Power distribution network voltage optimization method and system considering intelligent soft switching and maintenance plan

Abstract

The invention discloses a power distribution network voltage optimization method and system considering an intelligent soft switch and a maintenance plan, and the method comprises the steps: building a model of the intelligent soft switch and the maintenance plan, considering the uncertainty of photovoltaic output and load demands in a power distribution network, and obtaining a power distribution network voltage optimization decision method. Therefore, the voltage stability of power distribution network operation is realized. The invention provides a power distribution network voltage optimization method considering an intelligent soft switch and a maintenance plan, and aims to lay a foundation for the voltage stability problem of a power distribution network under the conditions of novel equipment access and complex operation, and the proposed method can effectively improve the voltage safety and stability of the power distribution network. And powerful technical support is provided for intelligent management and operation and maintenance of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network voltage optimization, and in particular to a power distribution network voltage optimization method and system taking into account intelligent soft switches and maintenance plans. Background Art

[0002] With the national strategy of "carbon peak and carbon neutrality", large-scale photovoltaic units are connected to the distribution network. However, with the continuous increase in the penetration rate of photovoltaic power sources, the voltage problem of the distribution network has become increasingly prominent, which has posed some new technical challenges to the reliability, stability and control and dispatch of the distribution network. In order to effectively respond to these challenges and ensure that the distribution network can always maintain a safe and stable operating state under a complex and changeable operating environment, it is urgent to propose an innovative and more refined distribution network voltage optimization method. Therefore, a distribution network voltage optimization method taking into account intelligent soft switching and maintenance plans is proposed, taking into account the uncertainty of photovoltaic output and load demand, so as to better guarantee the voltage state of the distribution network and ensure the safe and stable operation of the distribution network. Summary of the invention

[0003] The technical problem to be solved by the present invention is how to take into account the uncertainty of photovoltaic output and load demand, so as to better guarantee the voltage state of the distribution network and ensure the safe and stable operation of the distribution network.

[0004] The present invention solves the above technical problems through the following technical means:

[0005] The method for optimizing the voltage of a distribution network taking into account intelligent soft switching and maintenance plans comprises the following steps:

[0006] Study the operating characteristics of intelligent soft switches in distribution networks and establish corresponding intelligent soft switch constraints;

[0007] Analyze various factors that need to be considered in the distribution network maintenance plan and construct the distribution network maintenance plan constraints;

[0008] Considering the uncertainty of PV output and load demand in distribution network, fuzzy chance constraints are constructed and clear equivalence classes are derived;

[0009] Taking the minimum node voltage deviation of the distribution network as the objective function, combined with the intelligent soft switch constraints, maintenance plan constraints and uncertainty constraints of the distribution station area, the voltage optimization decision method of the distribution network is determined; the objective function is:

[0010]

[0011] Among them, U ref is the reference voltage of the distribution network node, U i is the voltage at node i, N b is the number of distribution network nodes.

[0012] Furthermore, the intelligent soft switch constraint includes:

[0013] Intelligent soft switches are back-to-back AC / DC / AC converters composed of high-power fully controlled power electronic devices. They can flexibly control the flow of power at both ends according to instructions, making the distribution network more flexible and avoiding the safety hazards caused by the disconnection of traditional switches in the distribution network.

[0014] The power self-balancing constraint of intelligent soft switch is:

[0015]

[0016] Where P i sop and They represent the active power injected into the intelligent soft switch from nodes i and j respectively; P i loss and They represent the internal power loss of the smart soft switch generated at nodes i and j respectively;

[0017] The reactive power and capacity constraints of the intelligent soft switch are:

[0018]

[0019] In the formula, enb k,t represents the power-on state of node k at time t, indicating that if the node is powered on, enb k,t =1, otherwise, if the node is not powered, enb k,t =0; represents the reactive power of the intelligent soft switch at node k; and is the minimum and maximum value of reactive power of the intelligent soft switch at node k; and They represent the active power and reactive power of the intelligent soft switch at node k at time t respectively; represents the capacity of the intelligent soft switch at node k;

[0020] The loss constraint of the smart soft switch itself is:

[0021]

[0022] Where: and is the loss coefficient of the intelligent soft switch;

[0023] In order to solve the problem of non-convex constraints in optimization calculations, the equality constraints in the above formula are further relaxed into inequality constraints:

[0024]

[0025] Furthermore, the distribution network maintenance plan constraints include:

[0026] When the distribution network is operating normally, it is necessary to consider the node voltage constraint, power balance constraint and network radial topology constraint; among them, the node voltage constraint means that when the branch is in the disconnected state, the branch end voltage is 0, and when the branch is in the closed state, the branch end voltage is equal to the branch end voltage; the power balance constraint includes the node power balance constraint and the upper and lower limit constraints of the branch power; the network radial topology constraint needs to meet the following conditions: the network is connected, and the number of closed branches is equal to the total number of nodes minus 1;

[0027] The maintenance plan constraints of the distribution network include that the cumulative maintenance time of any line l is not less than the set single maintenance time:

[0028]

[0029] Where Y t It is used to indicate the status of line l at hour t. The line is offline for maintenance, which is 0, and the line is available, which is 1. L,l is the total time required for maintenance of line l, NT is the total time of the maintenance plan, and They are the start and end time of maintenance respectively;

[0030] The relationship between maintenance variables and line state variables is:

[0031]

[0032] In the formula, p l,t and q l,t They are used to indicate the maintenance start variable and maintenance end variable respectively. The maintenance start variable is 1, indicating that the maintenance has started, and the maintenance start variable is 0, indicating that the maintenance has not started or has ended.

[0033] In addition, line maintenance can be divided into multiple stages:

[0034]

[0035] In the formula, MN l The upper limit of the number of line maintenance sections;

[0036] The constraints on the minimum duration and minimum time interval of line maintenance are:

[0037]

[0038] In the formula, MT l on and MT l offare the shortest duration and minimum time interval of the maintenance section of line l, and They are the start time and end time of maintenance of line l respectively.

[0039] Furthermore, the uncertain fuzzy opportunity constraints of photovoltaic output and load demand are constructed and clear equivalence classes are derived, including:

[0040] Due to the volatility of photovoltaic output and load demand, it is difficult to evaluate the operating status of the power system in real time, which makes it difficult for system operators to formulate dispatch plans, arrange backup plans, and other activities. Considering the influence of uncertain factors, the node power constraint balance is relaxed to be established under a certain confidence level γ, and the uncertainty factor set is constructed:

[0041]

[0042] In the formula, is the photovoltaic output fuzzy parameter at node i; P i d,cut is the load shedding power at node i; is the load power at node i; P i,j The power flowing from node i to node j;

[0043] The safety requirements in the power system are extremely high, so the confidence level needs to be set high; when the confidence level γ ≥ 0.5, the chance constraint P r The clear equivalence class of {g(x,ξ)≤0}≥γ is denoted as:

[0044]

[0045] In the formula, are two functions defined; h0(x) is a part of the function g(x,ξ);

[0046] The uncertainty fuzzy opportunity constraints of photovoltaic output and load demand are derived into corresponding clear equivalence classes according to the parameter transformation method, and the following is obtained:

[0047]

[0048] In the formula, is the membership parameter of photovoltaic output, is the membership parameter of load demand.

[0049] The present invention also provides a distribution network voltage optimization system taking into account intelligent soft switches and maintenance plans, comprising:

[0050] Constraint construction module: Study the operating characteristics of intelligent soft switches in distribution networks and establish corresponding intelligent soft switch constraints; analyze various factors that need to be considered in the distribution network maintenance plan and construct the distribution network maintenance plan constraints; consider the uncertainty of photovoltaic output and load demand in the distribution network to construct fuzzy opportunity constraints and derive clear equivalence classes;

[0051] Objective function construction module: Taking the minimum node voltage deviation of the distribution network as the objective function, combined with the intelligent soft switch constraints, maintenance plan constraints and uncertainty constraints of the distribution station area, the voltage optimization decision method of the distribution network is determined; the objective function is:

[0052]

[0053] Among them, U ref is the reference voltage of the distribution network node, U i is the voltage at node i, N b is the number of distribution network nodes.

[0054] Furthermore, the intelligent soft switch constraint includes:

[0055] The power self-balancing constraint of intelligent soft switch is:

[0056]

[0057] Where P i sop and They represent the active power injected into the intelligent soft switch from nodes i and j respectively; P i loss and They represent the internal power loss of the smart soft switch generated at nodes i and j respectively;

[0058] The reactive power and capacity constraints of the intelligent soft switch are:

[0059]

[0060] In the formula, enb k,t represents the power-on state of node k at time t, indicating that if the node is powered on, enb k,t =1, otherwise, if the node is not powered, enb k,t =0; represents the reactive power of the intelligent soft switch at node k; and is the minimum and maximum value of reactive power of the intelligent soft switch at node k; and They represent the active power and reactive power of the intelligent soft switch at node k at time t respectively; represents the capacity of the intelligent soft switch at node k;

[0061] The loss constraint of the smart soft switch itself is:

[0062]

[0063] Where: and is the loss coefficient of the intelligent soft switch;

[0064] In order to solve the problem of non-convex constraints in optimization calculations, the equality constraints in the above formula are further relaxed into inequality constraints:

[0065]

[0066] Furthermore, the distribution network maintenance plan constraints include:

[0067] When the distribution network is operating normally, it is necessary to consider the node voltage constraint, power balance constraint and network radial topology constraint; among them, the node voltage constraint means that when the branch is in the disconnected state, the branch end voltage is 0, and when the branch is in the closed state, the branch end voltage is equal to the branch end voltage; the power balance constraint includes the node power balance constraint and the upper and lower limit constraints of the branch power; the network radial topology constraint needs to meet the following conditions: the network is connected, and the number of closed branches is equal to the total number of nodes minus 1;

[0068] The maintenance plan constraints of the distribution network include that the cumulative maintenance time of any line l is not less than the set single maintenance time:

[0069]

[0070] Where Y t It is used to indicate the status of line l at hour t. The line is offline for maintenance, which is 0, and the line is available, which is 1. L,l is the total time required for maintenance of line l, NT is the total time of the maintenance plan, and They are the start and end time of maintenance respectively;

[0071] The relationship between maintenance variables and line state variables is:

[0072]

[0073] In the formula, p l,t and q l,t They are used to indicate the maintenance start variable and maintenance end variable respectively. The maintenance start variable is 1, indicating that the maintenance has started, and the maintenance start variable is 0, indicating that the maintenance has not started or has ended.

[0074] In addition, line maintenance can be divided into multiple stages:

[0075]

[0076] In the formula, MN l The upper limit of the number of line maintenance sections;

[0077] The constraints on the minimum duration and minimum time interval of line maintenance are:

[0078]

[0079]

[0080] In the formula, MT l on and MT l off are the shortest duration and minimum time interval of the maintenance section of line l, and They are the start time and end time of maintenance of line l respectively.

[0081] Furthermore, the uncertain fuzzy opportunity constraints of photovoltaic output and load demand are constructed and clear equivalence classes are derived, including:

[0082] Considering the influence of uncertain factors on photovoltaic output and load demand, the node power constraint balance is relaxed to be established under a certain confidence level γ, and the uncertainty factor set is constructed:

[0083]

[0084] In the formula, is the photovoltaic output fuzzy parameter at node i; P i d,cut is the load shedding power at node i; is the load power at node i; P i,j The power flowing from node i to node j;

[0085] The safety requirements in the power system are extremely high, so the confidence level needs to be set high; when the confidence level γ ≥ 0.5, the chance constraint P r The clear equivalence class of {g(x,ξ)≤0}≥γ is denoted as:

[0086]

[0087] In the formula, are two functions defined; h0(x) is a part of the function g(x,ξ);

[0088] The uncertainty fuzzy opportunity constraints of photovoltaic output and load demand are derived into corresponding clear equivalence classes according to the parameter transformation method, and the following is obtained:

[0089]

[0090] In the formula, is the membership parameter of photovoltaic output, is the membership parameter of load demand.

[0091] The present invention also provides a processing device, comprising at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the above method by calling the program instructions.

[0092] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the above method.

[0093] The advantages of the present invention are:

[0094] The present invention proposes a distribution network voltage optimization method taking into account intelligent soft switches and maintenance plans, which can fully guarantee the voltage stability of the distribution network under high photovoltaic penetration, realize accurate regulation and optimization of the voltage state of the distribution network, thereby comprehensively improving the safety, stability and operation efficiency of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 A schematic diagram of a method flow in an embodiment of the present invention;

[0096] Figure 2 An improved IEEE33 node power distribution system in an embodiment of the present invention;

[0097] Figure 3 This is a comparison diagram of node voltage curves before and after considering soft switching in an embodiment of the present invention. DETAILED DESCRIPTION

[0098] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] like Figure 1 As shown, a distribution network voltage optimization method considering intelligent soft switches and maintenance plans proposed by the present invention includes the following steps:

[0100] Study the operating characteristics of intelligent soft switches in distribution networks and establish corresponding intelligent soft switch constraints;

[0101] Combined with the distribution network operation constraints, various factors that need to be considered in the distribution network maintenance plan are analyzed, and the distribution network maintenance plan constraints are constructed;

[0102] Considering the uncertainty of PV output and load demand in distribution network, fuzzy chance constraints are constructed and clear equivalence classes are derived;

[0103] Taking the minimum node voltage deviation of the distribution network as the objective function, the voltage optimization decision method of the distribution network is determined by combining the intelligent soft switch constraints, maintenance plan constraints and uncertainty constraints of the distribution station area.

[0104] The technical solution of the present invention is described in detail below:

[0105] Step 1: Study the operating characteristics of the intelligent soft switch in the distribution network and establish the corresponding intelligent soft switch constraints. The specific implementation steps are as follows:

[0106] The intelligent soft switch is a back-to-back AC / DC / AC converter composed of high-power fully controlled power electronic devices. It can flexibly regulate the flow of power at both ends according to instructions, making the distribution network operation more flexible and avoiding the safety hazards caused by the disconnection of traditional switches in the distribution network.

[0107] The power self-balancing constraint of intelligent soft switch is:

[0108]

[0109] Where P i sop and They represent the active power injected into the intelligent soft switch from nodes i and j respectively. i loss and They represent the internal power loss of the smart soft switch generated at nodes i and j respectively.

[0110] The reactive power and capacity constraints of the intelligent soft switch are:

[0111]

[0112] In the formula, enb k,t represents the power-on state of node k at time t, indicating that if the node is powered on, enb k,t =1, otherwise, if the node is not powered, enb k,t =0. Represents the reactive power of the intelligent soft switch at node k. and is the minimum and maximum reactive power of the intelligent soft switch at node k. and They represent the active power and reactive power of the intelligent soft switch at node k at time t respectively. Represents the capacity of the intelligent soft switch at node k.

[0113] The loss constraint of the smart soft switch itself is:

[0114]

[0115] Where: and is the loss coefficient of the intelligent soft switch.

[0116] In order to solve the problem of non-convex constraints in optimization calculations, the equality constraints in the above formula are further relaxed into inequality constraints:

[0117]

[0118] Step 2: Analyze various factors that need to be considered in the distribution network maintenance plan in combination with the distribution network operation constraints, and construct the distribution network maintenance plan constraints. The specific implementation steps are as follows:

[0119] When the distribution network is operating normally, it is necessary to consider node voltage constraints, power balance constraints and network radial topology constraints. Among them, the node voltage constraint means that when the branch is in the open state, the branch end voltage is 0, and when the branch is in the closed state, the branch end voltage is equal to the branch end voltage; the power balance constraint includes the node power balance constraint and the upper and lower limit constraints of the branch power; the network radial topology constraint needs to meet the following conditions: the network is connected, and the number of closed branches is equal to the total number of nodes minus 1.

[0120] The maintenance plan constraints of the distribution network include that the cumulative maintenance time of any line l is not less than the set single maintenance time:

[0121]

[0122] Where Y t It is used to indicate the status of line l at hour t. If the line is offline for maintenance, it is 0; if the line is available, it is 1. MD L,l is the total time required for maintenance of line l, NT is the total time of the maintenance plan, and They are the start and end time of maintenance respectively.

[0123] The relationship between maintenance variables and line state variables is:

[0124]

[0125] In the formula, p l,t and q l,tThey are used to represent the maintenance start variable and the maintenance end variable respectively. The maintenance start variable is 1, which means the maintenance has started; the maintenance start variable is 0, which means the maintenance has not started or has ended.

[0126] In addition, line maintenance can be divided into multiple stages:

[0127]

[0128] In the formula, MN l It is the upper limit of the number of line maintenance sections.

[0129] The constraints on the minimum duration and minimum time interval of line maintenance are:

[0130]

[0131] In the formula, MT l on and MT l off are the shortest duration and minimum time interval of the maintenance section of line l, and They are the start time and end time of maintenance of line l respectively.

[0132] Step 3: Considering the uncertainty of PV output and load demand in the distribution network, fuzzy opportunity constraints are constructed and clear equivalence classes are derived. The specific implementation steps are as follows:

[0133] Due to the volatility of photovoltaic output and load demand, it is difficult to evaluate the operating status of the power system in real time, which makes it difficult for system operators to formulate dispatch plans, arrange backup plans, etc. Considering the influence of uncertain factors, the node power constraint balance is relaxed to be established under a certain confidence level γ, and the uncertainty factor set is constructed:

[0134]

[0135] In the formula, is the photovoltaic output fuzzy parameter at node i; P i d,cut is the load shedding power at node i; is the load power at node i; P i,j The power flowing from node i to node j.

[0136] The safety requirements in the power system are extremely high, so the confidence level needs to be set high. When the confidence level γ ≥ 0.5, the chance constraint P r The clear equivalence class of {g(x,ξ)≤0}≥γ is denoted as:

[0137]

[0138] In the formula, are two functions defined; h0(x) is part of the function g(x,ξ).

[0139] The uncertainty fuzzy opportunity constraints of photovoltaic output and load demand are derived into corresponding clear equivalence classes according to the parameter transformation method, and the following is obtained:

[0140]

[0141] In the formula, is the membership parameter of photovoltaic output, is the membership parameter of load demand.

[0142] Step 4: Taking the minimum node voltage deviation of the distribution network as the objective function, combined with the intelligent soft switch constraints, maintenance plan constraints and uncertainty constraints of the distribution station area, determine the voltage optimization decision method of the distribution network. The specific implementation steps are as follows:

[0143] Taking the minimum node voltage deviation of the distribution network as the objective function, combined with the intelligent soft switch constraints, maintenance plan constraints and uncertainty constraints of the distribution station area, the voltage optimization decision method of the distribution network is determined, where the optimization objective of the optimization decision model is:

[0144]

[0145] Among them, U ref is the reference voltage of the distribution network node, U i is the voltage at node i, N b is the number of distribution network nodes.

[0146] For the above-mentioned distribution network voltage optimization method taking into account intelligent soft switching and maintenance plan, the applicability of this method is evaluated through specific case analysis.

[0147] The improved IEEE33 node distribution system is tested, where node 25 is connected to the photovoltaic power station and there is an intelligent soft switch connection between node 18 and node 22. The node voltage curve of the distribution network voltage optimization method before and after considering the intelligent soft switch is as follows: Figure 3 As shown, it can be seen that the voltage drop at the nodes 18 and 22 at the end of the system line is significantly reduced due to the flexible voltage regulation of the intelligent soft switch.

[0148] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing distribution network voltage taking into account intelligent soft switching and maintenance planning, characterized in that: The method comprises the following steps: Study the operating characteristics of intelligent soft switches in distribution networks and establish corresponding intelligent soft switch constraints; Analyze various factors that need to be considered in the distribution network maintenance plan and construct the distribution network maintenance plan constraints; Considering the uncertainty of PV output and load demand in distribution network, fuzzy chance constraints are constructed and clear equivalence classes are derived; Taking the minimum node voltage deviation of the distribution network as the objective function, combined with the intelligent soft switch constraints, maintenance plan constraints and uncertainty constraints of the distribution station area, the voltage optimization decision method of the distribution network is determined; the objective function is: Among them, U ref is the reference voltage of the distribution network node, U i is the voltage at node i, N b is the number of distribution network nodes.

2. The method for optimizing distribution network voltage taking into account intelligent soft switches and maintenance plans according to claim 1, characterized in that: The intelligent soft switch constraint includes: Intelligent soft switches are back-to-back AC / DC / AC converters composed of high-power fully controlled power electronic devices. They can flexibly control the flow of power at both ends according to instructions, making the distribution network more flexible and avoiding the safety hazards caused by the disconnection of traditional switches in the distribution network. The power self-balancing constraint of intelligent soft switch is: In the formula, and They represent the active power injected into the intelligent soft switch from nodes i and j respectively; and They represent the internal power loss of the smart soft switch generated at nodes i and j respectively; The reactive power and capacity constraints of the intelligent soft switch are: In the formula, enb k,t represents the power-on state of node k at time t, indicating that if the node is powered on, enb k,t =1, otherwise, if the node is not powered, enb k,t =0; represents the reactive power of the intelligent soft switch at node k; and is the minimum and maximum value of reactive power of the intelligent soft switch at node k; and They represent the active power and reactive power of the intelligent soft switch at node k at time t respectively; represents the capacity of the intelligent soft switch at node k; The loss constraint of the smart soft switch itself is: Where: and is the loss coefficient of the intelligent soft switch; In order to solve the problem of non-convex constraints in optimization calculations, the equality constraints in the above formula are further relaxed into inequality constraints:

3. The method for optimizing distribution network voltage taking into account intelligent soft switches and maintenance plans according to claim 1, characterized in that: The distribution network maintenance plan constraints include: When the distribution network is operating normally, it is necessary to consider the node voltage constraint, power balance constraint and network radial topology constraint; among them, the node voltage constraint means that when the branch is in the disconnected state, the branch end voltage is 0, and when the branch is in the closed state, the branch end voltage is equal to the branch end voltage; the power balance constraint includes the node power balance constraint and the upper and lower limit constraints of the branch power; the network radial topology constraint needs to meet the following conditions: the network is connected, and the number of closed branches is equal to the total number of nodes minus 1; The maintenance plan constraints of the distribution network include that the cumulative maintenance time of any line l is not less than the set single maintenance time: Where Y t It is used to indicate the status of line l at hour t. The line is offline for maintenance, which is 0, and the line is available, which is 1. L,l is the total time required for maintenance of line l, NT is the total time of the maintenance plan, and They are the start and end time of maintenance respectively; The relationship between maintenance variables and line state variables is: In the formula, p l,t and q l,t They are used to indicate the maintenance start variable and maintenance end variable respectively. The maintenance start variable is 1, indicating that the maintenance has started, and the maintenance start variable is 0, indicating that the maintenance has not started or has ended. In addition, line maintenance can be divided into multiple stages: In the formula, MN l The upper limit of the number of line maintenance sections; The constraints on the minimum duration and minimum time interval of line maintenance are: In the formula, and are the shortest duration and minimum time interval of the maintenance section of line l, and They are the start time and end time of maintenance of line l respectively.

4. The method for optimizing distribution network voltage taking into account intelligent soft switches and maintenance plans according to claim 1, characterized in that: The uncertain fuzzy chance constraints of photovoltaic output and load demand are constructed and clear equivalence classes are derived, including: Due to the volatility of photovoltaic output and load demand, it is difficult to evaluate the operating status of the power system in real time, which makes it difficult for system operators to formulate dispatch plans, arrange backup plans, and other activities. Considering the influence of uncertain factors, the node power constraint balance is relaxed to be established under a certain confidence level γ, and the uncertainty factor set is constructed: In the formula, is the fuzzy parameter of photovoltaic output at node i; is the load shedding power at node i; is the load power at node i; P i,j The power flowing from node i to node j; The safety requirements in the power system are extremely high, so the confidence level needs to be set high; when the confidence level γ ≥ 0.5, the chance constraint P r The clear equivalence class of {g(x,ξ)≤0}≥γ is denoted as: In the formula, are two functions defined; h0(x) is a part of the function g(x,ξ); The uncertainty fuzzy opportunity constraints of photovoltaic output and load demand are derived into corresponding clear equivalence classes according to the parameter transformation method, and the following is obtained: In the formula, is the membership parameter of photovoltaic output, is the membership parameter of load demand.

5. Distribution network voltage optimization system taking into account intelligent soft switching and maintenance planning, characterized in that: include: Constraint building module: study the operating characteristics of intelligent soft switches in distribution networks and establish corresponding intelligent soft switch constraints; Analyze various factors that need to be considered in the distribution network maintenance plan and construct the distribution network maintenance plan constraints; consider the uncertainty of photovoltaic output and load demand in the distribution network to construct fuzzy opportunity constraints and derive clear equivalence classes; Objective function construction module: Taking the minimum node voltage deviation of the distribution network as the objective function, combined with the intelligent soft switch constraints, maintenance plan constraints and uncertainty constraints of the distribution station area, the voltage optimization decision method of the distribution network is determined; the objective function is: Among them, U ref is the reference voltage of the distribution network node, U i is the voltage at node i, N b is the number of distribution network nodes.

6. The distribution network voltage optimization system taking into account intelligent soft switches and maintenance plans according to claim 5, characterized in that: The intelligent soft switch constraint includes: The power self-balancing constraint of intelligent soft switch is: In the formula, and They represent the active power injected into the intelligent soft switch from nodes i and j respectively; and They represent the internal power loss of the smart soft switch generated at nodes i and j respectively; The reactive power and capacity constraints of the intelligent soft switch are: In the formula, enb k,t represents the power-on state of node k at time t, indicating that if the node is powered on, enb k,t =1, otherwise, if the node is not powered, enb k,t =0; represents the reactive power of the intelligent soft switch at node k; and is the minimum and maximum value of reactive power of the intelligent soft switch at node k; and They represent the active power and reactive power of the intelligent soft switch at node k at time t respectively; represents the capacity of the intelligent soft switch at node k; The loss constraint of the smart soft switch itself is: Where: and is the loss coefficient of the intelligent soft switch; In order to solve the problem of non-convex constraints in optimization calculations, the equality constraints in the above formula are further relaxed into inequality constraints:

7. A distribution network voltage optimization system taking into account intelligent soft switches and maintenance plans according to claim 5, characterized in that: The distribution network maintenance plan constraints include: When the distribution network is operating normally, it is necessary to consider the node voltage constraint, power balance constraint and network radial topology constraint; among them, the node voltage constraint means that when the branch is in the disconnected state, the branch end voltage is 0, and when the branch is in the closed state, the branch end voltage is equal to the branch end voltage; the power balance constraint includes the node power balance constraint and the upper and lower limit constraints of the branch power; the network radial topology constraint needs to meet the following conditions: the network is connected, and the number of closed branches is equal to the total number of nodes minus 1; The maintenance plan constraints of the distribution network include that the cumulative maintenance time of any line l is not less than the set single maintenance time: Where Y t It is used to indicate the status of line l at hour t. The line is offline for maintenance, which is 0, and the line is available, which is 1. L,l is the total time required for maintenance of line l, NT is the total time of the maintenance plan, and They are the start and end time of maintenance respectively; The relationship between maintenance variables and line state variables is: In the formula, p l,t and q l,t They are used to indicate the maintenance start variable and maintenance end variable respectively. The maintenance start variable is 1, indicating that the maintenance has started, and the maintenance start variable is 0, indicating that the maintenance has not started or has ended. In addition, line maintenance can be divided into multiple stages: In the formula, MN l The upper limit of the number of line maintenance sections; The constraints on the minimum duration and minimum time interval of line maintenance are: In the formula, and are the shortest duration and minimum time interval of the maintenance section of line l, and They are the start time and end time of maintenance of line l respectively.

8. The distribution network voltage optimization system taking into account intelligent soft switches and maintenance plans according to claim 5, characterized in that: The uncertain fuzzy chance constraints of photovoltaic output and load demand are constructed and clear equivalence classes are derived, including: Considering the influence of uncertain factors on photovoltaic output and load demand, the node power constraint balance is relaxed to be established under a certain confidence level γ, and the uncertainty factor set is constructed: In the formula, is the fuzzy parameter of photovoltaic output at node i; is the load shedding power at node i; is the load power at node i; P i,j The power flowing from node i to node j; The safety requirements in the power system are extremely high, so the confidence level needs to be set high; when the confidence level γ ≥ 0.5, the chance constraint P r The clear equivalence class of {g(x,ξ)≤0}≥γ is denoted as: In the formula, are two functions defined; h0(x) is a part of the function g(x,ξ); The uncertainty fuzzy opportunity constraints of photovoltaic output and load demand are derived into corresponding clear equivalence classes according to the parameter transformation method, and the following is obtained: In the formula, is the membership parameter of photovoltaic output, is the membership parameter of load demand.

9. A processing device, characterized in that: It comprises at least one processor and at least one memory in communication with the processor, wherein the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute any method according to claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method according to any one of claims 1 to 4.

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