Distributed photovoltaic collaborative optimization method

Abstract

The invention discloses a distributed photovoltaic collaborative optimization method. The method comprises the following steps: S1, data collection and analysis: resource evaluation and load analysis; s2, system design: carrying out system design through layout planning and capacity configuration; s3, optimizing the operation control, fault section positioning and fault recovery reconstruction technology of the power distribution network of the distributed power supply; s4, monitoring and adjusting: system installation, real-time monitoring, performance evaluation and optimization adjustment; and S5, optimization and improvement: performing optimization and improvement in three aspects of user feedback, energy sharing and technical maintenance. According to the distributed photovoltaic collaborative optimization method, system design is carried out through layout planning and capacity configuration, distributed photovoltaic modules are convenient to install, and capacity configuration is used for determining the capacity of a photovoltaic system and optimizing operation control, fault section positioning and fault recovery reconstruction technologies of a power distribution network. And the problem that the power supply reliability is influenced by relatively long response time is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field related to distributed power distribution networks, and in particular to a distributed photovoltaic collaborative optimization method. Background Art

[0002] Distributed photovoltaic power generation has become an increasingly important component of the power system due to its rich resources and less geographical restrictions. As distributed photovoltaic power generation is widely connected to the power system distribution network, its output power is affected by many factors and has uncertainty and volatility. Therefore, it is necessary to optimize the distributed power distribution network. At present, the optimization scheme is based on an accurate mathematical model, randomly selecting an initial value, and iterating repeatedly according to the defined objectives and constraints until the optimal solution is obtained. However, in real-time applications, the control signal obtained by this type of scheme may lag behind the change of photovoltaic power generation, thus failing to obtain the corresponding ideal control effect. In order to overcome the above defects, prior art 1 (Chinese patent with application number 202311782096.7 and application date 2023-12-22) is a coordinated optimization control method for a distributed photovoltaic distribution network, including obtaining data information of a target distributed photovoltaic distribution network; constructing a coordinated optimization control model of the target distributed photovoltaic distribution network; constructing a decision network; and realizing coordinated optimization control of the target distributed photovoltaic distribution network according to the constructed coordinated optimization control model and the decision network; There is also prior art 2 (Chinese patent with application number 202410353390.4 and application date 2024-03-27), a distributed photovoltaic double-layer collaborative optimization investment decision-making method for a distribution network, comprising: based on the historical annual data of all electricity consumption influencing items in the power consumption area and the historical annual electricity consumption data of the power consumption area, analyzing all quantitative impact relationships between electricity consumption influencing items and electricity consumption; based on all quantitative impact relationships and the predicted annual data of all electricity consumption influencing items in the power consumption area, predicting the electricity consumption data of the power consumption area in the current year; based on the electricity consumption data of the power consumption area in the current year and the current distributable power data of the distribution network, determining the photovoltaic power supply input adjustment amount for each divided time period in the current year; determining the optimal distributed photovoltaic input plan based on the photovoltaic power supply input adjustment amount and the unit input investment cost of each distributed photovoltaic input location in the distribution network; to meet the predicted electricity consumption and photovoltaic input cost of the power consumption area; And prior art 3 (Chinese patent with application number 202310807626.2 and application date 2023-07-03) a method, device and electronic equipment for coordinated optimization of distributed photovoltaics in distribution networks, the method comprising: establishing a coordinated framework for distributed photovoltaic distribution networks, wherein the coordinated framework comprises load aggregators, distribution system operators, smart residential users with adjustable devices and ordinary users without adjustable devices; determining the maximum reactive output of distributed photovoltaics in the distribution network based on a pre-built maximum reactive power model of distributed photovoltaic output and preset constraints; controlling the load flow of the distribution network based on the equilibrium price function of smart residential users, ordinary users and load aggregators and the maximum reactive output of distributed photovoltaics in the distribution network, iterating the equilibrium price function according to a preset voltage vector difference model, and determining the voltage distribution plan for each node of the distributed photovoltaics in the distribution network.

[0003] Although existing technologies can optimize power distribution after distributed photovoltaics are connected, during the working process, the optimization is performed by calculating existing data and predicted data, which makes it difficult to adapt to the dynamic characteristics of distributed power sources as a whole, resulting in insufficient adaptability to the power grid and easily leading to insufficient assessment of power grid security, which greatly increases the risk of system attacks or failures. In addition, when the entire system needs maintenance, fault detection and recovery may rely on traditional means, resulting in a long response time, affecting power supply reliability and increasing operating costs.

[0004] In response to the above problems, it is urgently necessary to carry out innovative design based on the original distributed photovoltaic collaborative optimization method. Therefore, we proposed a distributed photovoltaic collaborative optimization method that can solve the above problems well. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed photovoltaic collaborative optimization method to solve the problem raised by the above-mentioned background technology that the current market is optimized by calculating existing data and predicted data, which is difficult to adapt to the dynamic characteristics of distributed power sources as a whole, resulting in insufficient adaptability to the power grid, easily leading to insufficient assessment of the security of the power grid, and greatly increasing the risk of the system being attacked or failing. In addition, when maintenance is required as a whole, fault detection and recovery may rely on traditional means, resulting in a long response time, affecting power supply reliability, and increasing operating costs.

[0006] To achieve the above object, the present invention provides the following technical solutions: a distributed photovoltaic collaborative optimization method, S1: data collection and analysis includes: resource evaluation and load analysis, wherein resource evaluation is to collect relevant data and evaluate the availability of photovoltaic resources, and the collected relevant data includes: light, weather, terrain, and convenient data analysis and processing, and load analysis is to monitor and analyze power load data to understand the user's power consumption pattern and demand; S2: System design: System design is carried out through layout planning and capacity configuration to facilitate the installation of distributed photovoltaic components. Among them, capacity configuration is to determine the capacity of the photovoltaic system to meet the expected power generation demand and power load; S3: Distributed generation distribution network includes: optimization of distribution network operation control, fault section location and fault recovery and reconstruction technology; S4: Monitoring and adjustment include: system installation, real-time monitoring, performance evaluation and optimization adjustment. System installation is to install and debug the photovoltaic system according to the design plan, performance evaluation is to regularly evaluate the power generation efficiency and economic benefits of the system and find potential problems, and optimization adjustment is to optimize the system configuration and operation strategy according to the evaluation results to improve the overall performance; S5: Optimization and improvement: Optimization and improvement are carried out through three aspects: user feedback, energy sharing and technical maintenance; among which, user feedback is to establish a feedback mechanism for users and operators to collect opinions and suggestions.

[0007] Preferably, the layout planning is to optimize the installation position and angle of the photovoltaic modules according to the data analysis results to maximize the utilization of light.

[0008] Preferably, in terms of the operation control of the distribution network, the problem of voltage exceeding the limit when the distributed power source is connected to the distribution network needs to be handled by establishing a mathematical model, solving the model, introducing a mechanism, and conducting simulation verification.

[0009] Preferably, in locating the fault section of the distribution network, the problem that the traditional distribution network fault section locating method fails due to the access of distributed power sources is handled through power flow calculation, positioning model and introduction of algorithm.

[0010] Preferably, the introduced algorithms include: genetic algorithm, particle swarm algorithm and ant colony algorithm to solve the model.

[0011] Preferably, in terms of fault recovery and reconstruction of the distribution network, the problem that the traditional distribution network fault recovery and reconstruction method does not involve the island operation of distributed power sources, resulting in low utilization of distributed power sources, is addressed by establishing a model and introducing an algorithm.

[0012] Preferably, real-time monitoring uses sensors and monitoring systems to track photovoltaic power generation and power consumption in real time and adjust system operation strategies in a timely manner.

[0013] Preferably, energy sharing is achieved by building a community microgrid to achieve resource sharing and complementarity among multiple distributed photovoltaic systems.

[0014] Preferably, technical maintenance is to continuously update and upgrade the photovoltaic system according to the latest technological advances, provide professional training for operation and maintenance personnel, and improve overall operational efficiency.

[0015] Compared with the prior art, the invention has the following beneficial effects: the distributed photovoltaic collaborative optimization method performs system design through layout planning and capacity configuration, which facilitates the installation of distributed photovoltaic components. The capacity configuration is to determine the capacity of the photovoltaic system, optimize the operation control, fault section location and fault recovery reconstruction technology of the distribution network, and reduce the problem of long response time affecting power supply reliability. The specific contents are as follows: (1) Through layout planning and capacity configuration, the system is designed to facilitate the installation of distributed photovoltaic modules, optimize the operation control, fault section location and fault recovery and reconstruction technology of the distribution network, and reduce the problem of long response time affecting power supply reliability; (2) To address the voltage over-limit problem of distributed generation connected to the distribution network, a mathematical model of the distribution network is established to analyze the impact of distributed generation connected to the distribution network on voltage stability. By analyzing the comprehensive demand response characteristics on the load side, the dispatchable resources on the load side are introduced into the coordinated voltage control of the distribution network. (3) The model was solved using the CPLEX solver in Matlab to obtain the output of each coordinated control device, energy storage, and time-shiftable load participating in the coordinated control of distribution network voltage, in order to verify the feasibility of the method. By introducing a step-by-step carbon trading mechanism, a distribution network voltage coordinated control model with low-carbon characteristics was formed. (4) In order to solve the problem that the traditional distribution network fault section location method fails due to the access of distributed power sources, the power flow calculation of the distribution network containing distributed power sources is carried out to analyze the impact of the access of distributed power sources on the fault section location of the distribution network. By introducing artificial intelligence algorithms to solve the model, a fast and accurate section method is formed for single-point faults, multiple-point faults and information distortion faults; (5) In order to solve the problem that the traditional distribution network fault recovery and reconstruction method does not involve the island operation of distributed power sources, resulting in low utilization of distributed power sources, an island division model based on load level is established to divide the optimal island range of distributed power sources with independent power supply capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic diagram of data collection and analysis of the present invention; Figure 3 It is a schematic diagram of the system design of the present invention; Figure 4 This is a schematic diagram of a distributed power distribution network of the present invention; Figure 5 This is a schematic diagram of voltage crossing processing of the present invention; Figure 6 This is a schematic diagram of the fault section location processing of the present invention; Figure 7 The algorithm schematic diagram is introduced for the present invention; Figure 8 This is a schematic diagram of fault recovery and reconstruction of the present invention; Fig. 9 This is a monitoring and adjustment schematic diagram of the present invention; Fig.10 This is a schematic diagram for optimizing and improving the present invention. DETAILED DESCRIPTION

[0017] 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. Embodiment 1:

[0018] S1: Data collection and analysis include resource assessment and load analysis. Resource assessment is to collect relevant data and evaluate the availability of photovoltaic resources. The relevant data collected include: light, weather, and terrain, which are convenient for data analysis and processing. Load analysis is to monitor and analyze power load data to understand users' power consumption patterns and needs. S2: System design: System design is carried out through layout planning and capacity configuration to facilitate the installation of distributed photovoltaic modules. Layout planning is to optimize the installation position and angle of photovoltaic modules based on data analysis results to maximize the use of sunlight, and capacity configuration is to determine the capacity of the photovoltaic system to meet the expected power generation demand and power load; S3: Distributed generation distribution network includes: optimization of distribution network operation control, fault section location and fault recovery and reconstruction technology; In terms of the operation control of the distribution network, the problem of voltage exceeding the limit when the distributed power source is connected to the distribution network needs to be dealt with by establishing a mathematical model, solving the model, introducing a mechanism, and conducting simulation verification; Aiming at the problem of voltage over-limit when distributed generation is connected to distribution network, the mathematical model of distribution network is firstly established to analyze the influence of distributed generation connected to distribution network on voltage stability. By analyzing the comprehensive demand response characteristics of load side, the dispatchable resources on load side are introduced into the coordinated control of distribution network voltage. Secondly, the model is solved by CPLEX solver in Matlab to obtain the output of each coordinated control device, energy storage and time-shiftable load participating in the coordinated control of distribution network voltage, so as to verify the feasibility of this method. Finally, by introducing the step-type carbon trading mechanism, a distribution network voltage coordinated control model with low-carbon characteristics is formed. The objective function is established with the lowest network loss cost, the lowest interruptible load cost and the lowest carbon trading cost. The optimization strategy of the coordinated control method of distribution network voltage with distributed generation is proposed, and the simulation verification is carried out in combination with the improved IEEE 33-node system. S4: Monitoring and adjustment include: system installation, real-time monitoring, performance evaluation and optimization adjustment. System installation is to install and debug the photovoltaic system according to the design plan. Real-time monitoring is to use sensors and monitoring systems to track photovoltaic power generation and power consumption in real time and adjust the system operation strategy in time. Performance evaluation is to regularly evaluate the power generation efficiency and economic benefits of the system and find potential problems. Optimization adjustment is to optimize the system configuration and operation strategy according to the evaluation results to improve the overall performance. S5: Optimization and improvement: Optimization and improvement are carried out through three aspects: user feedback, energy sharing, and technical maintenance. Among them, user feedback is to establish a feedback mechanism for users and operators to collect opinions and suggestions. Energy sharing is to achieve resource sharing and complementarity among multiple distributed photovoltaic systems by building community microgrids. Technical maintenance is to continuously update and upgrade photovoltaic systems based on the latest technological advances, provide professional training for operations and maintenance personnel, and improve overall operational efficiency. Embodiment 2:

[0019] S1: Data collection and analysis include resource assessment and load analysis. Resource assessment is to collect relevant data and evaluate the availability of photovoltaic resources. The relevant data collected include: light, weather, and terrain, which are convenient for data analysis and processing. Load analysis is to monitor and analyze power load data to understand users' power consumption patterns and needs. S2: System design: System design is carried out through layout planning and capacity configuration to facilitate the installation of distributed photovoltaic modules. Layout planning is to optimize the installation position and angle of photovoltaic modules based on data analysis results to maximize the use of sunlight, and capacity configuration is to determine the capacity of the photovoltaic system to meet the expected power generation demand and power load; S3: Distributed generation distribution network includes: optimization of distribution network operation control, fault section location and fault recovery and reconstruction technology; In terms of fault section location of distribution network, the problem of failure of traditional fault section location method of distribution network caused by access of distributed power sources is solved through power flow calculation, location model and introduction of algorithm. Aiming at the problem that the traditional fault section location method of distribution network fails due to the access of distributed power sources, the influence of the access of distributed power sources on the fault section location of distribution network is analyzed by calculating the power flow of the distribution network containing distributed power sources. Based on the coding method that can reflect the direction of fault current, the switch function that can dynamically reflect the number of distributed power sources accessed, and the fitness function with fault-tolerant performance, a fault section location model of the distribution network containing distributed power sources is established. The model is solved by introducing artificial intelligence algorithm to form a fast and accurate section method for single-point faults, multi-point faults and information distortion faults. The introduced algorithms include: genetic algorithm, particle swarm algorithm and ant colony algorithm to solve the model; Genetic algorithm is an optimization algorithm that simulates the biological evolution process. It generates new solutions through operations such as selection, crossover and mutation to achieve the purpose of optimization. In the fault section location of the distribution network, genetic algorithm can be used to search for possible fault section combinations. Each possible fault section combination is encoded as a gene, and then selection, crossover and mutation operations are performed according to the fitness of these genes. Through multiple iterations, the algorithm can find the optimal or approximately optimal fault section combination; The particle swarm algorithm is an optimization algorithm based on group behavior. It achieves global optimization through information sharing and local search among particles. In the fault section location of the distribution network, the particle swarm algorithm can regard each possible fault section combination as a particle and update their position and speed according to the fitness of these particles. Through information sharing and collaboration among particles, the algorithm can gradually approach the optimal solution, that is, the most accurate fault section combination; The ant colony algorithm is an optimization algorithm that simulates the foraging behavior of ants. It uses pheromones to search and optimize paths. In the location of fault sections in the distribution network, the ant colony algorithm can regard each possible fault section as a path and select the next direction based on the pheromone concentration on these paths. By simulating the pheromones and path selection mechanism left by ants in the process of searching for food, the algorithm can gradually find the optimal or suboptimal combination of fault sections. Specifically, the algorithm will initialize the pheromone matrix and other parameters, and then let each "ant" build a path from the starting point to the end point based on the pheromone concentration and heuristic information. After all ants have completed the path construction, the algorithm will update the pheromone concentration based on the quality of the path, and repeat this process until the stop condition is reached; S4: Monitoring and adjustment include: system installation, real-time monitoring, performance evaluation and optimization adjustment. System installation is to install and debug the photovoltaic system according to the design plan. Real-time monitoring is to use sensors and monitoring systems to track photovoltaic power generation and power consumption in real time and adjust the system operation strategy in time. Performance evaluation is to regularly evaluate the power generation efficiency and economic benefits of the system and find potential problems. Optimization adjustment is to optimize the system configuration and operation strategy according to the evaluation results to improve the overall performance. S5: Optimization and improvement: Optimization and improvement are carried out through three aspects: user feedback, energy sharing, and technical maintenance. Among them, user feedback is to establish a feedback mechanism for users and operators to collect opinions and suggestions. Energy sharing is to achieve resource sharing and complementarity among multiple distributed photovoltaic systems by building community microgrids. Technical maintenance is to continuously update and upgrade photovoltaic systems based on the latest technological advances, provide professional training for operations and maintenance personnel, and improve overall operational efficiency. Embodiment three:

[0020] S1: Data collection and analysis include resource assessment and load analysis. Resource assessment is to collect relevant data and evaluate the availability of photovoltaic resources. The relevant data collected include: light, weather, and terrain, which are convenient for data analysis and processing. Load analysis is to monitor and analyze power load data to understand users' power consumption patterns and needs. S2: System design: System design is carried out through layout planning and capacity configuration to facilitate the installation of distributed photovoltaic modules. Layout planning is to optimize the installation position and angle of photovoltaic modules based on data analysis results to maximize the use of sunlight, and capacity configuration is to determine the capacity of the photovoltaic system to meet the expected power generation demand and power load; S3: Distributed generation distribution network includes: optimization of distribution network operation control, fault section location and fault recovery and reconstruction technology; In terms of fault recovery and reconstruction of distribution networks, the traditional distribution network fault recovery and reconstruction method does not involve the isolated operation of distributed power sources, resulting in low utilization of distributed power sources. This problem is addressed by establishing a model and introducing an algorithm. Aiming at the problem that the traditional distribution network fault recovery and reconstruction method does not involve the island operation of distributed power sources, resulting in low utilization of distributed power sources, an island division model based on load level is established to divide the optimal island range for distributed power sources with independent power supply capabilities, and an artificial intelligence algorithm is introduced to recover and reconstruct the network outside the island after the fault occurs. By reducing the scope of the solution space and improving the search rate, an efficient and reliable fault recovery and reconstruction method is formed; S4: Monitoring and adjustment include: system installation, real-time monitoring, performance evaluation and optimization adjustment. System installation is to install and debug the photovoltaic system according to the design plan. Real-time monitoring is to use sensors and monitoring systems to track photovoltaic power generation and power consumption in real time and adjust the system operation strategy in time. Performance evaluation is to regularly evaluate the power generation efficiency and economic benefits of the system and find potential problems. Optimization adjustment is to optimize the system configuration and operation strategy according to the evaluation results to improve the overall performance. S5: Optimization and improvement: Optimization and improvement are carried out through three aspects: user feedback, energy sharing, and technical maintenance. Among them, user feedback is to establish a feedback mechanism for users and operators to collect opinions and suggestions. Energy sharing is to achieve resource sharing and complementarity among multiple distributed photovoltaic systems by building community microgrids. Technical maintenance is to continuously update and upgrade photovoltaic systems based on the latest technological advances, provide professional training for operations and maintenance personnel, and improve overall operational efficiency.

[0021] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. 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 distributed photovoltaic collaborative optimization method, The method comprises the following steps, characterized in that: S1: Data collection and analysis include resource assessment and load analysis. Resource assessment is to collect relevant data and evaluate the availability of photovoltaic resources. The relevant data collected include: light, weather, and terrain, which are convenient for data analysis and processing. Load analysis is to monitor and analyze power load data to understand users' power consumption patterns and needs. S2: System design: System design is carried out through layout planning and capacity configuration to facilitate the installation of distributed photovoltaic components. Among them, capacity configuration is to determine the capacity of the photovoltaic system to meet the expected power generation demand and power load; S3: Distributed generation distribution network includes: optimization of distribution network operation control, fault section location and fault recovery and reconstruction technology; S4: Monitoring and adjustment include: system installation, real-time monitoring, performance evaluation and optimization adjustment. System installation is to install and debug the photovoltaic system according to the design plan, performance evaluation is to regularly evaluate the power generation efficiency and economic benefits of the system and find potential problems, and optimization adjustment is to optimize the system configuration and operation strategy according to the evaluation results to improve the overall performance; S5: Optimization and improvement: Optimization and improvement are carried out through three aspects: user feedback, energy sharing and technical maintenance; among which, user feedback is to establish a feedback mechanism for users and operators to collect opinions and suggestions.

2. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: Layout planning is to optimize the installation position and angle of photovoltaic modules based on data analysis results to maximize light utilization.

3. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: In terms of the operation and control of the distribution network, the problem of voltage exceeding the limit when distributed power sources are connected to the distribution network needs to be handled by establishing mathematical models, solving models, introducing mechanisms and simulation verification.

4. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: In terms of locating the faulty section of the distribution network, the problem of the failure of the traditional distribution network faulty section locating method due to the access of distributed power sources is solved through power flow calculation, positioning model and introduction of algorithm.

5. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: The introduced algorithms include genetic algorithm, particle swarm algorithm and ant colony algorithm to solve the model.

6. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: In terms of fault recovery and reconstruction of distribution networks, the traditional distribution network fault recovery and reconstruction method does not involve the isolated operation of distributed power sources, resulting in low utilization of distributed power sources. This problem is addressed by establishing a model and introducing an algorithm.

7. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: Real-time monitoring is the use of sensors and monitoring systems to track photovoltaic power generation and electricity consumption in real time and adjust system operation strategies in a timely manner.

8. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: Energy sharing is achieved by building community microgrids to achieve resource sharing and complementarity among multiple distributed photovoltaic systems.

9. A distributed photovoltaic collaborative optimization method according to claim 1, characterized in that: Technical maintenance is to continuously update and upgrade the photovoltaic system according to the latest technological advances, provide professional training for operation and maintenance personnel, and improve overall operational efficiency.

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