CN107591797A - A kind of collection of intelligent Sofe Switch neutralizes jointly controls tactful setting method on the spot - Google Patents

Abstract

A centralized and local joint control strategy setting method for intelligent soft switches: according to the selected active power distribution system, input the structure and parameters of the active power distribution system; according to the structure and parameters of the active power distribution system, consider the distributed power Based on the timing characteristics of output and load, the centralized and local joint control strategy tuning model of intelligent soft switch for active distribution network is established; The centralized and local joint control strategy setting model of intelligent soft switch in distribution network is transformed into a second-order cone model; the second-order cone model is calculated and solved by using the second-order cone programming algorithm: the centralized and local joint control strategy of intelligent soft switch The relevant parameters of the system, the voltage distribution in the system, the active power adjustment and reactive power compensation of the intelligent soft switch. The invention considers the randomness and fluctuation of distributed power sources and loads, and establishes a centralized and local joint control strategy setting model of an active distribution network intelligent soft switch.

Description

A centralized and local combined control strategy setting method for intelligent soft switches

technical field

The invention relates to an operation control strategy of an intelligent soft switch. In particular, it relates to a centralized and local combined control strategy setting method for an intelligent soft switch.

Background technique

The high attention to energy and the environment makes the development of distribution networks face new pressures and challenges, and these pressures and challenges are also important opportunities to promote the development of traditional distribution networks to active distribution networks. In recent years, the increasing penetration rate of distributed generation (DG), including photovoltaic (PV) and wind turbines, has made the active distribution network face a series of new problems, such as bidirectional power flow, voltage over-limit, Network congestion, etc., in which the voltage limit is particularly prominent. In the traditional power distribution system, its adjustment methods are limited, especially the control methods for the primary system are seriously lacking. Most of the existing equipment is for the adjustment of reactive power, such as capacitor banks, static var compensators, etc. However, in the distribution network, the relationship between active and reactive power is mutually coupled, and the influence of active power on voltage distribution is also significant. Therefore, especially for distribution networks with high-penetration distributed power sources, it is difficult to eliminate the problem of voltage over-limit by simply relying on reactive power regulation. Intelligent soft switch (soft open point, SOP) is a new power distribution device based on power electronics technology derived from the above background to replace the traditional contact switch. Intelligent soft switching can realize the joint adjustment of active power and reactive power, and the power control is simple and reliable, so as to effectively deal with a series of problems including voltage limit.

At present, the intelligent soft switch mainly adopts the centralized control strategy to realize its operation control. The centralized control strategy uses global information to optimize the controllable resources such as intelligent soft switches and distributed power sources globally, but excessive data volume will bring heavy communication and data processing burdens and increase time delay; in addition, sometimes due to Privacy and security considerations make it difficult to obtain global detailed information, and centralized control will not be suitable at this time. While local control only relies on local measurement information, although it cannot achieve global optimality, it does not require information exchange or remote measurement between nodes, thereby reducing the amount of communication data and the dimension of control variables; and, when distributed When the power generation fluctuates greatly, the local control strategy can respond quickly, thereby quickly suppressing the fluctuation.

At present, most of the research on the operation optimization of intelligent soft switches is carried out for the centralized control strategy of its power output. However, considering the fact that the reactive output of the two converters of the intelligent soft switch does not affect each other due to the isolation of the DC link, the active and reactive outputs can be controlled separately in a centralized manner to realize the centralized and on-site control of the intelligent soft switch. In-situ joint control, so as to achieve the global optimum as much as possible under the premise of reducing the computational burden.

Since the operation optimization of intelligent soft switches has strong time-sequential characteristics, the centralized and local joint control strategy tuning model of time-series active distribution network intelligent soft switches must be used as the basis for solving the optimization problem. The mathematics of this model is essentially a mixed integer nonlinear programming problem, which brings great challenges to the calculation and solution. Therefore, a model and algorithm that can quickly solve the above mixed integer nonlinear programming problem is needed to solve the centralized and local joint control strategy tuning model of the intelligent soft switch in the active distribution network, so as to formulate the centralized control strategy of the intelligent soft switch. And local joint control strategy, including active power centralized control strategy of intelligent soft switch and local voltage and reactive power control strategy.

Contents of the invention

The technical problem to be solved by the present invention is to provide a centralized and on-site joint control strategy setting model for establishing an intelligent soft switch in an active distribution network, and to formulate a centralized and on-site joint control strategy for an intelligent soft switch. and local joint control strategy tuning method.

The technical solution adopted in the present invention is: a centralized and local joint control strategy setting method for an intelligent soft switch, comprising the following steps:

1) According to the selected active power distribution system, input line parameters, load level, network topology connection relationship, system safe operation voltage constraints and branch current limits, access location and capacity of distributed power sources, distributed power sources and loads The prediction curve of the daily operation characteristics, the access position, capacity and parameters of the intelligent soft switch, the initial value of the system reference voltage and reference power;

2) Based on the structure and parameters of the active distribution system, considering the timing characteristics of distributed power output and load, a centralized and local joint control strategy setting model for the intelligent soft switch of the active distribution network is established, including: selecting the root node as the balance node, set the minimum sum of active power distribution system loss and voltage deviation as the objective function, and consider the system power flow constraints, system safety operation constraints, intelligent soft switching operation and capacity constraints respectively;

3) Perform linearization or second-order cone transformation on the objective function and nonlinear constraints in the model described in step 2), so that the centralized and local joint control strategy tuning model of the active distribution network intelligent soft switch is transformed into a second-order cone model;

4) Using the second-order cone programming algorithm to calculate and solve the second-order cone model: the relevant parameters of the centralized and local joint control strategy of the intelligent soft switch, the voltage distribution in the system, the active power adjustment and reactive power of the intelligent soft switch Compensation;

5) Output the solution result of step 4).

In step 2):

(1) The minimum sum of the active power distribution system loss and voltage deviation is the objective function expressed as

min f=αf _L +βf _V (1)

In the formula, α and β are the weight coefficients of the system loss f _L and the system voltage deviation f _V respectively, where the expressions of the system loss f _L and the system voltage deviation f _V are as follows:

In the formula, N _T and N _N are the number of time sections and the total number of system nodes respectively; Ω _b is the set of system branches; V _t,i is the voltage amplitude of node i in period t; R _ij is the resistance of branch ij, I _{t, ij} is the current amplitude flowing from node i to node j during the t period; is the active power loss value of the intelligent soft switch on node i in the period t; is the expected voltage range of V t, _i , when V _t,i is not in this range, the objective function f _V is used to reduce the voltage deviation;

(2) The power flow constraint of the system is expressed as

In the formula, Ω _b is the set of all branches in the system; P _t,ij is the active power flowing from node i to node j during t period, Q _t,ij is the reactive power flowing from node i to node j during t period; P _t ,ij _ik is the active power flowing from node i to node k in period t, Q _t,ik is the reactive power flowing from node i to node k in period t; R _ij is the resistance of branch ij, X _ij is the reactance of branch ij; I _{t , ij} is the current amplitude of node i flowing to node j in period t; V _t,i is the voltage amplitude of node i in period t, V _t,j is the voltage amplitude of node j in period t; P _t,i is the amplitude of node j in period t The sum of active power injected on node i, Respectively, the active power injected by the distributed power supply, the active power injected by the intelligent soft switch, and the active power consumed by the load on node i during the t period, Q _t,i is the sum of the reactive power injected on the node i during the t period, Respectively, the reactive power injected by the distributed power supply, the reactive power injected by the intelligent soft switch, and the reactive power consumed by the load on node i during the period t;

(3) The active power centralized control constraint of the intelligent soft switch is expressed as

In the formula, with are the active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss values of the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss coefficients of the intelligent soft switch on nodes i and j in the period t, respectively; with are the reactive power injected by the intelligent soft switch on nodes i and j in period t, respectively;

(4) The local voltage and reactive power control constraints of the intelligent soft switch are expressed as

In the formula, with Respectively, the maximum value of reactive power injected by intelligent soft switch on nodes i and j in period t; and g(V _t,j ) constitute the expression of the local voltage and reactive power control strategy of the intelligent soft switch, and g(V _t,j ) respectively have regulation dead zones with At this time, the reactive power generated by the intelligent soft switch is 0var; the following formulas respectively constitute and g(V _t,j ):

Step 3) includes:

(1) Use U _2,t,i and I _{2,t,ij to} replace the quadratic terms in the objective function, system power flow constraints and system operation constraints with Linearize the objective function and system power flow constraints:

(V ^min ) ² ≤U _2,t,i ≤(V ^max ) ² (20)

I _2,t,ij ≤(I ^max ) ² (21)

In the formula, Ω _b is the set of branches; P _t,ij is the active power flowing from node i to node j in period t, Q _t,ij is the reactive power flowing from node i to node j in period t; P _t,ik is t Active power flowing from node i to node k in time period, Q _t,ik is the reactive power flowing from node i to node k in time period t; R _ij is the resistance of branch ij, X _ij is the reactance of branch ij; P _t,i is The sum of active power injected on node i in period t, Q _t,i is the sum of reactive power injected on node i in period t, Respectively, the reactive power injected by the distributed power source and the reactive power consumed by the load on node i during the period t; is the active power loss value of the intelligent soft switch on node i in the period t; V ^max and V ^min are the maximum allowable voltage value and the minimum allowable voltage value of the system; I ^max is the maximum allowable current value of the branch; _NT and N _N are the time section and the total number of system nodes; is the minimum value of the desired voltage for V _t,i , is the maximum value of the expected voltage of V _t,i ;

(2) The objective function f _V contains an absolute value item |U _2,t,i -1|, introduces auxiliary variables A _t,i , and adds constraints:

At _t,i ≥ 0 (27);

(3) Expression of local voltage and reactive power control strategy of intelligent soft switch and g(V _t,j ) are nonlinear expressions, and piecewise linearization is used to realize the and g(V _t,j ); by introducing auxiliary variables a _t,i,n n=1,2,…,6, d _t,i,n n=1,2,…,5 and a _t,j,n n=1,2,…,6, d _t,j,n n=1,2,…,5, approximated by line segments and the curve defined by g(V _t,j ) as follows:

a _t,i,1 ≤d _t,i,1 ,a _t,i,6 ≤d _t,i,5 (30)

a _t,i,n ≤d _t,i,n +d _t,i,n-1 ,n=2,3,4,5 (31)

a _t,i,n ≥0,d _t,i,n ∈{0,1} (32)

In the formula, a _t,i,n n=1,2,…,6 are continuous variables, d _t,i,n n=1,2,…,5 are integer variables; with respectively The curve adjusts the minimum and maximum values of the dead zone;

g(V _t,j )=a _t,j,1 +a _t,j,2 -a _t,j,5 -a _t,j,6 (34)

a _t,j,1 ≤d _t,j,1 ,a _t,j,6 ≤d _t,j,5 (36)

a _t,j,n ≤d _t,j,n +d _t,j,n-1 ,n=2,3,4,5 (37)

a _t,j,n ≥ 0,d _t,j,n ∈ {0,1} (38)

In the formula, a _{t, j, n} n=1,2,...,6 are continuous variables, d _{t, j, n} n=1,2,...,5 are integer variables; with Adjust the minimum and maximum values of the dead zone for the g(V _t,j ) curve, respectively;

Introduce auxiliary integer variables c _i,1 , c _i,2 and c _j,1 , c _j,2 respectively to convert the non-linear product term with linearized, then with Respectively expressed as:

c _i,1 ≤ c i _,2 (42)

c _j,1 ≤ c j _,2 (45)

a _t,i,3 c _i,1 , a _t,i,4 c _i,2 and a _t,j,3 c _j,1 , a _t,j,4 c _j,2 are still nonlinear product terms, so Introduce binary variables l _i,1,m , l _i,2,m and l _j,1,m , l _j,2,m m=0,1,...,4 to represent a _t,i,3 c _i,1 , a _t,i,4 c _i,2 and a _t,j,3 c _j,1 , a _t,j,4 c _j,2 ;

Introducing auxiliary variables w _t,i,1,m = _at,i,3 l _i,1,m , w _t,i,2,m = _at,i,4 l _i,2,m and w _{t, j,1,m} ＝a _t,j,3 l _j,1,m 、w _t,j,2,m ＝a _t,j,4 l _j,2,m , and take M as 1000, and add the following constraint:

a _t,i,3 -(1 _i,1,m )M≤w _t,i,1,m ≤a t, _i,3 (50)

0≤w _t,i,1,m ≤l _i,1,m M (51)

a _t,i,4 -(1-l _i,2,m )M≤w _t,i,2,m ≤a t, _i,4 (52)

0≤w _t,i,2,m ≤l _i,2,m M (53)

a _t,j,3 -(1-l _j,1,m )M≤w _t,j,1,m ≤a t, _j,3 (54)

0≤w _t,j,1,m ≤l _j,1,m M (55)

a _t,j,4 -(1-l _j,2,m )M≤w _t,j,2,m ≤a t, _j,4 (56)

0≤w _t,j,2,m ≤l _j,2,m M (57)

(4) Convex relaxation is performed on the loss constraint of the intelligent soft switch, and then the rotating cone constraint is obtained:

(5) The constraint condition Perform linearization, replacing quadratic terms with U _2,t,i and I _2,t,ij with

Further convex relaxation is performed to obtain the second-order cone constraint formula:

In the formula, with are the active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss values of the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss coefficients of the intelligent soft switch on nodes i and j in the period t, respectively; with are the reactive power injected by the intelligent soft switch on nodes i and j in period t, respectively, with are the access capacity of the intelligent soft-switching two-terminal converter connected between node i and node j during the period t, respectively.

A centralized and local combined control strategy setting method for an intelligent soft switch of the present invention is based on solving the problem of setting a centralized and local combined control strategy for an intelligent soft switch under continuous time series, fully considering the randomness of distributed power sources and loads The centralized and local joint control strategy tuning model of the intelligent soft switch in active distribution network is established, and the solution is solved by the second-order cone programming method, and the centralized and local joint control strategy of the intelligent soft switch is obtained.

Description of drawings

Fig. 1 is the centralized and on-the-spot combined control strategy setting method flowchart of intelligent soft switch of the present invention;

Figure 2 is the structure diagram of the improved PG&E69 node calculation example;

Figure 3 is the prediction curve of distributed power supply and load operation characteristics;

Fig. 4a is the local voltage and reactive power control strategy of the converter at node 27 of the intelligent soft switch 1 after setting;

Fig. 4b is the local voltage and reactive power control strategy of the converter at node 54 of the intelligent soft switch 1 after setting;

Fig. 4c is the local voltage and reactive power control strategy of the converter at node 35 of the intelligent soft switch 2 after setting;

Fig. 4d is the local voltage and reactive power control strategy of the converter at node 48 of the intelligent soft switch 2 after setting;

Fig. 5a is the active power transmission situation of the intelligent soft switch 1 under scheme II;

Fig. 5b is the active power transmission situation of the intelligent soft switch 2 under scheme II;

Fig. 6a is the reactive power compensation situation of the intelligent soft switch 1 under scheme II;

Fig. 6b is the reactive power compensation situation of the intelligent soft switch 2 under scheme II;

Fig. 7 a is the voltage distribution situation at node 35 before and after optimization;

Fig. 7b shows the voltage distribution at node 54 before and after optimization.

detailed description

A centralized and local combined control strategy setting method for an intelligent soft switch of the present invention will be described in detail below in conjunction with the embodiments and the accompanying drawings.

As shown in Figure 1, a centralized and local joint control strategy setting method of an intelligent soft switch of the present invention comprises the following steps:

1) According to the selected active power distribution system, input line parameters, load level, network topology connection relationship, system safe operation voltage constraints and branch current limits, access location and capacity of distributed power sources, distributed power sources and loads The prediction curve of the daily operation characteristics, the access position, capacity and parameters of the intelligent soft switch, the initial value of the system reference voltage and reference power;

2) Based on the structure and parameters of the active distribution system provided in step 1), considering the timing characteristics of distributed power output and load, a centralized and local joint control strategy setting model for intelligent soft switches in active distribution networks is established, including: Select the root node as the balance node, set the minimum sum of the active power distribution system loss and voltage deviation as the objective function, and consider the system power flow constraints, system safety operation constraints, intelligent soft switching operation and capacity constraints respectively; where:

(1) The minimum sum of the active power distribution system loss and voltage deviation is the objective function expressed as

min f=αf _L +βf _V (1)

In the formula, α and β are the weight coefficients of the system loss f _L and the system voltage deviation f _V respectively, where the expressions of the system loss f _L and the system voltage deviation f _V are as follows:

In the formula, N _T and N _N are the number of time sections and the total number of system nodes respectively; Ω _b is the set of system branches; V _t,i is the voltage amplitude of node i in period t; R _ij is the resistance of branch ij, I _{t, ij} is the current amplitude flowing from node i to node j during the t period; is the active power loss value of the intelligent soft switch on node i in the period t; is the expected voltage range of V t, _i , when V _t,i is not in this range, the objective function f _V is used to reduce the voltage deviation;

(2) The power flow constraint of the system is expressed as

In the formula, Ω _b is the set of all branches in the system; P _t,ij is the active power flowing from node i to node j during t period, Q _t,ij is the reactive power flowing from node i to node j during t period; P _t ,ij _ik is the active power flowing from node i to node k in period t, Q _t,ik is the reactive power flowing from node i to node k in period t; R _ij is the resistance of branch ij, X _ij is the reactance of branch ij; I _{t , ij} is the current amplitude of node i flowing to node j in period t; V _t,i is the voltage amplitude of node i in period t, V _t,j is the voltage amplitude of node j in period t; P _t,i is the amplitude of node j in period t The sum of active power injected on node i, Respectively, the active power injected by the distributed power supply, the active power injected by the intelligent soft switch, and the active power consumed by the load on node i during the t period, Q _t,i is the sum of the reactive power injected on the node i during the t period, Respectively, the reactive power injected by the distributed power supply, the reactive power injected by the intelligent soft switch, and the reactive power consumed by the load on node i during the period t;

(3) The safe operation constraints of the system are expressed as

In the formula, V ^max and V ^min are the maximum allowable voltage value and the minimum allowable voltage value of the system; V _t,i is the voltage amplitude of node i during t period; I _t,ij is the current amplitude of node i flowing to node j during t period ; I ^max is the maximum allowable current value of the branch;

(4) The operating constraints of the intelligent soft switch are expressed as

In the formula, with are the active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with are the reactive power injected by the intelligent soft switch on nodes i and j in period t, respectively; with Respectively, the access capacity of the smart soft-switch two-terminal converter connected between node i and node j during the period t; with are the maximum active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with Respectively, the maximum value of reactive power injected by intelligent soft switch on nodes i and j in period t;

(5) The active power centralized control constraint of the intelligent soft switch is expressed as

In the formula, with are the active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss values of the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss coefficients of the intelligent soft switch on nodes i and j in the period t, respectively; with are the reactive power injected by the intelligent soft switch on nodes i and j in period t, respectively;

(6) The local voltage and reactive power control constraints of the intelligent soft switch are expressed as

In the formula, with Respectively, the maximum value of reactive power injected by intelligent soft switch on nodes i and j in period t; and g(V _t,j ) constitute the expression of the local voltage and reactive power control strategy of the intelligent soft switch, and g(V _t,j ) respectively have regulation dead zones with At this time, the reactive power generated by the intelligent soft switch is 0var; the following formulas respectively constitute and g(V _t,j ):

3) Perform linearization or second-order cone transformation on the objective function and nonlinear constraints in the model described in step 2), so that the centralized and local joint control strategy tuning model of the active distribution network intelligent soft switch is transformed into a second-order Cone model; includes:

(1) Use U _2,t,i and I _{2,t,ij to} replace the quadratic terms in the objective function, system power flow constraints and system operation constraints with Linearize the objective function, system power flow constraints, and system operating constraints:

(V ^min ) ² ≤U _2,t,i ≤(V ^max ) ² (28)

I _2,t,ij ≤(I ^max ) ² (29)

In the formula, Ω _b is the set of branches; P _t,ij is the active power flowing from node i to node j in period t, Q _t,ij is the reactive power flowing from node i to node j in period t; P _t,ik is t Active power flowing from node i to node k in time period, Q _t,ik is the reactive power flowing from node i to node k in time period t; R _ij is the resistance of branch ij, X _ij is the reactance of branch ij; P _t,i is The sum of active power injected on node i in period t, Q _t,i is the sum of reactive power injected on node i in period t, Respectively, the reactive power injected by the distributed power source and the reactive power consumed by the load on node i during the period t; is the active power loss value of the intelligent soft switch on node i in the period t; V ^max and V ^min are the maximum allowable voltage value and the minimum allowable voltage value of the system; I ^max is the maximum allowable current value of the branch; _NT and N _N are the time section and the total number of system nodes; is the minimum value of the desired voltage for V _t,i , is the maximum value of the expected voltage of V _t,i ;

(2) The objective function f _V contains an absolute value item |U _2,t,i -1|, introduces auxiliary variables A _t,i , and adds constraints:

At _t,i ≥ 0 (35);

(3) Expression of local voltage and reactive power control strategy of intelligent soft switch and g(V _t,j ) are nonlinear expressions, and piecewise linearization is used to realize the and g(V _t,j ); by introducing auxiliary variables a _t,i,n n=1,2,…,6, d _t,i,n n=1,2,…,5 and a _t,j,n n=1,2,…,6, d _t,j,n n=1,2,…,5, approximated by line segments and the curve defined by g(V _t,j ) as follows:

a _t,i,1 ≤d _t,i,1 ,a _t,i,6 ≤d _t,i,5 (38)

a _t,i,n ≤d _t,i,n +d _t,i,n-1 ,n=2,3,4,5 (39)

a _t,i,n ≥0,d _t,i,n ∈{0,1} (40)

In the formula, a _t,i,n n=1,2,…,6 are continuous variables, d _t,i,n n=1,2,…,5 are integer variables; with respectively The curve adjusts the minimum and maximum values of the dead zone;

g(V _t,j )=a _t,j,1 +a _t,j,2 -a _t,j,5 -a _t,j,6 (42)

a _t,j,1 ≤d _t,j,1 ,a _t,j,6 ≤d _t,j,5 (44)

a _t,j,n ≤d _t,j,n +d _t,j,n-1 ,n=2,3,4,5 (45)

a _t,j,n ≥ 0,d _t,j,n ∈ {0,1} (46)

In the formula, a _{t, j, n} n=1,2,...,6 are continuous variables, d _{t, j, n} n=1,2,...,5 are integer variables; with Adjust the minimum and maximum values of the dead zone for the g(V _t,j ) curve, respectively;

Introduce auxiliary integer variables c _i,1 , c _i,2 and c _j,1 , c _j,2 respectively to convert the non-linear product term with linearized, then with Respectively expressed as:

c _i,1 ≤ c i _,2 (50)

c _j,1 ≤c _j,2 (53)

a _t,i,3 c _i,1 , a _t,i,4 c _i,2 and a _t,j,3 c _j,1 , a _t,j,4 c _j,2 are still nonlinear product terms, so Introduce binary variables l _i,1,m , l _i,2,m and l _j,1,m , l _j,2,m m=0,1,...,4 to represent a _t,i,3 c _i,1 , a _t,i,4 c _i,2 and a _t,j,3 c _j,1 , a _t,j,4 c _j,2 ;

Introducing auxiliary variables w _t,i,1,m = _at,i,3 l _i,1,m , w _t,i,2,m = _at,i,4 l _i,2,m and w _{t, j,1,m} ＝a _t,j,3 l _j,1,m 、w _t,j,2,m ＝a _t,j,4 l _j,2,m , and take M as 1000, and add the following constraint:

a _t,i,3 -(1-l _i,1,m )M≤w _t,i,1,m ≤a t, _i,3 (58)

0≤w _t,i,1,m ≤l _i,1,m M (59)

a _t,i,4 -(1-l _i,2,m )M≤w _t,i,2,m ≤a t, _i,4 (60)

0≤w _t,i,2,m ≤l _i,2,m M (61)

a _t,j,3 -(1-l _j,1,m )M≤w _t,j,1,m ≤a t, _j,3 (62)

0≤w _t,j,1,m ≤l _j,1,m M (63)

a _t,j,4 -(1-l _j,2,m )M≤w _t,j,2,m ≤a t, _j,4 (64)

0≤w _t,j,2,m ≤l _j,2,m M (65)

(4) Convex relaxation is performed on the loss constraint of the intelligent soft switch, and then the rotating cone constraint is obtained:

(5) The constraint condition Perform linearization, replacing quadratic terms with U _2,t,i and I _2,t,ij with

Further convex relaxation is performed to obtain the second-order cone constraint formula:

In the formula, with are the active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss values of the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss coefficients of the intelligent soft switch on nodes i and j in the period t, respectively; with are the reactive power injected by the intelligent soft switch on nodes i and j in period t, respectively, with are the access capacity of the intelligent soft-switching two-terminal converter connected between node i and node j during the period t, respectively.

4) Using the second-order cone programming algorithm to calculate and solve the second-order cone model: the relevant parameters of the centralized and local joint control strategy of the intelligent soft switch, the voltage distribution in the system, the active power adjustment and reactive power of the intelligent soft switch Compensation;

5) Output the solution result of step 4).

The centralized and local joint control strategy setting method of the intelligent soft switch of the present invention realizes the solution of the centralized and local joint control strategy setting method of the active distribution network intelligent soft switch.

For the example of the present invention, first input the impedance value of the line element in the improved PG&E69 node system, the active power reference value and the power factor of the load element, the network topology connection relationship, the calculation example structure is as shown in Figure 2, and the detailed parameters are shown in Table 1 and Table 2; nodes 33, 35, 52 and 54 are respectively connected to a group of photovoltaic systems with a capacity of 1MVA; set two groups of intelligent soft switches connected to the distribution network between node 27 and node 54, node 35 and node 48 The capacity is 1MVA, and the loss coefficient is 0.01; set the reference voltage of the system to 12.66kV and the reference power to 1MVA, and process each value in the system as per unit; finally set the voltage amplitude of each node (per unit value) The upper and lower limits of safe operation are 1.10 and 0.90, respectively. The expected operating range of node voltage is 0.98p.u.-1.02p.u., and the weight coefficients of system loss and voltage deviation are 0.7 and 0.3 respectively. The prediction curves of distributed power supply and load operating characteristics are shown in Figure 3.

Three schemes are used for comparative analysis. Scheme I does not use control means, scheme II adopts the centralized and local joint control strategy of intelligent soft switch, and scheme III adopts the centralized control strategy of intelligent soft switch. The simulation results are shown in Table 3.

The computer hardware environment for performing optimization calculations is Intel(R) Xeon(R) CPU E5-1620, the main frequency is 3.70GHz, and the memory is 32GB; the software environment is Windows 10 operating system.

The intelligent soft switch centralized and local joint control strategy includes the intelligent soft switch active power centralized control strategy and the local voltage and reactive power strategy. The relevant parameters of the voltage and reactive power control strategy in the centralized and local joint control strategy of intelligent soft switching can be optimized by using the predicted data, as shown in Figure 4. Then the intelligent soft switch can adjust its active power transmission and reactive power compensation in real time according to the centralized and local joint control strategy, as shown in Figure 5 and Figure 6, so as to effectively reduce the voltage deviation and network loss, as shown in Table 3 and Figure 6 7. It can be seen from Figure 7 that when the control means are not used, the access of distributed power sources such as photovoltaics and wind turbines will cause severe voltage fluctuations; The voltage level of each node has been significantly improved, and it is close to the effect of centralized control of intelligent soft switches.

Table 1 Load connection position and power of PG&E69 node example

Table 2 Line parameters of PG&E69 node example

Table 3 Comparison of simulation results under different control strategies

Control Strategy Voltage min/p.u. Maximum voltage/p.u. Network loss/kWh I. Not using control strategies 0.9351 1.0460 1758.7 II. Joint Control Strategy 0.9694 1.0254 1311.6 III. Centralized Control Strategy 0.9701 1.0252 1250.5

Claims (3)

1. a kind of centralized and on-the-spot joint control strategy setting method of intelligent soft switch is characterized in that, comprises the steps: 1) According to the selected active power distribution system, input line parameters, load level, network topology connection relationship, system safe operation voltage constraints and branch current limits, access location and capacity of distributed power sources, distributed power sources and loads The prediction curve of the daily operation characteristics, the access position, capacity and parameters of the intelligent soft switch, the initial value of the system reference voltage and reference power; 2) Based on the structure and parameters of the active distribution system, considering the timing characteristics of distributed power output and load, a centralized and local joint control strategy setting model for the intelligent soft switch of the active distribution network is established, including: selecting the root node as the balance node, set the minimum sum of active power distribution system loss and voltage deviation as the objective function, and consider the system power flow constraints, system safety operation constraints, intelligent soft switching operation and capacity constraints respectively; 3) Perform linearization or second-order cone transformation on the objective function and nonlinear constraints in the model described in step 2), so that the centralized and local joint control strategy tuning model of the active distribution network intelligent soft switch is transformed into a second-order cone model; 4) Using the second-order cone programming algorithm to calculate and solve the second-order cone model: the relevant parameters of the centralized and local joint control strategy of the intelligent soft switch, the voltage distribution in the system, the active power adjustment and reactive power of the intelligent soft switch Compensation; 5) Output the solution result of step 4). 2. the centralized and on-the-spot combined control strategy setting method of a kind of intelligent soft switch according to claim 1, is characterized in that, in step 2): (1) The minimum sum of the active power distribution system loss and voltage deviation is the objective function expressed as minf=αf _L +βf _V (1) In the formula, α and β are the weight coefficients of the system loss f _L and the system voltage deviation f _v , respectively, where the expressions of the system loss f _L and the system voltage deviation f _v are as follows: <mrow><msub><mi>f</mi><mi>L</mi></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>T</mi></msub></msubsup><mrow><mo>(</mo><msub><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mi>j</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mrow></msub><msub><mi>R</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><msubsup><mi>I</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>+</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>N</mi></msub></msubsup><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mo>)</mo></mrow><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>f</mi><mi>V</mi></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>T</mi></msub></msubsup><msubsup><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>N</mi></msub></msubsup><mo>|</mo><mrow><msubsup><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mn>2</mn></msubsup><mo>-</mo><mn>1</mn></mrow><mo>|</mo><mo>,</mo><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>&amp;GreaterEqual;</mo><msubsup><mi>V</mi><mrow><mi>t</mi><mi>h</mi><mi>r</mi></mrow><mi>max</mi></msubsup><mo>|</mo><mo>|</mo><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>&amp;le;</mo><msubsup><mi>V</mi><mrow><mi>t</mi><mi>h</mi><mi>r</mi></mrow><mi>min</mi></msubsup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>3</mn><mo>)</mo></mrow></mrow> In the formula, N _T and N _N are the number of time sections and the total number of system nodes respectively; Ω _b is the set of system branches; V _t,i is the voltage amplitude of node i in period t; R _ij is the resistance of branch ij, I _{t, ij} is the current amplitude flowing from node i to node j during the t period; is the active power loss value of the intelligent soft switch on node i in the period t; is the expected voltage range of V t, _i , when V _t,i is not in this range, the objective function f _V is used to reduce the voltage deviation; (2) The power flow constraint of the system is expressed as <mrow><msub><mo>&amp;Sigma;</mo><mrow><mi>j</mi><mi>i</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mrow></msub><mrow><mo>(</mo><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow></msub><mo>-</mo><msub><mi>R</mi><mrow><mi>j</mi><mi>i</mi></mrow></msub><msubsup><mi>I</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow><mn>2</mn></msubsup><mo>)</mo></mrow><mo>+</mo><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>=</mo><msub><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mi>k</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mi>mrow></msub><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>k</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>4</mn><mo>)</mo></mrow></mrow> <mrow><msub><mo>&amp;Sigma;</mo><mrow><mi>j</mi><mi>i</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mrow></msub><mrow><mo>(</mo><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow></msub><mo>-</mo><msub><mi>X</mi><mrow><mi>j</mi><mi>i</mi></mrow></msub><msubsup><mi>I</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow><mn>2</mn></msubsup><mo>)</mo></mrow><mo>+</mo><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>=</mo><msub><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mi>k</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mi>mrow></msub><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>k</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>5</mn><mo>)</mo></mrow></mrow> <mrow><msubsup><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mn>2</mn></msubsup><mo>-</mo><msubsup><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mn>2</mn></msubsup><mo>+</mo><mrow><mo>(</mo><msubsup><mi>R</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>+</mo><msubsup><mi>X</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>)</mo></mrow><msubsup><mi>I</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>2</mn><mrow><mo>(</mo><msub><mi>R</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>+</mo><msub><mi>X</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>)</mo></mrow><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>6</mn><mo>)</mo></mrow></mrow> <mrow><msubsup><mi>I</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><msubsup><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mn>2</mn></msubsup><mo>=</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>+</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>7</mo>mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>=</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>D</mi><mi>G</mi></mrow></msubsup><mo>+</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>-</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>L</mi><mi>O</mi><mi>A</mi><mi>D</mi></mrow></msubsup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>8</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>=</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>D</mi><mi>G</mi></mrow></msubsup><mo>+</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>-</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>L</mi><mi>O</mi><mi>A</mi><mi>D</mi></mrow></msubsup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>9</mn><mo>)</mo></mrow></mrow> In the formula, Ω _b is the set of all branches in the system; P _t,ij is the active power flowing from node i to node j during t period, Q _t,ij is the reactive power flowing from node i to node j during t period; P _t ,ij _ik is the active power flowing from node i to node k in period t, Q _t,ik is the reactive power flowing from node i to node k in period t; R _ij is the resistance of branch ij, X _ij is the reactance of branch ij; I _{t , ij} is the current amplitude of node i flowing to node j in period t; V _t,i is the voltage amplitude of node i in period t, V _t,j is the voltage amplitude of node j in period t; P _t,i is the amplitude of node j in period t The sum of active power injected on node i, Respectively, the active power injected by the distributed power supply, the active power injected by the intelligent soft switch, and the active power consumed by the load on node i during the t period, Q _t,i is the sum of the reactive power injected on the node i during the t period, Respectively, the reactive power injected by the distributed power supply, the reactive power injected by the intelligent soft switch, and the reactive power consumed by the load on node i during the period t; (3) The active power centralized control constraint of the intelligent soft switch is expressed as <mrow><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>+</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>+</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mo>+</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mo>=</mo><mn>0</mn><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>10</mn><mo>)</mo></mrow></mrow> <mrow><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mo>=</mo><msubsup><mi>A</mi><mi>i</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><msqrt><mrow><msup><mrow><mo>(</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup></mrow></msqrt><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>11</mn><mo>)</mo></mrow></mrow> <mrow><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mo>=</mo><msubsup><mi>A</mi><mi>j</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><msqrt><mrow><msup><mrow><mo>(</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup></mrow></msqrt><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>12</mn><mo>)</mo></mrow></mrow> In the formula, with are the active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss values of the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss coefficients of the intelligent soft switch on nodes i and j in the period t, respectively; with are the reactive power injected by the intelligent soft switch on nodes i and j in period t, respectively; (4) The local voltage and reactive power control constraints of the intelligent soft switch are expressed as <mrow><mfrac><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><msubsup><mi>Q</mi><mi>j</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup></mfrac><mo>=</mo><mi>g</mi><mrow><mo>(</mo><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>)</mo></mrow><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>14</mn><mo>)</mo></mrow></mrow> In the formula, with Respectively, the maximum value of reactive power injected by intelligent soft switch on nodes i and j in period t; and g(V _t,j ) constitute the expression of the local voltage and reactive power control strategy of the intelligent soft switch, and g(V _t,j ) have adjustment dead zones [V _i ^{q, min} , V _i ^{q, max} ] and [V _j ^{q, min} ,V _j ^{q, max} ] respectively, at this time the reactive power generated by intelligent soft switching The power is 0var; the following formulas respectively constitute and g(V _t,j ): <mrow><mi>g</mi><mrow><mo>(</mo><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>)</mo></mrow><mo>=</mo><mfenced open = "{" close = ""><mtable><mtr><mtd><mn>1.0</mn></mtd><mtd><mrow><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>&amp;Element;</mo><mo>&amp;lsqb;</mo><mn>0.8</mn><mo>,</mo><mn>0.9</mn><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mrow><mfrac><mn>1</mn><mrow><mn>0.9</mn><mo>-</mo><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>min</mi></mrow></msubsup></mrow></mfrac><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>+</mo><mfrac><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>min</mi></mrow></msubsup><mrow><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>min</mi></mrow></msubsup><mo>-</mo><mn>0.9</mn></mrow></mfrac></mrow></mtd><mtd><mrow><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>&amp;Element;</mo><mo>&amp;lsqb;</mo><mn>0.9</mn><mo>,</mo><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>min</mi></mrow></msubsup><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mn>0</mn></mtd><mtd><mrow><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>&amp;Element;</mo><mo>&amp;lsqb;</mo><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>min</mi></mrow></msubsup><mo>,</mo><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>max</mi></mrow></msubsup><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mrow><mfrac><mn>1</mn><mrow><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>max</mi></mrow></msubsup><mo>-</mo><mn>1.1</mn></mrow></mi>mfrac><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>+</mo><mfrac><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>max</mi></mrow></msubsup><mrow><mn>1.1</mn><mo>-</mo><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>max</mi></mrow></msubsup></mrow></mfrac></mrow></mtd><mtd><mrow><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>&amp;Element;</mo><mo>&amp;lsqb;</mo><msubsup><mi>V</mi><mi>j</mi><mrow><mi>q</mi><mo>,</mo><mi>max</mi></mrow></msubsup><mo>,</mo><mn>1.1</mn><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mrow><mo>-</mo><mn>1.0</mn></mrow></mtd><mtd><mrow><msub><mi>V</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>&amp;Element;</mo><mo>&amp;lsqb;</mo><mn>1.1</mn><mo>,</mo><mn>1.2</mn><mo>&amp;rsqb;</mo></mrow></mtd></mtr></mtable></mfenced><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>16</mn><mo>)</mo></mrow><mo>.</mo></mrow> 3. the centralized and local joint control strategy setting method of a kind of intelligent soft switch according to claim 1, is characterized in that, step 3) comprises: (1) Use U _2,t,i and I _{2,t,ij to} replace the quadratic terms in the objective function, system power flow constraints and system operation constraints with Linearize the objective function and system power flow constraints: <mrow><msub><mo>&amp;Sigma;</mo><mrow><mi>j</mi><mi>i</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mrow></msub><mrow><mo>(</mo><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow></msub><mo>-</mo><msub><mi>R</mi><mrow><mi>j</mi><mi>i</mi></mrow></msub><msub><mi>I</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow></msub><mo>)</mo></mrow><mo>+</mo><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>=</mo><msub><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mi>k</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi>mi></msub></mrow></msub><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>k</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>17</mn><mo>)</mo></mrow></mrow> <mrow><msub><mo>&amp;Sigma;</mo><mrow><mi>j</mi><mi>i</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mrow></msub><mrow><mo>(</mo><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow></msub><mo>-</mo><msub><mi>X</mi><mrow><mi>j</mi><mi>i</mi></mrow></msub><msub><mi>I</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>j</mi><mi>i</mi></mrow></msub><mo>)</mo></mrow><mo>+</mo><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>=</mo><msub><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mi>k</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi>mi></msub></mrow></msub><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>k</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>18</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>-</mo><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>j</mi></mrow></msub><mo>+</mo><mrow><mo>(</mo><msubsup><mi>R</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>+</mo><msubsup><mi>X</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>)</mo></mrow><msub><mi>I</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>=</mo><mn>2</mn><mrow><mo>(</mo><msub><mi>R</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>+</mo><msub><mi>X</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>)</mo></mrow><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>19</mn><mo>)</mo></mrow></mrow> (V ^min ) ² ≤U _2,t,i ≤(V ^max ) ² (20) I _2,t,ij ≤(I ^max ) ² (21) <mrow><msub><mi>f</mi><mi>L</mi></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>T</mi></msub></msubsup><mrow><mo>(</mo><msub><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mi>j</mi><mo>&amp;Element;</mo><msub><mi>&amp;Omega;</mi><mi>b</mi></msub></mrow></msub><msub><mi>R</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>I</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>+</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>N</mi></msub></msubsup><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mo>)</mo></mrow><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>22</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>f</mi><mi>V</mi></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>T</mi></msub></msubsup><msubsup><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>N</mi></msub></msubsup><mo>|</mo><mrow><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>-</mo><mn>1</mn></mrow><mo>|</mo><mo>,</mo><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>&amp;GreaterEqual;</mo><msup><mrow><mo>(</mo><msubsup><mi>V</mi><mrow><mi>t</mi><mi>h</mi><mi>r</mi></mrow><mi>max</mi></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>|</mo><mo>|</mo><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>&amp;le;</mo><msup><mrow><mo>(</mo><msubsup><mi>V</mi><mrow><mi>t</mi><mi>h</mi><mi>r</mi></mrow><mi>min</mi></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>23</mn><mo>)</mo></mrow></mrow> In the formula, Ω _b is the set of branches; P _t,ij is the active power flowing from node i to node j in period t, Q _t,ij is the reactive power flowing from node i to node j in period t; P _t,ik is t Active power flowing from node i to node k in time period, Q _t,ik is the reactive power flowing from node i to node k in time period t; R _ij is the resistance of branch ij, X _ij is the reactance of branch ij; P _t,i is The sum of active power injected on node i in period t, Q _t,i is the sum of reactive power injected on node i in period t, Respectively, the reactive power injected by the distributed power source and the reactive power consumed by the load on node i during the period t; is the active power loss value of the intelligent soft switch on node i in the period t; V ^max and V ^min are the maximum allowable voltage value and the minimum allowable voltage value of the system; I ^max is the maximum allowable current value of the branch; _NT and N _N are the time section and the total number of system nodes; is the minimum value of the desired voltage for V _t,i , is the maximum value of the expected voltage of V _t,i ; (2) The objective function f _v contains an absolute value item |U _2,t,i -1|, introduce auxiliary variables A _t,i , and add constraints: <mrow><msub><mi>f</mi><mi>V</mi></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>T</mi></msub></msubsup><msubsup><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>N</mi><mi>N</mi></msub></msubsup><msub><mi>A</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>24</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>A</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>&amp;GreaterEqual;</mo><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>-</mo><msup><mrow><mo>(</mo><msubsup><mi>V</mi><mrow><mi>t</mi><mi>h</mi><mi>r</mi></mrow><mi>max</mi></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>25</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>A</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>&amp;GreaterEqual;</mo><mo>-</mo><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>V</mi><mrow><mi>t</mi><mi>h</mi><mi>r</mi></mrow><mi>min</mi></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>26</mn><mo>)</mo></mrow></mrow> At _t,i ≥ 0 (27); (3) Expression of local voltage and reactive power control strategy of intelligent soft switch and g(V _t,j ) are nonlinear expressions, and piecewise linearization is used to realize the and g(V _t,j ); by introducing auxiliary variables a _t,i,n n=1,2,…,6, d _t,i,n n=1,2,…,5 and a _t,j,n n=1,2,…,6, d _t,j,n n=1,2,…,5, approximated by line segments and the curve defined by g(V _t,j ) as follows: V _t,i ＝0.8a _t,i,1 +0.9a _t,i,2 +a _t,i,3 V _i ^q,min +a _t,i,4 V _i ^q,max +1.1a _{t,i ,5} +1.2a _t,i,6 (29) a _t,i,1 ≤d _t,i,1 ,a _t,i,6 ≤d _t,i,5 (30) a _t,i,n ≤d _t,i,n +d _t,i,n-1 ,n=2,3,4,5 (31) a _t,i,n ≥0,d _t,i,n ∈{0,1} (32) <mrow><msubsup><mo>&amp;Sigma;</mo><mrow><mi>n</mi><mo>=</mo><mn>1</mn></mrow><mn>6</mn></msubsup><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mo>,</mo><mi>n</mi></mrow></msub><mo>=</mo><mn>1</mn><mo>,</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>n</mi><mo>=</mo><mn>1</mn></mrow><mn>5</mn></msubsup><msub><mi>d</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mo>,</mo><mi>n</mi></mrow></msub><mo>=</mo><mn>1</mn><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>33</mn><mo>)</mo></mrow></mrow> In the formula, a _t,i,n n=1,2,…,6 are continuous variables, d _t,i,n n=1,2,…,5 are integer variables; V _i ^q,min and V _i ^{q , max} are respectively The curve adjusts the minimum and maximum values of the dead zone; g(V _t,j )=a _t,j,1 +a _t,j,2 -a _t,j,5 -a _t,j,6 (34) V _t,j ＝0.8a _t,j,1 +0.9a _t,j,2 +a _t,j,3 V _j ^q,min +a _t,j,4 V _j ^q,max +1.1a _{t,j ,5} +1.2a _t,j,6 (35) a _t,j,1 ≤d _t,j,1 ,a _t,j,6 ≤d _t,j,5 (36) a _t,j,n ≤d _t,j,n +d _t,j,n-1 ,n=2,3,4,5 (37) a _t,j,n ≥ 0,d _t,j,n ∈ {0,1} (38) <mrow><msubsup><mo>&amp;Sigma;</mo><mrow><mi>n</mi><mo>=</mo><mn>1</mn></mrow><mn>6</mn></msubsup><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mo>,</mo><mi>n</mi></mrow></msub><mo>=</mo><mn>1</mn><mo>,</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>n</mi><mo>=</mo><mn>1</mn></mrow><mn>5</mn></msubsup><msub><mi>d</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mo>,</mo><mi>n</mi></mrow></msub><mo>=</mo><mn>1</mn><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>39</mn><mo>)</mo></mrow></mrow> In the formula, a _t,j,n n=1,2,…,6 are continuous variables, d _t,j,n n=1,2,…,5 are integer variables; V _j ^q,min and V _j ^{q ,max} are the minimum and maximum values of the g(V _t,j ) curve adjustment dead zone respectively; Introduce the auxiliary integer variables c _i,1 , c _i,2 and c _j,1 , c _j,2 to make the non-linear product term a _t,i,3 V _i ^q,min , a _t,i,4 V _i ^{q ,max} and a _t,j,3 V _j ^q,min , a _t,j,4 V _j ^q,max are linearized, then a _t,i,3 V _i ^q,min , a _t,i,4 V _i ^q,max and a _t,j,3 V _j ^q,min , a _t,j,4 V _j ^q,max are respectively expressed as: a _t,i,3 V _i ^q,min ＝0.90a _t,i,3 +0.01a t, _i,3 c _i,1 ,0≤ci _,1 ≤20 (40) a _t,i,4 V _i ^q,max ＝0.90a _t,i,4 +0.01a t, _i,4 c _i,2 ,0≤c _i,2 ≤20 (41) c _i,1 ≤ c i _,2 (42) a _t,j,3 V _j ^q,min =0.90a _t,j,3 +0.01a t, _j,3 c _j,1 ,0≤c _j,1 ≤20 (43) a _t,j,4 V _j ^q,max ＝0.90a _t,j,4 +0.01a t, _j,4 c _j,2 ,0≤c _j,2 ≤20 (44) c _j,1 ≤ c j _,2 (45) a _t,i,3 c _i,1 , a _t,i,4 c _i,2 and a _t,j,3 c _j,1 , a _t,j,4 c _j,2 are still nonlinear product terms, so Introduce binary variables l _i,1,m , l _i,2,m and l _j,1,m , l _j,2,m m=0,1,...,4 to represent a _t,i,3 c _i,1 , a _t,i,4 c _i,2 and a _t,j,3 c _j,1 , a _t,j,4 c _j,2 ; <mrow><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mo>,</mo><mn>3</mn></mrow></msub><msub><mi>c</mi><mrow><mi>i</mi><mo>,</mo><mn>1</mn></mrow></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>m</mi><mo>=</mo><mn>0</mn></mrow><mn>4</mn></msubsup><msup><mn>2</mn><mi>m</mi></msup><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mo>,</mo><mn>3</mn></mrow></msub><msub><mi>l</mi><mrow><mi>i</mi><mo>,</mo><mn>1</mn><mo>,</mo><mi>m</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>46</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mo>,</mo><mn>4</mn></mrow></msub><msub><mi>c</mi><mrow><mi>i</mi><mo>,</mo><mn>2</mn></mrow></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>m</mi><mo>=</mo><mn>0</mn></mrow><mn>4</mn></msubsup><msup><mn>2</mn><mi>m</mi></msup><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mo>,</mo><mn>4</mn></mrow></msub><msub><mi>l</mi><mrow><mi>i</mi><mo>,</mo><mn>2</mn><mo>,</mo><mi>m</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>47</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mo>,</mo><mn>3</mn></mrow></msub><msub><mi>c</mi><mrow><mi>j</mi><mo>,</mo><mn>1</mn></mrow></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>m</mi><mo>=</mo><mn>0</mn></mrow><mn>4</mn></msubsup><msup><mn>2</mn><mi>m</mi></msup><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mo>,</mo><mn>3</mn></mrow></msub><msub><mi>l</mi><mrow><mi>j</mi><mo>,</mo><mn>1</mn><mo>,</mo><mi>m</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>48</mn><mo>)</mo></mrow></mrow> <mrow><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mo>,</mo><mn>4</mn></mrow></msub><msub><mi>c</mi><mrow><mi>j</mi><mo>,</mo><mn>2</mn></mrow></msub><mo>=</mo><msubsup><mo>&amp;Sigma;</mo><mrow><mi>m</mi><mo>=</mo><mn>0</mn></mrow><mn>4</mn></msubsup><msup><mn>2</mn><mi>m</mi></msup><msub><mi>a</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi><mo>,</mo><mn>4</mn></mrow></msub><msub><mi>l</mi><mrow><mi>j</mi><mo>,</mo><mn>2</mn><mo>,</mo><mi>m</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>49</mn><mo>)</mo></mrow></mrow> Introducing auxiliary variables w _t,i,1,m = _at,i,3 l _i,1,m , w _t,i,2,m = _at,i,4 l _i,2,m and w _{t, j,1,m} ＝a _t,j,3 l _j,1,m 、w _t,j,2,m ＝a _t,j,4 l _j,2,m , and take M as 1000, and add the following constraint: a _t,i,3 -(1-l _i,1,m )M≤w _t,i,1,m ≤a t, _i,3 (50) 0≤w _t,i,1,m ≤l _i,1,m M (51) a _t,i,4 -(1-l _i,2,m )M≤w _t,i,2,m ≤a t, _i,4 (52) 0≤w _t,i,2,m ≤l _i,2,m M (53) a _t,j,3 -(1-l _j,1,m )M≤w _t,j,1,m ≤a t, _j,3 (54) 0≤w _t,j,1,m ≤l _j,1,m M (55) a _t,j,4 -(1-l _j,2,m )M≤w _t,j,2,m ≤a t, _j,4 (56) 0≤w _t,j,2,m ≤l _j,2,m M (57) (4) Convex relaxation is performed on the loss constraint of the intelligent soft switch, and then the rotating cone constraint is obtained: <mrow><msup><mrow><mo>(</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>&amp;le;</mo><mn>2</mn><mfrac><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mrow><msqrt><mn>2</mn></msqrt><msubsup><mi>A</mi><mi>i</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup></mrow></mfrac><mfrac><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mrow><msqrt><mn>2</mn></msqrt><msubsup><mi>A</mi><mi>i</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup></mrow></mfrac><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>58</mn><mo>)</mo></mrow></mrow> <mrow><msup><mrow><mo>(</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>&amp;le;</mo><mn>2</mn><mfrac><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mrow><msqrt><mn>2</mn></msqrt><msubsup><mi>A</mi><mi>j</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup></mrow></mfrac><mfrac><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi><mo>,</mo><mi>L</mi></mrow></msubsup><mrow><msqrt><mn>2</mn></msqrt><msubsup><mi>A</mi><mi>j</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup></mrow></mfrac><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>59</mn><mo>)</mo></mrow></mrow> <mrow><msup><mrow><mo>(</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>&amp;le;</mo><mn>2</mn><mfrac><msubsup><mi>S</mi><mi>i</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><msqrt><mn>2</mn></msqrt></mfrac><mfrac><msubsup><mi>S</mi><mi>i</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><msqrt><mn>2</mn></msqrt></mfrac><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>60</mn><mo>)</mo></mrow></mrow> <mrow><msup><mrow><mo>(</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><mo>)</mo></mrow><mn>2</mn></msup><mo>&amp;le;</mo><mn>2</mn><mfrac><msubsup><mi>S</mi><mi>j</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><msqrt><mn>2</mn></msqrt></mfrac><mfrac><msubsup><mi>S</mi><mi>j</mi><mrow><mi>S</mi><mi>O</mi><mi>P</mi></mrow></msubsup><msqrt><mn>2</mn></msqrt></mfrac><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>61</mn><mo>)</mo></mrow></mrow> (5) The constraint condition Perform linearization, replacing quadratic terms with U _2,t,i and I _2,t,ij with <mrow><msub><mi>I</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>=</mo><msubsup><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>+</mo><msubsup><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>62</mn><mo>)</mo></mrow></mrow> Further convex relaxation is performed to obtain the second-order cone constraint formula: <mrow><msub><mrow><mo>||</mo><mtable><mtr><mtd><mrow><mn>2</mn><msub><mi>P</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub></mrow></mtd></mtr><mtr><mtd><mrow><mn>2</mn><msub><mi>Q</mi><mrow><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>I</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>-</mo><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></mtd></mtr></mo>mtable><mo>||</mo></mrow><mn>2</mn></msub><mo>&amp;le;</mo><msub><mi>I</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi><mi>j</mi></mrow></msub><mo>+</mo><msub><mi>U</mi><mrow><mn>2</mn><mo>,</mo><mi>t</mi><mo>,</mo><mi>i</mi></mrow></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>63</mn><mo>)</mo></mrow></mrow> In the formula, with are the active power injected by the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss values of the intelligent soft switch on nodes i and j in period t, respectively; with are the active power loss coefficients of the intelligent soft switch on nodes i and j in the period t, respectively; with are the reactive power injected by the intelligent soft switch on nodes i and j in period t, respectively, with are the access capacity of the intelligent soft-switching two-terminal converter connected between node i and node j during the period t, respectively.