Collaborative planning method and system for remote control switch and intelligent soft switch of power distribution network

Abstract

The invention belongs to the technical field of power distribution network planning, and provides a collaborative planning method and system for a remote control switch and an intelligent soft switch of a power distribution network, and the method comprises the steps: determining a power supply recovery process of the power distribution network after the power distribution network breaks down; according to the determined power supply recovery process, considering the cooperation of a remote control switch and an intelligent soft switch for multi-stage power supply recovery, taking the minimum sum of the full life cycle cost and the load power failure cost of switch equipment as an objective function, and considering the operation constraint, the power flow constraint and the network topology constraint of the intelligent soft switch for multi-stage power supply recovery; constructing a collaborative planning mathematical model of the remote control switch and the intelligent soft switch; and solving the constructed collaborative planning mathematical model, and completing collaborative planning of the remote control switch and the intelligent soft switch of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network planning, and in particular relates to a collaborative planning method and system for remote control switches and intelligent soft switches in a distribution network. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The distribution network is the final link in power transmission and a critical infrastructure for maintaining reliable power supply to end users. Traditional distribution networks use mechanical switches to rapidly isolate faults and transfer loads. However, mechanical switches only have discrete switching state control capabilities, making it difficult to achieve continuous power flow regulation and lacking voltage support. In recent years, intelligent soft open points (SOPs) based on back-to-back voltage source converters (VSCs) have garnered attention. SOPs are installed at interconnected feeders to replace mechanical tie switches. By implementing appropriate control strategies, they enable flexible and precise bidirectional power control and dynamic voltage support, thereby improving voltage distribution, balancing three-phase loads, reducing energy consumption, achieving rapid fault recovery, and enhancing power supply reliability and renewable energy absorption capacity.

[0004] Existing research has conducted in-depth discussions on the site selection, sizing and operation control strategy optimization of SOP from multiple dimensions, mainly focusing on the SOP configuration under normal operating conditions of the distribution network, but has not explored the application potential of SOP in improving system reliability.

[0005] Existing technologies have integrated SOPs into distribution network reliability assessment and resilience improvement frameworks, validating their ability to enhance power supply security. However, the high investment cost of SOPs necessitates their coordinated and optimized configuration with other switchgear. Existing technologies have conducted some research on the coordinated configuration of SOPs and remote-controlled switches (RCS), but research on the coordinated configuration of SOP location and capacity is insufficient, and the modeling of manual-controlled switches (MCS) to further narrow the fault area is not detailed enough. Summary of the Invention

[0006] To solve the above problems, the present invention proposes a collaborative planning method and system for remote-controlled switches and intelligent soft switches in a distribution network. In the collaborative planning, the intelligent soft switches and remote-controlled switches for multi-stage power restoration are fully considered, and the RCS and SOP collaborative power restoration process based on multiple stages such as fault degradation, fault isolation and power restoration is analyzed. With the goal of minimizing the sum of the full life cycle cost of the switchgear and the load power outage cost, the SOP operation constraints, power flow constraints, and network topology constraints in different power restoration stages are considered, and a mathematical model for collaborative planning of RCS and SOP is established. The collaborative planning mathematical model is converted into Mixed Integer Second-Order Cone Programming (MISOCP) to achieve the solution. While reducing the overall cost, the reliability of the distribution network power supply is improved.

[0007] According to some embodiments, a first solution of the present invention provides a method for collaborative planning of remote control switches and intelligent soft switches in a distribution network, which adopts the following technical solutions: A collaborative planning method for remote control switches and intelligent soft switches in a distribution network, comprising: Determine the distribution network power restoration process after a distribution network failure; Based on the determined power restoration process, the coordination of remote control switches and intelligent soft switches for multi-stage power restoration is considered. With the objective function of minimizing the sum of the switchgear lifecycle cost and the load outage cost, and considering the operation constraints, power flow constraints, and network topology constraints of the intelligent soft switches for multi-stage power restoration, a mathematical model for the coordinated planning of remote control switches and intelligent soft switches is constructed. Solve the constructed collaborative planning mathematical model and complete the collaborative planning of remote control switches and intelligent soft switches in the distribution network.

[0008] As a further technical limitation, in the process of solving the constructed collaborative planning mathematical model, the collaborative planning model is converted into a mixed integer second-order cone programming model, and the obtained mixed integer second-order cone programming model is solved to obtain a collaborative planning scheme for remote control switches and intelligent soft switches, thereby completing the collaborative planning of remote control switches and intelligent soft switches in the distribution network.

[0009] As a further technical limitation, the distribution network power supply restoration process includes at least a fault degradation stage, a fault isolation stage, a power supply restoration stage and a fault repair stage.

[0010] As a further technical limitation, when a fault occurs, the distribution network enters the fault degradation stage, the intelligent soft switch is immediately locked, the feeder outlet circuit breaker is disconnected, the nodes of the fault feeder lose power, the distribution network enters the fault isolation stage, the remote control switch is disconnected, the nodes of the fault feeder lose power, the remote control switch and the intelligent soft switch work together to restore power supply, and after the fault is located, the distribution network enters the power supply recovery stage, disconnects the upstream of the fault for fault isolation, and the power supply of the distribution network is fully restored. After the manual control switch, remote control switch and intelligent soft switch work together, the distribution network enters the fault recovery stage, the component fault is repaired, and the distribution network returns to its original operation.

[0011] Furthermore, the network topology constraints include topology constraints in the fault degradation stage, topology constraints in the fault isolation stage, and topology constraints in the power supply restoration stage.

[0012] As a further technical limitation, the constraints of the constructed collaborative planning mathematical model of remote control switches and intelligent soft switches also include line status constraints, reliability constraints, radial constraints, virtual power flow constraints and safety constraints.

[0013] According to some embodiments, a second solution of the present invention provides a coordinated planning system for remote control switches and intelligent soft switches in a distribution network, which adopts the following technical solutions: A coordinated planning system for remote control switches and intelligent soft switches in a distribution network, comprising: a determination module configured to determine a power supply restoration process of a distribution network after a failure occurs in the distribution network; A construction module is configured to construct a mathematical model for collaborative planning of the remote control switch and the intelligent soft switch based on the determined power restoration process, taking into account the coordination of the remote control switch and the intelligent soft switch for multi-stage power restoration, with the sum of the switchgear life cycle cost and the load outage cost as the objective function, and taking into account the operation constraints, power flow constraints, and network topology constraints of the intelligent soft switch for multi-stage power restoration; The planning module is configured to solve the constructed collaborative planning mathematical model to complete the collaborative planning of the distribution network remote control switches and intelligent soft switches.

[0014] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in the first embodiment of the present invention.

[0015] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps of the collaborative planning method for distribution network remote control switches and intelligent soft switches as described in the first embodiment of the present invention.

[0016] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code executes the steps of the collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in the first embodiment of the present invention.

[0017] Compared with the prior art, the present invention has the following beneficial effects: In collaborative planning, the present invention fully considers intelligent soft switches and remote control switches for multi-stage power restoration, analyzes the RCS and SOP collaborative power restoration process based on multiple stages such as fault degradation, fault isolation and power restoration; takes the minimization of the sum of the full life cycle cost of the switchgear and the load power outage cost as the goal, considers the SOP operation constraints, flow constraints and network topology constraints in different power restoration stages, establishes an RCS and SOP collaborative planning mathematical model, and converts the collaborative planning mathematical model into a mixed integer second-order cone programming to achieve the solution; while reducing the overall cost, the reliability of the distribution network power supply is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.

[0019] Figure 1 This is a flow chart of a method for collaborative planning of remote control switches and intelligent soft switches in a distribution network according to the first embodiment of the present invention; Figure 2 This is a structural diagram of the SOP control method after a fault occurs in Example 1 of the present invention; FIG3 (a) is a schematic diagram of a normal operation phase of a simple power distribution system during power restoration in the first embodiment of the present invention; FIG3( b ) is a schematic diagram of a fault recovery phase during power restoration of a simple power distribution system according to the first embodiment of the present invention; FIG3 (c) is a schematic diagram of the fault isolation stage during the power supply restoration process of the simple power distribution system in the first embodiment of the present invention; FIG3 (d) is a schematic diagram of the power supply restoration stage during the power supply restoration process of the simple power distribution system in the first embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a 21-node test system in Example 1 of the present invention; Figure 5 Schematic diagram of cost curves under different RCS quantity constraints in Example 1 of the present invention; Figure 6 Schematic diagram of reliability index curves under different RCS numbers in Example 1 of the present invention; Figure 7 Schematic diagram of SOP configuration quantity and capacity under different RCS quantities in Example 1 of the present invention; Figure 8 Schematic diagram of cost curves under different ASAI constraints in Example 1 of the present invention; Figure 9 Schematic diagram of RCS quantity curve under different ASAI constraints in Example 1 of the present invention; Figure 10 Schematic diagram of SOP quantity and capacity under different ASAI constraints in Example 1 of the present invention; Figure 11 This is a structural block diagram of the collaborative planning system for distribution network remote control switches and intelligent soft switches in Example 2 of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0022] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0023] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention, and should not be understood as limiting the present invention.

[0024] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0025] Example 1 Embodiment 1 of the present invention introduces a collaborative planning method for remote control switches and intelligent soft switches in a distribution network.

[0026] like Figure 1 A collaborative planning method for remote control switches and intelligent soft switches in a distribution network is shown, including: Determine the distribution network power restoration process after a distribution network failure; Based on the determined power restoration process, the coordination of remote control switches and intelligent soft switches for multi-stage power restoration is considered. With the objective function of minimizing the sum of the switchgear lifecycle cost and the load outage cost, and considering the operation constraints, power flow constraints, and network topology constraints of the intelligent soft switches for multi-stage power restoration, a mathematical model for the coordinated planning of remote control switches and intelligent soft switches is constructed. Solve the constructed collaborative planning mathematical model and complete the collaborative planning of remote control switches and intelligent soft switches in the distribution network.

[0027] This embodiment adopts Figure 2 A study on double-ended SOP was conducted, and the typical control mode of double-ended SOP is shown in Table 1.

[0028] Table 1 SOP typical control mode

[0029] During normal operation, the SOP side VSC adopts P / Q Control mode, responsible for regulating the active power passing through SOP and the reactive power on this side, while the VSC on the other side adopts V dc / Q Control mode, responsible for maintaining DC side voltage stability and regulating the reactive power on this side, namely control mode 1 and 2. After a fault occurs, the fault side VSC control mode switches to V / f Control, by controlling the conduction of the bridge arm to change the amplitude and phase of the AC side voltage, thereby achieving control of active and reactive injection power and providing voltage and frequency support for the fault side load; the VSC control mode on the non-fault side is switched to V dc / Q Control is used to maintain the stability of the DC voltage inside the SOP and regulate the reactive power on this side, namely control modes 3 and 4.

[0030] After a fault occurs, the distribution network goes through the stages of fault degradation, fault isolation, power supply restoration, and fault repair in sequence.

[0031] This embodiment takes the simple power distribution system shown in Figure 3(a) as an example. t f 、 t iso 、t sr 、 t rp They represent the fault occurrence time, fault isolation time, power supply restoration time, and fault repair time, respectively. The multi-stage power supply restoration process after a fault occurs is as follows: (1) Fault degradation stage: at the moment of fault occurrence t f , SOP is immediately locked, and the feeder outlet circuit breaker S10 is disconnected, and all nodes 2 to 5 of the faulty feeder lose power (in this embodiment, it is assumed that only the first switch is equipped with a relay protection device, and the protection zone is the entire feeder), as shown in Figure 3(b). t f ~ t iso During this period, the operating personnel formulated RCS, MCS and SOP action strategies based on the fault location information.

[0032] (2) Fault isolation stage: t iso At this moment, the RCS S7 closest to the downstream of the fault is remotely disconnected to isolate the fault. After the SOP detects the fault, it automatically switches to V dc / QV / f Control mode, providing frequency and voltage support for the fault side; due to SOP capacity limitations, only nodes 2 and 3 are restored to power, so S5 needs to be remotely disconnected, and nodes 4 and 5 are still out of power, as shown in Figure 3(c). The power restoration strategy implemented in this stage, which combines RCS and SOP, starts calculation and generation immediately after locating the fault. t iso ~ t sr During this period, operation and maintenance personnel rushed to the site to operate the MCS.

[0033] (3) Power supply restoration stage: t sr At this moment, the operation and maintenance personnel arrived at the scene and disconnected MCS S8, the nearest upstream MCS, to further isolate the fault. Simultaneously, they closed S10, S5, and tie switch R1, completely restoring the system power supply, as shown in Figure 3(d). The power restoration strategy implemented in this phase, which coordinated RCS, MCS, and SOP, was calculated and generated immediately after locating the fault.

[0034] (4) Fault repair phase: t rp After a certain time, the component failure is repaired and the system returns to its original operating mode.

[0035] By providing voltage support during the fault isolation and power restoration phases and collaborating with the RCS and MCS, the SOP can effectively improve system load recovery and significantly reduce load outage losses. In traditional distribution networks, the SOP is located at a mechanical tie switch, which has a long operating time and lacks voltage support capability. If a single tie switch is used to transfer power, voltage limitations may make it difficult to restore all loads. If multiple tie switches are used to transfer power, the load outage will be prolonged due to the MCS operating time limit.

[0036] In this embodiment, all MCSs are used as candidate locations for RCSs and all contact lines are used as candidate locations for SOPs. The decision variables are defined as follows: (1) (2) To consider any MCS configuration, define the parameters m ij : (3) Among them, Ω l is the set of all lines (including tie lines); Ω tie A collection of all contact lines.

[0037] The goal is to minimize the sum of RCS and SOP investments, annual operation and maintenance value, and expected annual power outage losses: (4) (5) (6) in, LCC (Life Cycle Cost) is the sum of the annual value of the full life cycle investment and operation and maintenance costs of all RCS and SOPs; CIC (Customer Interruption Cost) is the user's expected annual power outage loss; P i,L 、 Q i,L Node i Active and reactive loads; c RCS The investment cost of a single RCS; c SOP is the investment cost per unit capacity of SOP; The operational life of RCS and SOP; r 0 is the discount rate; h is the ratio of operation and maintenance costs to investment costs; For failure scenarios s probability of occurrence; For communication lines i - j Corresponding SOP configuration capacity; n i,s,c is a binary variable, representing the node i In the failure scenario s stage c Is there a power outage? If so, n i,s,c is 1, otherwise 0; They represent the fault degradation, fault isolation, and power restoration stages respectively; is the set of all nodes; T c For the stage c duration, 、 、 ; The power outage cost per unit of electricity consumed by the user (Customer Damage Function), which is related to the load type and the duration of the power outage; Ω s is a set of all fault scenarios, and in this embodiment, is a set of all normally closed line fault scenarios.

[0038] (1) SOP operation constraints The optimization variables of the SOP include the active and reactive power of the two VSCs, with injection into the grid on both sides as the positive direction. Power losses are ignored, and each stage of power restoration must meet the following constraints: (7) (8) (9) (10) (11) (12) (13) in, 、 Fault scenarios s stage c Next SOP Injection Node i Active and reactive power; is the voltage amplitude of the fault side VSC (assuming i Located on the fault side); V set is the preset voltage of the SOP on the fault side; Mis a large positive number. Formula (7) is the active power transmission constraint; Formula (8) and Formula (9) respectively ensure that the SOP does not inject active and reactive power into the power-off node; Formula (10) is the SOP capacity constraint; Formula (11) is the SOP fault side voltage support constraint; Formula (12) and Formula (13) respectively ensure that the active and reactive power injected on both sides of the SOP are only available after the SOP is configured.

[0039] (2) Flow constraints The loss-ignoring LinDisFlow power flow model is adopted, with the injection node as positive, and the power flow balance constraints at each stage are: (14) (15) (16) (17) in, Pij,s,c 、 Qij,s,c Fault scenarios s stage c line i - j Active and reactive power; V i,s, c Load point i In the failure scenario s stage c The voltage amplitude; r ij 、 x ij Line ij resistance and reactance; z ij,s,c For the line ij In the failure scenario s stage c Binary variable of the open and close state, if the line ij If connected, it is 1, otherwise it is 0; S ij,max For the line i - j The maximum apparent power of Ω b\root The set of all nodes except the root node.

[0040] (3) Line status constraints The contact line for the installation SOP should always remain disconnected: (18) (4) Reliability constraints Taking ASAI as the reliability index, the calculation method is as follows: (19) in, Load point i Average duration of power outages; N i Load point i The number of users (assuming all are 1); K ASAI It is the lower limit of ASAI.

[0041] (5) Radial constraints The radial network topology should be maintained during power restoration, so each line ij Define two binary variables a ij,s,c and a ji,s,c : (20) To ensure a radial topology, it is necessary to ensure that the root node has no parent node and the remaining nodes have only one parent node: (twenty one) (twenty two) (twenty three) in, N ( i ) is a node i The set of all adjacent nodes of Ω root is the set of all root nodes.

[0042] (6) Virtual power flow constraints To ensure that no unnecessary switching operations occur in the fault area during power restoration and that the radial structure remains, a virtual power flow constraint is added: (twenty four) (25) in, H kj,s,c 、 H ij,s,c Fault scenarios s stage c Virtual Circuit k - i 、 ij Virtual power of Ω h A collection of all virtual circuits.

[0043] (7) Safety constraints The voltage amplitude of each node should satisfy the constraint: (26) in,V min 、 V max are the upper and lower bounds of the voltage respectively, and the upper and lower bounds of the root node voltage are both 1.0 pu.

[0044] (8) Topological constraints at each stage 1) Fault degradation stage ( c =1): The first switch of the faulty feeder is disconnected, all nodes lose power, and non-faulty feeders are not affected.

[0045] 2) Fault isolation phase ( c =2) The following constraints must be met: (27) (28) (29) (30) in, For failure scenarios s Downline i - j Is there a fault? If it is faulty, it is 1, otherwise it is 0. Formula (27) ensures that if the line is not configured with RCS, the connectivity remains unchanged; Formula (28) ensures that if the faulty line is configured with RCS, it must be disconnected; Formula (29) ensures that if the faulty line is not configured with RCS, the nodes at both ends are located in the fault area; Formula (30) ensures that the nodes on both sides of the closed line must be located in the fault area or the non-fault area at the same time.

[0046] 3) Power supply restoration phase ( c =3) The following constraints must be met: (31) (32) (33) (34) Formula (31) ensures that if the line is not configured with MCS, the connectivity status remains unchanged; (32) ensures that if the faulty line is configured with MCS, it must be disconnected; Formula (33) ensures that if the faulty line is not configured with MCS, the nodes at both ends are located in the fault area; Formula (34) ensures that the nodes on both sides of the closed line must be located in the fault area or the non-fault area at the same time.

[0047] The model constructed in this embodiment is a mixed integer nonlinear programming problem, which is difficult to solve quickly, so it is relaxed into a MISOCP problem.

[0048] Power flow constraint conversion Introducing auxiliary variablesu i,s,c replace , Formula (16) and Formula (26) are changed to: (35) (36) Formula (7), Formula (11) and Formula (35) are constraints with specified conditions, and are converted using the Big M method. Constraint (7) is converted to: (37) For the construction of formula (11), the fault line is first removed. At this time, the distribution network is divided into two connected components: the downstream part of the fault line and the remaining part, which correspond to the node sets S dn 、 S up , then formula (11) is replaced by: (38) Formula (35) is converted to: (39) The presence of bilinear terms in formula (8) and formula (9) causes the model to be non-convex, so the auxiliary variable is introduced. Convert to linear constraints: (40) (41) (42) (43) After the above steps, the model is converted into a MISOCP model, which can be quickly solved using a commercial solver. The complete model includes formulas (4) to (6), (8) to (10), (12) to (15), (17) to (25), (27) to (34), and (36) to (43).

[0049] Case Analysis A 21-node system was used for testing, including two feeder lines and three tie lines. Figure 4 As shown, the total load is 18.2188MW and 8.8034MVar. Assume that MCS is configured at both ends of all lines except the feeder outlet. Fault location time t loc Set to 0.02 h ; The action time of RCS and MCS is set to 0.02 h , 0.5 h , fault repair timet rep Set to 4 h The unit power outage losses corresponding to the RCS and MCS operation times and fault repair times are 0.6 $ / kWh, 31.6 $ / kWh, and 50 $ / kWh, respectively. The line failure rate is set at 0.046 events / year. The cost and lifespan of the MCS to RCS conversion are set at 14,286 $ and 20 years, respectively. The unit capacity investment cost and lifespan of the SOP are set at 143 $ / kVA and 20 years, respectively. The ratio of RCS and SOP operation and maintenance costs to installation costs is 0.03. The discount rate is 0.1, the upper and lower voltage bounds are set at 0.95 and 1.05 pu, and the line capacity is 10 MVA. YALMIP modeling is used in MATLAB, and the Gurobi solver is called for, with each computation taking 10 minutes.

[0050] The test was conducted with the RCS configuration number as a constraint. The RCS and SOP configuration results when the RCS configuration number is 15 are as follows: Figure 4 The investment cost, power outage loss and total cost curves under different RCS quantities are shown in Figure 5 As shown in the figure, as the number of RCSs increases, investment costs increase linearly, while outage losses decrease rapidly and then slow down. The total cost shows a trend of first decreasing and then increasing. The total cost inflection point occurs when the number of RCSs reaches 16. This is because when the number of RCSs is small, relying solely on MCSs to isolate faults leads to high outage losses. Increasing the number of RCSs improves fault isolation capabilities and significantly reduces outage losses. Subsequently, equipment investment costs gradually become dominant, causing the total cost to gradually increase.

[0051] The reliability index ASAI and SAIDI curves under different RCS numbers are as follows: Figure 6 As shown in the figure, both indicators improve rapidly with the increase in the number of RCSs, but then slow down. This is because when there are only a few RCSs, fault isolation relies solely on MCSs, resulting in longer outages. Increasing the number of RCSs can improve fault isolation speed and reduce outage duration. ASAI improves from 99.94% with two RCSs to 99.99% when all MCSs are converted to RCSs, while SAIDI decreases from 0.25h with two RCSs to 0.04h when all MCSs are converted to RCSs. However, even when all MCSs are converted to RCSs, the reliability indicators still cannot reach higher levels. This is due to the line-wide outage during the fault degradation phase following a line fault.

[0052] The number and capacity of SOP configurations under different RCS numbers are as follows: Figure 7As shown in the figure, as the number of RCSs increases, the number of SOPs remains constant at 1, deployed on tie lines 17-21, and the capacity initially increases and then decreases. This is because when there are few RCSs, relying solely on MCSs to isolate faults results in high power outage losses, necessitating the deployment of SOPs. However, due to line capacity constraints and SOP cost constraints, even if an SOP can provide voltage support to the fault area, its ability to restore load is limited, increasing investment costs. Therefore, the SOP capacity is only 3.3 MVA.

[0053] A sensitivity analysis was conducted using ASAI as a constraint, with the lower limit of ASAI set to 99.90%, the upper limit to 99.99%, and the step size to 0.002%. A total of 45 sets of calculations were performed. The investment cost, power outage loss, and total cost curves under different ASAI constraints are shown in Figure 2. Figure 8 As shown in the figure, when the ASAI constraint is less than 99.982%, 16 RCSs must always be configured. This is because the configuration scheme corresponds to Figure 8 The lowest total cost point in the model represents the optimal solution under the ASAI constraint. At this point, the actual ASAI value exceeds 99.982%, making the ASAI constraint invalid. If the number of RCSs exceeds 16, the investment cost is excessive; if the number of RCSs is less than 16, the power outage losses are excessive, both of which lead to suboptimal solutions. When the ASAI constraint is further tightened, the power outage losses decrease, while the investment and total costs increase rapidly. At this point, the ASAI constraint becomes valid.

[0054] The RCS quantity curves under different ASAI constraints are as follows: Figure 9 As shown in the figure, when the ASAI constraint is less than 99.982%, 16 RCSs must always be configured; when the ASAI constraint is further tightened, the number of RCSs increases rapidly, and Figure 8 The conclusions are consistent.

[0055] The number and capacity of SOP configurations under different ASAI constraints are as follows: Figure 10 As shown in the figure, when the ASAI constraint is less than 99.982%, it is always necessary to configure SOP on the tie line 17-21, and the capacity is always 3.3MVA. This is also because this configuration scheme is the optimal solution under the set ASAI constraint, corresponding to Figure 5 When the ASAI constraint is further tightened, the number and capacity of SOPs are increased, and power supply is restored in coordination with RCS to meet the set ASAI constraint.

[0056] This example addresses the lack of precision in existing SOP planning models. Aiming to minimize the sum of the switchgear lifecycle cost and the load outage cost, this example considers the SOP operational constraints, power flow constraints, and network topology constraints at different power restoration stages. A mathematical model for collaborative planning of RCS and SOP is established, and the effectiveness of the proposed method is verified through typical case studies. Test results demonstrate that the introduction of SOP significantly improves the power restoration capability and reliability of the distribution network, and that their coordinated configuration maintains high reliability at lower RCS configuration levels.

[0057] Example 2 The second embodiment of the present invention introduces a collaborative planning system for remote control switches and intelligent soft switches in a distribution network.

[0058] like Figure 11 A coordinated planning system for remote control switches and intelligent soft switches in a distribution network is shown, comprising: a determination module configured to determine a power supply restoration process of a distribution network after a failure occurs in the distribution network; A construction module is configured to construct a mathematical model for collaborative planning of the remote control switch and the intelligent soft switch based on the determined power restoration process, taking into account the coordination of the remote control switch and the intelligent soft switch for multi-stage power restoration, with the sum of the switchgear life cycle cost and the load outage cost as the objective function, and taking into account the operation constraints, power flow constraints, and network topology constraints of the intelligent soft switch for multi-stage power restoration; The planning module is configured to solve the constructed collaborative planning mathematical model to complete the collaborative planning of the distribution network remote control switches and intelligent soft switches.

[0059] The detailed steps are the same as those of the collaborative planning method for the distribution network remote control switch and the intelligent soft switch provided in Example 1, and will not be repeated here.

[0060] Example 3 A third embodiment of the present invention provides a computer-readable storage medium.

[0061] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in the first embodiment of the present invention.

[0062] The detailed steps are the same as those of the collaborative planning method for the distribution network remote control switch and the intelligent soft switch provided in Example 1, and will not be repeated here.

[0063] Example 4 A fourth embodiment of the present invention provides an electronic device.

[0064] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps of the collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in Example 1 of the present invention.

[0065] The detailed steps are the same as those of the collaborative planning method for the distribution network remote control switch and the intelligent soft switch provided in Example 1, and will not be repeated here.

[0066] Example 5 A fifth embodiment of the present invention provides a computer program product.

[0067] A computer program product includes software code, wherein the program in the software code executes the steps of the collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in the first embodiment of the present invention.

[0068] The detailed steps are the same as those of the collaborative planning method for the distribution network remote control switch and the intelligent soft switch provided in Example 1, and will not be repeated here.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk drives, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0073] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0074] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0075] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.

Claims

1. A collaborative planning method for remote control switches and intelligent soft switches in a distribution network, characterized in that: include: Determine the distribution network power restoration process after a distribution network failure; Based on the determined power restoration process, the coordination of remote control switches and intelligent soft switches for multi-stage power restoration is considered. With the objective function of minimizing the sum of the switchgear lifecycle cost and the load outage cost, and considering the operation constraints, power flow constraints, and network topology constraints of the intelligent soft switches for multi-stage power restoration, a mathematical model for the coordinated planning of remote control switches and intelligent soft switches is constructed. Solve the constructed collaborative planning mathematical model and complete the collaborative planning of remote control switches and intelligent soft switches in the distribution network.

2. A method for collaborative planning of remote control switches and intelligent soft switches in a distribution network as claimed in claim 1, characterized in that: In the process of solving the constructed collaborative planning mathematical model, the collaborative planning model is converted into a mixed integer second-order cone programming model. The obtained mixed integer second-order cone programming model is solved to obtain the collaborative planning scheme of the remote control switch and the intelligent soft switch, thereby completing the collaborative planning of the remote control switch and the intelligent soft switch in the distribution network.

3. A method for collaborative planning of remote control switches and intelligent soft switches in a distribution network as claimed in claim 1, characterized in that: The power supply restoration process of the distribution network includes at least a fault degradation stage, a fault isolation stage, a power supply restoration stage and a fault repair stage.

4. A method for collaborative planning of remote control switches and intelligent soft switches in a distribution network as claimed in claim 3, characterized in that: When a fault occurs, the distribution network enters the fault degradation stage, the intelligent soft switch is immediately locked, the feeder outlet circuit breaker is disconnected, the nodes of the faulty feeder lose power, and the distribution network enters the fault isolation stage. The remote control switch is disconnected, the nodes of the faulty feeder lose power, and the remote control switch and the intelligent soft switch work together to restore power. After the fault is located, the distribution network enters the power supply restoration stage, disconnecting the upstream of the fault for fault isolation. The power supply of the distribution network is fully restored. After the manual control switch, remote control switch and intelligent soft switch work together, the distribution network enters the fault recovery stage, the component fault is repaired, and the distribution network returns to its original operation.

5. A method for collaborative planning of remote control switches and intelligent soft switches in a distribution network as claimed in claim 4, characterized in that: The network topology constraints include topology constraints in the fault degradation phase, topology constraints in the fault isolation phase, and topology constraints in the power supply restoration phase.

6. A method for collaborative planning of remote control switches and intelligent soft switches in a distribution network as claimed in claim 1, characterized in that: The constraints of the constructed collaborative planning mathematical model of remote control switches and intelligent soft switches also include line status constraints, reliability constraints, radial constraints, virtual power flow constraints and safety constraints.

7. A collaborative planning system for remote control switches and intelligent soft switches in a distribution network, characterized in that: include: a determination module configured to determine a power supply restoration process of a distribution network after a failure occurs in the distribution network; A construction module is configured to construct a mathematical model for collaborative planning of the remote control switch and the intelligent soft switch based on the determined power restoration process, taking into account the coordination of the remote control switch and the intelligent soft switch for multi-stage power restoration, with the sum of the switchgear life cycle cost and the load outage cost as the objective function, and taking into account the operation constraints, power flow constraints, and network topology constraints of the intelligent soft switch for multi-stage power restoration; The planning module is configured to solve the constructed collaborative planning mathematical model to complete the collaborative planning of the distribution network remote control switches and intelligent soft switches.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for collaborative planning of a distribution network remote control switch and an intelligent soft switch are implemented as described in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the method for collaborative planning of a distribution network remote control switch and an intelligent soft switch are implemented as described in any one of claims 1 to 6.

10. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the method for collaborative planning of a distribution network remote control switch and an intelligent soft switch according to any one of claims 1 to 6.

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