shows that Scheme 1 takes very little time to solve. This is because Scheme 1 does not analyze normal scenarios and fails to consider the impact of energy storage degradation. Compared with other schemes, Scheme 1 calculates relatively low reliability indicators (SAIFI, SAIDI, and EENS). This overly idealizes the performance of energy storage in fault scenarios, resulting in unrealistic results. Furthermore, Scheme 1's calculation time is only three-quarters of that of Schemes 2 and 3, not significantly reducing computational time. It is worth noting that Scheme 4 is very time-consuming because the optimization model established in Section 4 increases the complexity of energy storage operation, resulting in a relatively long solution time.

[0086] Comparing Scheme 2 and Scheme 3, we can see that the solution time difference is only 39 seconds, a small difference, demonstrating the high efficiency of the energy storage performance quantification method proposed in this article for normal scenarios. Secondly, there is a certain gap between the results of the two methods with respect to the reliability assessment indicators, with the reliability indicators calculated by Scheme 3 being more serious. This is because Scheme 2 does not consider the performance degradation under normal scenarios, resulting in relatively ideal energy storage performance under fault scenarios. The energy storage's charge and discharge capacity under fault scenarios is overestimated, resulting in an overly high and unrealistic reliability assessment result.

[0087] Scheme 3 takes performance degradation into account in normal scenarios. Load shedding does not occur in normal scenarios, so the difference in reliability indicators between Schemes 2 and 3 stems from the level of energy storage restoration in fault scenarios. Comparing the number of energy storage replacements reveals that quantifying energy storage performance in normal scenarios significantly impacts the number of replacements, demonstrating the necessity of quantifying energy storage performance in normal scenarios.

[0088] As can be seen from the number of energy storage replacements, Scheme 4 reduces the number of energy storage replacements. The proposed energy storage performance optimization model can provide greater potential for energy storage restoration in fault scenarios. This method addresses energy storage performance degradation in normal scenarios while maintaining solution efficiency, providing more accurate reliability assessment results.

[0089] The node load failure rate diagram shows that the average annual failure rate of loads at energy storage access nodes is lower. Compared to Scheme 2, Scheme 3 has a slightly higher overall node load failure rate. Due to Scheme 3's lower energy storage performance, in a failure scenario, the available dispatchable resources for energy storage are smaller, and the load restoration capability is also relatively limited. The failure rate of loads at nodes close to energy storage is more significantly affected, demonstrating the importance of energy storage performance degradation on distribution network reliability. Scheme 4, which considers energy storage O&M costs, has a similar node failure rate to Scheme 3, but the number of energy storage replacements varies significantly, highlighting the importance of developing an energy storage O&M strategy.

[0090] This paper proposes a rapid reliability assessment method for distribution networks that accounts for energy storage performance degradation. An energy storage performance degradation model is established, and a framework for distribution network reliability assessment that accounts for energy storage performance degradation is proposed. This framework addresses all scenarios while ensuring accuracy and efficiency. A rapid quantification method for energy storage performance degradation indicators under normal scenarios is proposed, significantly improving the efficiency of reliability assessment.

[0091] Numerical results demonstrate the importance of modeling energy storage performance degradation and rapidly quantifying it across all scenarios. The proposed rapid reliability assessment framework can rapidly calculate energy storage performance degradation under normal scenarios, improving the overall reliability calculation speed.

[0092] This research can further integrate the self-discharge characteristics of energy storage with the increasing load demands of distribution networks to achieve more refined reliability assessments. Regarding the limitations of this research, errors caused by repeatedly using rated capacity when quantifying the rapid degradation of energy storage performance cannot be avoided. Future research could focus on this area.

[0093] This technical solution studies the performance degradation of energy storage under various conditions, including ambient temperature, state of charge range, and distribution system charge and discharge schedules. It proposes a rapid reliability assessment framework for distribution networks that accounts for energy storage performance degradation, thereby overcoming the shortcomings of reliability assessments for distribution networks containing energy storage. The main technical features of this example are as follows: (1) A storage performance degradation model considering the influence of temperature, SOC range, and time was established. This model serves as a link between energy storage performance and distribution network scheduling decisions. It can be analyzed jointly with distribution network scheduling to obtain the performance of energy storage in various scenarios.

[0094] (2) A rapid reliability assessment framework for distribution networks taking into account the degradation of energy storage performance is proposed. This framework combines detailed optimal power flow calculations for fault scenarios with rapid quantification of energy storage performance for normal scenarios to achieve rapid reliability assessment of distribution networks taking into account the degradation of energy storage performance.

[0095] (3) A clustering-based rapid quantification method for energy storage performance under normal scenarios is proposed, which effectively addresses the computational complexity caused by the performance calculation of energy storage over time in reliability assessment. In addition, the energy storage operation and maintenance cost is designed under normal scenarios to reduce the overcharge and over-discharge behavior of energy storage to reduce the energy storage performance degradation, so that the energy storage scheduling decision can be made with the maximum recovery load point load in the fault scenario.

[0096] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0097] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0098] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the above method.

[0099] Example 4 The purpose of this embodiment is to provide a distribution network reliability rapid assessment system that takes into account energy storage performance degradation, including: The energy storage performance degradation model construction module is configured to: establish an energy storage performance degradation model that considers the effects of temperature, SOC range, and time. The energy storage performance degradation model includes a battery cell capacity degradation model and a battery power degradation model; a reliability assessment model module configured to: describe the duration of each state of the device based on the established model, thereby obtaining a reliability assessment model that takes into account energy storage performance degradation under fault scenarios, wherein the reliability assessment model includes the established distribution network optimization scheduling model; The energy storage performance degradation calculation module under normal scenarios is configured to: modify the distribution network optimization scheduling model, and calculate the energy storage performance degradation under normal scenarios based on the modified scheduling model; The evaluation module is configured to calculate a reliability index of the distribution network based on the energy storage performance attenuation, wherein the reliability index is used to quickly evaluate the reliability of the distribution network.

[0100] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any of the above embodiments. The steps involved in the apparatus of the above embodiment correspond to those of the method embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0101] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0102] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A rapid evaluation method for distribution network reliability taking into account energy storage performance degradation is characterized by: include: Establish an energy storage performance degradation model that considers the effects of temperature, SOC range, and time. The energy storage performance degradation model includes a battery cell capacity degradation model and a battery power degradation model. Based on the established model, the duration of each state of the equipment is described, thereby obtaining a reliability assessment model that takes into account the degradation of energy storage performance under fault scenarios. The reliability assessment model includes the established distribution network optimization scheduling model; The distribution network optimization dispatch model is modified, and the energy storage performance degradation under normal scenarios is calculated based on the modified dispatch model; The reliability index of the distribution network is calculated based on the energy storage performance attenuation, and the reliability index is used to quickly evaluate the reliability of the distribution network.

2. The method for rapid evaluation of distribution network reliability taking into account energy storage performance degradation according to claim 1, characterized in that: include: The steps of linearizing the established distribution network optimization dispatching model; After linearization processing, the optimization scheduling problem corresponding to the above distribution network optimization scheduling model is transformed into a mixed integer second-order cone programming problem, which can solve the scenario.

3. The method for rapid evaluation of distribution network reliability taking into account energy storage performance degradation as claimed in claim 1, wherein: The distribution network optimization dispatch model taking into account the energy storage performance degradation under the fault scenario includes: The construction goal is to minimize the total cost of the system during the scheduling period; The corresponding constraints include: power flow constraints, energy storage operation constraints, safety constraints, load reduction constraints and new energy DG constraints.

4. The method for rapid evaluation of distribution network reliability taking into account energy storage performance degradation as claimed in claim 1, wherein: The energy storage performance attenuation under normal scenarios is calculated, including: Under normal operating scenarios, by clustering temperature and DG output, we determine the temperatures and DG outputs that affect energy storage performance degradation in typical scenarios and calculate the energy storage performance degradation for these scenarios. Then, based on the cluster center of DG output and temperature in each scenario, we determine the energy storage performance degradation for that scenario.

5. The method for rapid evaluation of distribution network reliability taking into account energy storage performance degradation according to claim 1, characterized in that: include: After solving each scheduling cycle, the optimization scheduling model records the cumulative energy throughput of the energy storage in that cycle to obtain the cumulative energy throughput from the initial moment of the energy storage to the current cycle, and calculates the current energy storage performance through the energy storage performance decay model for the distribution network optimization scheduling in the next scheduling cycle.

6. A rapid distribution network reliability assessment system taking into account energy storage performance degradation, characterized by: include: The energy storage performance degradation model construction module is configured to: establish an energy storage performance degradation model that considers the effects of temperature, SOC range, and time. The energy storage performance degradation model includes a battery cell capacity degradation model and a battery power degradation model; a reliability assessment model module configured to: describe the duration of each state of the device based on the established model, thereby obtaining a reliability assessment model that takes into account energy storage performance degradation under fault scenarios, wherein the reliability assessment model includes the established distribution network optimization scheduling model; The energy storage performance degradation calculation module under normal scenarios is configured to: modify the distribution network optimization scheduling model, and calculate the energy storage performance degradation under normal scenarios based on the modified scheduling model; The evaluation module is configured to calculate a reliability index of the distribution network based on the energy storage performance attenuation, wherein the reliability index is used to quickly evaluate the reliability of the distribution network.

7. The distribution network reliability rapid assessment system taking into account energy storage performance degradation as claimed in claim 1, characterized in that: The distribution network optimization dispatch model taking into account the energy storage performance degradation under the fault scenario includes: The construction goal is to minimize the total cost of the system during the scheduling period; The corresponding constraints include: power flow constraints, energy storage operation constraints, safety constraints, load reduction constraints and new energy DG constraints.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method described in any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are performed.

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