Optical storage integrated AC / DC system topology optimization method based on adaptive algorithm

Abstract

The invention discloses a topological optimization method for an optical storage integrated AC / DC system based on an adaptive algorithm, and the method comprises the following steps: obtaining an initial topological structure and operation parameters of the optical storage integrated AC / DC system, including photovoltaic power generation, configuration of an energy storage system, operation state parameters of a microgrid, and external environment parameters; optimizing and adjusting the topological structure, and dynamically generating a plurality of candidate topological structures according to system operation data and environment change which are acquired in real time; carrying out analogue simulation on the candidate topological structure, and evaluating the stability, power distribution and energy utilization efficiency of the system under different operation conditions; performing reliability analysis on the optimized topological structure, and monitoring and adjusting the topological structure of the system in real time; and performing simulation verification on the adjusted topological structure to obtain an optimal topological structure. According to the invention, the system stability, reliability and fault processing capability can be improved.

Description

Technical Field

[0001] The present invention relates to the field of fuzzy evaluation technology, and more specifically, to a topology optimization method for a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm. Background Art

[0002] With the rapid development of renewable energy technology, photovoltaic-storage integrated AC / DC systems play an increasingly important role in modern power systems. By integrating photovoltaic power generation with energy storage systems, such systems achieve efficient utilization and balanced dispatch of electric energy, becoming an important means to solve energy crises and environmental problems. However, photovoltaic-storage integrated systems still face many challenges in practical applications, especially in terms of system topology optimization, stability and reliability.

[0003] In the existing technology, traditional topology optimization methods for integrated photovoltaic and energy storage systems usually rely on static design principles and fixed control strategies. These methods often seem to be powerless when faced with complex and changing operating conditions, and have the following obvious shortcomings: 1. Lack of adaptability: Traditional topology optimization methods are usually based on fixed topology structures and control parameters, and cannot be dynamically adjusted to adapt to different operating conditions. This makes it difficult for the system to maintain optimal operating conditions when facing load changes, faults or environmental fluctuations, reducing the stability and efficiency of the system.

[0004] 2. Single control strategy: Existing photovoltaic storage integrated systems usually adopt a single or small number of control strategies, which fail to fully consider the diverse needs of the system. Especially in complex operating scenarios, traditional methods are difficult to achieve multi-dimensional control and cannot effectively cope with power balance and energy management under various operating conditions.

[0005] 3. Insufficient fault diagnosis and self-healing capabilities: Traditional solar storage systems usually rely on manual intervention or simple automation methods when facing faults, and lack intelligent fault diagnosis and self-healing mechanisms. This approach not only has a slow response speed, but may also cause the system to be in a non-optimal state for a long time, increasing the operational risk of the system.

[0006] 4. Limitations of simulation and verification methods: The system optimization methods in existing technologies usually rely on a single simulation model and lack the support of experimental verification. This limits the credibility and applicability of the optimization results and makes it difficult to achieve the expected results in practical applications.

[0007] Therefore, how to provide a topology optimization method for an integrated photovoltaic and energy storage AC / DC system based on an adaptive algorithm is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a topology optimization method for a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm, which can improve system stability, reliability and fault handling capability.

[0009] To achieve the above object, the present invention provides the following technical solutions: A method for topology optimization of an integrated photovoltaic and energy storage AC / DC system based on an adaptive algorithm comprises the following steps: obtaining an initial topology structure and operating parameters of the integrated photovoltaic and energy storage AC / DC system, including photovoltaic power generation, configuration of an energy storage system, operating status parameters of a microgrid, and external environmental parameters; optimizing and adjusting the topology structure, and dynamically generating a variety of candidate topologies according to real-time acquired system operating data and environmental changes; simulating the candidate topologies, and evaluating the stability, power distribution, and energy utilization efficiency of the system under different operating conditions; performing reliability analysis on the optimized topology structure, and monitoring and adjusting the system topology structure in real time; and simulating and verifying the adjusted topology structure to obtain the optimal topology structure.

[0010] In the above scheme, the operating status parameters of the microgrid include the access mode of the distributed power source in the microgrid operating status, the real-time power of the load, and the load priority; the external environmental parameters include the real-time solar irradiance, ambient temperature, meteorological conditions of the geographical location of the microgrid, and the voltage and frequency of the power grid.

[0011] In the above scheme, the topology structure adopts the following calculation formula: ; In the formula, For the system at time The total power, and Respectively The photovoltaic power generation unit and The power output of each energy storage unit, For the moment The load demand, is the power loss of the system.

[0012] In the above scheme, the topology structure is optimized and adjusted, and multiple candidate topologies are dynamically generated according to the real-time system operation data and environmental changes. Specifically, the target optimization function is established based on the initial topology structure and operation parameters of the photovoltaic storage integrated AC / DC system. ; Use the adaptive algorithm to optimize the topology structure and obtain the optimized parameter set ; Apply genetic algorithm to iteratively select, crossover and mutate candidate topological structures, generate new topological structures, and calculate their fitness function ; The final optimized topology Evaluate and make the overall fitness function of the system maximize; Among them, the objective optimization function It is expressed as: ; In the formula, For the The stability index of the topological structure is is the power balance indicator, For energy efficiency, is the reliability index, , , , are the weight coefficients of the corresponding indicators, is the number of candidate topologies; Fitness function It is expressed as: ; In the formula, Indicates The first candidate topology The fitness index, is the weight coefficient of each fitness indicator, is the number of fitness indicators.

[0013] In the above scheme, the topological structure is optimized and adjusted by using an adaptive algorithm to obtain an optimized parameter set. , also includes performance constraints, whose constraints are: ; In the formula, For the A photovoltaic power generation unit at a time The power output, For the The energy storage unit at time of power output.

[0014] In the above scheme, the candidate topology is simulated to evaluate the stability, power distribution and energy utilization efficiency of the system under different operating conditions, specifically: each candidate topology is simulated and the power flow of the system is dynamically adjusted; the candidate topologies under different operating conditions are evaluated and a scheduling plan for load power supply is formulated; the system stability of each candidate topology is analyzed and the frequency stability and voltage stability of the system are evaluated; the simulation results are evaluated, the candidate topologies are screened, and simulation verification is performed.

[0015] In the above scheme, the reliability analysis of the optimized topology structure is carried out, and the topology structure of the system is monitored and adjusted in real time, specifically including: for each candidate topology structure, the reliability of the system under random fault conditions is analyzed using Monte Carlo simulation technology; in each Monte Carlo simulation, the impact of random faults on photovoltaic power generation units, energy storage systems and microgrid nodes is considered, and the output power of the system after the fault occurs is calculated; the stability of the system under fault conditions is analyzed, and the frequency and voltage fluctuations of the system when the fault occurs are evaluated; according to each simulation result, the fault recovery time and the system self-healing ability and fault recovery time are calculated; Among them, the system reliability index for: ; In the formula, is the number of simulations, For the Topology in sub-simulation Probability of failure under fault conditions; Fault self-healing capability It is expressed as: ; in, For the Topology in sub-simulation Failure recovery time, is the number of simulations; The output power of the system after a fault occurs is expressed as: ; In the formula, For the A photovoltaic power generation unit at a time The output power, For the The energy storage unit at time The output power, For the moment The load demand, is the power loss, is the power imbalance caused by the fault.

[0016] The reliability analysis of the optimized topology structure, real-time monitoring and adjustment of the system topology structure, and intelligent diagnosis and fault repair steps are also included, specifically including: obtaining the system operation data, the operation data including photovoltaic power generation, charging and discharging power of the energy storage system, load power demand, grid power and environmental parameters; applying intelligent fault diagnosis technology according to the operation data to build a fault diagnosis model; when a fault occurs, the system automatically triggers the self-healing mechanism and performs dynamic adjustment of the topology structure to cope with the fault; establishing an index model to evaluate the system operation status after self-healing and confirm the stability of the system, the index model includes the stability index of the system after self-healing and the self-healing effect evaluation index Among them, the fault diagnosis model adopts the following formula:

[0017] In the formula, is the fault diagnosis result. is the system voltage, is the system frequency, Represents the function model of fault diagnosis, Photovoltaic power generation The charging and discharging power of the energy storage system, Load power demand, Grid power.

[0018] The stability index and self-healing effect evaluation index model are as follows: ; ; In the formula, is the stability index of the system after self-healing, is the stability index of the system before the fault, is the stability change caused by the fault; It is an evaluation index of self-healing effect. For the evaluation period, For the moment self-healing ability.

[0019] The method of simulating and verifying the adjusted topology structure to obtain the optimal topology structure specifically includes: verifying the performance of the optimized photovoltaic-storage integrated AC / DC system through simulation; during the simulation process, observing the response of the system by changing the system operating conditions, and evaluating the stability and reliability of the system under different operating conditions; verifying the simulation results through experimental verification in combination with actual data, including simulating the actual operating environment of the system on an experimental platform, introducing operating conditions consistent with the simulation model, and recording the actual response data of the system; and optimizing the parameter settings of the simulation model based on the simulation and experimental results.

[0020] The technical effects and advantages of the topology optimization method of the photovoltaic-storage integrated AC / DC system based on the adaptive algorithm of the present invention are as follows: (1) The present invention combines adaptive algorithms, Monte Carlo simulation technology, microgrid control strategies, power balance control technology, and intelligent fault diagnosis and self-healing technology to achieve dynamic topology optimization of the photovoltaic-storage integrated AC / DC system, enabling the system to adapt to different operating conditions and complex scenarios, thereby significantly improving the flexibility and stability of the system. In particular, when load changes and faults occur, the system can quickly adjust and optimize the topology structure to ensure that the system can operate efficiently under various conditions.

[0021] (2) The present invention significantly improves the system's fault response and self-healing capabilities through intelligent fault diagnosis and self-healing mechanisms, combined with real-time data monitoring and dynamic control strategies. The system can identify faults in real time and automatically adjust the topology structure, reducing the impact of faults on system operation and improving the overall reliability and safety of the system.

[0022] (3) The present invention ensures the credibility and operability of the optimization method by combining simulation and experimental verification technology. By simulating and experimentally verifying the optimized topological structure, the system model is further optimized and corrected, making the optimization effect more practical and improving the application performance and reliability of the system in actual engineering.

[0023] (4) The integrated application of the multi-dimensional control strategy and energy management strategy of the present invention effectively improves the energy utilization efficiency and power balancing capability of the system, makes the energy scheduling between photovoltaic power generation and energy storage system more accurate, optimizes the distribution and utilization of electric energy, and reduces the energy loss of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is the overall framework diagram of the present invention; Figure 2 is a flow chart of the method of the present invention; Figure 3 The diagram is a process diagram of combining simulation and experimental verification to optimize the system according to the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] refer to Figure 1-Figure 3 , Figure 1The present invention provides a method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm, comprising the following steps: S1. Obtain the initial topological structure and operating parameters of the integrated photovoltaic and energy storage AC / DC system, including the configuration of photovoltaic power generation, energy storage system and the operating status of the microgrid; In this implementation, S1 specifically includes: S11. Obtain the initial parameters of the photovoltaic power generation system, including the rated power of the photovoltaic modules , connection method of photovoltaic array, temperature coefficient of components , parameter setting of the maximum power point tracking algorithm, and the performance of photovoltaic modules under different irradiances and temperature Output characteristics under conditions: ; in, For photovoltaic modules at the moment The output power, For the moment The solar irradiance, is the reference irradiance, For the moment The ambient temperature, is the reference temperature, is the temperature coefficient of the photovoltaic module; S12. Obtain the configuration parameters of the energy storage system, including the rated capacity of the energy storage battery , rated voltage , state of charge Upper and lower limits and , charge and discharge efficiency , as well as the lifetime attenuation model and the discharge depth limit conditions of the energy storage system: ; in, For energy storage system at all times The output power, For the moment The state of charge of the energy storage battery, is the minimum available capacity of the energy storage battery, is the discharge time of the energy storage system, is the charge and discharge efficiency; S13. Obtain the operating status parameters of the microgrid, including the topology of the microgrid, the access method of each distributed power source, and the real-time power demand of the load , load priority , as well as the grid-connected and islanded operation modes between the microgrid and the main grid: ; in, For the A photovoltaic power generation unit at a time The output power, For the The energy storage unit at time The output power, For the moment The power obtained from the main grid, is the system power loss; S14, obtaining external environment parameters, including real-time solar irradiance , ambient temperature , the meteorological conditions of the microgrid’s geographical location, and the voltage of the grid and frequency : ; in, For photovoltaic modules at the moment The actual operating temperature, For the moment The ambient temperature, is the nominal operating temperature of the PV module; S15. Obtain historical operation data and forecast data, including historical power generation of photovoltaic power generation , the historical charge and discharge records of the energy storage system , the historical change curve of load , and the predicted values ​​of solar irradiance and load demand in a certain period of time in the future : ; in, For the moment The predicted power, For historical moments The power value, and are the irradiance and temperature changes relative to the reference values, respectively; S16. Based on the above parameters, the initial topology of the photovoltaic-storage integrated AC / DC system is established, and the mathematical model of the system is generated: ; in, For the system at time The total power, and Respectively The photovoltaic power generation unit and The power output of each energy storage unit, For the moment The load demand, is the power loss of the system.

[0027] S2. Applying an adaptive algorithm to optimize and adjust the topology of the system, and dynamically generating a variety of candidate topologies according to real-time acquired system operation data and environmental changes; In this implementation, S2 specifically includes: S21. Establish the target optimization function based on the initial topology and operating parameters of the integrated AC / DC system. , which comprehensively considers the stability of the system , Power Balance , Energy efficiency and reliability : ; in, For the The stability index of the topological structure is is the power balance indicator, For energy efficiency, is the reliability index, , , , are the weight coefficients of the corresponding indicators, is the number of candidate topologies; S22, using adaptive algorithms to optimize the topology, the optimization process aims to maximize the target optimization function , candidate topology The corresponding optimization parameter set : ; in, is a stability indicator, is the power balance indicator, For energy efficiency, is the reliability index, is the weight coefficient of each performance index, is the number of performance indicators; S23. In the process of adaptive algorithm optimization, the genetic algorithm is used to optimize the candidate topology. Perform iterative selection, crossover and mutation operations to generate a new topological structure and calculate its fitness function : ; in, Indicates The first candidate topology fitness indicators, including stability, power balance, energy efficiency and reliability, is the weight coefficient of each fitness indicator, is the number of fitness indicators; S24. During the optimization process, the power balance equation of the candidate topology is adjusted, and the optimized parameter set Meet the system's stability, power balance and reliability requirements: ; in, For the A photovoltaic power generation unit at a time The power output, For the The energy storage unit at time The power output, For the moment The load demand, is the power loss of the system; S25. The final optimized topology Evaluate, and make the overall fitness function of the system maximize: ; in, For the final optimized topology, Represents the fitness function Get the topology with the maximum value.

[0028] S3. Comprehensively apply microgrid control strategy, power balance control strategy and energy management strategy to simulate candidate topologies and evaluate the stability, power distribution and energy utilization efficiency of the system under different operating conditions; In this implementation, S3 specifically includes: S31. For each candidate topology Conduct simulation and dynamically adjust the power flow of the system through microgrid control strategy: ; ; in, For the system at time Total input power, including photovoltaic power , the discharge power of the energy storage system and the power input from the grid , is the total output power of the system, including the load demand power , Charging power of energy storage system , power output to the grid And the system at time Power loss ; S32, apply power balancing control strategy to candidate topologies under different operating conditions To conduct an assessment: ; in, is the total time of the evaluation period, is the weight coefficient of power loss, For the moment The power imbalance of the system, For the moment System power loss; S33, Comprehensive application of energy management strategies for different candidate topologies Based on the operation status of the photovoltaic power generation, energy storage system charging and discharging, and load power supply scheduling plan: ; in, is the photovoltaic power generation efficiency, For energy storage system at all times The discharge power, For energy storage system at all times The charging power, is the charging and discharging efficiency of the energy storage system, for costs associated with power losses; S34. Analyze the system stability of each candidate topology, especially when the load fluctuates and the external environment changes, and evaluate the frequency stability and voltage stability of the system: ; in, For the moment The frequency deviation, is the nominal frequency of the system, is the inertia constant of the system, and are the input and output power of the system respectively; S35. Evaluate the simulation results, select candidate topologies with high energy efficiency, good power balance and strong system stability, and perform further simulation verification on them: ; in, is the optimal topology, For the The weight of the indicator, For the The topology-related Performance indicators, is the number of performance indicators.

[0029] S4. Combined with Monte Carlo simulation technology, reliability analysis is performed on the optimized topology structure to simulate the performance of the system under random fault conditions; In this implementation, S4 specifically includes: S41. For each candidate topology , Monte Carlo simulation technology is used to analyze the reliability of the system under random fault conditions. By randomly generating a sequence of system fault events, the system response when each fault event occurs is calculated, and the system reliability index for: ; in, is the number of simulations, For the Topology in sub-simulation Probability of failure under fault conditions; S42. In each Monte Carlo simulation, the impact of random faults on photovoltaic power generation units, energy storage systems and microgrid nodes is considered, and the power balance of the system after the fault occurs is calculated: ; in, For the A photovoltaic power generation unit at a time The output power, For the The energy storage unit at time The output power, For the moment The load demand, is the power loss, is the power imbalance caused by the fault; S43. Analyze the stability of the system under fault conditions and evaluate the frequency and voltage fluctuations of the system when the fault occurs: ; in, The time when the fault occurred The frequency deviation, is the inertia constant of the system; S44. Calculate the fault recovery time and system self-healing capability based on each simulation result. It is expressed as the time required for the system to recover from a fault state to a normal state, and the fault self-healing capability for: ; in, For the Topology in sub-simulation Failure recovery time, is the number of simulations; S45. Based on the results of Monte Carlo simulation, evaluate the reliability, stability and self-healing ability of each candidate topology, and select the topology with strong fault response capability and high reliability.

[0030] S5. Based on intelligent fault diagnosis and self-healing technology, the system topology is monitored and adjusted in real time, so that the fault can be quickly identified and repaired when it occurs, maintaining the stable operation of the system; In this implementation, S5 specifically includes: S51, real-time monitoring of the operating status of the optimized photovoltaic storage integrated AC / DC system, and collection of system operating data, including photovoltaic power generation , the charging and discharging power of the energy storage system , Load power demand , Grid power and environmental parameters such as solar irradiance and temperature ; S52. Based on the collected operating data, apply intelligent fault diagnosis technology to identify potential faults in the system: ; in, is the fault diagnosis result. is the system voltage, is the system frequency, A function model representing fault diagnosis; S53. When a fault occurs, the system automatically triggers the self-healing mechanism and performs dynamic adjustment of the topology to cope with the fault. The self-healing control strategy is based on the optimized topology. And real-time operating data to adjust power flow: ; ; in, and are the input power and output power of the system after self-healing, respectively; S54. Evaluate the system operation status after self-healing and confirm that the system stability is not affected: ; in, is the stability index of the system after self-healing, is the stability index of the system before the fault, is the stability change caused by the fault; S55. Through continuous monitoring and evaluation, the system continuously updates its fault diagnosis and self-healing strategies to adapt to possible changes in the operating environment and new fault conditions, thereby improving the long-term stability and reliability of the system: ; in, It is an evaluation index of self-healing effect. For the evaluation period, For the moment self-healing ability.

[0031] S6. Verify the performance of the optimized topology through simulation and experimental verification, and apply the optimal topology to the integrated photovoltaic and energy storage AC / DC system.

[0032] In this implementation, S6 specifically includes: S61. Verify the performance of the optimized photovoltaic and energy storage integrated AC / DC system through simulation: ; in, For the system at time The total power, For the The output power of each photovoltaic power generation unit is For the The output power of each energy storage unit is is the load demand power, is the grid power, is the system power loss; S62. During the simulation, by changing the system operating conditions and observing the system response, the stability and reliability of the system under different operating conditions are evaluated: ; in, For the moment The system voltage, is the nominal voltage, is the time interval, For the system at time Stability indicators; S63. Verify the simulation results through experimental verification and actual data, including simulating the actual operating environment of the system on the experimental platform, introducing operating conditions consistent with the simulation model, and recording the actual response data of the system: ; in, is the relative error between the simulation results and the experimental results, is the power obtained by simulation, is the power measured in the experiment; S64. Based on the simulation and experimental results, optimize the parameter settings of the simulation model: ; in, is the optimized simulation model power, is the original simulation model power, is the percentage error between simulation and experiment.

[0033] Example 1: In order to verify the feasibility of the present invention, the present invention is applied to the energy management system of a coastal industrial park. The energy supply of the park mainly relies on distributed photovoltaic power generation systems and energy storage units, and is uniformly managed through an AC / DC hybrid microgrid. Due to frequent production activities in the park, electricity demand fluctuates greatly, especially during peak hours in summer. Traditional energy management systems are difficult to effectively respond to complex load changes and sudden equipment failures, resulting in low system operation efficiency and insufficient stability.

[0034] In order to address these problems, the photovoltaic-storage integrated AC / DC system topology optimization method based on an adaptive algorithm proposed in the present invention is deployed in the energy management system of the park. First, the system collects real-time operating data of photovoltaic power generation units and energy storage systems in the park, including parameters such as the output power of photovoltaic modules, the charge and discharge status of energy storage batteries, ambient temperature and irradiance. In addition, the power demand of each major load unit in the park and the power supply status of the power grid are also obtained. The system uses an adaptive algorithm to dynamically analyze these data, generates a variety of candidate topologies, and evaluates the reliability, stability and fault response capability of each topology through Monte Carlo simulation technology.

[0035] In actual application, the system was simulated to analyze the power balance and energy utilization efficiency of each candidate topology under different load changes and environmental conditions. For example, on a typical hot summer day, the total power consumption of the park reached 60MWh, of which photovoltaic power generation contributed 36MWh, the energy storage system released 18MWh of electricity during peak hours, and the remaining 6MWh was supplied by the grid. In this process, the system's optimized topology made the energy scheduling between photovoltaic power generation and energy storage systems more efficient, significantly reduced dependence on the grid, and improved the overall energy efficiency of the system.

[0036] During the actual operation of the system, the system collected a lot of data under different load conditions. For example, during the three-month monitoring period, the system's operating data is shown in Table 1 below: Table 1 Industrial Park Energy Management System Performance Improvement Report

[0037] As can be seen from the above table, by applying the present invention, the energy management system of the industrial park has been significantly improved before and after optimization. Before optimization, the park needed to purchase about 12MWh of electricity from the power grid on average per month, but after implementing the present invention, this figure dropped significantly to 3MWh. At the same time, the self-consumption rate of photovoltaic power generation increased from 60% to 80%, indicating that the energy utilization efficiency of the system has been significantly improved after optimization. Especially during peak load periods, the power support of the energy storage system increased from 10MWh to 18MWh, greatly reducing dependence on the external power grid.

[0038] In addition, the system effectively handled several sudden failures of photovoltaic power generation units and energy storage systems during operation through intelligent fault diagnosis and self-healing mechanisms. A typical failure occurred one day in August when the inverter of a photovoltaic power generation unit failed, causing the output power of the unit to suddenly drop. After the system detected this anomaly in real time, it immediately activated the self-healing mechanism, and successfully made up for the power gap caused by the failure by dynamically adjusting the output power of the energy storage unit and redistributing the load power supply path. The system resumed normal operation in just 2 minutes, while the system before optimization took an average of 15 minutes to recover under similar circumstances.

[0039] Through experimental verification and data analysis, the present invention has shown high adaptability and effectiveness under different seasons and load conditions. Within three months after the optimization, the energy utilization efficiency of the system increased by an average of 15%, the operational stability of the system was greatly improved, and no large-scale load abandonment or power outage occurred. The data in Table 1 further verify the significant advantages of the present invention in improving the operational performance of the photovoltaic storage integrated system.

[0040] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0041] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm, characterized in that: The steps include: Obtain the initial topological structure and operating parameters of the integrated photovoltaic and energy storage AC / DC system, including the configuration of photovoltaic power generation and energy storage system, the operating status parameters of the microgrid, and the external environmental parameters; Optimize and adjust the topology structure, and dynamically generate multiple candidate topologies based on real-time system operation data and environmental changes; Simulating the candidate topology to evaluate the stability, power distribution and energy efficiency of the system under different operating conditions; Conduct reliability analysis on the optimized topology structure, monitor and adjust the system topology structure in real time; The adjusted topology is simulated and verified to obtain the optimal topology.

2. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 1, characterized in that: The operating status parameters of the microgrid include the access mode of the distributed power source in the microgrid operating status, the real-time power of the load, and the load priority; the external environmental parameters include the real-time solar irradiance, ambient temperature, meteorological conditions of the geographical location of the microgrid, and the voltage and frequency of the power grid.

3. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 2, characterized in that: The topology is calculated using the following formula: ； In the formula, For the system at time The total power, and Respectively The photovoltaic power generation unit and The power output of each energy storage unit, For the moment The load demand, is the power loss of the system.

4. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 3 is characterized in that: The optimization and adjustment of the topology structure dynamically generates a variety of candidate topologies according to the real-time acquired system operation data and environmental changes, specifically including: Based on the initial topology and operating parameters of the integrated photovoltaic and energy storage AC / DC system, the target optimization function is established. ; Use the adaptive algorithm to optimize the topology structure and obtain the optimized parameter set. ; Apply genetic algorithm to iteratively select, crossover and mutate candidate topological structures, generate new topological structures, and calculate their fitness functions ; The final optimized topology Evaluate and make the overall fitness function of the system maximize; Among them, the objective optimization function It is expressed as: ； In the formula, For the The stability index of the topological structure is is the power balance indicator, For energy efficiency, is the reliability index, , , , are the weight coefficients of the corresponding indicators, is the number of candidate topologies; Fitness function It is expressed as: ； In the formula, Indicates The first candidate topology The fitness index, is the weight coefficient of each fitness indicator, is the number of fitness indicators.

5. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 4 is characterized in that: The optimization adjustment of the topological structure by using the adaptive algorithm is performed to obtain the optimization parameter set , also includes performance constraints, whose constraints are: ； In the formula, For the A photovoltaic power generation unit at the time The power output, For the The energy storage unit at time power output.

6. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 5, characterized in that: The simulation of the candidate topology structure is performed to evaluate the stability, power distribution and energy utilization efficiency of the system under different operating conditions, specifically: Simulate each candidate topology and dynamically adjust the power flow of the system; Evaluate candidate topologies under different operating conditions and develop load power supply scheduling plans; Analyze the system stability of each candidate topology and evaluate the frequency stability and voltage stability of the system; Evaluate the simulation results, screen candidate topologies, and perform simulation verification.

7. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 6, characterized in that: The reliability analysis of the optimized topology structure and real-time monitoring and adjustment of the system topology structure specifically include: For each candidate topology, Monte Carlo simulation technique is used to analyze the reliability of the system under random fault conditions; In each Monte Carlo simulation, the impact of random faults on the photovoltaic power generation unit, energy storage system and microgrid nodes is considered, and the output power of the system after the fault occurs is calculated; Analyze the stability of the system under fault conditions and evaluate the frequency and voltage fluctuations of the system when a fault occurs; According to each simulation result, calculate the fault recovery time and system self-healing capacity, fault recovery time; Among them, the system reliability index for: ； In the formula, is the number of simulations, For the Topology in the simulation Probability of failure under fault conditions; Fault self-healing capability It is expressed as: ； in, For the Topology in the simulation Failure recovery time, is the number of simulations; The output power of the system after a fault occurs is expressed as: ； In the formula, For the A photovoltaic power generation unit at the time The output power, For the The energy storage unit at time The output power, For the moment The load demand, is the power loss, is the power imbalance caused by the fault.

8. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 7, characterized in that: The reliability analysis of the optimized topology structure, real-time monitoring and adjustment of the system topology structure, and intelligent diagnosis and fault repair steps are specifically included: Acquire system operation data, including photovoltaic power generation, energy storage system charge and discharge power, load power demand, grid power and environmental parameters; Based on the operation data, intelligent fault diagnosis technology is applied to build a fault diagnosis model; When a fault occurs, the system automatically triggers the self-healing mechanism and performs dynamic adjustments to the topology to cope with the fault; Establish an index model to evaluate the operating status of the system after self-healing and confirm the stability of the system. The index model includes the stability index of the system after self-healing and the self-healing effect evaluation index. Among them, the fault diagnosis model adopts the following formula: In the formula, is the fault diagnosis result. is the system voltage, is the system frequency, Represents the function model of fault diagnosis, Photovoltaic power generation The charging and discharging power of the energy storage system, Load power demand, Grid power.

9. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 8, characterized in that: The stability index and self-healing effect evaluation index model are as follows: ; ; In the formula, is the stability index of the system after self-healing, is the stability index of the system before the fault, is the stability change caused by the fault; It is an evaluation index of self-healing effect. For the evaluation period, For the moment self-healing ability.

10. The method for topology optimization of a photovoltaic-storage integrated AC / DC system based on an adaptive algorithm according to claim 1, characterized in that: The simulation verification of the adjusted topological structure to obtain the optimal topological structure specifically includes: The performance of the optimized photovoltaic-storage integrated AC / DC system is verified through simulation; During the simulation, the system operating conditions are changed, the system response is observed, and the stability and reliability of the system under different operating conditions are evaluated; Verify the simulation results with actual data through experimental verification, including simulating the actual operating environment of the system on the experimental platform, introducing operating conditions consistent with the simulation model, and recording the actual response data of the system; Based on the simulation and experimental results, the parameter settings of the simulation model are optimized.

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