Power distribution network photovoltaic and energy storage configuration method considering island division and network reconstruction

Abstract

The invention discloses a power distribution network photovoltaic and energy storage configuration method considering island division and network reconstruction. The method comprises the following steps: collecting basic information of a power distribution network; monitoring output change and load fluctuation of a photovoltaic power supply in real time, constructing a photovoltaic power supply output model and a load time-varying model, and predicting photovoltaic output data and load demand data in the fault duration; determining an island range by using a breadth-first search algorithm by taking a load node directly supplied with power by the photovoltaic power supply as a starting point; based on an island division result, identifying residual load nodes of which the power supply is not recovered and network lines connected with the load nodes; the lowest comprehensive cost is determined as an optimization target, an electrical constraint condition is converted into a second-order cone constraint form, the optimization target and the electrical constraint condition are used for solving a network reconstruction problem for the fault duration, and a switch operation strategy is determined; power supply of the fault area is recovered; and performing configuration optimization on the photovoltaic power supply according to the load operation state in the island during the fault period of the power distribution network.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a photovoltaic and energy storage configuration method for a distribution network taking into account island division and network reconstruction. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy technology, photovoltaic power generation, as a clean and renewable energy form, has an increasing penetration rate in distribution networks. However, photovoltaic power generation is greatly affected by the natural environment, and its output power has significant intermittency and uncertainty, which brings new challenges to the safe and stable operation of distribution networks. Especially when a distribution network fails, how to effectively use photovoltaic power sources and energy storage equipment to ensure the continuous power supply of critical loads has become an urgent problem to be solved.

[0003] Traditional distribution networks often adopt a passive approach when dealing with faults. That is, after a fault occurs, they cut off the connection between the faulty area and the main grid to prevent the fault from expanding, and then wait for manual maintenance to restore power. This approach not only causes long power outages in the faulty area, affecting user experience, but may also cause secondary disasters due to prolonged power outages. In addition, traditional methods fail to fully utilize the potential of photovoltaic power sources and energy storage equipment, fail to form an effective island power supply mode during faults, and waste renewable energy resources.

[0004] At present, although some studies have explored the islanding and restoration strategies during distribution network faults, most of these methods are based on static or simplified models and fail to fully consider the time-varying nature of photovoltaic power output changes and load fluctuations, resulting in inaccurate islanding results and limited power restoration effects. At the same time, these studies often ignore the precise processing of electrical constraints during network reconstruction, resulting in optimization results that may not meet actual operation requirements.

[0005] In addition, for the configuration of photovoltaic power sources and energy storage equipment, existing methods mostly focus on a single economic or technical indicator, and lack an optimization solution that comprehensively considers system reliability, economy, and technical feasibility. Especially in the event of a fault, how to dynamically adjust the configuration strategy based on the load operation status within the island and the reliability of the photovoltaic power source to achieve rapid recovery and stable operation during the fault is a key issue that needs to be urgently addressed in the current distribution network configuration field.

[0006] In summary, the passive recovery method of traditional distribution networks in response to faults, the inadequacy of island division and recovery strategies, the improper handling of electrical constraints in network reconstruction, and the single configuration method of photovoltaic power sources and energy storage devices together constitute major defects in the current operation and management of distribution networks. These defects not only affect the power supply reliability and economy of distribution networks, but also restrict the large-scale access and efficient use of renewable energy. Summary of the invention

[0007] In view of this, an embodiment of the present application provides a distribution network photovoltaic and energy storage configuration method and computer device taking into account island division and network reconstruction.

[0008] According to one aspect of the present application, a method for configuring photovoltaic and energy storage in a distribution network considering island division and network reconstruction is provided, the method comprising:

[0009] Collect basic information of the distribution network, including topological structure information, load distribution data, photovoltaic power output power, energy storage device status and capacity, fault location, and fault duration;

[0010] Real-time monitoring of photovoltaic power output changes and load fluctuations, building a photovoltaic power output model and a load time-varying model, and using the photovoltaic power output model and the load time-varying model to predict photovoltaic output data and load demand data within the duration of the fault, wherein the photovoltaic power source includes photovoltaic power generation equipment and energy storage equipment;

[0011] Based on the fault location, the topological structure information, the load distribution data, the photovoltaic output forecast data and the load demand forecast data within the fault duration, the load node directly powered by the photovoltaic power source is used as the starting point to determine the island range using a breadth-first search algorithm;

[0012] Based on the islanding results, the remaining load nodes and their connected network lines that have not yet restored power supply are identified to form a basic framework for dynamic network reconstruction; the lowest comprehensive cost is determined as the optimization goal, the electrical constraints are converted into a second-order cone constraint form, and the network reconstruction problem is solved for the fault duration using the optimization goal and the electrical constraints to determine the switch operation strategy; the distribution network is configured according to the islanding results of the fault duration and the switch operation strategy to restore power supply to the fault area;

[0013] According to the load operation status inside the isolated island during the distribution network fault, the reliability of the photovoltaic power source at the fault location is evaluated, and the configuration of the photovoltaic power source is optimized based on the reliability of the photovoltaic power source.

[0014] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the program, the method for configuring photovoltaic and energy storage in a distribution network considering island division and network reconstruction is implemented.

[0015] By means of the above technical scheme, the embodiment of the present application provides a distribution network photovoltaic and energy storage configuration method considering island division and network reconstruction, which collects basic information of the distribution network, wherein the basic information includes topological structure information, load distribution data, photovoltaic power supply output power, energy storage device status and capacity, fault location, and fault duration; real-time monitoring of photovoltaic power supply output changes and load fluctuations, constructing a photovoltaic power supply output model and a load time-varying model, and using the photovoltaic power supply output model and the load time-varying model to predict photovoltaic output data and load demand data within the fault duration, wherein the photovoltaic power supply includes photovoltaic power generation equipment and energy storage equipment; based on the fault location, the topological structure information, the load distribution data, the photovoltaic output prediction data within the fault duration, and the load demand prediction Measure data, take the load node directly powered by the photovoltaic power source as the starting point, and use the breadth-first search algorithm to determine the scope of the island; based on the results of island division, identify the remaining load nodes that have not restored power supply and their connected network lines, and form the basic architecture of dynamic network reconstruction; determine the lowest comprehensive cost as the optimization goal, convert the electrical constraints into the second-order cone constraint form, use the optimization goal and electrical constraints to solve the network reconstruction problem for the fault duration, and determine the switch operation strategy; configure the distribution network according to the island division results and switch operation strategy of the fault duration to restore the power supply to the fault area; according to the load operation status inside the island during the distribution network fault, evaluate the reliability of the photovoltaic power supply at the fault location, and optimize the configuration of the photovoltaic power supply based on the reliability of the photovoltaic power supply. This application can provide continuous power supply to critical loads during the fault by real-time monitoring and prediction of photovoltaic output and load demand, combined with island division and network reconstruction technology, improve the power supply reliability of the distribution network, and reduce the time and scope of power outages. Network reconstruction with the lowest comprehensive cost as the optimization goal ensures that economic costs are reduced while restoring power supply, which helps to improve the economy of the distribution network and bring practical benefits to operators and users. Configuration optimization based on the reliability of photovoltaic power sources can ensure that photovoltaic power sources are fully utilized during faults, avoid waste of resources, and improve the absorption rate of renewable energy. Ultimately, through dynamic network reconstruction and photovoltaic power configuration optimization, the flexibility and adaptability of the distribution network in dealing with different fault scenarios are enhanced, which helps to improve the overall operating efficiency and stability of the distribution network. In summary, the embodiments of the present application can effectively solve the problems of low power supply reliability, high recovery cost, and unreasonable resource allocation in traditional distribution networks when dealing with faults by comprehensively considering island division, network reconstruction, and photovoltaic power configuration optimization. At the same time, this solution also enhances the flexibility and adaptability of the distribution network and improves its overall operating efficiency and stability.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A flow chart of a method for configuring photovoltaic and energy storage in a distribution network taking into account island division and network reconstruction provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0020] In this embodiment, a distribution network photovoltaic and energy storage configuration method considering island division and network reconstruction is provided. Figure 1 As shown, the method includes:

[0021] Step 101, collecting basic information of the distribution network, the basic information includes topological structure information, load distribution data, photovoltaic power output power, energy storage device status and capacity, fault location, and fault duration.

[0022] Step 102, real-time monitoring of photovoltaic power output changes and load fluctuations, building a photovoltaic power output model and a load time-varying model, and using the photovoltaic power output model and the load time-varying model to predict photovoltaic output data and load demand data within the fault duration, the photovoltaic power source includes photovoltaic power generation equipment and energy storage equipment.

[0023] Step 103, based on the fault location, the topology information, the load distribution data, the photovoltaic output forecast data and the load demand forecast data within the fault duration, and taking the load node directly powered by the photovoltaic power source as the starting point, a breadth-first search algorithm is used to determine the scope of the island.

[0024] Step 104, based on the islanding division result, identify the remaining load nodes and their connected network lines that have not yet restored power supply, and form an infrastructure for dynamic network reconstruction; determine the lowest comprehensive cost as the optimization goal, convert the electrical constraints into a second-order cone constraint form, use the optimization goal and the electrical constraints to solve the network reconstruction problem for the fault duration, and determine the switch operation strategy; configure the distribution network according to the islanding division result of the fault duration and the switch operation strategy to restore power supply to the fault area.

[0025] Step 105 , evaluating the reliability of the photovoltaic power source at the fault location according to the load operation status inside the isolated island during the distribution network fault, and optimizing the configuration of the photovoltaic power source based on the photovoltaic power source reliability.

[0026] In the above embodiment, first, the basic information of the distribution network is collected, including the topological structure, load distribution, the status and capacity of photovoltaic power sources and energy storage devices, the fault location and duration, etc., as the basis for subsequent analysis. Then, by real-time monitoring of the output changes and load fluctuations of photovoltaic power sources, the corresponding output model and time-varying model are constructed. These models are used to predict the photovoltaic output and load demand during the fault duration, so as to more accurately reflect the actual situation and provide strong support for subsequent island division and network reconstruction. Next, based on the fault location, topological structure, load distribution, and predicted photovoltaic output and load demand data, the load node directly powered by the photovoltaic power source is used as the starting point, and the breadth-first search algorithm is used to determine the scope of the island. The island division aims to ensure that during the fault period, the photovoltaic power source and the energy storage device work together to provide continuous power supply to the critical load. Furthermore, based on the island division, the remaining load nodes and their connected network lines that have not been restored to power supply are identified to form the basic framework of dynamic network reconstruction. Then, with the lowest comprehensive cost as the optimization goal, the electrical constraints are converted into second-order cone constraints, the network reconstruction problem is solved, and the switch operation strategy is determined so that the fault area can be quickly restored to power supply and the cost of the restoration process can be reduced by optimizing the network structure. Finally, according to the load operation status inside the island, the reliability of the photovoltaic power source is evaluated, and based on the reliability evaluation results, the photovoltaic power source is configured and optimized to improve the utilization and reliability of the photovoltaic power source during the fault period and ensure the stability and continuity of the island power supply.

[0027] By applying the technical solution of this embodiment, by real-time monitoring and prediction of photovoltaic output and load demand, combined with island division and network reconstruction technology, it is possible to provide continuous power supply for critical loads during faults, improve the power supply reliability of the distribution network, and reduce the power outage time and scope. Network reconstruction is carried out with the lowest comprehensive cost as the optimization goal, ensuring that the economic cost is reduced while restoring power supply, which helps to improve the economy of the distribution network and bring practical benefits to operators and users. Configuration optimization based on the reliability of photovoltaic power supply can ensure that the photovoltaic power supply is fully utilized during the fault, avoid waste of resources, and improve the absorption rate of renewable energy. Finally, through dynamic network reconstruction and photovoltaic power supply configuration optimization, the flexibility and adaptability of the distribution network in dealing with different fault scenarios are enhanced, which helps to improve the overall operation efficiency and stability of the distribution network. In summary, the embodiment of the present application can effectively solve the problems of low power supply reliability, high recovery cost, and unreasonable resource allocation in traditional distribution networks when dealing with faults by comprehensively considering island division, network reconstruction and photovoltaic power supply configuration optimization. At the same time, this solution also enhances the flexibility and adaptability of the distribution network and improves its overall operation efficiency and stability.

[0028] In an embodiment of the present application, optionally, in step 102, the photovoltaic power output model and the load time-varying model are used to predict the photovoltaic output data and the load demand data within the fault duration, including: dividing the fault duration into multiple time periods, and for the first time period, using the photovoltaic power output model and the load time-varying model to predict the photovoltaic output data and the load demand data of the first time period; step 103 includes: based on the fault location, the topological structure information, the load distribution data, the photovoltaic output prediction data and the load demand prediction data of the first time period, taking the load node directly powered by the photovoltaic power source and the energy storage device as the starting point, using the breadth-first search algorithm to determine the island range; step 104 includes: based on the first time period The islanding results of the interval are used to identify the remaining load nodes and their connected network lines that have not yet restored power supply, so as to form an infrastructure for dynamic network reconstruction; the lowest comprehensive cost is determined as the optimization goal, the electrical constraints are converted into a second-order cone constraint form, the network reconstruction problem is solved for the first time period using the optimization goal and the electrical constraints, and the switch operation strategy is determined; the distribution network is configured according to the islanding results of the first time period and the switch operation strategy to restore power supply to the faulty area; the photovoltaic power output changes and load fluctuations in the first time period inside the island are monitored, the photovoltaic output data and load demand data in the second time period are predicted using the photovoltaic power output model and the load time-varying model, and the islanding and network reconstruction are continued until the distribution network fault is restored.

[0029] In this embodiment, the fault duration can be divided into multiple continuous time periods, and the length of each time period can be set according to actual needs to reflect the changes in photovoltaic output and load demand in a short period of time. At the beginning of each time period, the photovoltaic power output model and the load time-varying model are used to predict the photovoltaic output and load demand data in the time period, and these data will serve as the basis for subsequent island division and network reconstruction. Taking the load node directly powered by the photovoltaic power source as the starting point, combined with the fault location, topological structure information, load distribution data, and photovoltaic output and load demand forecast data for the current time period, the breadth-first search algorithm (BFS) is used to determine the preliminary island range. As time goes by, when entering the next time period, the photovoltaic output and load demand are re-predicted, and the island division is updated based on the new prediction results to ensure the dynamic balance of power and load in the island. Based on the results of island division, the remaining load nodes and their connected network lines that have not been restored to power supply are identified, and the basic architecture of dynamic network reconstruction is constructed. The optimization goal (such as recovery time, operation cost, power supply reliability, etc.) is set to minimize the comprehensive cost, and the electrical constraints (such as voltage limit, current limit, power balance, etc.) are converted into second-order cone constraint forms for efficient solution. Using the optimization goal and electrical constraints, the network reconstruction problem is solved for each time period, and the optimal switch operation strategy is determined. According to the results of island division and the switch operation strategy, the distribution network configuration is adjusted to restore the power supply to the fault area. In addition, inside the island, the output change of photovoltaic power source and load fluctuation are monitored in real time to ensure the stability and reliability of the island operation. As time goes by, the above photovoltaic output and load demand prediction, island division and network reconstruction process are repeated until the distribution network fault is completely restored. The embodiment of the present application combines the dynamic division of time periods, the island division based on the predicted data, the network reconstruction guided by the optimization goal, and the continuous monitoring and adjustment, which effectively improves the power supply recovery capability and operation efficiency of the distribution network during the fault period, while reducing the overall cost.

[0030] The expression of the photovoltaic power output model is: PV,t =ηAS t , where P PV,t is the photovoltaic power output at time t, η is the photoelectric conversion efficiency, A is the area of ​​the photovoltaic module, S t is the light intensity at time t; the expression of the load time-varying model is: P i (t) = f i (x)×Δt, where P i (t) is the average power of load i from time t to t+Δt, f i (x) is the daily load curve of load i, and Δt is the time interval.

[0031] In an embodiment of the present application, optionally, a breadth-first search algorithm is used to determine the island range, including: using a breadth-first search algorithm to determine a preliminary island range, using a depth-first search and an island fusion strategy to optimize the preliminary island range, ensuring that the power supply inside the island meets the load demand, and verifying the island division result of the first time period to ensure that each island division result meets the constraints of island power balance, node voltage and branch current; wherein the island power balance constraint is: is the output of photovoltaic power source n in the island at time t, is the output of energy storage device n operating in conjunction with the photovoltaic power source at time t, is the active power of load j in the island at time t, P loss,t is the network loss of the island at time t; the node voltage constraint condition is: U min ≤U i,t ≤U max , U i,t is the voltage amplitude of node i at time t, U min and U max They represent the minimum and maximum values ​​of the node voltage respectively; the branch current constraint condition is: I ij,t ≤I ij,max , I ij,t is the current flowing through branch ij at time t, I ij,max is the maximum current allowed to pass through branch ij.

[0032] In this embodiment, in the process of determining the scope of the island by using the breadth-first search algorithm (BFS), the depth-first search (DFS) and the island fusion strategy can be further combined to optimize the preliminary island scope and ensure that the power supply inside the island can meet the load demand and meet various electrical constraints. First, the load node directly powered by the photovoltaic power source is used as the starting point of the search. Starting from the starting point, the search scope is expanded outward layer by layer until a fault point, a visited node or a search boundary is encountered. During the search process, all reachable load nodes and corresponding network lines are recorded, and all searched reachable nodes and lines are regarded as a preliminary island scope. Within the preliminary island scope, DFS is used to further explore the network structure inside the island, especially those potential connections that may be ignored due to the hierarchical search of BFS. If it is found that there is a connectable path between two or more preliminary islands (that is, the switch between them is in the disconnected state, but the line connecting them is intact), then consider merging these islands into a larger island. When merging, it is necessary to consider whether the power supply capacity inside the island can meet the total demand of the merged load. Calculate the total capacity of all power sources (including photovoltaic power sources and energy storage devices) inside the island and compare it with the total demand of all loads in the island. If the power capacity is not enough to meet the load demand, it is necessary to readjust the scope of the island or consider other recovery strategies (such as introducing power from the external power grid). Ensure that the power output power of each island is balanced with the load demand, that is, there is no power shortage or surplus inside the island. Verify whether the node voltage and branch current of each island meet the preset constraints. This usually includes the upper and lower limits of the voltage amplitude, the thermal stability limit of the current, etc. If the verification result does not meet the requirements, it is necessary to adjust or correct the scope of the island according to the specific situation until all islands meet the power balance and electrical constraints. In addition, for subsequent time periods, repeat the above prediction, island division, optimization and verification process to dynamically adapt to changes in photovoltaic output and load demand. Through the above steps, it can be ensured that during the distribution network failure, through reasonable island division and network reconstruction strategies, the power supply of the fault area is effectively restored and various electrical constraints are met.

[0033] In the embodiment of the present application, optionally, the optimization goal is to minimize the comprehensive cost, the electrical constraints are converted into a second-order cone constraint form, the network reconstruction problem is solved for the first time period using the optimization goal and the electrical constraints, and the switch operation strategy is determined, including: based on the load recovery amount, the number of switch operations and the network loss factor, an objective function with the minimum comprehensive cost as the optimization goal is set, wherein the objective function is min(c1g 1,t +c2g 2,t +c3g 3,t), c1, c2, c3 are the equivalent power loss load cost coefficient, switch operation cost coefficient and network loss cost coefficient respectively, g 1,t , g 2,t , g 3,t They are the equivalent load recovery amount, number of switch operations, and network loss in period t respectively.

[0034] In this embodiment, the objective function aims to minimize the comprehensive cost, which consists of three parts: equivalent power loss load cost, switch operation cost and network loss cost. The specific form is as follows: min(c1g 1,t +c2g 2,t +c3g 3,t ), where c1, c2, and c3 are the equivalent power loss load cost coefficient, switch operation cost coefficient, and network loss cost coefficient, respectively. These coefficients reflect the relative importance of different cost items in the total cost. 1,t It represents the equivalent load recovery amount in period t, that is, the load amount that successfully restored power supply during this period. 2,t Indicates the number of switch operations during period t, including the number of times the switch is opened and closed. 3,t Represents the network loss in time period t, that is, the energy loss caused by the flow of current in the power grid. Electrical constraints usually include node voltage limits, branch current limits, power balance, etc. In order to convert these constraints into second-order cone constraint form, some mathematical transformations are required. The general form of the second-order cone constraint is: ∥Ax+b∥2≤cTx+d, where A, b, c, and d are constant matrices or vectors, and x is the decision variable. In the distribution network reconstruction problem, electrical constraints can be converted into a similar form. For example, the node voltage constraint can be expressed as: U min ≤U i,t ≤U max , U i,t is the voltage amplitude at node i at time t. These constraints can be converted to second-order cone constraints through appropriate transformations (such as using the square of the voltage amplitude, etc.). Branch current constraints and power balance constraints can also be handled similarly. It should be noted that the specific conversion method depends on the specific form of the problem and the optimization tool used. After setting the objective function and converting the electrical constraints into the second-order cone constraint form, an appropriate optimization algorithm (such as the interior point method, second-order cone programming solver, etc.) can be used to solve the network reconstruction problem. The solution process will involve the following steps: Initialization: Set the initial switch state, load recovery status, etc. Construct an optimization model: Construct an optimization model based on the objective function and electrical constraints. Solution: Use the optimization algorithm to solve the model and obtain the optimal switch operation strategy. Verification: Verify whether the solution meets all constraints and evaluate the comprehensive cost. Adjustment and iteration: If necessary, adjust the initial conditions or optimization model according to the solution results, and repeat the solution process.

[0035] In an embodiment of the present application, optionally, according to the load operation state inside the isolated island during the distribution network failure, the reliability of the photovoltaic power supply at the fault location is evaluated, and the configuration of the photovoltaic power supply is optimized based on the reliability of the photovoltaic power supply, including: according to the load operation state inside the isolated island during the distribution network failure and the preset reference load operation state, evaluating whether the photovoltaic power supply at the fault location provides electric energy that meets the preset reference load operation state for the load inside the isolated island during the distribution network failure; wherein the preset reference load operation state refers to the preset first-level load maintaining a normal operating state; if not, determining that the reliability of the photovoltaic power supply at the fault location is unreliable, based on the load operation state inside the isolated island during the distribution network failure and the preset reference load operation state, determining the required capacity corresponding to the photovoltaic power supply at the fault location, and based on the required capacity and the existing capacity of the photovoltaic power supply at the fault location, determining the incremental configuration requirement of the photovoltaic power supply, and based on the incremental configuration requirement of the photovoltaic power supply and the light intensity information of the fault location and the output power of the photovoltaic power supply at the fault location, determining to increase the number of photovoltaic panels and perform a first configuration optimization; wherein the required capacity L actual (t) is the actual load demand of the primary load inside the island in each time period t, L baseline (t) is the preset benchmark load demand for each time period t.

[0036] In this embodiment, during the distribution network failure, evaluating the reliability of the photovoltaic power source and optimizing the configuration is an important step to ensure that the load inside the island can be powered stably and reliably. Specifically, during the distribution network failure, the actual operating status of each load inside the island is recorded, including power demand, operating time, etc., and the electric energy and power required for the first-level load to maintain normal operation are preset. The load operating status inside the island is compared with the preset benchmark load operating status to evaluate whether the photovoltaic power source can meet the normal operation requirements of the first-level load. If the electric energy provided by the photovoltaic power source during the failure cannot meet the preset benchmark load operating status, the reliability of the photovoltaic power source at the fault location is considered to be unreliable. Then perform the following steps for optimization: First, based on the load operating status inside the island and the preset benchmark load operating status, calculate the minimum electric energy and power that the photovoltaic power source needs to provide during the failure, and determine the required capacity corresponding to the photovoltaic power source, that is, the minimum photovoltaic power source capacity that can stably provide the required electric energy and power. Secondly, compare the required capacity with the existing capacity of the photovoltaic power source at the fault location to determine the incremental configuration requirements of the photovoltaic power source. Then, according to the incremental configuration requirements of the photovoltaic power source and the light intensity information of the fault location, the number of photovoltaic panels that need to be added is calculated, and the number of photovoltaic panels = incremental configuration requirements / (single photovoltaic panel output power*light intensity coefficient), where the light intensity coefficient is the coefficient for correcting the output power of the photovoltaic panel according to the actual lighting conditions. Finally, the calculated number of photovoltaic panels is added to the fault location to improve the reliability and power supply capacity of the photovoltaic power source. In addition, other factors need to be considered when optimizing the configuration of photovoltaic power sources, such as the installation space, cost budget, and maintenance difficulty of photovoltaic panels. Light intensity information should be obtained based on long-term statistical data or professional prediction models to ensure the accuracy of the calculation of the number of photovoltaic panels. In addition, the configuration optimization of photovoltaic power sources should be a continuous process. With the changes in the distribution network structure and load demand, the configuration of photovoltaic power sources may need to be re-evaluated and adjusted. Through the above steps, the reliability of photovoltaic power sources during distribution network failures can be effectively evaluated, and reasonable configuration optimization can be performed to ensure that the loads inside the island can be stably and reliably powered during the failure.

[0037] In an embodiment of the present application, optionally, after evaluating whether the photovoltaic power source at the fault location provides electric energy that meets a preset benchmark load operating state for the load inside the island during the distribution network failure, the method further includes: if so, determining that the reliability of the photovoltaic power source at the fault location is reliable, determining the remaining capacity of the photovoltaic power source at the fault location based on the load operating state inside the island during the distribution network failure and the preset benchmark load operating state, and determining to increase the capacity of the energy storage device of the photovoltaic power source at the fault location based on the remaining capacity and performing a second configuration optimization; wherein, the remaining capacity P PV(t) is the actual power generation of the photovoltaic power source at the fault location in each time period t.

[0038] In this embodiment, after evaluating whether the photovoltaic power source at the fault location provides electric energy that meets the preset benchmark load operating state to the load inside the island during the distribution network failure, if the answer is affirmative, that is, the photovoltaic power source does meet this requirement, the remaining capacity of the photovoltaic power source can be determined next, and the increased capacity configuration of the energy storage device can be optimized based on the remaining capacity. First, if the photovoltaic power source successfully provides electric energy that meets the preset benchmark load operating state to the load inside the island during the distribution network failure, then it can be considered that the reliability of the photovoltaic power source at the current fault location is reliable. Next, the remaining capacity of the photovoltaic power source is calculated. The remaining capacity refers to the additional electric energy capacity that the photovoltaic power source can provide after the preset benchmark load operating state is met. The remaining capacity PPV(t) is the actual power generation of the photovoltaic power source at the fault location in each time period t. Finally, since energy storage devices play a key role in distribution network fault recovery and island operation, they can store excess power and release it when needed to balance load demand and power supply. Therefore, based on the remaining capacity of the photovoltaic power source, it is possible to evaluate whether the capacity of the energy storage device needs to be increased in order to better utilize the excess power generated by the photovoltaic power source and provide additional power supply support when needed. Increase the capacity of the energy storage device at the fault location to improve the reliability and stability of the power supply within the island, increase the capacity according to the determined energy storage device, select appropriate energy storage devices (such as battery packs, supercapacitors, etc.) and install and configure them. In addition, during the configuration process, factors such as the charging and discharging efficiency, maintenance cost, service life, and compatibility with other power grid equipment of the energy storage device need to be considered. It should be noted that when optimizing the configuration of increasing the capacity of energy storage equipment, it should be ensured that the technical indicators and performance of the selected energy storage equipment meet the requirements of island operation, the increase in the capacity of energy storage equipment should be based on reasonable predictions and planning, and over-investment or under-investment should be avoided. The configuration optimization of energy storage equipment should be coordinated with the configuration of other power grid equipment to ensure the stability and reliability of the entire distribution network. Through the above steps, on the basis of confirming the reliability of photovoltaic power sources, the configuration of energy storage equipment inside the island can be further optimized to improve the reliability and stability of power supply during distribution network failures and in normal times.

[0039] In an embodiment of the present application, optionally, before performing the first configuration optimization, the method further includes: judging whether the photovoltaic power source at the fault location after optimization can meet the maximum actual load demand among the actual load demands of the primary load within the island in each time period if the first configuration optimization is performed, and performing the first configuration optimization if it is determined that the maximum actual load demand is met, otherwise re-determining the number of photovoltaic panels to be increased based on the increased number of photovoltaic panels and the preset coefficient and performing the first configuration optimization.

[0040] In this embodiment, before the first configuration optimization is performed, in order to ensure that the optimized photovoltaic power source can fully meet the actual demand of the primary load inside the island, especially the demand during peak hours, it is indeed necessary to add a judgment step so as to evaluate the maximum actual load demand among the actual load demands of the primary load inside the island in each time period. First, the actual load demand data of the primary load inside the island in each time period are collected and analyzed, and the maximum actual load demand, that is, the value with the highest load demand in all time periods, is found from these data. Then, based on the number of photovoltaic panels preliminarily calculated, combined with the output power of the photovoltaic panels and the light intensity information of the fault location, the total output power of the optimized photovoltaic power source is estimated, and the estimated total output power is compared with the maximum actual load demand to determine whether the optimized photovoltaic power source can meet the maximum load demand. If the optimized photovoltaic power source can meet the maximum actual load demand, the first configuration optimization is performed according to the original plan, that is, the corresponding number of photovoltaic panels is increased. If the optimized photovoltaic power source cannot meet the maximum actual load demand, it is necessary to readjust the number of photovoltaic panels to be added, and the number of photovoltaic panels preliminarily calculated can be adjusted based on the preset coefficient. The preset coefficient can be set according to actual conditions and experience to adjust the increase in the number of photovoltaic panels to ensure that the optimized photovoltaic power source can meet the maximum load demand. Use the preset coefficient to adjust the preliminarily calculated number of increased photovoltaic panels to obtain a new number of increased photovoltaic panels. Re-evaluate whether the photovoltaic power source based on the new number can meet the maximum actual load demand. If it still does not meet the requirements, it may be necessary to further adjust the preset coefficient or consider other factors (such as the configuration of energy storage equipment, load management strategy, etc.). After confirming that the optimized photovoltaic power source can meet the maximum actual load demand, the first configuration optimization is performed, that is, installation and configuration according to the final number of photovoltaic panels.

[0041] In an embodiment of the present application, optionally, after performing the second configuration optimization, the method further includes: determining the total actual load demand of the primary load within the island during the fault duration based on the actual load demand of the primary load within the island in each time period, and determining the redundant capacity of the photovoltaic power supply at the fault location based on the energy storage device capacity of the photovoltaic power supply at the fault location after the second configuration optimization and the total actual load demand, and performing power access operations in non-fault periods based on the redundant capacity to utilize the redundant capacity in non-fault periods.

[0042] In this embodiment, after the second configuration optimization is performed, in order to more effectively utilize the redundant capacity of the photovoltaic power source and the energy storage device, especially during non-fault periods, the electricity grid-connection operation can be further planned. First, the actual load demand data of the primary load inside the island in each time period are collected, and based on these data, the total actual load demand of the primary load inside the island during the fault duration is calculated, for example, the load demand of each time period is added. Based on the result after the second configuration optimization, the energy storage device capacity of the photovoltaic power source at the fault location is obtained, and the energy storage device capacity is compared with the total actual load demand to determine the additional electric energy that the energy storage device can provide during the fault duration, that is, the redundant capacity, which is at least not less than the remaining electric energy after meeting the primary load demand. Based on the redundant capacity, the electricity grid-connection operation is performed during the non-fault period (that is, the normal operation period of the distribution network), and the excess electric energy is transmitted to the distribution network for use by other users or devices.

[0043] By applying the technical solution of this embodiment, the following beneficial effects can be achieved: 1. Improve the reliability and stability of power supply: By evaluating the ability of photovoltaic power supply to provide power to the load inside the island during the distribution network failure, and optimizing the configuration accordingly, it can be ensured that when the failure occurs, the key load inside the island (such as the primary load) can obtain continuous and stable power supply, which greatly improves the reliability and stability of power supply and reduces the power outage time and impact range caused by the failure. 2. Optimize resource configuration: The determination of the number of photovoltaic panels and the capacity of energy storage equipment involved in the embodiment is based on actual load demand and prediction. This data-based resource configuration method can ensure the effective use of resources and avoid over-investment or insufficient resources. At the same time, by continuously adjusting and optimizing the configuration, the efficiency of resource use can be further improved. 3. Increase economic benefits: In non-fault periods, the redundant capacity of photovoltaic power supply and energy storage equipment is used to operate the power grid, which can convert excess electricity into economic benefits, which can not only increase electricity bill income, but also reduce the idle cost of energy storage equipment and improve the overall economic benefits. 4. Promote the use of renewable energy: By increasing the configuration of photovoltaic power supply and optimizing the use of energy storage equipment, renewable energy such as solar energy can be used more effectively. This helps reduce dependence on fossil fuels, lower carbon emissions, and promote sustainable development. 5. Enhance emergency response capabilities: When a distribution network failure occurs, it can quickly form an island operation and ensure the power supply of key loads, which greatly enhances the emergency response capabilities of the power grid. In emergencies such as natural disasters and equipment failures, power supply can be quickly restored to reduce losses and impacts.

[0044] The embodiment of the present application also provides a computer device, which can be a personal computer, a server, a network device, etc. The computer device includes a bus, a processor, a memory and a communication interface, and can also include an input and output interface and a display device. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in each method embodiment are implemented.

[0045] Those skilled in the art will appreciate that the structure of the above-mentioned computer device is only a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components, or combine certain components, or have a different arrangement of components.

[0046] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium may be non-volatile or volatile, and stores a computer program thereon. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0047] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0048] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0049] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0050] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distribution network photovoltaic and energy storage configuration method considering island division and network reconstruction, characterized in that: The method comprises: Collect basic information of the distribution network, including topological structure information, load distribution data, photovoltaic power output power, energy storage device status and capacity, fault location, and fault duration; Real-time monitoring of photovoltaic power output changes and load fluctuations, building a photovoltaic power output model and a load time-varying model, and using the photovoltaic power output model and the load time-varying model to predict photovoltaic output data and load demand data within the duration of the fault, wherein the photovoltaic power source includes photovoltaic power generation equipment and energy storage equipment; Based on the fault location, the topological structure information, the load distribution data, the photovoltaic output forecast data and the load demand forecast data within the fault duration, the load node directly powered by the photovoltaic power source is used as the starting point to determine the island range using a breadth-first search algorithm; Based on the islanding results, the remaining load nodes and their connected network lines that have not yet restored power supply are identified to form a basic framework for dynamic network reconstruction; the lowest comprehensive cost is determined as the optimization goal, the electrical constraints are converted into a second-order cone constraint form, and the network reconstruction problem is solved for the fault duration using the optimization goal and the electrical constraints to determine the switch operation strategy; the distribution network is configured according to the islanding results of the fault duration and the switch operation strategy to restore power supply to the fault area; According to the load operation status inside the isolated island during the distribution network fault, the reliability of the photovoltaic power source at the fault location is evaluated, and the configuration of the photovoltaic power source is optimized based on the reliability of the photovoltaic power source.

2. The method according to claim 1, characterized in that The photovoltaic power output model and the load time-varying model are used to predict the photovoltaic output data and the load demand data within the fault duration, including: Divide the fault duration into multiple time periods, and for a first time period, use the photovoltaic power output model and the load time-varying model to predict the photovoltaic output data and load demand data of the first time period; Based on the fault location, the topological structure information, the load distribution data, the photovoltaic output forecast data and the load demand forecast data within the fault duration, the load node directly powered by the photovoltaic power source is used as the starting point to determine the island range using a breadth-first search algorithm, including: Based on the fault location, the topological structure information, the load distribution data, the photovoltaic output forecast data and the load demand forecast data for the first time period, the load node directly powered by the photovoltaic power source and the energy storage device is used as the starting point to determine the island range using a breadth-first search algorithm; Based on the islanding results, the remaining load nodes and their connected network lines that have not been restored to power supply are identified to form a basic framework for dynamic network reconstruction; the lowest comprehensive cost is determined as the optimization goal, the electrical constraints are converted into a second-order cone constraint form, the network reconstruction problem is solved for the fault duration using the optimization goal and the electrical constraints, and the switch operation strategy is determined; the distribution network is configured according to the islanding results of the fault duration and the switch operation strategy to restore power supply to the fault area, including: Based on the islanding results of the first time period, the remaining load nodes and their connected network lines that have not yet restored power supply are identified to form a basic framework for dynamic network reconstruction; the lowest comprehensive cost is determined as the optimization goal, the electrical constraints are converted into a second-order cone constraint form, and the network reconstruction problem is solved for the first time period using the optimization goal and the electrical constraints to determine the switch operation strategy; the distribution network is configured according to the islanding results of the first time period and the switch operation strategy to restore power supply to the fault area; Monitor the photovoltaic power output changes and load fluctuations in the first time period within the island, use the photovoltaic power output model and the load time-varying model to predict the photovoltaic output data and load demand data in the second time period, and continue to perform island division and network reconstruction until the distribution network fault is restored.

3. The method according to claim 2, characterized in that The expression of the photovoltaic power output model is: PV,t =ηAS t , where P PV,t is the photovoltaic power output at time t, η is the photoelectric conversion efficiency, A is the area of ​​the photovoltaic module, S t is the light intensity at time t; The expression of the load time-varying model is: i (t) = f i (x)×Δt, where P i (t) is the average power of load i from time t to t+Δt, f i (x) is the daily load curve of load i, and Δt is the time interval.

4. The method according to claim 3, characterized in that Use the breadth-first search algorithm to determine the scope of the island, including: Use the breadth-first search algorithm to determine the initial island range, use the depth-first search and island fusion strategy to optimize the initial island range to ensure that the power supply inside the island meets the load demand, and verify the island division results of the first time period to ensure that each island division result meets the constraints of island power balance, node voltage and branch current; Among them, the island power balance constraint is: is the output of photovoltaic power source n in the island at time t, is the output of energy storage device n operating in conjunction with the photovoltaic power source at time t, is the active power of load j in the island at time t, P loss,t is the network loss of the island at time t; the node voltage constraint condition is: U min ≤U i,t ≤U max , U i,t is the voltage amplitude of node i at time t, U min and U max They represent the minimum and maximum values ​​of the node voltage respectively; the branch current constraint condition is: I ij,t ≤I ij,max , I ij,t is the current flowing through branch ij at time t, I ij,max is the maximum current allowed to pass through branch ij.

5. The method according to claim 4, characterized in that The optimization goal is to minimize the comprehensive cost, and the electrical constraints are converted into second-order cone constraints. The network reconstruction problem is solved for the first time period using the optimization goal and electrical constraints, and the switch operation strategy is determined, including: Based on the load recovery amount, the number of switch operations and the network loss factors, the objective function with the lowest comprehensive cost as the optimization goal is set, where the objective function is min(c1g 1,t +c2g 2,t +c3g 3,t ), c1, c2, c3 are the equivalent power loss load cost coefficient, switch operation cost coefficient and network loss cost coefficient respectively, g 1,t , g 2,t , g 3,t They are the equivalent load recovery amount, number of switch operations, and network loss in period t respectively.

6. The method according to claim 5, characterized in that According to the load operation state inside the isolated island during the distribution network fault, the reliability of the photovoltaic power source at the fault location is evaluated, and the configuration of the photovoltaic power source is optimized based on the reliability of the photovoltaic power source, including: According to the load operation state inside the isolated island during the distribution network failure and the preset reference load operation state, assess whether the photovoltaic power source at the fault location provides electric energy that meets the preset reference load operation state for the load inside the isolated island during the distribution network failure; wherein the preset reference load operation state refers to the preset first-level load maintaining a normal operating state; If not, the reliability of the photovoltaic power source at the fault location is determined to be unreliable, and the required capacity corresponding to the photovoltaic power source at the fault location is determined based on the load operation status inside the island during the distribution network fault and the preset reference load operation status, and the incremental configuration requirement of the photovoltaic power source is determined based on the required capacity and the existing capacity of the photovoltaic power source at the fault location, and based on the incremental configuration requirement of the photovoltaic power source and the light intensity information of the fault location and the output power of the photovoltaic power source at the fault location, it is determined to increase the number of photovoltaic panels and perform the first configuration optimization; wherein, the required capacity L actual (t) is the actual load demand of the primary load inside the island in each time period t, L baseline (t) is the preset benchmark load demand for each time period t.

7. The method according to claim 6, characterized in that After evaluating whether the photovoltaic power source at the fault location provides electric energy that satisfies a preset reference load operating state to the load inside the island during the distribution network fault, the method further includes: If so, the reliability of the photovoltaic power source at the fault location is determined to be reliable, and the remaining capacity of the photovoltaic power source at the fault location is determined based on the load operation status inside the island during the distribution network failure and the preset reference load operation status, and the energy storage device of the photovoltaic power source at the fault location is increased in capacity based on the remaining capacity and a second configuration optimization is performed; wherein, the remaining capacity P PV (t) is the actual power generation of the photovoltaic power source at the fault location in each time period t.

8. The method according to claim 6, characterized in that Before performing the first configuration optimization, the method further includes: According to the maximum actual load demand among the actual load demands of the primary load within the island in each time period, it is determined whether the photovoltaic power source at the fault location after optimization can meet the maximum actual load demand if the first configuration optimization is performed, and the first configuration optimization is performed if it is determined that the maximum actual load demand is met, otherwise the number of photovoltaic panels to be increased is re-determined based on the increased number of photovoltaic panels and the preset coefficient and the first configuration optimization is performed.

9. The method according to claim 7, characterized in that: After performing the second configuration optimization, the method further includes: According to the actual load demand of the primary load inside the island in each time period, the total actual load demand of the primary load inside the island during the fault duration is determined, and according to the energy storage device capacity of the photovoltaic power supply at the fault location after the second configuration optimization and the total actual load demand, the redundant capacity of the photovoltaic power supply at the fault location is determined, and based on the redundant capacity, the power grid operation is performed in the non-fault period to utilize the redundant capacity in the non-fault period.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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