Flexible power distribution network dynamic capacity configuration method, system, equipment and medium considering power supply reliability constraint

Abstract

The invention relates to the technical field of power system power distribution network planning and operation, and discloses a flexible power distribution network dynamic capacity configuration method and system considering power supply reliability constraint, equipment and a medium, and the method comprises the steps: collecting the basic data of a power distribution network, and building an equipment fault rate database; constructing a system state space model based on the Markov chain theory, and establishing a reliability evaluation index system; probability distribution is adopted to describe uncertainty factors, and a random scene is generated and reduced through Monte Carlo simulation; establishing a multi-objective optimization model, and solving a Pareto optimal solution set by adopting an improved genetic algorithm; and dynamic capacity configuration is implemented based on a rolling optimization strategy, a trigger adjustment mechanism is established, a configuration scheme is adjusted in real time according to a system operation state, and an optimization configuration strategy is evaluated. According to the method, the capacity dynamic optimization configuration is realized on the premise of ensuring the power supply reliability, and the economy and the operation reliability of the power distribution network planning are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network planning and operation technology, and in particular to a method, system, equipment and medium for dynamic capacity configuration of flexible distribution networks considering power supply reliability constraints. Background Technology

[0002] With the large-scale integration of photovoltaic, energy storage, and charging microgrids and the rapid development of flexible distribution network technology, traditional distribution network capacity configuration methods face severe challenges. Existing technologies mainly suffer from the following drawbacks: Traditional capacity configuration methods employ a static design concept, determining equipment capacity based on historical maximum loads, which cannot adapt to the dynamic and uncertain characteristics of photovoltaic, energy storage, and charging microgrid output. This method often leads to over-configuration of equipment capacity, resulting in high investment costs, while simultaneously potentially causing capacity shortages under extreme operating conditions.

[0003] Existing reliability assessment methods primarily target traditional distribution network designs, lacking in-depth analysis of the reliability characteristics of new equipment such as flexible interconnect devices, energy storage systems, and electric vehicle charging facilities. Traditional N-1 criteria and average outage time are insufficient to comprehensively reflect the reliability level of flexible distribution networks, especially their system resilience under multiple fault modes and extreme weather conditions.

[0004] Current capacity optimization methods mostly employ deterministic optimization models, lacking effective handling of uncertainties. Factors such as the intermittency of photovoltaic power generation, the state dependence of energy storage systems, and the stochastic nature of electric vehicle charging make it difficult for traditional optimization methods to find the true optimal solution. Furthermore, existing methods lack quantitative analysis tools for balancing economic efficiency and reliability, failing to provide a scientific basis for investment decisions. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention provides a method, system, device and medium for dynamic capacity configuration of flexible distribution networks that takes into account power supply reliability constraints.

[0006] Therefore, the technical problem solved by this invention is: to establish a dynamic reliability assessment model suitable for flexible distribution networks, accurately quantify the impact of various equipment failures on system reliability; to construct a dynamic capacity optimization model that considers uncertain factors, and realize intelligent configuration of the capacity of flexible interconnection devices; and to establish a multi-objective balance optimization framework for economy and reliability, providing a quantitative analysis tool for distribution network investment decisions.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints, comprising: collecting distribution network topology, equipment parameters, historical fault data, and load characteristic data; establishing an equipment failure rate database; and setting optimization algorithm parameters. A system state-space model is established based on Markov chain theory, the state transition probability matrix is ​​calculated, and a reliability assessment index calculation model is established. Probability distribution models for random variables such as photovoltaic power generation, load demand, and equipment failure are established. Monte Carlo simulation is used to generate random scenarios, and scenario reduction is employed to reduce computational complexity. A multi-objective optimization model balancing economy and reliability is established, and an improved non-dominated sorting genetic algorithm is used to solve the problem, obtaining the Pareto optimal solution set. Dynamic capacity allocation is implemented based on a rolling optimization strategy, updating the allocation scheme according to load forecasting and renewable energy output forecasting, and a capacity adjustment triggering mechanism is established. The effectiveness of capacity allocation is evaluated, sensitivity analysis is performed, and model parameters and optimization strategies are adjusted based on actual operating results.

[0008] As a preferred embodiment of the flexible distribution network dynamic capacity configuration method considering power supply reliability constraints described in this invention, the establishment of the system state space model based on Markov chain theory includes identifying all possible operating states and fault states of the system, calculating the state transition probability matrix, and establishing evaluation index calculation models for system availability, average outage time, and outage frequency to obtain the quantitative results of system reliability.

[0009] As a preferred embodiment of the flexible distribution network dynamic capacity configuration method considering power supply reliability constraints described in this invention, the probability distribution model includes: using a Beta distribution to describe the uncertainty of photovoltaic power generation output, using a normal distribution to describe the uncertainty of load demand, using an exponential distribution to describe the uncertainty of equipment failure, generating random scenarios through Monte Carlo simulation, and using a scenario reduction method to reduce the number of scenarios while retaining statistical characteristics, thereby obtaining a set of random scenarios after processing uncertainty.

[0010] As a preferred embodiment of the dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints described in this invention, the establishment of a multi-objective optimization model for economy and reliability includes setting an economic objective function and a reliability objective function, solving the problem using an improved non-dominated sorting genetic algorithm, ensuring the quality of the solution through an elite retention strategy, and determining the optimal solution set based on the Pareto dominance relationship to obtain a trade-off scheme for capacity configuration.

[0011] As a preferred embodiment of the dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints described in this invention, the step of implementing dynamic capacity configuration based on a rolling optimization strategy includes calculating the optimal capacity configuration for the next time step based on load forecasts and renewable energy output forecasts. In the formula, This represents the optimal capacity configuration at time t+1. This is the load forecast value. Let F be the predicted photovoltaic power output, and F be the objective function. Establish a capacity adjustment trigger mechanism to dynamically adjust the configuration strategy when system reliability indicators deviate from the target value. In the formula, For capacity adjustment amount, S(t-1) is the capacity configuration at time t-1, which is the adjustment factor. Implement capacity configuration schemes and monitor system operation status and reliability levels in real time.

[0012] As a preferred embodiment of the dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints described in this invention, the evaluation of capacity configuration effectiveness includes calculating power supply reliability risk by defining power supply reliability risk as the product of fault probability and fault consequences. In the formula, For power supply reliability risks, This represents the probability of failure. As a consequence of the malfunction; Calculate the conditional value of risk to obtain the risk quantification results of the system at different confidence levels: In the formula, Confidence level Conditional Value at Risk (VaR) Value at risk.

[0013] As a preferred embodiment of the dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints described in this invention, the sensitivity analysis includes calculating the sensitivity of capacity configuration to reliability indicators. In the formula, The sensitivity of system availability to the capacity of the i-th device. To increase capacity The availability of the system afterwards The system availability under the current capacity. This represents the capacity increment of the i-th device; Analyzing the sensitivity of economic indicators reveals the degree of influence of key parameters on system performance and economy: In the formula, Let be the sensitivity of total cost to the failure rate of the i-th device. Increased failure rate The final total cost, The total cost at the current failure rate. This represents the failure rate increment for the i-th device.

[0014] This invention provides a dynamic capacity configuration system for flexible distribution networks that takes into account power supply reliability constraints.

[0015] As a preferred embodiment of the flexible distribution network dynamic capacity configuration system considering power supply reliability constraints described in this invention, it includes a data management module, an optimization decision module, and a dynamic control module. The data management module is responsible for integrating power grid topology, equipment parameters and operating data, establishing reliability models and probability distributions, and providing data foundation and calculation models for the system. The optimization decision module constructs a multi-objective optimization problem based on a data model, solves it using an improved algorithm, and outputs a set of optimal configuration schemes. The dynamic control module performs rolling capacity configuration, adjusts parameters in real time according to the system status, and forms closed-loop control through monitoring feedback.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a dynamic capacity configuration method for a flexible distribution network that takes into account power supply reliability constraints.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a dynamic capacity configuration method for a flexible distribution network that takes into account power supply reliability constraints.

[0018] The beneficial effects of this invention are as follows: Firstly, a dynamic reliability assessment system for flexible distribution networks is established, which can accurately quantify the impact of various equipment failures on system reliability, providing a reliable theoretical basis for capacity configuration; Secondly, a multi-objective balance optimization model for economy and reliability is constructed, achieving the best trade-off between investment cost and power supply reliability, significantly improving the economic benefits of distribution network investment; Thirdly, a dynamic capacity configuration strategy based on uncertainty is developed, which can adapt to the randomness of power output from photovoltaic-storage-charging microgrids and changes in load demand, greatly improving the adaptability and robustness of the configuration scheme; Fourthly, a complete risk assessment and sensitivity analysis system is established, providing quantitative tools for distribution network operation, maintenance, and risk management. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic flowchart of a dynamic capacity configuration method for flexible distribution networks that takes into account power supply reliability constraints, provided as an embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints, including: S1: Collect data on distribution network topology, equipment parameters, historical fault data, and load characteristic data; establish an equipment failure rate database; and set optimization algorithm parameters.

[0023] S2: Based on Markov chain theory, establish a system state-space model, calculate the state transition probability matrix, and establish a reliability assessment index calculation model.

[0024] S3: Establish a probability distribution model for random variables of photovoltaic power generation, load demand and equipment failure, use Monte Carlo simulation to generate random scenarios, and reduce computational complexity by scenario reduction.

[0025] S4: Establish a multi-objective optimization model that balances economy and reliability, and solve it using an improved non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set.

[0026] S5: Implement dynamic capacity configuration based on rolling optimization strategy, update the configuration scheme according to load forecast and renewable energy output forecast, and establish a capacity adjustment trigger mechanism.

[0027] S6: Evaluate the effectiveness of capacity configuration, conduct sensitivity analysis, and adjust model parameters and optimization strategies based on actual operating results.

[0028] It should be noted that, compared to the current situation where existing static configuration methods cannot adapt to the dynamic output of photovoltaic-storage-charging microgrids, traditional reliability assessment indicators cannot fully reflect the characteristics of flexible equipment, and deterministic optimization models cannot handle uncertainties, by establishing a dynamic reliability assessment and multi-objective optimization model, combined with uncertainty handling and rolling optimization strategies, dynamic and precise configuration of flexible distribution network capacity is achieved. This significantly improves power supply reliability while effectively reducing investment and operating costs, providing key technical support for the scientific planning and economic operation of high-proportion renewable energy access to the distribution network.

[0029] Example 2 is an embodiment of the present invention. Based on the above embodiment, a dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints is provided.

[0030] Furthermore, in this embodiment of the application, step S1 collects the distribution network topology, equipment parameters, historical fault data, and load characteristic data to establish an equipment failure rate database and sets optimization algorithm parameters. Specific steps include: Collect data on distribution network topology, equipment parameters, historical fault data, and load characteristics. Establish a fault rate database for various types of equipment, including fault statistics for flexible interconnection devices, energy storage systems, lines, transformers, and other equipment. Set optimization algorithm parameters, including genetic algorithm parameters such as population size, crossover probability, and mutation probability, as well as convergence criteria and maximum number of iterations.

[0031] Reliability modeling for flexible distribution networks needs to consider the failure characteristics of both traditional and novel flexible equipment. Equipment reliability status is defined as follows: In the formula, This refers to the voltage state of device i. It is in the current state. Temperature status, Humidity status, These are the equipment operating status parameters.

[0032] The failure rate model for flexible interconnected devices takes into account the effects of environmental factors and operating conditions. In the formula, Let t be the failure rate of the flexible interconnect device. Based on the failure rate, This is a temperature correction factor. As the load correction factor, It is an aging correction factor.

[0033] The reliability model of an energy storage system considers the effects of charge-discharge cycle count and state of charge: In the formula, For the reliability of energy storage systems, For initial reliability, To accumulate the number of loops, For rated cycle life, The cyclic decay coefficient is... This is the influencing factor of the state of charge.

[0034] In an optional embodiment, the reliability model of the energy storage system can also be implemented using a linear decay model. Specifically, this model uses the cumulative operating time of the equipment as the main aging factor to establish a model in which the reliability of the energy storage system decays linearly with operating time. This model directly calculates the current reliability level by recording the total operating time of the energy storage system and combining it with its design life data.

[0035] In another optional embodiment, the reliability model of the energy storage system can also be implemented through a comprehensive evaluation model. Specifically, this model introduces battery health state parameters as core indicators and evaluates the reliability of the energy storage system by monitoring its key performance parameters. This model comprehensively considers actual operating data such as voltage consistency and internal resistance changes, directly reflecting the overall health state and remaining lifespan of the energy storage system, thereby assessing its reliability.

[0036] Furthermore, in this embodiment, step S2 establishes a system state-space model based on Markov chain theory, calculates the state transition probability matrix, and establishes a reliability assessment index calculation model. Specific steps include: A state-space model of the system is established based on Markov chain theory to identify and enumerate all possible system operating states and failure states. The system's state transition probability matrix is ​​calculated, which comprehensively considers the impact of equipment failure rate, repair rate, and actual operating conditions. A calculation model for system reliability assessment indicators is established, with core indicators including system availability, average outage time index (SAIDI), and average outage frequency index (SAIFI), thereby quantitatively assessing the system's reliability level.

[0037] Establish a system state transition model based on Markov chain theory: In the formula, Let i be the probability of transitioning from state i to state j. Let be the system state at time n.

[0038] System availability calculation formula: In the formula, For system availability, Mean time between failures (MTBF) This represents the average repair time.

[0039] Power supply reliability metrics include average outage time and outage frequency: In the formula, This is an indicator of the system's average power outage duration. For user i, the annual power outage time Let i be the number of users i.

[0040] In the formula, This is an indicator of the system's average power outage frequency. The number of power outages per year for user i.

[0041] Furthermore, in this embodiment, step S3 establishes a probability distribution model for random variables of photovoltaic power generation, load demand, and equipment failure, generates random scenarios using Monte Carlo simulation, and reduces computational complexity through scenario reduction. Specific steps include: Probability distribution models for random variables such as photovoltaic (PV) power generation, wind power generation, and load demand are established. PV output adopts a Beta distribution, load demand adopts a normal distribution, and equipment failure time adopts an exponential distribution. A large number of correlated random operating scenarios are generated using Monte Carlo simulation. A scenario reduction model is then established to effectively reduce the number of scenarios while preserving the statistical characteristics of the original scenario set to the greatest extent possible, thereby reducing the complexity of subsequent optimization calculations.

[0042] The uncertainty of photovoltaic power generation output is described using a Beta distribution: In the formula, Let be the probability density function of photovoltaic power output. , For Beta distribution parameters, This is a gamma function.

[0043] The uncertainty of load demand is described using a normal distribution: In the formula, Let be the probability density function of load demand. The mean, The standard deviation is denoted as .

[0044] The uncertainty of equipment failure is described using an exponential distribution: In the formula, Let be the fault time probability density function. This refers to the failure rate.

[0045] Furthermore, in the embodiments of this application, step S4 establishes a multi-objective optimization model considering both economy and reliability, and solves it using an improved non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set. Specific steps include: Establish a multi-objective optimization model that comprehensively considers both economic efficiency and reliability: In the formula, Let the objective function vector be... For economic purposes, For the reliability objective, x is the decision variable.

[0046] Objective function for capacity configuration optimization of flexible interconnected devices: In the formula, For investment costs, For operating costs, For reliability costs.

[0047] Investment cost model: In the formula, This refers to the number of flexible interconnect devices. Let i be the capacity of the i-th device. , This is the investment cost coefficient. For the number of energy storage systems, Let the capacity of the j-th energy storage system be... , This represents the energy storage investment cost coefficient.

[0048] Operating costs include energy loss costs and maintenance costs: In the formula, T is the running time. Let be the cost of energy loss at time t. To maintain costs, For time step.

[0049] Reliability costs take into account power outage losses: In the formula, For the number of load nodes, The power outage value of the k-th load node. This is in anticipation of a power shortage.

[0050] An improved non-dominated sorting genetic algorithm is used to solve the problem: In the formula, For the fitness of individual i, The number of individuals dominating individual i; the quality of the solution is guaranteed through an elite retention strategy. The Pareto optimal solution set is calculated, providing decision-makers with multiple trade-off options. The criteria for determining the Pareto optimal solution are: In the formula, For the Pareto optimal solution set, This is the feasible solution space. This is a Pareto dominance relationship.

[0051] The optimization process must meet the following constraints: In the formula, To meet minimum availability requirements, , These represent the maximum permissible power outage time and frequency, respectively.

[0052] Capacity constraints: In the formula, , These are the minimum and maximum capacities of the i-th device, respectively. This is a limit based on total capacity.

[0053] Power grid operation constraints: In the formula, , These are the upper and lower limits of the voltage. is the maximum allowable current for line ij.

[0054] In an optional embodiment, solving the multi-objective optimization model can also be achieved using a Pareto evolutionary algorithm. Specifically, the Intensity Pareto Evolutionary Algorithm (SPEA2) is used as the solver for the multi-objective optimization. This is achieved by maintaining an external archive set to store excellent individuals and using a density estimation method based on k-nearest neighbors to maintain the distribution of the solution set, thereby effectively searching for the Pareto optimal solution set.

[0055] In another alternative embodiment, the multi-objective optimization model can also be solved using a multi-objective particle swarm optimization algorithm. Specifically, by simulating the behavior of social groups, the particle swarm (i.e., the candidate solution set) is guided to fly toward the Pareto front, and external archiving and density estimation strategies are used to maintain the diversity and convergence of the non-dominated solutions found.

[0056] Furthermore, in this embodiment, step S5 implements dynamic capacity configuration based on a rolling optimization strategy, updates the configuration scheme according to load forecasting and renewable energy output forecasting, and establishes a capacity adjustment triggering mechanism. Specific steps include: Dynamic capacity allocation is implemented based on a rolling optimization strategy, updating the optimal capacity allocation scheme for the next time step according to load forecasting and renewable energy output forecasting. In the formula, This represents the optimal capacity configuration at time t+1. This is the load forecast value. This represents the predicted output value for photovoltaic power.

[0057] Establish a capacity adjustment trigger mechanism to adjust the configuration strategy promptly when system reliability indicators deviate from the target value. In the formula, For capacity adjustment amount, For adjustment coefficients; Implement capacity configuration schemes and monitor system operation status and reliability levels.

[0058] In an optional embodiment, the capacity adjustment triggering mechanism can also be event-triggered, specifically by pre-setting thresholds for key operating parameters (such as system availability and load loss power). The system continuously monitors these actual operating parameters, and once any parameter exceeds its preset safety threshold, the capacity adjustment process is immediately triggered.

[0059] In another optional embodiment, the capacity adjustment trigger mechanism can also be configured for periodic verification. Specifically, a fixed time interval (such as every 24 hours or weekly) is set to periodically evaluate the system configuration. At the end of each time period, regardless of the current operating state of the system, a capacity optimization calculation is forcibly initiated, and an adjustment is determined based on the result.

[0060] Furthermore, in this embodiment, step S6 evaluates the capacity configuration effect, performs sensitivity analysis, and adjusts model parameters and optimization strategies based on actual operating results. Specific steps include: Evaluate the effectiveness of capacity configuration, and calculate actual reliability and economic indicators. Conduct sensitivity analysis and risk assessment, including calculating power supply reliability risk and conditional risk value, analyzing the sensitivity of system availability to equipment capacity and the sensitivity of total cost to equipment failure rate, and identifying key factors affecting system performance. Establish a feedback mechanism to adjust model parameters and optimization strategies based on actual operating results, and continuously improve the accuracy and effectiveness of the configuration scheme.

[0061] Power supply reliability risk is defined as: In the formula, For power supply reliability risks, This represents the probability of failure. This is a consequence of the malfunction.

[0062] Conditional Value at Risk Assessment: In the formula, Confidence level Conditional Value at Risk (VaR) Value at risk.

[0063] Sensitivity of capacity configuration to reliability metrics: In the formula, The sensitivity of system availability to the capacity of the i-th device. To increase capacity The availability of the system afterwards The system availability under the current capacity. This represents the capacity increment of the i-th device.

[0064] Sensitivity analysis of economic indicators: In the formula, Let be the sensitivity of total cost to the failure rate of the i-th device. Increased failure rate The final total cost, The total cost at the current failure rate. This represents the failure rate increment for the i-th device.

[0065] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides a dynamic capacity configuration system for flexible distribution networks that takes into account power supply reliability constraints, including: a data management module, an optimization decision module, and a dynamic control module; The data management module is responsible for integrating power grid topology, equipment parameters and operating data, establishing reliability models and probability distributions, and providing the system with data foundation and calculation models; The optimization decision-making module constructs a multi-objective optimization problem based on a data model, solves it using an improved algorithm, and outputs a set of optimal configuration schemes. The dynamic control module performs rolling capacity configuration, adjusts parameters in real time according to system status, and forms closed-loop control through monitoring feedback.

[0066] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the flexible distribution network dynamic capacity configuration method considering power supply reliability constraints as proposed in the above embodiment.

[0067] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the dynamic capacity configuration method for flexible distribution networks that considers power supply reliability constraints as proposed in the above embodiments.

[0068] The storage medium proposed in this embodiment and the method for dynamic capacity configuration of flexible distribution networks considering power supply reliability constraints proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0069] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0071] Example 4 is the first embodiment of the present invention, used to verify the dynamic capacity configuration method of flexible distribution network considering power supply reliability constraints.

[0072] This embodiment uses a modified IEEE 69-node distribution system for simulation verification, employing a co-simulation platform of MATLAB R2023a and DIgSILENT PowerFactory. The simulation system considers four flexible interconnection device access points and six energy storage system access points, with a total installed capacity of 8.5MW. The experiment uses actual operating data from a city's distribution network from 2022 to 2024, including 26,280 hours of continuous monitoring data. The flexible interconnection devices utilize modular multilevel converter technology, and the energy storage systems employ lithium-ion battery technology. Equipment fault data is based on reliability reports provided by the manufacturer and on-site operating statistics.

[0073] Table 1: Performance Comparison of Different Configuration Methods

[0074] As shown in Table 1, the method in this embodiment significantly outperforms the traditional method in all indicators. Compared with the traditional static configuration, the investment cost is reduced by 32.5%, the operating cost by 35.1%, the system availability is increased by 1.13 percentage points, the SAIDI is reduced by 48.7%, and the total cost is reduced by 32.7%. This fully demonstrates the superiority and economic benefits of the dynamic capacity configuration method.

[0075] Table 2: Reliability Analysis under Different Operating Scenarios

[0076] Table 2 illustrates the reliability performance of the method in this embodiment under different operating scenarios. Even under the most severe superimposed fault scenario, the system availability remains at 98.67%, and the fault recovery time is controlled within 3.42 hours, demonstrating good resilience. During normal operation and high load periods, the system availability exceeds 99.4%, meeting the requirements for high-reliability power supply.

[0077] Table 3: Pareto Optimal Solution Analysis

[0078] Table 3 provides the Pareto optimal solutions under different weights, offering decision-makers multiple options. Solution 2 (the recommended solution in this embodiment) achieves the best balance between investment cost and reliability, resulting in the highest overall evaluation index. This solution is suitable for application scenarios with high requirements for both economy and reliability.

[0079] Further sensitivity analysis revealed that the failure rate of flexible interconnect devices had the most significant impact on system reliability; a 10% increase in the failure rate led to a 0.12 percentage point decrease in system availability. Energy storage system capacity configuration had a substantial impact on economics; a 20% increase in capacity could reduce operating costs by 8.5%, but increase investment costs by 15.2%. Load growth exhibited a non-linear relationship with capacity configuration requirements; when the load growth rate exceeded 8%, a significant increase in the capacity of flexible interconnect devices was necessary.

Claims

1. A dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints, characterized in that: include, Collect data on distribution network topology, equipment parameters, historical fault data, and load characteristics; establish an equipment failure rate database; and set optimization algorithm parameters. A system state-space model is established based on Markov chain theory, the state transition probability matrix is ​​calculated, and a reliability assessment index calculation model is established. A probability distribution model for random variables of photovoltaic power generation, load demand and equipment failure is established. Monte Carlo simulation is used to generate random scenarios, and the computational complexity is reduced by scenario reduction. A multi-objective optimization model balancing economy and reliability was established, and an improved non-dominated sorting genetic algorithm was used to solve it, yielding the Pareto optimal solution set. Dynamic capacity allocation is implemented based on a rolling optimization strategy, the allocation scheme is updated according to load forecast and renewable energy output forecast, and a capacity adjustment trigger mechanism is established. Evaluate the effectiveness of capacity configuration, conduct sensitivity analysis, and adjust model parameters and optimization strategies based on actual operating results.

2. The dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in claim 1, characterized in that: The establishment of the system state-space model based on Markov chain theory includes identifying all possible operating and fault states of the system, calculating the state transition probability matrix, and establishing evaluation index calculation models for system availability, average power outage time, and power outage frequency to obtain the quantitative results of system reliability.

3. The dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in claim 2, characterized in that: The probability distribution model includes using a Beta distribution to describe the uncertainty of photovoltaic power generation output, a normal distribution to describe the uncertainty of load demand, and an exponential distribution to describe the uncertainty of equipment failure. Random scenarios are generated through Monte Carlo simulation, and the number of scenarios is reduced by using a scenario reduction method while retaining statistical characteristics, resulting in a set of random scenarios after uncertainty processing.

4. The dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in claim 3, characterized in that: The establishment of the multi-objective optimization model for economy and reliability includes setting an economic objective function and a reliability objective function, solving the problem using an improved non-dominated sorting genetic algorithm, ensuring the quality of the solution through an elite retention strategy, determining the optimal solution set based on the Pareto dominance relationship, and obtaining a trade-off scheme for capacity allocation.

5. The dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in claim 4, characterized in that: The implementation of dynamic capacity allocation based on the rolling optimization strategy includes calculating the optimal capacity allocation for the next time step based on load forecasts and renewable energy output forecasts. In the formula, This represents the optimal capacity configuration at time t+1. This is the load forecast value. Let F be the predicted photovoltaic power output, and F be the objective function. Establish a capacity adjustment trigger mechanism to dynamically adjust the configuration strategy when system reliability indicators deviate from the target value. In the formula, For capacity adjustment amount, S(t-1) is the capacity configuration at time t-1, which is the adjustment factor. Implement capacity configuration schemes and monitor system operation status and reliability levels in real time.

6. The dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in claim 5, characterized in that: The evaluation of capacity configuration effectiveness includes calculating power supply reliability risk by defining it as the product of the probability of failure and the consequences of failure. In the formula, For power supply reliability risks, This represents the probability of failure. As a consequence of the malfunction; Calculate the conditional value of risk to obtain the risk quantification results of the system at different confidence levels: In the formula, Confidence level Conditional Value at Risk (VaR) Value at risk.

7. The dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in claim 6, characterized in that: The sensitivity analysis includes calculating the sensitivity of capacity configuration to reliability metrics. In the formula, The sensitivity of system availability to the capacity of the i-th device. To increase capacity The availability of the system afterwards The system availability under the current capacity. This represents the capacity increment of the i-th device; Analyzing the sensitivity of economic indicators reveals the degree of influence of key parameters on system performance and economy: In the formula, Let be the sensitivity of total cost to the failure rate of the i-th device. Increased failure rate The final total cost, The total cost at the current failure rate. This represents the failure rate increment for the i-th device.

8. A flexible distribution network dynamic capacity configuration system considering power supply reliability constraints, employing the method for flexible distribution network dynamic capacity configuration considering power supply reliability constraints as described in any one of claims 1 to 7, characterized in that, include: Data management module, optimization decision-making module, dynamic control module; The data management module is responsible for integrating power grid topology, equipment parameters and operating data, establishing reliability models and probability distributions, and providing data foundation and calculation models for the system. The optimization decision module constructs a multi-objective optimization problem based on a data model, solves it using an improved algorithm, and outputs a set of optimal configuration schemes. The dynamic control module performs rolling capacity configuration, adjusts parameters in real time according to the system status, and forms closed-loop control through monitoring feedback.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic capacity configuration method for flexible distribution networks considering power supply reliability constraints as described in any one of claims 1 to 7.

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