A power distribution network reconstruction method based on distributed power supply and SOP optimal configuration

Abstract

This invention relates to a distribution network reconfiguration method based on distributed generation and SOP (Standard Operating Procedure) optimization configuration, belonging to the technical field of new energy grid connection and distribution network optimization operation. The method includes: performing site selection and capacity determination on the original distribution network using an improved gray wolf algorithm based on a constructed distributed generation optimization configuration model; updating the distribution network structure to form a new distribution network structure, constructing an SOP optimization configuration model, and optimizing the SOP configuration using an improved sensitivity analysis method; connecting the optimal access location and capacity of the distributed generation obtained from site selection and capacity determination, along with the SOP optimized configuration capacity and location, to the distribution network, constructing a distribution network demand response model; constructing a distribution network reconfiguration model based on the distribution network demand response model, reconfiguring the distribution network, obtaining the distribution network disconnection results, and thus obtaining a new topology. It aims to solve the technical problems of increased voltage fluctuations, increased network losses, and bidirectional power flow caused by a high proportion of distributed generation access to the distribution network.

Description

Technical Field

[0001] This invention relates to a distribution network reconfiguration method based on distributed power generation and SOP optimized configuration, belonging to the field of new energy grid connection and distribution network optimized operation technology. Background Technology

[0002] The deep integration of distributed generation (DG) with the distribution network is an important future development direction for low-carbon power systems, and their coordinated operation meets the energy requirements for low-carbon and sustainable development of the power system. The integration of high-penetration DG into the distribution network significantly alters the original operating characteristics of the system, transforming it from a traditional passive radial network into an active network with multi-source coordinated power supply. However, the randomness and uncertainty of DG integration pose dual risks to the safe and stable operation of the distribution network: firstly, the risk of power supply and demand imbalance caused by fluctuations in DG output; and secondly, the operational risks of node voltage deviation instability and persistently high network losses. Distribution network reconfiguration, as a key technical means to optimize the operating characteristics of the distribution network and improve the system's operational flexibility, reconfigures the network topology by rationally switching the on / off states of tie switches and sectionalizing switches in the distribution network. This enables optimized power flow allocation, improves the absorption capacity of DG, and simultaneously enhances the reliability of the distribution network's power supply.

[0003] However, the scale, location, and capacity configuration of distributed generation (DG) are core parameters for distribution network planning and operation optimization. They significantly impact the global distribution characteristics of bus voltage, active power loss, and reactive power loss levels of the distribution network. Inappropriate DG connection to the distribution network can easily lead to problems such as node voltage exceeding limits, reverse power flow, and a surge in line losses, even disrupting the original stability of the distribution network. Therefore, it is essential to conduct reasonable optimization of DG configuration. Meanwhile, Soft Open Points (SOPs) can optimize the global power flow distribution and power transmission path of the distribution network by precisely, rapidly, and continuously controlling the active power of the lines they connect to, effectively reducing energy losses caused by branch overload, power flow detours, and redundant transmission; and providing effective voltage support for the distribution system through reactive power compensation. However, the investment cost of connecting SOPs to the distribution network is relatively high. Therefore, before putting them into actual operation, it is urgent to conduct special studies on site selection and capacity determination to minimize their investment costs. Summary of the Invention

[0004] The purpose of this invention is to provide a distribution network reconfiguration method based on distributed generation and SOP optimized configuration, which aims to solve the technical problems of increased voltage fluctuation, increased network loss, and bidirectional power flow caused by the high proportion of distributed generation access to the distribution network.

[0005] To achieve the above objectives, the technical solution of this invention is: a distribution network reconfiguration method based on the optimized configuration of distributed generation sources and Standard Operating Procedures (SOPs). This method mitigates the negative impact of disorderly access on the distribution network bus voltage distribution, power flow distribution, and line losses by simultaneously optimizing parameters such as the number, location, and capacity of SOPs and distributed generation sources during the planning phase. The method includes the following steps: Step 1: Construct a distributed power supply optimization configuration model; Step 2: Improve the traditional Grey Wolf algorithm to obtain the improved Grey Wolf algorithm; Step 3: Based on the distributed power source optimization configuration model, the improved Grey Wolf algorithm is used to perform site selection and capacity determination on the original distribution network to obtain the optimal access location and capacity of the distributed power source. Step 4: Update the distribution network structure based on the optimal access location and capacity of the distributed power source to form a new distribution network structure. Based on the new distribution network structure, construct the SOP optimization configuration model and use the improved sensitivity analysis method to optimize the configuration of the SOP to obtain the optimized configuration capacity and location of the SOP. Step 5: Connect the optimal access location and capacity of the distributed power source with the optimized configuration capacity and location of the SOP to the distribution network, and construct the distribution network demand response model; Step 6: Construct a distribution network reconfiguration model based on the distribution network demand response model, and reconfigure the distribution network using the distribution network reconfiguration model to obtain the distribution network disconnection results, thereby obtaining a new topology.

[0006] Optionally, the distributed power supply optimization configuration model is specifically as follows:

[0007] In the formula, F z The objective function is... Cost of active power loss in the system; Cost of system voltage deviation; Cost of distributed power supply in the system; Costs associated with voltage exceeding limits; To cover the cost of exceeding current limits, These are the system active power loss cost, system voltage deviation cost, and system distributed power source absorption cost coefficient, respectively.

[0008] Optionally, the improvement to the traditional gray wolf algorithm specifically involves: Improvement 1: First, generate GWO candidate solutions, then generate DLH perturbation candidate solutions based on neighborhood distance. A two-layer selection mechanism is used to retain the better solution, thereby enhancing global exploration and local development capabilities. The expression for the two-layer selection mechanism is:

[0009] In the formula, , These represent the optimal position and fitness of the current gray wolf. , Let be the optimal position and optimal fitness of the i-th wolf, respectively; , Let GWO and DLH be the positions of the i-th wolf, respectively. , These are the fitness values ​​corresponding to their respective positions; Improvement 2: Introducing Tent chaotic mapping and a nonlinear adjustment strategy for the convergence factor; wherein the expression for the nonlinear adjustment strategy for the convergence factor is:

[0010] In the formula, , Nonlinear factors a The maximum and minimum values; t This represents the current iteration number; =0.6 This is the critical value for the number of iterations. This represents the maximum number of iterations.

[0011] Optionally, the SOP optimized configuration model is specifically as follows: The SOP is optimized using the lowest annual comprehensive cost as the objective function, and the expression is:

[0012] In the formula, f To optimize the total configuration cost, The annual investment cost for SOP (Start of Production) Annual operating costs for SOP, C loss The annual power supply loss cost of the power distribution system, C u Penalty cost for voltage deviation; Among them, the annual investment cost of the SOP The expression is:

[0013] In the formula, The number of nodes; For nodes The set of adjacent nodes; The unit capacity investment cost of SOP; For the node and nodes The SOP capacity configured between them; The service life of the SOP; Among them, the annual operation and maintenance cost of the SOP The expression is:

[0014] In the formula, This represents the annual maintenance cost coefficient for Standard Operating Procedures (SOP). Among them, the annual power supply loss cost of the power distribution system C loss The expression is:

[0015] In the formula, This is the annual power supply loss cost coefficient for the distribution network. Total number of time periods; The duration of each time period; for Time period nodes The injected active power; for Time-based SOP at node The active power loss at point A is expressed as:

[0016] In the formula, They are nodes and nodes Active power injected at SOP; They are respectively Time period nodes The active power loss at SOP of node j; The loss factor for SOP; , They are respectively Time period nodes and nodes No power injected at SOP; Among them, the voltage deviation penalty cost C u The expression is:

[0017] In the formula, T This refers to the total time period. n d For the number of nodes, For nodes j Node voltage; This is the system's reference voltage.

[0018] Optionally, the expression for the improved sensitivity analysis method is:

[0019] In the formula, S ji For sensitivity, for The sensitivity of the power change at node i to the voltage at node j during a given time period; For time period Weighting coefficients; for The number of nodes in the system that exceed voltage limits during a given time period; for The maximum value of the voltage exceeding the limit at the node during the time period.

[0020] Optionally, the distribution network demand response model is specifically as follows:

[0021]

[0022]

[0023]

[0024] In the formula, The price elasticity coefficients at different times for each node; The change in electricity consumption before and after implementing demand response at different times for each node; This represents the difference between the initial electricity price and the price after considering DR (Derivative Price). , These represent peak and off-peak load times, respectively. , These are the electricity prices during peak and off-peak hours, respectively. It is the set of all branches in the system; and They are time intervals Electricity prices before and after the implementation of DR; To consider the load output of DR and To take into account the output of the DR preload; and These are the nodes after implementing DR. time The upper and lower limits of electricity prices.

[0025] Optionally, the distribution network reconfiguration model is specifically as follows: The objective function is to minimize the sum of network losses, wind curtailment, solar curtailment, SOP losses, and switching costs. The expression is:

[0026] In the formula,C z The total cost of power distribution network reconfiguration; The length of each time period; It is the set of all branches in the system; for t Time Branch ij The current, branch road ij The resistance between; To connect to the set of wind turbine nodes; To connect to the photovoltaic unit node set; The set of nodes for system access to SOP; and They are respectively Time Node The power emitted by WT and PV; and They are respectively Time Node The actual power of WT and PV connected to the grid; and These are 0-1 variables, representing branches in the initial network state and after distribution network reconfiguration, respectively. The opening and closing status is indicated by a value of 1, which means the branch is closed, and a value of 0 means the branch is open. , , , and These are the unit prices for network loss costs, switch costs, wind curtailment costs, solar curtailment costs, and SOP operating costs, respectively.

[0027] The beneficial effects of this invention are: (1) The improved Grey Wolf algorithm of this invention has excellent overall performance in distributed power source location and capacity determination, and can guarantee good voltage quality. The performance of IGWO in terms of voltage qualification rate and standard deviation of the system after distributed power source location and capacity determination is significantly better than other comparative optimization algorithms, and the overall optimization effect is the best.

[0028] (2) This invention optimizes the configuration of SOP by taking into account the improved sensitivity analysis method of the additional voltage limit cost, and obtains the optimal access node, capacity and quantity of SOP. By connecting the optimized SOP to the distribution network, the system network loss can be effectively reduced and the system economy can be optimized.

[0029] (3) The present invention considers the distribution network reconfiguration method of distributed power source and SOP optimization configuration, which significantly improves the overall voltage quality of the distribution network. At the same time, there is an optimal SOP configuration scheme that ensures the optimal optimization cost during the SOP planning stage, so that the voltage quality of the connected distribution network and the total operating cost of the system are optimal. Attached Figure Description

[0030] Figure 1 This is a flowchart of the steps of the present invention; Figure 2 This is a diagram of the power distribution network topology of the present invention; Figure 3 This is the convergence graph of the objective function of the optimization algorithm of this invention; Figure 4 This is a comparison chart of the number of iterations of the optimization algorithm of this invention; Figure 5 This is a comparison diagram of node voltages after grid connection of various optimization algorithms DG in this invention; Figure 6 This is a voltage distribution diagram of each node in the distribution network at 03:00 during off-peak hours according to the present invention; Figure 7 This is a voltage distribution diagram of each node in the distribution network at 20:00 during peak hours according to the present invention; Figure 8 This is a voltage distribution curve of 33 nodes at 24 time points in the power distribution network system of the present invention; Figure 9 This is a diagram of the voltage distribution of each node in the distribution network at 03:00 during the valley time of demand response, as presented in this invention. Figure 10 This is a diagram showing the voltage distribution of each node in the distribution network at 20:00 during peak hours, taking into account demand response, according to the present invention. Figure 11 This is a new power distribution network topology diagram of the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0032] Example 1: As Figure 1 As shown, a distribution network reconfiguration method based on distributed generation and SOP optimized configuration includes the following steps: Step 1: Construct a distributed power supply optimization configuration model; It is important to understand that the location and capacity of distributed generation (DG) connected to the distribution network will have a variety of impacts on the distribution network. Among them, the system network loss and the voltage distribution of each node are the main indicators characterizing the economic efficiency and stability of the system operation. The system active power loss, voltage deviation, distributed generation absorption rate, voltage and current over-limit, and five other indicators can effectively reflect the system network loss and voltage distribution. Therefore, this embodiment uses the above five indicators as the main optimization targets to analyze the location and capacity of DG. Optionally, the distributed power supply optimization configuration model is specifically as follows:

[0033] In the formula,F z The objective function is... Cost of active power loss in the system; Cost of system voltage deviation; Cost of distributed power supply in the system; Costs associated with voltage exceeding limits; To cover the cost of exceeding current limits, These are the system active power loss cost, system voltage deviation cost, and system distributed power source absorption cost coefficient, respectively.

[0034] Specifically, the active power loss cost of the system is as follows: In a power distribution network, the formula for calculating the active power loss of a single branch is:

[0035] In the formula, The active power loss of the branch between node i and node j; Let be the current flowing through branches ij; The resistance between branches ij; Let be the active power at node j; Let J be the reactive power at node j. Let i be the voltage at node i; To calculate the active power loss in a distribution network system, we need to find the sum of the active power losses of all branches. The formula is as follows:

[0036] In the formula, For the number of nodes, It is a resistor, and .

[0037] Specifically, the system voltage deviation cost is as follows: In a power distribution network, the objective function is to minimize the total voltage deviation at all nodes. The calculation formula is as follows:

[0038] In the formula, n d For the number of nodes, Let be the node voltage at node j; This is the system's reference voltage, with a per-unit value of 1.

[0039] Specifically, the distributed power consumption cost of the system is as follows: In power distribution networks, the renewable energy absorption rate is an important operational evaluation indicator. The formula for calculating renewable energy absorption output is as follows:

[0040] In the formula, This refers to the total output power of new energy sources.

[0041] Specifically, the voltage over-limit cost is as follows: In a power distribution network, the objective function is to minimize the total voltage deviation at all nodes. The calculation formula is as follows:

[0042] In the formula, This represents the total number of nodes in the system. The penalty coefficient is... , These are the upper and lower limits of the voltage.

[0043] Specifically, the current over-limit cost is as follows: In a distribution network, the objective function is to minimize the sum of all current exceeding limits. The calculation formula is as follows:

[0044] In the formula, The total number of branch roads, Let g be the current amplitude of the g-th branch. The penalty coefficient is... This is the upper limit of the branch current.

[0045] Step 2: Improve the traditional Grey Wolf algorithm to obtain the improved Grey Wolf algorithm; It should be understood that, in determining the optimization algorithm, this embodiment selected the particle swarm optimization algorithm, whale optimization algorithm, gray wolf algorithm, African vulture optimization algorithm, and improved gray wolf optimization algorithm for comparative optimization experiments; Specifically, this experiment uses an IEEE 33-node distribution network system containing energy storage, capacitors, and distributed generation (DG) for effectiveness analysis. The system topology is as follows: Figure 2 As shown, the distribution network comprises 37 branches, equipped with 32 normally closed switches and 5 tie switches. The initial network load is 3715kW + j2300kvar. The base voltage is set at 12.66kV, and the base power is 10MW. Node 1 is selected as the slack node, and its per-unit voltage value is set to 1.0. The system voltage operating constraints are per-unit values ​​of 0.95~1.05, and the line operation must meet the maximum current limit requirement of 1000A.

[0046] Furthermore, this experiment selected a test function, specifically test function F10 from MATLAB's CEC2005 library. The convergence graph of the target function is shown below. Figure 3 As shown in the figure, the number of iterations among Particle Swarm Optimization, Whale Optimization, Gray Wolf Optimization, African Vulture Optimization, and the improved Gray Wolf Optimization is compared. Figure 4As shown in the figure, the IGWO, AVOA, and WOA optimization algorithms converge the fastest, reaching the global optimum within about 50 iterations. The objective function value approaches 0, and there is no late-stage oscillation. They have extremely strong optimization efficiency and stability. Therefore, in this embodiment, the improved Grey Wolf algorithm is selected as the optimization algorithm.

[0047] Optionally, the improvement to the traditional gray wolf algorithm specifically involves: Improvement 1: Traditional GWO updates candidate solutions solely based on the positional weights of α / β / δ wolves. Therefore, this embodiment first generates GWO candidate solutions, then generates DLH perturbation candidate solutions based on neighborhood distance. A two-layer selection mechanism is used to retain better solutions, thereby enhancing global exploration and local exploitation capabilities. The expression for the two-layer selection mechanism is as follows:

[0048] In the formula, , These represent the optimal position and fitness of the current gray wolf. , Let be the optimal position and optimal fitness of the i-th wolf, respectively; , Let GWO and DLH be the positions of the i-th wolf, respectively. , These are the fitness values ​​corresponding to their respective positions; Improvement 2: Introduce Tent chaotic mapping and a nonlinear adjustment strategy for the convergence factor; Optionally, since the traditional gray wolf algorithm struggles to guarantee a uniform distribution of individuals in the initial population across the entire search space, it hinders the improvement of the algorithm's global search performance. Therefore, to address this issue, this embodiment employs a Tent mapping chaotic sequence to generate the initial population, ensuring a uniform and unbiased distribution of individuals. This improves the ergodicity and global adaptability of the initial population, providing a better starting point for subsequent iterations and accelerating population convergence and evolutionary efficiency.

[0049] Specifically, the Tent mapping equation is:

[0050] In the formula: The state value of the i-th iteration has a range of [0,1] and is the input of the current iteration. Let i be the state value of the (i+1)th iteration; i is the index of the iteration number, representing the current iteration number.

[0051] Alternatively, in the traditional grey wolf algorithm, the convergence factor aThe convergence factor decreases linearly with the number of iterations. This change pattern is simple in mechanism but has limited adjustment capability, easily leading to problems such as excessive local search in the early stage and insufficient global search in the later stage. Therefore, this embodiment introduces a nonlinear adjustment strategy for the convergence factor to enable it to dynamically change non-uniformly and more closely fit the optimization law as the iteration process progresses. The expression of the nonlinear adjustment strategy for the convergence factor is:

[0052] In the formula, , Nonlinear factors a The maximum and minimum values; t This represents the current iteration number; =0.6 This is the critical value for the number of iterations. This represents the maximum number of iterations.

[0053] Step 3: Based on the distributed power source optimization configuration model, the improved Grey Wolf algorithm is used to perform site selection and capacity determination on the original distribution network to obtain the optimal access location and capacity of the distributed power source. Optionally, a distributed power source addressing and capacity determination simulation was performed, with the algorithm parameters set as follows: population size of 300; maximum number of iterations (Miteration = 100). Based on the parameter simulation, the results were compared with those of the unconnected DG, particle swarm optimization algorithm, gray wolf algorithm, whale algorithm, African vulture algorithm, and improved gray wolf algorithm, as shown in Table 1.

[0054] Table 1 Comparison of optimization configuration results for different algorithms

[0055] From the perspectives of three core indicators—network loss, optimization target cost, and voltage deviation—the improved Grey Wolf algorithm significantly outperforms other algorithms, making it the optimal choice for distributed generation (DG) location and capacity determination. Conversely, the scheme without DG integration performs the worst, highlighting the importance of DG integration and optimization algorithms. The improved Grey Wolf algorithm achieves a network loss of only 104.6kW, a 48.4% reduction compared to the unintegrated scheme, making it the lowest-loss algorithm among all options. This demonstrates the algorithm's superior capabilities in power flow optimization and reducing transmission loss.

[0056] Furthermore, Figure 5 The graph shows a comparison of node voltages after grid connection for each DG optimization algorithm. As can be seen from the graph, the voltage levels of each node are improved after IGWO optimizes the DG configuration. Compared with other optimization algorithms, IGWO's improvement effect is more significant.

[0057] Step 4: Update the distribution network structure based on the optimal access location and capacity of the distributed power source to form a new distribution network structure. Based on the new distribution network structure, construct the SOP optimization configuration model and use the improved sensitivity analysis method to optimize the configuration of the SOP to obtain the optimized configuration capacity and location of the SOP. Optionally, in this embodiment, the distribution network access nodes are first updated, and the PV is connected to system nodes 17 and 22 according to the improved Grey Wolf algorithm for site selection and capacity determination, with access capacities of 300 and 400kW respectively; WT is connected to nodes 9, 25 and 32 with access capacities of 800, 500 and 500kW respectively.

[0058] Furthermore, an SOP optimization configuration model is constructed, which specifically includes: The SOP is optimized using the lowest annual comprehensive cost as the objective function, and the expression is:

[0059] In the formula, f To optimize the total configuration cost, The annual investment cost for SOP (Start of Production) Annual operating costs for SOP, C loss The annual power supply loss cost of the power distribution system, C u Penalty cost for voltage deviation; Among them, the annual investment cost of the SOP The expression is:

[0060] In the formula, The number of nodes; For nodes The set of adjacent nodes; The unit capacity investment cost of SOP; For the node and nodes The SOP capacity configured between them; The service life of the SOP; Among them, the annual operation and maintenance cost of the SOP The expression is:

[0061] In the formula, This represents the annual maintenance cost coefficient for Standard Operating Procedures (SOP). Among them, the annual power supply loss cost of the power distribution system C loss The expression is:

[0062] In the formula, This is the annual power supply loss cost coefficient for the distribution network. Total number of time periods; The duration of each time period; for Time period nodes The injected active power; for Time-based SOP at node The active power loss at point A is expressed as:

[0063] In the formula, They are nodes and nodes Active power injected at SOP; They are respectively Time period nodes The active power loss at SOP of node j; The loss factor for SOP; , They are respectively Time period nodes and nodes No power injected at SOP; Among them, the voltage deviation penalty cost C u The expression is:

[0064] In the formula, T This refers to the total time period. n d For the number of nodes, For nodes j Node voltage; This is the system's reference voltage.

[0065] Optionally, this embodiment employs an improved sensitivity analysis method that incorporates the cost of voltage over-limit penalties to perform site selection and capacity optimization configuration analysis on the SOP. The expression for the improved sensitivity analysis method is:

[0066] In the formula, S ji For sensitivity, for The sensitivity of the power change at node i to the voltage at node j during a given time period; For time period Weighting coefficients; for The number of nodes in the system that exceed voltage limits during a given time period; for The maximum value of the voltage exceeding the limit at the node during the time period.

[0067] Furthermore, since connecting too many smart soft switches increases the system's security risks and processing burden, leading to a decrease in system performance, this embodiment only considers optimized configuration schemes with three or fewer smart soft switches connected to the system. The specific location and capacity comparison results are shown in Table 2.

[0068] Table 2. Cost Comparison of Access Schemes with Different Number of SOPs

[0069] Table 2 shows the optimal access nodes and capacities for different numbers of SOPs when the system's annual overall cost is minimized. The table shows that both SOP access cost and maintenance cost increase with increasing access capacity. Considering the need for the minimum annual overall system cost, accessing two SOPs results in the highest economic efficiency.

[0070] Furthermore, by employing improved sensitivity analysis to optimize the configuration of SOPs, the optimal configuration capacity and access nodes for different numbers of SOPs connected were determined. During the optimization process, given the significant investment cost of SOPs, to maximize system economics, the lowest overall investment cost should be prioritized when connecting SOPs to the original distribution network structure.

[0071] Step 5: Connect the optimal access location and capacity of the distributed power source with the optimized configuration capacity and location of the SOP to the distribution network, and construct the distribution network demand response model; Optionally, considering load capacity constraints and electricity price constraints, the distribution network demand response model is specifically as follows:

[0072]

[0073]

[0074]

[0075] In the formula, The price elasticity coefficients at different times for each node; The change in electricity consumption before and after implementing demand response at different times for each node; This represents the difference between the initial electricity price and the price after considering DR (Derivative Price). , These represent peak and off-peak load times, respectively. , These are the electricity prices during peak and off-peak hours, respectively. It is the set of all branches in the system; and They are time intervals Electricity prices before and after the implementation of DR; To consider the load output of DR and To take into account the output of the DR preload; and These are the nodes after implementing DR. time The upper and lower limits of electricity prices.

[0076] Step 6: Construct a distribution network reconfiguration model based on the distribution network demand response model, and reconfigure the distribution network using the distribution network reconfiguration model to obtain the distribution network disconnection results, thereby obtaining a new topology.

[0077] It is important to understand that this embodiment proposes a distribution network reconfiguration strategy based on distributed generation (DR) and SOP (Standard Operating Procedure) location and capacity selection. Considering the high investment cost of SOPs, the SOP system cost has been minimized and optimally configured during the SOP optimization phase. Therefore, in this embodiment, the distribution network reconfiguration model does not consider the investment cost of SOPs, but only their loss costs.

[0078] Optionally, the distribution network reconfiguration model is specifically as follows: The objective function is to minimize the sum of network losses, wind curtailment, solar curtailment, SOP losses, and switching costs. The expression is:

[0079] In the formula, C z The total cost of power distribution network reconfiguration; The length of each time period; It is the set of all branches in the system; for t Time Branch ij The current, branch road ij The resistance between; To connect to the set of wind turbine nodes; To connect to the photovoltaic unit node set; The set of nodes for system access to SOP; and They are respectively Time Node The power emitted by WT and PV; and They are respectively Time Node The actual power of WT and PV connected to the grid; and These are 0-1 variables, representing branches in the initial network state and after distribution network reconfiguration, respectively. The opening and closing status is indicated by a value of 1, which means the branch is closed, and a value of 0 means the branch is open. , , , and These are the unit prices for network loss costs, switch costs, wind curtailment costs, solar curtailment costs, and SOP operating costs, respectively.

[0080] Furthermore, distribution network reconfiguration constraints are constructed, including DistFlow constraints, topology constraints, voltage and current constraints, renewable energy output constraints, energy storage constraints, SOP constraints, and capacitor switching constraints. Specifically, the DistFlow power flow constraint is achieved by introducing a line interruption variable. Relax the power flow equations.

[0081] Specifically, the topological constraints are as follows: To ensure that the number of branches in the distribution network system is equal to the number of nodes minus the number of power sources, and that the restructured system does not contain ring networks or islands, the following constraints are imposed on the system's topology:

[0082]

[0083]

[0084] In the formula, Let be the opening and closing state of branch ij at time t; , Branch ij and branch ji are in a unidirectional conduction state; It is the set of branches in the system; This represents the number of power supply nodes in the system. T The total time period is 24 in this embodiment; 0-1 variables, nodes For nodes The value is 1 if it is the parent node, otherwise it is 0; Specifically, the voltage and current constraints are as follows: The system voltage and current must be within the voltage amplitude safety constraints and the maximum current transmission safety limits, as expressed by:

[0085]

[0086] In the formula, Let be the square of the voltage at node i; Let be the square of the current at node i; , For nodes Minimum and maximum allowable voltage values; branch road The maximum current value that can be transmitted.

[0087] Specifically, the power output constraint of the new energy source is as follows: The actual output of the wind turbines and photovoltaic units connected to the system should not exceed the upper and lower limits of the allowable output. The output constraints are as follows:

[0088]

[0089] In the formula, and These represent the lower and upper limits of wind turbine output, respectively. and These represent the lower and upper limits of the photovoltaic unit's output, respectively. and These are the sets of nodes that connect wind turbines and photovoltaic units to the system, respectively.

[0090] Specifically, the energy storage constraints are: Considering the energy storage capacity constraint, charge / discharge state constraint, and charge / discharge power constraint of the energy storage system, the expression is:

[0091]

[0092]

[0093] In the formula, and Variables are 0-1, representing respectively node The charging and discharging status of the ESS at any given time; , Let be the charging and discharging power at node t at time i, respectively; , These are the maximum charging and discharging power, respectively. For ESS system in node Energy storage capacity at any given time; and These are the minimum and maximum charge that the ESS can store, respectively. and These represent the charging and discharging efficiencies of the ESS, respectively.

[0094] Specifically, the SOP constraint is as follows: SOP transmission active power constraint:

[0095]

[0096] In the formula, , They are nodes and nodes Active power injected at SOP; These are nodes for time period t. The active power loss at SOP of node j; The loss factor for SOP; , These are nodes for time period t. and nodes No power is injected at the SOP.

[0097] SOP capacity constraints:

[0098]

[0099] In the formula, and Connected to the node respectively and nodes SOP capacity at both ends.

[0100] Specifically, the capacitor switching constraint is as follows: Introducing capacitors into the system can provide reactive power compensation and alleviate problems such as undervoltage caused by reactive power deficit. The capacitors should meet the following constraints:

[0101] In the formula, for Time period nodes The reactive power compensation capacity of the capacitor; This indicates the reactive power compensation capacity of a single capacitor. for Time period nodes The number of capacitors switched on at each location; For nodes The total number of capacitors available for switching.

[0102] Based on the specific implementation details, the effectiveness of the technical solution of the present invention will be demonstrated through experiments.

[0103] Specifically, the voltage quality of each node and the total system cost are analyzed during peak and off-peak hours. Peak hour voltage distribution is taken at 20:00, and off-peak hour voltage distribution is taken at 03:00. The results are as follows: Figure 6 and Figure 7 As shown in the figure, it can be seen that after optimizing the configuration of the SOP device and distributed generation in the distribution network, the voltage quality of the system is higher and the voltage fluctuation is smaller than that without optimization.

[0104] Furthermore, the voltage curve of the system at 24 hours is as follows: Figure 8 As shown in the figure, from the perspective of voltage quality, the optimized distribution network is better able to adapt to the randomness of distributed power sources and loads in the power grid, respond in real time to changes in the power grid state, and actively adjust active and reactive power control strategies, thereby improving the voltage distribution of the power grid. As can be seen from the figure, the overall voltage fluctuation of the optimized system is significantly reduced, and the voltage quality is improved. Furthermore, a comparative analysis of the distribution network reconfiguration costs of the two schemes is presented in Table 3.

[0105] Table 3 Comparison of total costs for different power distribution network reconfiguration schemes

[0106] Table 3 shows that the network loss cost after optimization is significantly lower than before, and the optimization effect on power flow is improved. Switching costs and wind / solar curtailment costs also decrease, demonstrating that adding and optimizing intelligent soft switches before distribution network reconfiguration can effectively improve system voltage quality, reduce voltage fluctuations, lower network losses, and increase the utilization rate of renewable energy.

[0107] Furthermore, by adding an optimized Standard Operating Procedure (SOP) device to the original distribution network structure, and considering the inclusion of time-of-use demand response, the power supply quality and reliability of the distribution network can be improved, while simultaneously enhancing system operational flexibility and reducing distribution network losses. Considering the integration of one optimized SOP into the distribution network, a comparative analysis is conducted on whether demand response was considered before the distribution network reconfiguration.

[0108] Considering that demand response can play a role in peak shaving and valley filling of system load, this study analyzes the voltage quality of each node and the total system cost during peak and valley periods by performing demand response on the system load before distribution network reconfiguration. The voltage distribution of each node is taken at 20:00 during peak hours and at 03:00 during valley hours, as shown below. Figure 9 and Figure 10 As shown.

[0109] right Figure 9 and Figure 10Comparative analysis reveals that during peak load hours (20:00), the overall voltage of the distribution network considering demand response is higher than that without it. Demand response, by reducing peak load, lowers line flow and reactive power losses, fundamentally mitigating voltage dips and ensuring voltage quality during peak hours. Conversely, during off-peak hours, the voltage of the distribution network considering demand response is lower than that without it, generally concentrated between 0.97-0.99 pu, effectively preventing overvoltage. Demand response, by guiding some transferable load to use electricity during off-peak hours, moderately increases off-peak load levels, smoothing out excessively high voltage and keeping the voltage operating within a more reasonable range.

[0110] Finally, in this embodiment, the distribution network disconnection branches of the distribution network reconfiguration scheme considering distributed generation and SOP location and capacity are determined to be 6-7, 11-12, 14-15, 17-18, and 25-29. The new distribution network topology is as follows: Figure 11 As shown.

[0111] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A power distribution network reconstruction method based on distributed power source and SOP optimized configuration, characterized in that, The method includes the following steps: Step 1: Construct a distributed power supply optimization configuration model; Step 2: Improve the traditional Grey Wolf algorithm to obtain the improved Grey Wolf algorithm; Step 3: Based on the distributed power source optimization configuration model, the improved Grey Wolf algorithm is used to perform site selection and capacity determination on the original distribution network to obtain the optimal access location and capacity of the distributed power source. Step 4: Update the distribution network structure based on the optimal access location and capacity of the distributed power source to form a new distribution network structure. Based on the new distribution network structure, construct the SOP optimization configuration model and use the improved sensitivity analysis method to optimize the configuration of the SOP to obtain the optimized configuration capacity and location of the SOP. Step 5: Connect the optimal access location and capacity of the distributed power source with the SOP optimized configuration capacity and location to the distribution network, and construct the distribution network demand response model; Step 6: Construct a distribution network reconfiguration model based on the distribution network demand response model, and reconfigure the distribution network using the distribution network reconfiguration model to obtain the distribution network disconnection results, thereby obtaining a new topology.

2. The power distribution network reconfiguration method based on optimal configuration of distributed power sources and SOP according to claim 1, characterized in that, The specific distributed power supply optimization configuration model is as follows: ； In the formula, F z is a target function; is a system active power loss cost; is a system voltage deviation cost; is a system distributed power consumption cost; is a voltage out-of-limit cost; is a current out-of-limit cost, respectively, are system active power loss cost, system voltage deviation cost and system distributed power consumption cost coefficients.

3. The power distribution network reconfiguration method based on optimal configuration of distributed power sources and SOP according to claim 1, characterized in that, The improvement to the traditional Grey Wolf algorithm specifically involves: Improvement 1: First, generate GWO candidate solutions, then generate DLH perturbation candidate solutions based on neighborhood distance. A two-layer selection mechanism is used to retain the better solution, thereby enhancing global exploration and local development capabilities. The expression for the two-layer selection mechanism is: ； where, , are the current optimal position and fitness of the grey wolf, , are the individual optimal position and optimal fitness of the ith wolf; , are the GWO and DLH positions of the ith wolf, , are the fitnesses corresponding to their positions, respectively. Improvement 2: Introducing Tent chaotic mapping and a nonlinear adjustment strategy for the convergence factor; wherein the expression for the nonlinear adjustment strategy for the convergence factor is: ； In the formula, , Nonlinear factors a The maximum and minimum values; t This represents the current iteration number; =0.6 This is the critical value for the number of iterations. This represents the maximum number of iterations.

4. The distribution network reconfiguration method based on distributed generation and SOP optimized configuration according to claim 1, characterized in that, The SOP optimized configuration model is specifically as follows: The SOP is optimized using the lowest annual comprehensive cost as the objective function, and the expression is: ； In the formula, f To optimize the total configuration cost, The annual investment cost for SOP (Start of Production) Annual operating costs for SOP, C loss The annual power supply loss cost of the power distribution system, C u Penalty cost for voltage deviation; Wherein, the SOP annual investment cost The expression is: ； In the formula, The number of nodes; For nodes The set of adjacent nodes; The unit capacity investment cost of SOP; For the node and nodes The SOP capacity configured between them; The service life of the SOP; The expression of the SOP annual operation and maintenance cost is: The expression of the SOP annual operation and maintenance cost is: ； In the formula, SOP is the annual operation and maintenance cost coefficient; The power distribution system annual power supply loss cost C loss The expression is: ； In the formula, This is the annual power supply loss cost coefficient for the distribution network. Total number of time periods; The duration of each time period; for Time period nodes The injected active power; for Time-based SOP at node The active power loss at point A is expressed as: ； In the formula, They are nodes and nodes Active power injected at SOP; They are respectively Time period nodes The active power loss at SOP of node j; The loss factor for SOP; , They are respectively Time period nodes and nodes No power injected at SOP; Among them, the voltage deviation penalty cost C u The expression is: ； In the formula, T This refers to the total time period. n d For the number of nodes, For nodes j The node voltage; This is the system's reference voltage.

5. A distribution network reconfiguration method based on distributed generation and SOP optimized configuration according to claim 1, characterized in that, The expression for the improved sensitivity analysis method is: ； In the formula, S ji For sensitivity, for The sensitivity of the power change at node i to the voltage at node j during a given time period; For time period Weighting coefficients; for The number of nodes in the system that exceed voltage limits during a given time period; for The maximum value of the voltage exceeding the limit at the time node.

6. The distribution network reconfiguration method based on distributed generation and SOP optimized configuration according to claim 1, characterized in that, The specific demand response model for the distribution network is as follows: ； ； ； ； In the formula, The price elasticity coefficients at different times for each node; The change in electricity consumption before and after demand response is implemented at different times for each node; This represents the difference between the initial electricity price and the price after considering DR (Derivative Price). , These represent peak and off-peak hours, respectively. , These are the electricity prices during peak and off-peak hours, respectively. It is the set of all branches in the system; and They are time points Electricity prices before and after the implementation of DR; To consider the load output of DR and To take into account the output of the DR preload; and These are the nodes after implementing DR. time The upper and lower limits of electricity prices.

7. The distribution network reconfiguration method based on distributed generation and SOP optimized configuration according to claim 1, characterized in that, The specific distribution network reconfiguration model is as follows: The objective function is to minimize the sum of network losses, wind curtailment, solar curtailment, SOP losses, and switching costs. The expression is: ； In the formula, C z The total cost of power distribution network reconfiguration; The length of each time period; It is the set of all branches in the system; for t Time Branch ij The current, branch road ij The resistance between; To connect to the set of wind turbine nodes; To connect to the photovoltaic unit node set; The set of nodes for system access to SOP; and They are respectively Time Node The power emitted by WT and PV; and They are respectively Time Node The actual power of WT and PV connected to the grid; and These are 0-1 variables, representing branches in the initial network state and after distribution network reconfiguration, respectively. The opening and closing status is indicated by a value of 1, which means the branch is closed, and a value of 0 means the branch is open. , , , and These are the unit prices for network loss costs, switch costs, wind curtailment costs, solar curtailment costs, and SOP operating costs, respectively.

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