A collaborative planning method and system of a power distribution network remote control switch and an intelligent soft switch

Abstract

The application 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, comprising: determining a power supply recovery process of the power distribution network after a fault occurs in the power distribution network; according to the determined power supply recovery process, considering the collaboration of the remote control switch and the intelligent soft switch in multi-stage power supply recovery, taking the sum of the full life cycle cost of the switch device and the load power outage cost as an objective function, considering the intelligent soft switch operation constraint, the power flow constraint and the network topology constraint in multi-stage power supply recovery, constructing a collaborative planning mathematical model for the remote control switch and the intelligent soft switch; solving the constructed collaborative planning mathematical model to complete the collaborative planning of the remote control switch and the intelligent soft switch of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network planning technology, specifically relating to a collaborative planning method and system for power distribution network remote control switches and intelligent soft switches. Background Technology

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

[0003] The power 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 achieve rapid fault isolation and load transfer. However, mechanical switches only have discrete switch state control capabilities, making it difficult to achieve continuous power flow regulation and lacking voltage support functions. In recent years, intelligent soft switches (SOPs) based on back-to-back voltage source converters (VSCs) have attracted attention. SOPs are installed at associated feeders to replace mechanical tie switches. By implementing appropriate control strategies, bidirectional flexible and precise power control and dynamic voltage support can be achieved, thereby improving voltage distribution, balancing three-phase loads, saving energy and reducing consumption, achieving rapid fault recovery, and enhancing power supply reliability and renewable energy absorption capacity.

[0004] Existing research has explored the optimization of SOP location, capacity determination, and operation control strategies from multiple dimensions, mainly focusing on SOP configuration under normal distribution network conditions, but has not explored the application potential of SOP in improving system reliability.

[0005] Existing technologies have integrated Standard Operating Procedures (SOPs) into the framework for distribution network reliability assessment and resilience enhancement, verifying their ability to enhance power supply security. However, the high investment cost of SOPs necessitates coordinated optimization with other switching equipment. 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 further narrowing the fault area for manual-controlled switches (MCS) is not refined enough. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a collaborative planning method and system for remote-controlled switches and intelligent soft switches in distribution networks. The collaborative planning fully considers the multi-stage power restoration of intelligent soft switches and remote-controlled switches, analyzing the collaborative power restoration process based on multiple stages such as fault degradation, fault isolation, and power restoration using RCS and SOP. With the objective of minimizing the sum of the total lifecycle cost of the switching equipment and the load outage cost, the invention considers SOP operation constraints, power flow constraints, and network topology constraints at different power restoration stages, establishing a mathematical model for RCS and SOP collaborative planning. This model is then transformed into a mixed-integer second-order cone programming (MISOCP) solution. This approach reduces overall costs while improving the reliability of power supply in the distribution network.

[0007] According to some embodiments, the first aspect of the present invention provides a collaborative planning method for remote control switches and intelligent soft switches in a power distribution network, employing the following technical solution:

[0008] A collaborative planning method for remote control switches and intelligent soft switches in a power distribution network includes:

[0009] Determine the power supply restoration process of the distribution network after a fault occurs;

[0010] Based on the determined power restoration process, considering the coordination of remote control switches and intelligent soft switches in multi-stage power restoration, and taking the minimum sum of the total life cycle cost of the switching equipment and the load outage cost as the objective function, and considering the operation constraints, power flow constraints and network topology constraints of intelligent soft switches in multi-stage power restoration, a collaborative planning mathematical model of remote control switches and intelligent soft switches is constructed.

[0011] Solve the constructed collaborative programming mathematical model to complete the collaborative programming of remote control switches and intelligent soft switches in the distribution network.

[0012] As a further technical limitation, in the process of solving the constructed collaborative programming mathematical model, the collaborative programming model is transformed into a mixed integer second-order cone programming model. Solving the obtained mixed integer second-order cone programming model yields a collaborative programming scheme for remote control switches and intelligent soft switches, thus completing the collaborative programming of remote control switches and intelligent soft switches in the distribution network.

[0013] As a further technical limitation, the power distribution network power restoration process includes at least a fault degradation stage, a fault isolation stage, a power restoration stage, and a fault repair stage.

[0014] As a further technical limitation, when a fault occurs, the distribution network enters the fault degradation stage, the intelligent soft switch immediately locks out, the feeder outlet circuit breaker opens, the node of the faulty feeder loses power, the distribution network enters the fault isolation stage, the remote control switch opens, the node of the faulty feeder loses power, the remote control switch and the intelligent soft switch work together to restore power supply, after the fault is located, the distribution network enters the power supply recovery stage, the upstream of the fault is disconnected to isolate the fault, the power supply of the distribution network is fully restored, after the manual control switch, the remote control switch and the 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.

[0015] Furthermore, the network topology constraints include topology constraints for the fault degradation phase, topology constraints for the fault isolation phase, and topology constraints for the power restoration phase.

[0016] As a further technical constraint, the constraints of the constructed collaborative planning mathematical model of remote control switch and intelligent soft switch also include line state constraints, reliability constraints, radial constraints, virtual power flow constraints, and security constraints.

[0017] According to some embodiments, a second aspect of the present invention provides a collaborative planning system for remote control switches and intelligent soft switches in a power distribution network, employing the following technical solution:

[0018] A collaborative planning system for remote control switches and intelligent soft switches in a power distribution network, comprising:

[0019] The module is configured to determine the power supply restoration process of the distribution network after a fault occurs.

[0020] The module is configured to construct a collaborative planning mathematical model of remote control switches and smart soft switches based on the determined power restoration process, considering the coordination of remote control switches and smart soft switches in multi-stage power restoration, with the objective function being the minimum sum of the total life cycle cost of the switching equipment and the load outage cost, and taking into account the operating constraints, power flow constraints, and network topology constraints of smart soft switches in multi-stage power restoration.

[0021] The planning module is configured to solve the constructed collaborative planning mathematical model to complete the collaborative planning of remote control switches and smart soft switches in the distribution network.

[0022] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution:

[0023] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the collaborative planning method for distribution network remote control switches and intelligent soft switches as described in the first aspect of the present invention.

[0024] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution:

[0025] 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 in the collaborative planning method for remote control switches and intelligent soft switches in a power distribution network as described in the first aspect of the present invention.

[0026] According to some embodiments, the fifth aspect of the present invention provides a computer program product, which adopts the following technical solution:

[0027] A computer program product includes software code, wherein the program in the software code performs the steps in the collaborative planning method of distribution network remote control switch and intelligent soft switch as described in the first aspect of the present invention.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] This invention fully considers intelligent soft switches and remote control switches for multi-stage power restoration in collaborative planning, and analyzes the RCS and SOP collaborative power restoration process based on multiple stages such as fault degradation, fault isolation and power restoration. With the goal of minimizing the sum of the total life cycle cost of switching equipment and the load outage cost, it considers the SOP operation constraints, power flow constraints and network topology constraints at different power restoration stages, establishes a mathematical model for RCS and SOP collaborative planning, and transforms the collaborative planning mathematical model into a mixed integer second-order cone programming solution. While reducing the overall cost, it improves the reliability of power distribution network supply. Attached Figure Description

[0030] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0031] Figure 1 This is a flowchart of the collaborative planning method for distribution network remote control switches and intelligent soft switches in Embodiment 1 of the present invention;

[0032] Figure 2 This is a schematic diagram of the SOP control method after a fault occurs in Embodiment 1 of the present invention;

[0033] Figure 3(a) is a schematic diagram of the normal operation stage during the power supply restoration process of a simple power distribution system in Embodiment 1 of the present invention;

[0034] Figure 3(b) is a schematic diagram of the fault recovery stage in the power supply restoration process of a simple power distribution system in Embodiment 1 of the present invention;

[0035] Figure 3(c) is a schematic diagram of the fault isolation stage during the power supply restoration process of a simple power distribution system in Embodiment 1 of the present invention;

[0036] Figure 3(d) is a schematic diagram of the power restoration stage in the power restoration process of a simple power distribution system in Embodiment 1 of the present invention;

[0037] Figure 4 This is a schematic diagram of the 21-node test system in Embodiment 1 of the present invention;

[0038] Figure 5 This is a schematic diagram of cost curves under different RCS quantity constraints in Embodiment 1 of the present invention;

[0039] Figure 6 This is a schematic diagram of reliability index curves under different RCS numbers in Embodiment 1 of the present invention;

[0040] Figure 7 This is a schematic diagram showing the number and capacity of SOP configurations under different RCS numbers in Embodiment 1 of the present invention;

[0041] Figure 8 This is a schematic diagram of cost curves under different ASAI constraints in Embodiment 1 of the present invention;

[0042] Figure 9 This is a schematic diagram of the RCS quantity curves under different ASAI constraints in Embodiment 1 of the present invention;

[0043] Figure 10 This is a schematic diagram showing the number and capacity of SOPs under different ASAI constraints in Embodiment 1 of the present invention;

[0044] Figure 11 This is a structural block diagram of the collaborative planning system for distribution network remote control switches and intelligent soft switches in Embodiment 2 of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0049] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0050] Example 1

[0051] Embodiment 1 of this invention introduces a collaborative planning method for remote control switches and intelligent soft switches in power distribution networks.

[0052] like Figure 1 The method for collaborative planning of remote control switches and smart soft switches in a distribution network, as shown, includes:

[0053] Determine the power supply restoration process of the distribution network after a fault occurs;

[0054] Based on the determined power restoration process, considering the coordination of remote control switches and intelligent soft switches in multi-stage power restoration, and taking the minimum sum of the total life cycle cost of the switching equipment and the load outage cost as the objective function, and considering the operation constraints, power flow constraints and network topology constraints of intelligent soft switches in multi-stage power restoration, a collaborative planning mathematical model of remote control switches and intelligent soft switches is constructed.

[0055] Solve the constructed collaborative programming mathematical model to complete the collaborative programming of remote control switches and intelligent soft switches in the distribution network.

[0056] This embodiment adopts Figure 2 A study was conducted on two-terminal SOPs, and the typical control modes of two-terminal SOPs are shown in Table 1.

[0057] Table 1 Typical SOP Control Modes

[0058]

[0059] During normal operation, the VSC on the SOP side adopts... P / QThe control mode is responsible for regulating the active power passing through the SOP and the reactive power on this side. The other side VSC uses... V dc / Q The control modes responsible for maintaining DC-side voltage stability and regulating reactive power on this side are control modes 1 and 2. After a fault occurs, the VSC control mode on the fault side switches to... V / f Control, by controlling the conduction of the bridge arms, changes the amplitude and phase of the AC side voltage, thereby controlling the injected active and reactive power, providing voltage and frequency support to the fault-side load; the non-fault-side VSC control mode switches to... V dc / Q Control is used to maintain the stability of the DC voltage inside the SOP and to regulate the reactive power on this side, i.e., control modes 3 and 4.

[0060] After a fault occurs, the distribution network goes through the following stages in sequence: fault degradation, fault isolation, power restoration, and fault repair.

[0061] This embodiment takes the simple power distribution system shown in Figure 3(a) as an example, and lets... t f , t iso , t sr , t rp Let represent the time of fault occurrence, the time of fault isolation, the time of power restoration, and the time of fault repair, respectively. The multi-stage power restoration process after a fault occurs is as follows:

[0062] (1) Fault degradation stage: at the moment the fault occurs t f SOP immediately locks out, and simultaneously the feeder outlet circuit breaker S10 opens, de-energizing all nodes 2-5 of the faulty feeder (this embodiment assumes 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, operators formulate RCS, MCS, and SOP action strategies based on fault location information.

[0063] (2) Fault isolation phase: In t iso At any time, the remote control disconnects the nearest downstream RCS S7 to isolate the fault. Upon detecting the fault, the Standard Operating Procedure (SOP) automatically switches to... V dc / QV / fIn the control mode, frequency and voltage support are provided to the faulty side. Due to SOP capacity limitations, only nodes 2 and 3 are restored to power, therefore S5 needs to be remotely disconnected, while nodes 4 and 5 remain de-energized, as shown in Figure 3(c). The RCS and SOP coordinated power restoration strategy implemented in this stage is calculated and generated immediately after the fault is located. t iso ~ t sr During this period, maintenance personnel rushed to the site to operate the MCS.

[0064] (3) Power restoration phase: In t sr At that moment, maintenance personnel arrived on site and disconnected the nearest upstream MCS S8 to further isolate the fault. Simultaneously, they closed S10, S5, and tie switch R1, and the system power supply was fully restored, as shown in Figure 3(d). The power restoration strategy implemented in this stage, which coordinates RCS, MCS, and SOP, was calculated and generated immediately after the fault was located.

[0065] (4) Fault Repair Phase: In t rp After a period of time, the component failure was repaired, and the system returned to its original operating mode.

[0066] SOPs (Standard Operating Procedures) can effectively improve system load recovery levels and significantly reduce load outage losses by providing voltage support and coordinating with RCS (Regulatory Switch) and MCS (Mechanical Switch) during both fault isolation and power restoration phases. In traditional distribution networks, the SOP location is a mechanical tie switch, which has a long operating time and lacks voltage support capability. If a single tie switch is used for power transfer, it may be difficult to restore all loads due to voltage limitations; if multiple tie switches are used for power transfer, the load outage time will be longer due to the operating time limitation of the MCS.

[0067] In this embodiment, all MCSs are considered as candidate locations for RCSs, and all tie lines are considered as candidate locations for SOPs. The decision variables are defined as follows:

[0068] (1)

[0069] (2)

[0070] To account for arbitrary MCS configurations, define parameters. m ij :

[0071] (3)

[0072] Among them, Ω l For all lines (including connecting lines); Ω tie This is a collection of all connection lines.

[0073] The objective is to minimize the sum of expected RCS and SOP investment, annual maintenance value, and annual power outage losses.

[0074] (4)

[0075] (5)

[0076] (6)

[0077] in, LCC (Life Cycle Cost) is the sum of the annual value of the total life cycle investment and maintenance costs for all RCS and SOPs; CIC (Customer Interruption Cost) represents the user's expected annual power outage loss; P i,L , Q i,L They are nodes i Active and reactive loads; c RCS Cost of investment for a single RCS; c SOP The investment cost per unit capacity of SOP; For the operating life of RCS and SOP; r 0 represents the discount rate; h The proportion of operation and maintenance costs to investment costs; For fault scenarios s The probability of occurrence; For connection lines i - j Corresponding SOP configuration capacity; n i,s,c A binary variable representing a node. i In failure scenarios s stage c Is there a power outage? If so, n i,s,c It is 1 if it is true, otherwise it is 0; These represent the fault degradation, fault isolation, and power restoration stages, respectively. For the set of all nodes; T c For the stage c Duration, , , ; The customer damage function (CFD) is related to the load type and the duration of the outage; Ω sThis refers to the set of all fault scenarios; in this embodiment, it refers to the set of all normally closed circuit fault scenarios.

[0078] (1) SOP operating constraints

[0079] The optimization variables of SOP include the active and reactive power of the two VSCs, with the injection into the grid on both sides as the positive direction, ignoring power loss, and each stage of power restoration must meet the following constraints:

[0080] (7)

[0081] (8)

[0082] (9)

[0083] (10)

[0084] (11)

[0085] (12)

[0086] (13)

[0087] in, , Each is a fault scenario s stage c SOP injection node i Active and reactive power; It is the voltage amplitude of the fault-side VSC (assuming) i (Located on the fault side) V set It is the preset voltage of the fault-side SOP; M It is a large positive number. Formula (7) is the active power transmission constraint; Formulas (8) and (9) respectively ensure that the SOP does not inject active and reactive power into the power failure node; Formula (10) is the SOP capacity constraint; Formula (11) is the SOP fault side voltage support constraint; Formulas (12) and (13) respectively ensure that the active and reactive power injected by the SOP on both sides is only available after the SOP is configured.

[0088] (2) Current constraints

[0089] Using the LinDisFlow power flow model that ignores losses, with the injected node as positive, the power flow balance constraints at each stage are as follows:

[0090] (14)

[0091] (15)

[0092] (16)

[0093] (17)

[0094] in, Pij,s,c , Qij,s,c Each is a fault scenario s stage c line i - j Active and reactive power; Vi,s, c For load point i In failure scenarios s stage c The voltage amplitude; r ij , x ij The lines are respectively ij Resistance and reactance; z ij,s,c For the line ij In failure scenarios s stage c The binary variable representing the state of separation and combination, if the line ij A value of 1 indicates connectivity, otherwise a value of 0. S ij,max For the line i - j Maximum apparent power; Ω b\root It is the set of all nodes except the root node.

[0095] (3) Line state constraints

[0096] The connection line for installing the SOP should always be disconnected:

[0097] (18)

[0098] (4) Reliability constraints

[0099] ASAI is used as a reliability metric, and the calculation method is as follows:

[0100] (19)

[0101] in, For load point i The average power outage time; N i For load point i The number of users (assuming all are 1); K ASAI This is the lower limit of ASAI.

[0102] (5) Radial constraint

[0103] During power restoration, a radial network topology should be maintained; therefore, for each line... ij Define two binary variables a ij,s,c and a ji,s,c :

[0104] (20)

[0105] To ensure a radial topology, the root node must have no parent node, and all other nodes must have exactly one parent node.

[0106] (twenty one)

[0107] (twenty two)

[0108] (twenty three)

[0109] in, N ( i ) is a node i The set of all adjacent nodes; Ω root This is the set of all root nodes.

[0110] (6) Virtual power flow constraints

[0111] To ensure that no unnecessary switching operations occur in the fault area during power restoration and that the structure remains radial, virtual power flow constraints are added:

[0112] (twenty four)

[0113] (25)

[0114] in, H kj,s,c , H ij,s,c Each is a fault scenario s stage c Virtual lines k - i , ij Virtual power; Ω h This is the set of all virtual lines.

[0115] (7) Safety constraints

[0116] The voltage magnitude at each node should meet the following constraints:

[0117] (26)

[0118] in, V min , V max These are the upper and lower bounds of the voltage, respectively, with both the upper and lower bounds of the root node voltage being 1.0 pu.

[0119] (8) Topological constraints at each stage

[0120] 1) Fault degradation stage ( c =1): The first switch of the faulty feeder is open, all nodes lose power, and the non-faulty feeders are unaffected.

[0121] 2) Fault isolation phase ( c =2) The following constraints must be satisfied:

[0122] (27)

[0123] (28)

[0124] (29)

[0125] (30)

[0126] in, For fault scenarios s Downline i - j Whether it is faulty, if it is faulty, it is 1, otherwise it is 0. Formula (27) ensures that if the line is not configured with RCS, the connection state 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 zone; Formula (30) ensures that the nodes on both sides of the closed line are always located in the fault zone or the non-fault zone at the same time.

[0127] 3) Power restoration phase ( c =3) The following constraints must be satisfied:

[0128] (31)

[0129] (32)

[0130] (33)

[0131] (34)

[0132] Formula (31) ensures that the connection status remains unchanged if the line is not configured with MCS; (32) ensures that the faulty line must be disconnected if it is configured with MCS; Formula (33) ensures that the nodes at both ends are located in the fault zone if the faulty line is not configured with MCS; Formula (34) ensures that the nodes on both sides of the closed line are simultaneously located in the fault zone or the non-fault zone.

[0133] The model constructed in this embodiment is a mixed integer nonlinear programming problem, which is difficult to solve quickly. Therefore, it is relaxed into a MISOCP problem.

[0134] Trend constraint transformation

[0135] Introducing auxiliary variables u i,s,c replace Formulas (16) and (26) are revised as follows:

[0136] (35)

[0137] (36)

[0138] Formulas (7), (11), and (35) are constraints with specified conditions, which are transformed using the Big M method. Constraint (7) is transformed into:

[0139] (37)

[0140] To construct formula (11), the faulty line is first removed. At this point, the distribution network is divided into two connected components: the downstream part of the faulty line and the remaining part, which correspond to the node sets respectively. S dn , S up Then formula (11) is replaced with:

[0141] (38)

[0142] Formula (35) is converted to:

[0143] (39)

[0144] The presence of bilinear terms in formulas (8) and (9) leads to a non-convex model. An auxiliary variable is then introduced. Convert to linear constraints:

[0145] (40)

[0146] (41)

[0147] (42)

[0148] (43)

[0149] After the above steps, the model is transformed into the MISOCP model, which can be solved quickly 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).

[0150] Case Analysis

[0151] The test was conducted using a 21-node system, including two feeders and three tie lines, such as... Figure 4 As shown, the total load is 18.2188MW and 8.8034MVar. Assume that MCS is installed at both ends of all lines except the feeder outlet. Fault location time. t loc Set to 0.02 h The motion times for RCS and MCS are each set to 0.02. h 0.5 h Fault repair time t rep Set to 4 h The unit power outage losses corresponding to the RCS and MCS action times and fault repair times are $0.6 / kWh, $31.6 / kWh, and $50 / kWh, respectively. The line fault rate is set at 0.046 times / year. The cost and lifespan of converting MCS to RCS are set at $14286 and 20 years, respectively. The unit capacity investment cost and lifespan of SOP are set at $143 / kVA and 20 years, respectively. The RCS and SOP maintenance costs account for 0.03% of the installation cost, the discount rate is 0.1%, the voltage upper and lower limits are set at 0.95 and 1.05 pu, respectively, and the line capacity is 10 MVA. The modeling is performed using YALMIP in MATLAB, and the solution is obtained using the Gurobi solver. Each calculation takes 10 minutes.

[0152] The test was conducted with the number of RCS configurations as a constraint. The RCS and SOP configuration results when the number of RCS configurations was 15 are as follows: Figure 4 As shown in the figure. The curves for investment cost, power outage loss, and total cost under different RCS values ​​are as follows. Figure 5As shown in the figure, the investment cost increases linearly with the increase of the number of RCSs, while the power outage loss decreases rapidly and then the rate of decrease slows down. The total cost shows a trend of first decreasing and then increasing. When the number of RCSs reaches 16, the total cost reaches an inflection point. This is because when the number of RCSs is small, relying solely on MCSs to isolate faults leads to high power outage losses. Increasing the number of RCSs can improve fault isolation capabilities and significantly reduce power outage losses. Subsequently, equipment investment costs gradually become dominant, causing the total cost to gradually increase.

[0153] The reliability indices ASAI and SAIDI curves for different RCS values ​​are as follows: Figure 6 As shown in the figure, both indicators improve rapidly with the increase of the number of RCSs, but then the rate of improvement slows down. This is because when the number of RCSs is small, relying solely on MCSs to isolate faults leads to longer power outage times. Increasing the number of RCSs improves fault isolation speed and reduces power outage time. ASAI improved from 99.94% with 2 RCSs to 99.99% with all MCSs upgraded to RCSs, and SAIDI decreased from 0.25h with 2 RCSs to 0.04h with all MCSs upgraded to RCSs. However, even when all MCSs are upgraded to RCSs, the reliability indicators still cannot reach a higher level. This is because a complete power outage occurs during the fault degradation phase after a line fault.

[0154] The number and capacity of SOP configurations under different RCS numbers are as follows: Figure 7 As shown in the figure, as the number of RCSs increases, the number of SOPs remains constant at 1, configured on tie lines 17-21, with the capacity first increasing and then decreasing. This is because when the number of RCSs is small, relying solely on MCSs to isolate faults results in high power outage losses, thus requiring the configuration of SOPs. However, due to line capacity constraints and SOP cost constraints, even if SOPs can provide voltage support for the fault area, their load restoration capacity is limited, unnecessarily increasing investment costs. Therefore, the SOP capacity is only 3.3 MVA.

[0155] Sensitivity analysis was performed using ASAI as a constraint, with a lower limit of 99.90%, an upper limit of 99.99%, and a step size of 0.002%. A total of 45 calculations were conducted. The curves for investment cost, power outage loss, and total cost under different ASAI constraints are shown below. 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 this configuration scheme corresponds to... Figure 8The point where the total cost is lowest is the optimal solution of the model under the set ASAI constraint. At this point, the actual value of ASAI is greater than 99.982%, and the set ASAI constraint is actually an invalid constraint. If the number of RCS configurations exceeds 16, the investment cost is too high; if the number of RCS configurations is less than 16, the power outage loss is too high, both of which will lead to a suboptimal solution. When the ASAI constraint is further tightened, the power outage loss decreases, while the investment cost and total cost increase rapidly. At this point, the set ASAI constraint has become an effective constraint.

[0156] 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 are always required; as the ASAI constraint tightens further, the number of RCSs increases rapidly, becoming... Figure 8 The conclusions are consistent.

[0157] The number and capacity of SOP configurations under different ASAI constraints, such as Figure 10 As shown in the figure, when the ASAI constraint is less than 99.982%, a Standard Operating Procedure (SOP) must always be configured on tie line 17-21, with a capacity of 3.3 MVA. This is because this configuration is the optimal solution under the set ASAI constraint. Figure 5 The lowest total cost is achieved. As ASAI constraints tighten further, the number and capacity of SOPs increase, and power restoration is carried out in conjunction with RCS to meet the set ASAI constraints.

[0158] This embodiment addresses the issue of insufficient refinement in existing SOP planning models. Aiming to minimize the sum of the total lifecycle cost of switching equipment and the cost of load outages, it establishes a mathematical model for collaborative planning of RCS and SOP, considering SOP operational constraints, power flow constraints, and network topology constraints at different power restoration stages. The effectiveness of the proposed method is verified through typical case studies. Test results show that the introduction of SOP significantly improves the power restoration capability and reliability of the distribution network, and that collaborative configuration of the two can maintain high reliability even at a relatively low RCS configuration level.

[0159] Example 2

[0160] Embodiment 2 of the present invention introduces a collaborative planning system for remote control switches and intelligent soft switches in power distribution networks.

[0161] like Figure 11 The system shown is a collaborative planning system for remote control switches and intelligent soft switches in a power distribution network, comprising:

[0162] The module is configured to determine the power supply restoration process of the distribution network after a fault occurs.

[0163] The module is configured to construct a collaborative planning mathematical model of remote control switches and smart soft switches based on the determined power restoration process, considering the coordination of remote control switches and smart soft switches in multi-stage power restoration, with the objective function being the minimum sum of the total life cycle cost of the switching equipment and the load outage cost, and taking into account the operating constraints, power flow constraints, and network topology constraints of smart soft switches in multi-stage power restoration.

[0164] The planning module is configured to solve the constructed collaborative planning mathematical model to complete the collaborative planning of remote control switches and smart soft switches in the distribution network.

[0165] The detailed steps are the same as the collaborative planning method for distribution network remote control switches and intelligent soft switches provided in Example 1, and will not be repeated here.

[0166] Example 3

[0167] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0168] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the collaborative planning method for distribution network remote control switches and intelligent soft switches as described in Embodiment 1 of the present invention.

[0169] The detailed steps are the same as the collaborative planning method for distribution network remote control switches and intelligent soft switches provided in Example 1, and will not be repeated here.

[0170] Example 4

[0171] Embodiment 4 of the present invention provides an electronic device.

[0172] 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 in the collaborative planning method for remote control switches and intelligent soft switches in a power distribution network as described in Embodiment 1 of the present invention.

[0173] The detailed steps are the same as the collaborative planning method for distribution network remote control switches and intelligent soft switches provided in Example 1, and will not be repeated here.

[0174] Example 5

[0175] Embodiment 5 of the present invention provides a computer program product.

[0176] A computer program product includes software code, wherein the program in the software code performs the steps in the collaborative planning method of distribution network remote control switch and intelligent soft switch as described in Embodiment 1 of the present invention.

[0177] The detailed steps are the same as the collaborative planning method for distribution network remote control switches and intelligent soft switches provided in Example 1, and will not be repeated here.

[0178] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0179] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0180] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0183] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0184] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A collaborative planning method for remote control switches and intelligent soft switches in a power distribution network, characterized in that, include: Determine the power supply restoration process of the distribution network after a fault occurs; The power supply restoration process of the power distribution network includes at least the fault degradation stage, the fault isolation stage, the power supply restoration stage, and the fault repair stage; During the fault degradation phase, operators formulate SOP action strategies for remote control switch RCS, manual control switch MCS, and smart soft switch based on fault location information. During the fault isolation phase, the SOP automatically switches to V after detecting a fault. dc / QV / f control mode provides frequency and voltage support for the fault side; During the period from the moment of fault isolation to the moment of power restoration, maintenance personnel rushed to the site to operate the MCS; During the power restoration phase, when power is restored, maintenance personnel arrive on site and disconnect the nearest upstream MCS to further isolate the fault; Based on the determined power restoration process, considering the coordination of remote control switches and intelligent soft switches in multi-stage power restoration, and taking the minimum sum of the total life cycle cost of the switching equipment and the load outage cost as the objective function, and considering the operation constraints, power flow constraints and network topology constraints of intelligent soft switches in multi-stage power restoration, a collaborative planning mathematical model of remote control switches and intelligent soft switches is constructed. The constraints of the constructed collaborative planning mathematical model for remote control switches and intelligent soft switches also include line state constraints, reliability constraints, radial constraints, virtual power flow constraints, and security constraints. To ensure that no unnecessary switching operations occur in the fault area during power restoration and that the structure remains radial, virtual power flow constraints are added: Among them, H kj,s,c H ij,s,c These represent the virtual power of virtual lines ki and ij in phase c of fault scenario s, respectively; Ω h For the set of all virtual lines; Ω l For the set of all lines; n i,s,c This is a binary variable representing whether node i loses power during fault scenario s stage c. If power is lost, then n... i,s,c Ω is 1 if it is Ω, otherwise it is 0. b\root It is the set of all nodes except the root node; Let be the maximum or minimum value of the binary variable of line ij in the c-state of fault scenario s; Solve the constructed collaborative planning mathematical model to complete the collaborative planning of remote control switches and intelligent soft switches in the distribution network; CIC Expected annual power outage losses for users; P i,L For nodes i Active load; For fault scenarios s The probability of occurrence; n i,s,c A binary variable representing a node. i In failure scenarios s stage c Is there a power outage? If so, n i,s,c It is 1 if it is true, otherwise it is 0; These represent the fault degradation, fault isolation, and power restoration stages, respectively. For the set of all nodes; T c For the stage c Duration, The cost of a power outage per unit of electricity consumed by a user depends on the load type and the duration of the outage; Ω s This is a set of all failure scenarios.

2. The collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in claim 1, characterized in that, In the process of solving the constructed collaborative programming mathematical model, the collaborative programming model is transformed into a mixed integer second-order cone programming model. Solving the obtained mixed integer second-order cone programming model yields a collaborative programming scheme for remote control switches and intelligent soft switches, thus completing the collaborative programming of remote control switches and intelligent soft switches in the distribution network.

3. The collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in claim 1, characterized in that, When a fault occurs, the distribution network enters the fault degradation stage. The intelligent soft switch immediately locks out, the feeder output circuit breaker opens, the node of the faulty feeder loses power, and the distribution network enters the fault isolation stage. The remote control switch opens, the node of the faulty feeder loses power, and the remote control switch and intelligent soft switch work together to restore power supply. After the fault is located, the distribution network enters the power supply recovery stage. The upstream of the fault is disconnected to isolate the fault. 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.

4. The collaborative planning method for remote control switches and intelligent soft switches in a distribution network as described in claim 3, characterized in that, The network topology constraints include topology constraints for the fault degradation phase, topology constraints for the fault isolation phase, and topology constraints for the power restoration phase.

5. A collaborative planning system for remote control switches and intelligent soft switches in a power distribution network, characterized in that, The collaborative planning method for distribution network remote control switches and intelligent soft switches according to any one of claims 1-4 includes: The module is configured to determine the power supply restoration process of the distribution network after a fault occurs. The module is configured to construct a collaborative planning mathematical model of remote control switches and smart soft switches based on the determined power restoration process, considering the coordination of remote control switches and smart soft switches in multi-stage power restoration, with the objective function being the minimum sum of the total life cycle cost of the switching equipment and the load outage cost, and taking into account the operating constraints, power flow constraints, and network topology constraints of smart soft switches in multi-stage power restoration. The planning module is configured to solve the constructed collaborative planning mathematical model to complete the collaborative planning of remote control switches and smart soft switches in the distribution network.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the collaborative planning method for distribution network remote control switches and intelligent soft switches as described in any one of claims 1-4.

7. 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, it implements the steps of the collaborative planning method for distribution network remote control switches and intelligent soft switches as described in any one of claims 1-4.

8. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the collaborative planning method for distribution network remote control switches and intelligent soft switches as described in any one of claims 1-4.

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