A site selection and capacity planning method based on reconfigurable multi-terminal soft switch

Abstract

The present application relates to the technical field of power distribution system operation planning optimization, and particularly relates to a site selection and capacity planning method based on a reconfigurable multi-terminal soft switch. The technical scheme comprises the following steps: establishing a steady-state operation model of a reconfigurable multi-terminal soft switch R-SOP, wherein the model comprises power balance constraints and capacity limit constraints; constructing a R-SOP candidate location selection model considering interconnection demand, wherein the model performs two-dimensional evaluation on the nodes of the power distribution network through a loss sensitivity index LSI and a voltage deviation index VDI. The present application precisely identifies the optimal interconnection nodes of the power distribution network by constructing a two-dimensional interconnection demand evaluation system integrating loss sensitivity and voltage deviation, and realizes precise site selection and optimal configuration of the reconfigurable multi-terminal soft switch in combination with a double-layer collaborative capacity optimization model considering economy and safety, thereby significantly improving system operation economy, effectively improving power supply power quality, and greatly improving equipment utilization and operation flexibility.

Description

Technical Field

[0001] This invention relates to the field of power distribution system operation planning and optimization technology, and in particular to a location and capacity planning method based on reconfigurable multi-terminal soft switches. Background Technology

[0002] With the continued deepening of the global energy transition and the steady advancement of the "dual-carbon" strategy, a large proportion of distributed generation (DG), represented by photovoltaic and wind power, has been connected to the distribution network, driving the traditional distribution network to evolve from a passive mode of single power flow and unidirectional power supply to an active mode of multi-source interaction and bidirectional power flow. This structural change has significantly exacerbated the complexity and uncertainty of distribution network operation: the intermittency and volatility of distributed generation output have led to increasingly prominent problems such as unbalanced feeder power distribution, frequent voltage overruns, and increased network losses. Especially in differentiated operating scenarios where source and load are mismatched in time and space, the regulation capabilities of the traditional distribution network are no longer sufficient to meet the dual requirements of high power quality and high operational economy.

[0003] To address these challenges, soft open points (SOPs), as flexible interconnection devices based on power electronics technology, have gained widespread attention in the distribution network field in recent years due to their precise power flow regulation capabilities and flexible feeder interconnection functions. By replacing traditional tie switches, SOPs can achieve continuous power regulation and reactive power voltage support between feeders, thereby effectively improving system operation. However, traditional SOPs typically employ a symmetrical structure design with fixed port capacities and an invariable number of ports. In practical applications, this results in low equipment utilization and insufficient adaptability to various scenarios, making it difficult to fully meet the dynamic interconnection needs under the differentiated operating characteristics of distribution networks.

[0004] Against this backdrop, the Reconfigurable Soft Open Point (R-SOP) has emerged as a novel power electronic device. Leveraging its unique reconfigurable topology, the R-SOP significantly improves operational flexibility and capacity utilization by dynamically adjusting the number and capacity allocation of connected Voltage Source Converters (VSCs). Each branch is equipped with a feeder selection switch, enabling flexible connection to any feeder, overcoming the limitations of fixed port numbers in traditional interconnection devices. Simultaneously, the asymmetric VSC size design allows for more efficient matching of power transmission requirements from different feeders, providing superior technical support for flexible interconnection in complex distribution networks.

[0005] However, existing research mainly focuses on the topology design and operation control strategies of R-SOPs, and has not yet addressed planning methods for installation location and capacity configuration. The location and capacity determination of R-SOPs directly affect the investment efficiency and system performance: improper installation location selection makes it difficult to fully utilize their cross-feeder power mutual assistance potential; unreasonable capacity configuration may lead to low equipment utilization or unsatisfactory return on investment. Especially in practical application scenarios with significant differences in distribution network structure and diverse source-load characteristics, traditional equipment configuration schemes based on experience or single indicators are no longer suitable for the multi-port reconfigurable characteristics of R-SOPs. Therefore, this application proposes a location and capacity determination planning method based on reconfigurable multi-terminal soft switches. Summary of the Invention

[0006] The purpose of this invention is to address the problem that existing research in the background art mainly focuses on the topology design and operation control strategy of R-SOP, and has not yet addressed the planning methods for installation location and configuration capacity. This invention proposes a location and capacity planning method based on reconfigurable multi-terminal soft switches.

[0007] Firstly, this application provides the following steps:

[0008] Step 1: Establish a steady-state operation model for the reconfigurable multi-terminal soft-switching R-SOP, which includes power balance constraints and capacity limitation constraints.

[0009] Step 2: Construct an R-SOP candidate location selection model that considers interconnection requirements. The model evaluates distribution network nodes in two dimensions using the Loss Sensitivity Index (LSI) and Voltage Deviation Index (VDI) to quantify the interconnection potential and voltage improvement requirements of each node and select candidate installation nodes.

[0010] Step 3: Establish an R-SOP two-layer location and capacity optimization model. The two-layer model includes an upper-layer planning model with the goal of minimizing the annual comprehensive cost and a lower-layer operation model with the goal of minimizing the operating cost and the voltage deviation conversion cost.

[0011] Step 4: Perform mathematical transformation on the two-layer model described in Step 3, and use a hybrid solution strategy combining differential evolution algorithm and second-order cone programming to obtain the optimal installation location and optimal configuration capacity of R-SOP.

[0012] Optionally, the power balance constraint mentioned in step 1 is characterized by the following equation:

[0013]

[0014]

[0015]

[0016] in, for Time Node Active power on the DC side of the voltage source converter VSC; for Time Node Active power loss at R-SOP; for Time Node The actual active power transmitted by the voltage source converter (VSC); For the first Loss factor of a voltage source converter (VSC); Indicates the first connected to R-SOP Branch power transmission capacity, The total number of branches connected to R-SOP;

[0017] The capacity constraint is characterized by the following formula:

[0018]

[0019]

[0020]

[0021]

[0022] in, for Time Node The actual active power transmitted by the voltage source converter (VSC); for Time Node The actual reactive power transmitted by the voltage source converter (VSC); Indicates the first The capacity of the voltage source converter (VSC); Indicates the first connected to R-SOP Branch power transmission capacity; Indicates the first The voltage source converter VSC and the first The status of the switch on the branch line; The total number of branches connected to R-SOP; The maximum reactive power output of the nth voltage source converter VSC is negative, representing its lower limit of reactive power output. This represents the upper limit of reactive power output of the nth voltage source converter VSC;

[0023] Optionally, the loss sensitivity index LSI mentioned in step 2 is calculated using the following formula:

[0024]

[0025] The voltage deviation index VDI is calculated using the following formula:

[0026]

[0027] in, Represents a node The active and reactive power loss sensitivity index; S represents the number of scenarios; T is the power supply time; For the first A scenario Time Branch The power flowing through it; For the first The probability of each scenario; For the first A scenario Time Branch The reactive power flowing upstream; One simulation cycle is 1 hour in this case. Represents a node Voltage deviation index; For the line The resistance; Standard voltage; No. A scenario Time Node The voltage value;

[0028] After normalizing and summing the LSI and VDI of each node, the nodes are sorted and selected as candidate installation nodes for R-SOP with comprehensive indicators greater than the set threshold.

[0029] Optionally, the objective function of the upper-level planning model in step 3 is:

[0030]

[0031] in, The objective function of the upper-level optimization model; Annual investment cost for R-SOP; Annual operating and maintenance costs for R-SOP; Cost of annual power supply loss in the power distribution network; This is a weighting factor used to convert voltage deviation into energy efficiency costs; Costs calculated based on annual voltage deviation in the distribution network;

[0032] The objective function of the lower-level operating model is:

[0033]

[0034] in, The objective function of the lower-level optimization model is... For electricity price; The total number of nodes; These are the weighting coefficients; For the first A scenario Time Node The sum of active power injected at each point; Indicates the first A scenario Time Node Power loss at R-SOP; For the first A scenario Time Node The per-unit voltage value; Standard value for node voltage;

[0035] The lower-level operation model must meet the power flow constraints of the distribution network, the output constraints of distributed generation sources, the R-SOP operation constraints, and the system security constraints.

[0036] Optionally, the power flow constraints of the distribution network are described by the DistFlow branch power flow equations; the system security constraints include node voltage magnitude constraints and branch current magnitude constraints.

[0037] Optionally, the mathematical transformation described in step 4 includes: variable substitution. = , = By introducing auxiliary variables to handle absolute value terms and using second-order cone relaxation techniques to transform the original nonlinear constraints into second-order cone constraints, the original mixed-integer nonlinear programming model is transformed into a mixed-integer second-order cone programming model.

[0038] Optionally, the hybrid solution strategy is as follows: the upper layer uses the differential evolution algorithm to optimize the installation capacity of R-SOP, and the lower layer calls the second-order cone programming solver to calculate the optimal operating cost for each given capacity and returns it to the upper layer. The globally optimal location and capacity scheme is obtained through iterative optimization.

[0039] In a second aspect, this application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect.

[0040] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0041] Compared with the prior art, this application includes at least one of the following beneficial technical effects:

[0042] This invention integrates the loss reduction potential quantification capability of the Loss Sensitivity Index (LSI) with the voltage improvement demand identification capability of the Voltage Deviation Index (VDI), establishing a two-dimensional evaluation model that considers multiple scenarios and time periods. This model accurately quantifies the interconnection potential of each node in loss reduction and voltage optimization. This system overcomes the limitations of traditional site selection that relies solely on the location of tie switches, effectively narrowing the search space for candidate installation nodes, improving planning efficiency, and providing a scientific basis for accurate site selection in R-SOP.

[0043] A two-layer collaborative capacity-determining framework is established, consisting of an upper-level planning model aimed at minimizing annual comprehensive costs and a lower-level operation model aimed at minimizing operating costs and voltage deviation-based costs. The upper-level model determines the optimal capacity of the R-SOP (Resource-Based System Operation), while the lower-level model simulates the optimal operating state under a given capacity, achieving collaborative optimization of equipment capacity configuration and system operation. This model overcomes the shortcomings of traditional single-layer planning, such as a singular objective and neglect of safety constraints, effectively balancing investment economy and operational safety.

[0044] Leveraging the physical characteristics of R-SOP's multi-port reconfigurability and asymmetric capacity design, and combining interconnection demand quantification and two-layer optimization results, flexible power sharing and dynamic capacity allocation across feeders are achieved. This method effectively addresses issues such as power flow reversal and voltage fluctuations caused by a high proportion of distributed power sources, significantly improving the distribution network's adaptability to spatiotemporal mismatches between sources and loads, while also increasing equipment capacity utilization and return on investment.

[0045] In summary, this invention constructs a dual-dimensional interconnection demand assessment system that integrates loss sensitivity and voltage deviation, accurately identifies the optimal interconnection nodes in the distribution network, and combines a two-layer collaborative capacity optimization model that takes into account both economy and safety, thereby achieving precise location and optimized configuration of reconfigurable multi-terminal soft switches. This significantly improves the economic efficiency of system operation, effectively improves the power supply quality, and greatly enhances equipment utilization and operational flexibility. Attached Figure Description

[0046] Figure 1 This is the topology diagram of the multi-feeder R-SOP model;

[0047] Figure 2 This is a logic diagram of the distribution network interconnection demand assessment system;

[0048] Figure 3 This is a framework diagram of the two-stage R-SOP planning methodology;

[0049] Figure 4 This is a test system diagram;

[0050] Figure 5This is a graph showing the LSI and VDI metrics of a node.

[0051] Figure 6 This is a diagram of a test system with R-SOP installed;

[0052] Figure 7 Here is a schematic diagram of the iteration process of the differential evolution algorithm: (a) the upper-level annual comprehensive cost iteration curve; (b) the lower-level operating cost iteration curve;

[0053] Figure 8 This is a schematic diagram showing the changes in transmission power and capacity of the R-SOP connected feeder over time: (a) changes in active power over time; (b) changes in reactive power over time; (c) changes in capacity over time.

[0054] Figure 9 It is a full-time voltage operating range diagram before and after planning R-SOP;

[0055] Figure 10 Here is a schematic diagram showing the power transmission variation of feeders connected to SOP and R-SOP: (a) Feeder 16; (b) Feeder 31; (c) Feeder 22;

[0056] Figure 11 This is a flowchart illustrating the implementation of the method described in the invention. Detailed Implementation

[0057] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0058] Example: Figure 11 As shown, the present invention proposes an addressing and capacity planning method based on reconfigurable multi-terminal soft switches, the specific steps of which are as follows:

[0059] Step 1, as follows Figure 1 The diagram shows the topology of the multi-feeder R-SOP model, from which the operational constraints of the reconfigurable multi-terminal soft switch R-SOP are constructed.

[0060] Step 1.1: Construct the power balance constraints of R-SOP using equations (1)-(3):

[0061]

[0062]

[0063]

[0064] In equations (1)-(3), for Time Node Active power on the DC side of the voltage source converter VSC; for Time Node Active power loss at R-SOP; for Time Node The actual active power transmitted by the voltage source converter (VSC); For the first Loss factor of a voltage source converter (VSC); Indicates the first connected to R-SOP Branch power transmission capacity; This represents the total number of branches connected to R-SOP.

[0065] Step 1.2: Construct the capacity constraint of R-SOP using equations (4)-(7):

[0066]

[0067]

[0068]

[0069]

[0070] In equations (4)-(7), for Time Node The actual active power transmitted by the voltage source converter (VSC); for Time Node The actual reactive power transmitted by the voltage source converter (VSC); Indicates the first The capacity of the voltage source converter (VSC); Indicates the first connected to R-SOP Branch power transmission capacity; Indicates the first The voltage source converter VSC and the first The status of the switch on the branch line; The total number of branches connected to R-SOP; The maximum reactive power output of the nth voltage source converter VSC is negative, representing its lower limit of reactive power output. This represents the upper limit of reactive power output of the nth voltage source converter (VSC).

[0071] Step 2, as follows Figure 2 The diagram shows the logic of the distribution network interconnection demand assessment system. The loss sensitivity index (LSI) and voltage deviation index (VDI) of the nodes are analyzed respectively, and an R-SOP candidate location selection model considering interconnection demand is constructed.

[0072] Step 2.1: Construct the loss sensitivity index LSI using equations (8)-(12):

[0073]

[0074]

[0075]

[0076]

[0077] In equations (8)-(12), and For the flow through the line Total active power and reactive power; Flowing through the line Power loss; For the line The resistance; For nodes Voltage amplitude at the location; Represents a node For active power, The LSI value indicates the sensitivity to changes in reactive power. This represents the maximum absolute value after grouping each LSI value by positive and negative. Represents a node By maximum value Normalized LSI value.

[0078] Step 2.2: Construct the voltage deviation index VDI using equations (13)-(14):

[0079]

[0080] In equations (13)-(14), Standard voltage (1 p.u.); To represent nodes The VDI value of voltage deviation, Indicate each The maximum value in; Represents a node By maximum value The normalized VDI value.

[0081] Step 2.3: Calculate the LSI and VDI values ​​of each node using equations (15) and (16):

[0082]

[0083]

[0084] In equations (15)-(16), For power supply time; Number of scenes; For the first A scenario Time Branch The power flowing through, For the first The probability of each scenario.

[0085] Step 3, as follows Figure 3 The diagram shows the framework of the two-stage R-SOP planning method, which includes two parts: site selection and capacity determination. The R-SOP two-layer site selection and capacity determination model is constructed by equations (17)-(31):

[0086] Step 3.1: Construct the upper-level optimization model using equations (17)-(22):

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] In equations (17)-(22), The objective function of the upper-level optimization model; Annual investment cost for R-SOP; Annual operating and maintenance costs for R-SOP; Cost of annual power supply loss in the power distribution network; This is a weighting factor used to convert voltage deviation into energy efficiency costs; Costs calculated based on annual voltage deviation in the distribution network; The discount rate; The service life of R-SOP; and The capacity and unit capacity investment cost of the installed R-SOP; This is the annual operation and maintenance cost coefficient; For electricity price; This represents the number of system nodes. For the first A scenario Time Node The sum of active power injected at each point; For the first A scenario Time Node The per-unit voltage value; Indicates the first A scenario Time Node Power loss at R-SOP; Standard value for node voltage; The maximum capacity of the R-SOP that can be installed.

[0094] Step 3.2: Construct the lower-level optimization model using equations (23)-(31):

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] Equation (23) is the objective function of the lower-level optimization model, where, The objective function of the lower-level optimization model; and These are the weighting coefficients; Indicates the first A scenario Time Node Power loss at R-SOP; For the first A scenario Time Node The per-unit voltage value; This is the per-unit value of the reference voltage; One simulation cycle; For each scene The probability of.

[0104] Equations (24) and (25) show that the active and reactive power of the branch is balanced, where, For the set of routes; Represented as a line The resistance; and For the first A scenario Time Branch Current and reactance; For the first A scenario Time Branch The active power flowing through, For the first A scenario Time Branch The reactive power flowing through; For the first A scenario Time Branch The reactive power flowing through; For the first A scenario Time Branch The active power flowing through, For the first A scenario Time Branch The reactive power flowing through.

[0105] Equations (26) and (27) show the horizontal constraints between node voltage and branch current, where, The first A scenario Time Node The per-unit voltage value; For the first A scenario Time Node The sum of active power injected at each point; For the first A scenario Time Node The sum of reactive power injected at each point.

[0106] Equations (28) and (29) show the active and reactive power balance of the nodes, where, For photovoltaics in the first A scenario Injecting nodes at all times active power, For photovoltaics in the first A scenario Injecting nodes at all times reactive power; For the wind turbine in the first A scenario Injecting nodes at all times The active power; For R-SOP in the first A scenario Injecting nodes at all times active power, For R-SOP in the first A scenario Injecting nodes at all times reactive power, Indicates the first A scenario Time Node The active power demand of the load. Indicates the first A scenario Time Node The reactive power demand of the load.

[0107] Equations (30) and (31) are the limit constraints for node voltage and branch current. , These are the lower and upper voltage limits for the node, respectively. This is the maximum current limit for the branch.

[0108] Step 4: Model transformation, and solve the planning model using a hybrid optimization algorithm based on DE algorithm and SOCP to obtain the optimal planning strategy of R-SOP.

[0109] Step 4.1: Establish the objective function of the location selection model:

[0110]

[0111] Step 4.2, Model Transformation: The mixed-integer nonlinear programming model is transformed into a mixed-integer second-order cone programming model through methods such as variable substitution, Big-M method, and second-order cone transformation, so as to quickly obtain the optimal strategy with the help of mature commercial solvers.

[0112] Using variable substitution and the Big-M method, nonlinear constraint equations (24)-(27) are transformed into equations (33)-(36), nonlinear constraint equations (30)-(31) are transformed into equations (37)-(38), and nonlinear constraint equations (21), (23), and (4) are transformed into equations (39)-(44):

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] In the formula express , and express and ; For the introduced auxiliary voltage variable, For the first A scenario Time of the first The capacity of a voltage source converter (VSC).

[0124] Step 4.3: Construct the R-SOP location model using equations (8)-(16) and (32), and call the solver to solve the optimized operation model to obtain the values ​​of each node. and The value is calculated by summing the two indicators, taking the average, and sorting them in descending order. Nodes with values ​​greater than the threshold are candidate installation nodes.

[0125] Step 4.4: Construct a two-layer location and capacity model using equations (17)-(20), (22), and (28)-(29). Call the solver to efficiently solve the two-layer planning model using a hybrid optimization algorithm based on differential evolution algorithm and second-order cone programming to obtain the optimal installation location and optimal installation capacity of R-SOP.

[0126] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0127] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0128] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components:

[0129] I. Case Description and Simulation Result Analysis

[0130] To verify its effectiveness, the present invention employs, as follows: Figure 4 The improved IEEE 33-node test system shown is used to verify and analyze the proposed two-stage addressing and sizing strategy. Figure 4 In the test system shown, the active power requirement is 3715kW, the reactive power requirement is 2300kVar, the system reference voltage is set to 12.66kV, the voltage safety range is set to 0.95pu-1.05pu, and the voltage non-overshoot range is set to 0.97pu-1.03pu. Additional photovoltaic systems are deployed at nodes 7, 13, and 22, and wind turbine systems are deployed at nodes 16 and 27. The simulation timescale is set to 1 hour, and the simulation time is from 1:00 to 24:00.

[0131] To fully verify the effectiveness of the R-SOP site selection method, photovoltaic, wind power, and load data under five scenarios were first created using scenario generation and reduction techniques. The system power flow was then calculated using the site selection method to obtain the voltage amplitude and branch current of each node, and the LSI and VDI of each node were calculated. Figure 5 As shown, nodes 16, 13, 15, and 22 have interconnection requirements with nodes 28, 29, 6, and 31. Furthermore, considering that interconnecting closely adjacent nodes would significantly reduce the interconnection efficiency, nodes 16, 13, and 15, and nodes 28, 29, and 31 are not considered for simultaneous interconnection. All possible connection scenarios are divided into three groups: two-feeder, three-feeder, and four-feeder R-SOP. There are 16 possibilities for two-feeder R-SOP, 24 for three-feeder R-SOP, and 9 for four-feeder R-SOP. The search space for candidate R-SOP installation locations is shown in Table 1. It can be seen that the location strategy reduces the search space from 147,113 to 49, significantly improving location efficiency.

[0132] Table 1

[0133] No location strategy Site selection strategy Search space 147113 49

[0134] A two-layer addressing and capacity determination strategy was used to test the installation of each R-SOP candidate, as shown in Table 2. The optimal data in each group was recorded. It can be seen that the optimal node locations are 16, 22, and 31, with an optimal capacity of 1.5 MVA. At this point, a three-feeder R-SOP should be installed. Figure 6 The figure shows a 33-node test system with R-SOP installed in the optimal position. The R-SOP has a three-feeder, four-VSC structure, and the VSC capacities are configured according to the golden ratio: 0.75MVA, 0.4635MVA, 0.2864MVA, and 0.1095MVA.

[0135] like Figure 7 The iterations of the DE algorithm show that, due to the single unknown (R-SOP capacity) and limited solution space, the algorithm is slow in the initial optimization phase. The objective function value changes in the 7th iteration and reaches its minimum in the 9th iteration, remaining unchanged in subsequent iterations, indicating that the model has achieved the global optimum at this point. This convergence characteristic demonstrates the feasibility and effectiveness of the hybrid algorithm based on DE and SOCP for solving multi-objective bilevel optimization models.

[0136] Table 2

[0137] Number of feeders Installation location Capacity / MVA Total active power loss / MWh Annual comprehensive cost (ten thousand yuan) 2 16,31 1.6 749.94 110.37 3 16,22,31 1.5 564.62 98.21 4 16,22,31,6 1.5 571.31 100.09

[0138] like Figure 8 The diagram illustrates the changes in power and capacity transmitted by feeders connected to R-SOP over time. It shows that throughout the entire control period, feeders 16 and 22 can transmit active power to feeder 31 via R-SOP. Specifically, from 01:00 to 05:00, the output of the DG and the load are relatively low and basically matched, resulting in a small capacity allocated to each feeder by R-SOP. From 07:00 to 11:00 and from 15:00 to 20:00, due to a severe mismatch between DG output and load, R-SOP continuously transmits larger amounts of power to feeder 31, causing a significant increase in the capacity allocation to feeder 31, exceeding 0.5 times at times 14 and 17. This indicates that the load demand in this area far exceeds the output of the nearby distributed generation (DG). The R-SOP needs to transmit a large amount of active and reactive power to feeder 31 to meet its power demand and voltage safety requirements, while simultaneously allocating the capacity of multiple distribution control stations (VSCs) to this port to support high-power transmission. This demonstrates that the R-SOP possesses excellent power flow regulation performance and reconfigurability, and also proves that the proposed R-SOP location and capacity planning method can scientifically quantify the distribution network interconnection demand, improve the dynamic regulation capability of the distribution network, and adapt to the differentiated characteristics of the mismatch between power supply and demand in the distribution network.

[0139] To fully demonstrate the effectiveness and superiority of the addressing and capacity planning method based on reconfigurable multi-terminal soft switches, five schemes (Case 1-Case 5) are set up for comparison in the example section. Case 1 is based on the planning and installation of an asymmetrical R-SOP using the addressing and capacity planning method; Case 2 is based on the test system before planning and installing the R-SOP; Case 3 configures an asymmetrical R-SOP according to the traditional installation position of a three-feeder R-SOP; Case 4 configures an asymmetrical R-SOP according to the traditional capacity of a three-feeder R-SOP; and Case 5 replaces the asymmetrical R-SOP in Case 2 with a symmetrical SOP.

[0140] Case 1: Based on the R-SOP case which uses site selection and volume determination methods to plan and install asymmetrical sizes;

[0141] Case 2: Taking the test system before planning and installing R-SOP as the initial case;

[0142] Case 3: Configure an asymmetrical R-SOP according to the traditional installation position of a three-feeder R-SOP;

[0143] Case 4: R-SOP with asymmetrical size configured according to the traditional capacity of three-feeder R-SOP;

[0144] Case 5: Replace the asymmetric R-SOP in Case 2 with a symmetric SOP.

[0145] All numerical simulations in the examples section were performed in MATLAB R2021b and solved using the YALMIP toolbox and Gurobi solver in a 64-bit Windows environment.

[0146] exist Figure 4 In the IEEE 33-node test system shown, the above five schemes are executed simultaneously. The installation location, capacity, total active power loss, annual comprehensive cost, and voltage over-limit rate of R-SOP in Cases 1-5 are obtained, as shown in Table 3.

[0147] Table 3

[0148] Case Location / Node Capacity / MVA Total active power loss / MWh Annual comprehensive cost / 10,000 yuan Voltage over-limit rate (0.97 pu~1.03 pu) Case 1 16,22,31 1.5 564.62 98.21 10.30% Case 2 / / 725.81 144.94 27.36% Case 3 18,22,33 1.5 571.47 102.08 11.77% Case 4 16,22,31 4 719.75 113.98 0 Case 5 16,22,31 1.5 576.06 105.71 13.38%

[0149] Table 3 shows the simulation results of the five cases. It can be seen that the optimization model proposed in this paper (Case 1) performs best in terms of comprehensively considering system economy and power quality improvement. Its overall operation effect is significantly better than the other four schemes, which further verifies the effectiveness and advancement of the proposed model in multi-objective coordinated optimization.

[0150] like Figure 9The full-time voltage operating range diagram before and after the R-SOP planning shows that before the planning, there were numerous voltage exceedances at distribution network nodes with large fluctuations. After the planning, the voltage exceedances were improved, and the fluctuation range was significantly narrowed. This proves that R-SOP can accurately provide reactive power, thereby alleviating voltage exceedances and improving power quality. Table 3 shows that the annual comprehensive cost of Case 1 was reduced by RMB 467,300 compared to Case 2, a reduction of 32.24%. Total active power loss was reduced by 161.19 MWh, a reduction of 22.20%, significantly improving the system's operational economy. Furthermore, due to the comprehensive consideration of power quality, the voltage exceedance rate decreased from 27.36% to 10.30%, a reduction of 62.35%, resulting in a significant improvement in system power quality and further enhancing the practical value of R-SOP.

[0151] Compared to Case 3, Case 1 reduced the annual comprehensive cost by RMB 38,700, a decrease of 3.8%, and the voltage over-limit rate decreased from 11.77% to 10.30%, a decrease of 19.34%. This demonstrates that the proposed location strategy can identify the interconnection potential of distribution network nodes, scientifically quantify the interconnection needs of the distribution network, and reduce line power loss through precise location selection. Compared to Case 4, the optimal capacity determined through capacity optimization in Case 1 was significantly reduced. Although the voltage over-limit rate increased, the annual comprehensive cost decreased by RMB 157,700, a decrease of 26.53%, significantly improving the system's economic efficiency. This proves the correctness and effectiveness of the proposed two-layer R-SOP capacity optimization strategy.

[0152] Figure 10 The diagram shows the power transmission variation of the feeder connected to SOP and R-SOP. It can be seen that the total capacity... Meanwhile, during the periods from 7:00 to 11:00 and from 15:00 to 20:00 when load demand is difficult to meet, the apparent power transmitted from R-SOP to feeder 31 exceeds the SOP port capacity limit. Furthermore, relying on its reconfigurable characteristics, the transmitted power reaches half of the total capacity, and exceeds 0.5 times the limit at times 14:00 and 17:00. However, due to the limited port capacity of the SOP, the energy transmitted by feeder 22 cannot be fully transferred to feeder 33 through the SOP. A large amount of power is forced to be transmitted to feeder 16 for reverse transmission, thus increasing the capacity allocated to feeder 16. However, this behavior not only causes unnecessary energy loss and equipment capacity loss, but also further increases the active power loss of the line. This proves that R-SOP has better control efficiency and higher capacity utilization than SOP when dealing with large power transfer demands. Moreover, as shown in Table 3, compared with Case 5, configuring R-SOP with the same capacity in Case 1 reduces the total active power loss of the system by 1.99%, the annual comprehensive cost by 7.09%, and the voltage over-limit rate by 23.02%, further proving that the asymmetric VSC structure of R-SOP has better control performance and higher capacity utilization than the symmetric multi-port SOP structure.

[0153] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A method for addressing and sizing planning based on reconfigurable multi-terminal soft switches, characterized in that, Includes the following steps: Step 1: Establish a steady-state operation model for the reconfigurable multi-terminal soft-switching R-SOP, which includes power balance constraints and capacity limitation constraints. Step 2: Construct an R-SOP candidate location selection model that considers interconnection requirements. The model evaluates distribution network nodes in two dimensions using the Loss Sensitivity Index (LSI) and Voltage Deviation Index (VDI) to quantify the interconnection potential and voltage improvement requirements of each node and select candidate installation nodes. Step 3: Establish an R-SOP two-layer location and capacity optimization model. The two-layer model includes an upper-layer planning model with the goal of minimizing the annual comprehensive cost and a lower-layer operation model with the goal of minimizing the operating cost and the voltage deviation conversion cost. Step 4: Perform mathematical transformation on the two-layer model described in Step 3, and use a hybrid solution strategy combining differential evolution algorithm and second-order cone programming to obtain the optimal installation location and optimal configuration capacity of R-SOP.

2. The addressing and capacity planning method based on reconfigurable multi-terminal soft switching according to claim 1, characterized in that, The power balance constraint mentioned in step 1 is characterized by the following formula: ； ； ； in, for Time Node Active power on the DC side of the voltage source converter VSC; for Time Node Active power loss at R-SOP; for Time Node The actual active power transmitted by the voltage source converter (VSC); For the first Loss factor of a voltage source converter (VSC); Indicates the first connected to R-SOP Branch power transmission capacity, The total number of branches connected to R-SOP; The capacity constraint is characterized by the following formula: ； ； ； ； in, for Time Node The actual active power transmitted by the voltage source converter (VSC); for Time Node The actual reactive power transmitted by the voltage source converter (VSC); Indicates the first The capacity of the voltage source converter (VSC); Indicates the first connected to R-SOP Branch power transmission capacity; Indicates the first The voltage source converter VSC and the first The status of the switch on the branch line; The total number of branches connected to R-SOP; The maximum reactive power output of the nth voltage source converter VSC is negative, representing its lower limit of reactive power output. This represents the upper limit of reactive power output of the nth voltage source converter (VSC).

3. The addressing and capacity planning method based on reconfigurable multi-terminal soft switching according to claim 2, characterized in that, The loss sensitivity index (LSI) mentioned in step 2 is calculated using the following formula: ； The voltage deviation index VDI is calculated using the following formula: ； in, Represents a node The active and reactive power loss sensitivity index; S represents the number of scenarios; T is the power supply time; For the first A scenario Time Branch The power flowing through it; For the first The probability of each scenario; For the first A scenario Time Branch The reactive power flowing upstream; One simulation cycle is 1 hour in this case. Represents a node Voltage deviation index; For the line The resistance; Standard voltage; No. A scenario Time Node The voltage value; After normalizing and summing the LSI and VDI of each node, the nodes are sorted and selected as candidate installation nodes for R-SOP with comprehensive indicators greater than the set threshold.

4. The addressing and capacity planning method based on reconfigurable multi-terminal soft switching according to claim 3, characterized in that, The objective function of the upper-level planning model in step 3 is: ； in, The objective function of the upper-level optimization model; Annual investment cost for R-SOP; Annual operating and maintenance costs for R-SOP; Cost of annual power supply loss in the power distribution network; This is a weighting factor used to convert voltage deviation into energy efficiency costs; Costs calculated based on annual voltage deviation in the distribution network; The objective function of the lower-level operating model is: ； in, The objective function of the lower-level optimization model is... For electricity price; The total number of nodes; These are the weighting coefficients; For the first A scenario Time Node The sum of active power injected at each point; Indicates the first A scenario Time Node Power loss at R-SOP; For the first A scenario Time Node The per-unit voltage value; Standard value for node voltage; The lower-level operation model must meet the power flow constraints of the distribution network, the output constraints of distributed generation sources, the R-SOP operation constraints, and the system security constraints.

5. The addressing and capacity planning method based on reconfigurable multi-terminal soft switching according to claim 4, characterized in that, The power flow constraints of the distribution network are described by the DistFlow branch power flow equations; the system security constraints include node voltage magnitude constraints and branch current magnitude constraints.

6. The addressing and capacity planning method based on reconfigurable multi-terminal soft switching according to claim 1, characterized in that, The mathematical transformation described in step 4 includes: variable substitution = , = By introducing auxiliary variables to handle absolute value terms and using second-order cone relaxation techniques to transform the original nonlinear constraints into second-order cone constraints, the original mixed-integer nonlinear programming model is transformed into a mixed-integer second-order cone programming model.

7. The addressing and capacity planning method based on reconfigurable multi-terminal soft switching according to claim 6, characterized in that, The hybrid solution strategy is as follows: the upper layer uses the differential evolution algorithm to optimize the installation capacity of R-SOP, and the lower layer calls the second-order cone programming solver to calculate the optimal operating cost for each given capacity and returns it to the upper layer. The globally optimal location and capacity scheme is obtained through iterative optimization.

8. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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