CN111969662A - Data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method - Google Patents

Data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method Download PDF

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CN111969662A
CN111969662A CN202010844136.6A CN202010844136A CN111969662A CN 111969662 A CN111969662 A CN 111969662A CN 202010844136 A CN202010844136 A CN 202010844136A CN 111969662 A CN111969662 A CN 111969662A
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冀浩然
霍彦达
王成山
宋关羽
于浩
赵金利
李鹏
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Abstract

A data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method comprises the following steps: inputting basic parameter information of the system according to the selected active power distribution network; respectively obtaining the voltage of each node of the power distribution network and the measured variable quantity of active and reactive power, and determining the effective regulation area of each intelligent soft switch; defining a region close to a source node as an upstream region, and defining a region close to the tail end of a branch as a downstream region; establishing an intelligent soft switch self-adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of the upstream region as targets, and solving; establishing an intelligent soft switch self-adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of the downstream region as targets, and solving; judging whether the time-shifting step number of the control domain reaches a set value or not; and if the current time t reaches the total control duration, ending the adaptive voltage control process. The invention is beneficial to improving the voltage optimization control effect of the power distribution network, thereby improving the safety and reliability of the power distribution network.

Description

Data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method
Technical Field
The invention relates to a voltage control method. In particular to a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method.
Background
The power distribution network is a junction for receiving a power generation system, a power transmission system and a user side, and plays an important role in safe, reliable and economic power supply, and the voltage level of the power distribution network directly influences the safety and reliability of equipment on the user side. The optimal control of the voltage of the power distribution network is beneficial to improving the satisfaction degree of users, and the importance of the optimal control is self-evident. The intelligent soft switching technology is a new power electronic technology, and can realize the optimal operation of a system by improving reactive power distribution and active power flow in a network, thereby becoming a current research hotspot. The current power distribution system is increasingly large in scale, the operation task is increasingly complex, and the active and reactive power output of intelligent soft switches in all areas in the network can be dynamically adjusted through reasonable power distribution network partitions, so that the problem of power distribution network voltage optimization can be effectively solved.
Most of traditional power distribution network voltage optimization control methods adopt mathematical models to describe the state of a power distribution network. However, in actual operation, accurate power distribution network parameters are difficult to obtain due to the influences of power distribution network operation conditions, line environments and the like; in addition, after a large amount of renewable energy is accessed at high permeability, the operating characteristics of the renewable energy are greatly influenced by the environment, and the output has obvious randomness and fluctuation. Therefore, it is difficult to describe the state of the distribution network with an accurate mathematical model. This also makes voltage optimization methods that rely on mathematical models of power distribution networks problematic.
In recent years, power distribution system measurement and communication systems have been rapidly developed. Including wide area measurement systems, synchronous phasor measurement systems, advanced measurement systems, etc., have become mature and widely used; communication systems have enabled real-time transmission of data. The operation data of the power distribution network contains a large amount of information, and important information contained in the operation data can be fully mined by a data driving method. The power distribution network model is constructed by using a data driving method, and the method has the advantages of avoiding complicated mathematical models, simplifying the solving process and the like. According to the data-driven multi-intelligent-soft-switch partition cooperative operation optimization method, a detailed mathematical model of the power distribution network is not needed to be known, a data model is established in a partition mode according to real-time operation data of the power distribution network, information interaction is carried out in an interval mode, the goal of partition-area cooperative power distribution network voltage control can be achieved, the problem that the overall information amount is increased sharply due to centralized control is solved, and the calculation efficiency is guaranteed.
Therefore, a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method is researched and mastered, a new thought is provided for the problem of power distribution network voltage partition cooperative operation optimization, the effect of power distribution network voltage optimization control is improved, and the safety and reliability of a power distribution network are improved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method capable of solving the problem of multi-intelligent soft switch partition cooperative operation optimization.
The technical scheme adopted by the invention is as follows: a data-driven multi-intelligent soft switch partition cooperation adaptive voltage control method comprises the following steps:
1) inputting basic parameter information of the system according to the selected active power distribution network, wherein the basic parameter information comprises the following steps: the access position, capacity and active/reactive output power upper limit of the intelligent soft switch, the node voltage reference value, the control error precision, the initial value of the pseudo Jacobian matrix of the controller and the initial time t0Optimizing the total control time length to be T, and setting the current time T to be T0The predicted domain time interval delta T is s delta T, the domain time interval delta T is controlled, and the control time-shifting step number k is 1;
2) according to the active power distribution network given in the step 1), in an optimization time period [ t, t + n delta t ], adjusting active transmission power and reactive power output levels of two ports of an intelligent soft switch, respectively obtaining the voltage of each node of the power distribution network and the variable quantity of active and reactive power measurement, calculating the pseudo-electric distance between nodes of the active power distribution network, clustering the nodes by using each intelligent soft switch access node as a clustering center and adopting a clustering density peak method, and determining an effective adjustment area of each intelligent soft switch;
3) according to the effective adjusting area of each intelligent soft switch determined in the step 2), defining an area close to a source node as an upstream area, and an area close to the tail end of a branch as a downstream area, judging whether the maximum values of the voltage control error of each area and the boundary iteration convergence error of the adjacent subarea meet the precision requirement, if so, turning to the step 6), and if not, turning to the next step;
4) in a control time period [ t, t + delta t ], establishing an intelligent soft switch adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of an upstream region as targets, solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, sending the intelligent soft switch adaptive voltage control strategy to the upstream region intelligent soft switch to obtain the voltage measurement of each region node, and transmitting the voltage measurement of the boundary node and the active and reactive power measurement of a boundary branch circuit to a downstream region as boundary information;
5) establishing an intelligent soft switch adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of a downstream region as targets, solving the intelligent soft switch adaptive voltage control model driven by the downstream region data by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, issuing the intelligent soft switch adaptive voltage control strategy to the downstream region, and transmitting boundary node voltage measurement and boundary branch active and reactive power requirements as boundary information to an upstream region;
6) updating the control time t to be t + delta t, the time-shifting step number k to be k +1, judging whether the time-shifting step number k in the control domain reaches a set value s, and if not, returning to the step 4); if yes, entering step 7);
7) and judging whether the current time T reaches the total control duration T, if not, making k equal to 1, returning to the step 2), and if so, ending the adaptive voltage control process.
According to the data-driven multi-intelligent-soft-switch-partition cooperative adaptive voltage control method, the unknown parameters of the power distribution network line, the distributed power supply position and the uncertainty of the output condition are comprehensively considered, the multi-intelligent-soft-switch-partition cooperative operation optimization strategy of data driving is adopted, the multi-intelligent-soft-switch-partition cooperative operation optimization problem is solved, the voltage optimization control effect of the power distribution network is improved, and the safety and the reliability of the power distribution network are improved.
Drawings
FIG. 1 is a flow chart of a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method of the present invention;
fig. 2 is a selected power distribution network topology and 10: 00 area division situation graph;
fig. 3 is 10: 00 active power distribution network voltage change curve;
fig. 4 is 10: 00 voltage change curves of 18 nodes and 33 nodes of the active power distribution network;
fig. 5 is 10: 00 active output force change curve of the intelligent soft switch;
fig. 6 is 10: 00 idle output change curve of intelligent soft switch;
fig. 7 is a 24-hour active output curve of the intelligent soft switch;
FIG. 8 is a 24-hour reactive power output curve for the intelligent soft switch;
FIG. 9 is a graph of 24 hour power loss versus active power distribution network;
fig. 10 is a graph comparing the maximum voltage of the active distribution network for 24 hours.
Detailed Description
The data-driven multi-intelligent soft-switch partition cooperative adaptive voltage control method is described in detail below with reference to embodiments and drawings.
As shown in fig. 1, the data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method of the present invention includes the following steps:
1) according to selected havingThe source power distribution network inputs basic parameter information of a system and comprises the following steps: the access position, capacity and active/reactive output power upper limit of the intelligent soft switch, the node voltage reference value, the control error precision, the initial value of the pseudo Jacobian matrix of the controller and the initial time t0Optimizing the total control time length to be T, and setting the current time T to be T0The predicted domain time interval delta T is n delta T, the domain time interval delta T is controlled, and the control time-shifting step number k is 1;
2) according to the active power distribution network given in the step 1), in an optimization time period [ t, t + n delta t ], adjusting active transmission power and reactive power output levels of two ports of an intelligent soft switch, respectively obtaining the voltage of each node of the power distribution network and the variable quantity of active and reactive power measurement, calculating the pseudo-electric distance between nodes of the active power distribution network, clustering the nodes by using each intelligent soft switch access node as a clustering center and adopting a clustering density peak method, and determining an effective adjustment area of each intelligent soft switch; wherein the content of the first and second substances,
(1) the pseudo-electric distance between the nodes of the active power distribution network is calculated by adopting the following formula:
Figure BDA0002642459030000031
in the formula, sijThe pseudo-electric distance between the node i and the node j of the active power distribution network, alpha and beta are weight coefficients,
Figure BDA0002642459030000032
representing a measure of the magnitude of the voltage at node i,
Figure BDA0002642459030000033
representing the active power variation measurement of node j,
Figure BDA0002642459030000034
represents the measurement of the reactive power variation of the node j,
Figure BDA0002642459030000035
to represent
Figure BDA0002642459030000036
To pair
Figure BDA0002642459030000037
The partial derivative of (a) of (b),
Figure BDA0002642459030000038
to represent
Figure BDA0002642459030000039
To pair
Figure BDA00026424590300000310
The partial derivative of (a) of (b),
Figure BDA00026424590300000311
the method is an active power distribution network node set.
(2) The node clustering by adopting a clustering density peak method is represented as follows:
Figure BDA00026424590300000312
in the formula (I), the compound is shown in the specification,irepresents the node density of the node i,
Figure BDA00026424590300000313
indicating a cut operation, when (·) < 0
Figure BDA00026424590300000314
When (-) is not less than 0
Figure BDA00026424590300000315
Sigma is a set pseudo-electrical distance threshold,
Figure BDA00026424590300000316
set of nodes representing access of intelligent soft switches in an active distribution network, diRepresents the distance of node i to the rest of the nodes in the active distribution network, ciDenotes the cluster density index, sijIs the pseudo-electrical distance between node i and node j of the active distribution network.
3) According to the effective adjusting area of each intelligent soft switch determined in the step 2), defining an area close to a source node as an upstream area, and an area close to the tail end of a branch as a downstream area, judging whether the maximum values of the voltage control error of each area and the boundary iteration convergence error of the adjacent subarea meet the precision requirement, if so, turning to the step 6), and if not, turning to the next step; the voltage control error of each region and the boundary iteration convergence error of the adjacent subareas are expressed as follows:
Figure BDA0002642459030000041
in the formula, R1Indicates the voltage control error of each region, X [ t ]]And X [ t- Δ t]Expressing the intelligent soft switch output at t moment and t-delta t moment, YrefRepresenting the respective zone voltage and net load reference vectors,
Figure BDA0002642459030000042
representing the voltage and payload estimates for each region at time t + Δ t, R2Representing the boundary iterative convergence error, r, of adjacent partitionspRepresents the iterative convergence error of the boundary power, rvThe iterative convergence error of the boundary voltage is represented,
Figure BDA0002642459030000043
and
Figure BDA0002642459030000044
respectively represents the active power transmitted by the boundary line l of the area a and the area b at the time t,and
Figure BDA0002642459030000046
respectively representing the reactive power transmitted by the lines l of the area a and the area b at the time t,
Figure BDA0002642459030000047
Figure BDA0002642459030000048
the voltages at the boundary nodes m and n in the regions a and b at time t are shown, respectively.
4) In a control time period [ t, t + delta t ], establishing an intelligent soft switch adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of an upstream region as targets, solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, sending the intelligent soft switch adaptive voltage control strategy to the upstream region intelligent soft switch to obtain the voltage measurement of each region node, and transmitting the voltage measurement of the boundary node and the active and reactive power measurement of a boundary branch circuit to a downstream region as boundary information; wherein the content of the first and second substances,
(1) the target of the upstream area voltage deviation and the network loss minimum is expressed as follows:
Figure BDA0002642459030000049
in the formula, Xa[t]And Xa[t-Δt]Respectively representing intelligent soft switch output vector X 'in a region a at the time t and the time t-delta t'a[t]The auxiliary variable is represented by a number of variables,
Figure BDA00026424590300000410
representing the voltage reference value of the boundary node n in the area a,
Figure BDA00026424590300000411
and
Figure BDA00026424590300000412
respectively representing the boundary active and reactive power transferred by the downstream region b at the time t-deltat,
Figure BDA00026424590300000413
represents the voltage estimate at the boundary node n of region a at time t + at, represents a weighting factor,
Figure BDA00026424590300000414
representing the voltage and payload reference vectors in region a,
Figure BDA00026424590300000415
representing the voltage and incoming power estimate vectors in region a at time t + deltat,
Figure BDA00026424590300000416
and
Figure BDA00026424590300000417
respectively representing the node voltage and the inflow power estimate in region a at time t + deltat,
Figure BDA00026424590300000418
and
Figure BDA00026424590300000419
respectively, the node voltage and the payload reference value in the region a.
(2) The intelligent soft switch adaptive voltage control model driven by the upstream region data is expressed as follows:
Figure BDA00026424590300000420
in the formula, Ya[t]And
Figure BDA00026424590300000421
a measured value and an estimated value vector, X, respectively representing the voltage and the inflow power of the region a at time ta[t]And Xa[t-Δt]Respectively represents the output vector phi of the intelligent soft switch in the t moment and the t-delta t moment area aa[t]And phia[t-Δt]Respectively representing a pseudo Jacobian matrix of the region a at the time t and the time t-delta t, and mu represents a weight coefficient.
(3) The intelligent soft switch adaptive voltage control strategy obtained by solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method is expressed as follows:
Figure BDA0002642459030000051
in the formula, Xa[t]And Xa[t-Δt]Respectively represents the intelligent soft switch output vector P in the t moment and the t-delta t moment area arA projection operator representing the intelligent soft switch output constraint domain,
Figure BDA0002642459030000052
representing the voltage and payload reference vectors in region a,
Figure BDA0002642459030000053
representing the vector of voltage and inflow power estimates, phi, in region a at time t + deltata[t]And phia[t-Δt]Respectively representing a pseudo Jacobian matrix, DeltaY, of the region a at the time t and the time t-Deltata[t]Represents Ya[t]-Ya[t-Δt]Wherein Y isa[t]And Ya[t-Δt]Vector of measured values, Δ X, representing the voltage and the power flowing in the region a at time t and at-time t- Δ t, respectivelya[t-Δt]Represents Xa[t-Δt]-Xa[t-2Δt]，Xa[t-Δt]And Xa[t-2Δt]Respectively representing intelligent soft switch output vectors in a region a at the time t and the time t-delta t, wherein rho, lambda, mu and eta represent weight coefficients.
5) Establishing an intelligent soft switch adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of a downstream region as targets, solving the intelligent soft switch adaptive voltage control model driven by the downstream region data by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, issuing the intelligent soft switch adaptive voltage control strategy to the downstream region, and transmitting boundary node voltage measurement and boundary branch active and reactive power requirements as boundary information to an upstream region; wherein the content of the first and second substances,
(1) the target of the minimum voltage deviation and network loss of the downstream area is expressed as follows:
Figure BDA0002642459030000054
in the formula, Xb[t]And Xb[t-Δt]Respectively representing intelligent soft switch output vector X 'in a t moment area b and a t-delta t moment area b'b[t]The extension variable is represented by a number of extension variables,
Figure BDA0002642459030000055
representing the voltage reference value of node m in region b,
Figure BDA0002642459030000056
representing the voltage estimate at node m in region b at time t + at, lambda represents a weighting factor,
Figure BDA0002642459030000057
and
Figure BDA0002642459030000058
respectively representing the boundary active and reactive power transferred by the upstream zone a at times t-deltat,
Figure BDA0002642459030000059
representing the voltage and payload reference vectors in region b,
Figure BDA00026424590300000510
representing the voltage and incoming power estimate vectors in region b at time t + deltat,
Figure BDA00026424590300000511
and
Figure BDA00026424590300000512
respectively representing the node voltage and the inflow power estimate in region b at time t + deltat,
Figure BDA00026424590300000513
and
Figure BDA00026424590300000514
respectively, the node voltage and the payload reference value in region b.
(2) The downstream region data-driven intelligent soft switch adaptive voltage control model is expressed as follows:
Figure BDA00026424590300000515
in the formula (I), the compound is shown in the specification,
Figure BDA00026424590300000516
representing the estimated values of voltage and power flowing in the region b at time t + Deltat, Yb[t]And
Figure BDA00026424590300000517
a measured value and an estimated value vector X respectively representing the voltage and the inflow power in the t-time region bb[t]And Xb[t-Δt]Respectively represents the output vector phi of the intelligent soft switch in the t moment and t-delta t moment region bb[t]And phib[t-Δt]And respectively representing a pseudo Jacobian matrix of the region b at the time t and the time t-delta t, and mu represents a weight coefficient.
(3) The intelligent soft-switch adaptive voltage control strategy obtained by solving the intelligent soft-switch adaptive voltage control model driven by the downstream region data by adopting the gradient descent method is expressed as follows:
Figure BDA0002642459030000061
in the formula, Xb[t]And Xb[t-Δt]Respectively representing intelligent soft switch output vectors P in t moment and t-delta t moment regions brA projection operator representing the intelligent soft switch output constraint domain,
Figure BDA0002642459030000062
representing the voltage and payload reference vectors in region b,
Figure BDA0002642459030000063
representing the vector of voltage and inflow power estimates, phi, in region b at time t + deltatb[t]And phib[t-Δt]Respectively representing the t time and t-delta t time regions b pseudo Jacobian matrix, delta Yb[t]Represents Yb[t]-Yb[t-Δt]Wherein Y isb[t]And Yb[t-Δt]Vector of measurements, Δ X, representing the voltage and the power flowing in the region b at time t and at-time t- Δ t, respectivelyb[t-Δt]Represents Xb[t-Δt]-Xb[t-2Δt]，Xb[t-Δt]And Xb[t-2Δt]Respectively representing intelligent soft switch output vectors in a t moment region b and a t-delta t moment region b, wherein rho, lambda, mu and eta represent weight coefficients.
6) Updating the control time t to be t + delta t, the time-shifting step number k to be k +1, judging whether the time-shifting step number k in the control domain reaches a set value s, and if not, returning to the step 4); if yes, entering step 7);
7) and judging whether the current time T reaches the total control duration T, if not, making k equal to 1, returning to the step 2), and if so, ending the adaptive voltage control process.
Specific examples are given below:
for the present embodiment, the topology connection condition of the distribution network with IEEE33 nodes is as shown in fig. 2, and 3 sets of capacity 1MVA are respectively connected to nodes 25-29, 12-22, 8-21, and the upper limits of active and reactive output power are 800kW and 500kvar, respectively; 7. the nodes 13 and 27 are connected into the photovoltaic system; 10. the 16, 17, 30 and 33 nodes are connected into the fan; optimizing and controlling the total time length T to be 24 h; the prediction domain time interval Δ T is 1 h; control field time interval Δ t is 20 s; the sensitivity zone threshold is set to 0.5; setting the voltage reference value of the power distribution network to be 1.0 p.u; voltage control error requirement is 10-2. The weighting coefficients lambda, rho, eta and mu take the values of 1, 1 and 5. The data-driven multi-intelligent soft switch partition cooperative self-adaptive voltage control method is adopted for optimization, and intelligent soft switch output strategies at all times can be obtained. In order to verify the effectiveness of the method, the following two control schemes are adopted for comparison aiming at the power distribution network.
The first scheme is as follows: the intelligent soft switch is not controlled, and the initial running state of the power distribution network is obtained;
scheme II: a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method is adopted.
The computer hardware environment for executing the optimized calculation is Intel (R) Xeon (R) CPU E5-16030, the main frequency is 2.8GHz, and the memory is 16 GB; the software environment is a Windows 10 operating system.
By adopting the data-driven multi-intelligent-soft-switch-partition cooperative adaptive voltage control method, the result of partitioning each intelligent soft-switch control area of the power distribution network in the embodiment is shown in fig. 2. The method comprises the following steps of (1) in a ratio of 10: 00 as an example, the comparison result of the voltage values of the nodes after the voltage control of the scheme one and the scheme two is shown in fig. 3, the voltage change curves of the 18 node and the 33 node are shown in fig. 4, and the voltage change curves of 10: 00 the active transmission power and reactive output change of the intelligent soft switch are shown in fig. 5 and 6; active transmission power and reactive output of the 24-hour intelligent soft switch are shown in fig. 7 and 8, network loss conditions after voltage control of the scheme I and the scheme II are shown in fig. 9, the comparison result of the maximum node voltage is shown in fig. 10, and the optimization result is shown in table 1. It can be seen from fig. 3 to fig. 10 and table 1 that the data-driven multi-intelligent-soft-switch-partition cooperative adaptive voltage control method of the present invention can effectively solve the problem of partition cooperative operation optimization control of the power distribution network, and has an important meaning for the adaptive operation optimization of the active power distribution network.
TABLE 1 comparison of control effects of scheme one and scheme two
Voltage minimum/p.u. Voltage maximum/p.u. Voltage deviation/p.u. Energy loss/kWh
Scheme one 0.9330 1.0313 0.0172 1179.7000
Scheme two 0.9710 1.0065 0.0035 865.2918

Claims (6)

1. A data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method is characterized by comprising the following steps:
1) inputting basic parameter information of the system according to the selected active power distribution network, wherein the basic parameter information comprises the following steps: the access position, capacity and active/reactive output power upper limit of the intelligent soft switch, the node voltage reference value, the control error precision, the initial value of the pseudo Jacobian matrix of the controller and the initial time t0Optimizing the total control time length to be T, and setting the current time T to be T0The predicted domain time interval delta T is s delta T, the domain time interval delta T is controlled, and the control time-shifting step number k is 1;
2) according to the active power distribution network given in the step 1), in an optimization time period [ t, t + n delta t ], adjusting active transmission power and reactive power output levels of two ports of an intelligent soft switch, respectively obtaining the voltage of each node of the power distribution network and the variable quantity of active and reactive power measurement, calculating the pseudo-electric distance between nodes of the active power distribution network, clustering the nodes by using each intelligent soft switch access node as a clustering center and adopting a clustering density peak method, and determining an effective adjustment area of each intelligent soft switch;
3) according to the effective adjusting area of each intelligent soft switch determined in the step 2), defining an area close to a source node as an upstream area, and an area close to the tail end of a branch as a downstream area, judging whether the maximum values of the voltage control error of each area and the boundary iteration convergence error of the adjacent subarea meet the precision requirement, if so, turning to the step 6), and if not, turning to the next step;
4) in a control time period [ t, t + delta t ], establishing an intelligent soft switch adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of an upstream region as targets, solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, sending the intelligent soft switch adaptive voltage control strategy to the upstream region intelligent soft switch to obtain the voltage measurement of each region node, and transmitting the voltage measurement of the boundary node and the active and reactive power measurement of a boundary branch circuit to a downstream region as boundary information;
5) establishing an intelligent soft switch adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of a downstream region as targets, solving the intelligent soft switch adaptive voltage control model driven by the downstream region data by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, issuing the intelligent soft switch adaptive voltage control strategy to the downstream region, and transmitting boundary node voltage measurement and boundary branch active and reactive power requirements as boundary information to an upstream region;
6) updating the control time t to be t + delta t, the time-shifting step number k to be k +1, judging whether the time-shifting step number k in the control domain reaches a set value s, and if not, returning to the step 4); if yes, entering step 7);
7) and judging whether the current time T reaches the total control duration T, if not, making k equal to 1, returning to the step 2), and if so, ending the adaptive voltage control process.
2. The data-driven multi-intelligent-soft-switching-partition cooperative adaptive voltage control method according to claim 1, wherein the voltage control error of each region and the boundary iterative convergence error of the adjacent partitions in the step 3) are represented as:
Figure FDA0002642459020000011
in the formula, R1Indicates the voltage control error of each region, X [ t ]]And X [ t- Δ t]Expressing the intelligent soft switch output at t moment and t-delta t moment, YrefRepresenting the respective zone voltage and net load reference vectors,
Figure FDA0002642459020000021
representing the voltage and net load estimate vectors, R, for each region at time t + Δ t2Representing the boundary iterative convergence error, r, of adjacent partitionspRepresents the iterative convergence error of the boundary power, rvThe iterative convergence error of the boundary voltage is represented,
Figure FDA0002642459020000022
and
Figure FDA0002642459020000023
respectively represents the active power transmitted by the boundary line l of the area a and the area b at the time t,
Figure FDA0002642459020000024
and
Figure FDA0002642459020000025
respectively representing the reactive power transmitted by the lines l of the area a and the area b at the time t,
Figure FDA0002642459020000026
Figure FDA0002642459020000027
the voltages at the boundary nodes m and n in the regions a and b at time t are shown, respectively.
3. The data-driven multi-intelligent-soft-switch-partition-coordinated-adaptive voltage control method according to claim 1, wherein the target J '(X') is the upstream region voltage deviation and the network loss minimum in the step 4)a[t]，X′a[t]) Expressed as:
Figure FDA0002642459020000028
in the formula, Xa[t]And Xa[t-Δt]Respectively representing intelligent soft switch output vector X 'in a region a at the time t and the time t-delta t'a[t]The auxiliary variable is represented by a number of variables,
Figure FDA00026424590200000222
representing the voltage reference value of the boundary node n in the area a,
Figure FDA00026424590200000210
and
Figure FDA00026424590200000211
respectively representing the boundary active and reactive power transferred by the downstream region b at the time t-deltat,
Figure FDA00026424590200000212
represents the voltage estimate at the boundary node n of region a at time t + at, represents a weighting factor,
Figure FDA00026424590200000213
representing the voltage and payload reference vectors in region a,
Figure FDA00026424590200000214
representing the voltage and incoming power estimate vectors in region a at time t + deltat,
Figure FDA00026424590200000215
and
Figure FDA00026424590200000216
respectively representing the node voltage and the inflow power estimate in region a at time t + deltat,
Figure FDA00026424590200000217
and
Figure FDA00026424590200000218
respectively, the node voltage and the payload reference value in the region a.
4. The method for partitioned cooperative adaptive voltage control according to claim 1, wherein the upstream region data-driven intelligent soft-switch adaptive voltage control model in step 4) is represented as:
Figure FDA00026424590200000219
in the formula, Ya[t]And
Figure FDA00026424590200000220
a measured value and an estimated value vector, X, respectively representing the voltage and the inflow power of the region a at time ta[t]And Xa[t-Δt]Respectively represents the output vector phi of the intelligent soft switch in the t moment and the t-delta t moment area aa[t]And phia[t-Δt]Respectively representing a pseudo Jacobian matrix of the region a at the time t and the time t-delta t, and mu represents a weight coefficient.
5. The data-driven multi-intelligent-soft-switch-partition cooperative adaptive voltage control method according to claim 1, wherein the target expression of the downstream region voltage deviation and the network loss minimum in the step 5) is as follows:
Figure FDA00026424590200000221
Figure FDA0002642459020000031
in the formula, Xb[t]And Xb[t-Δt]Respectively representing time t andintelligent soft switch output vector X 'in t-delta t time region b'b[t]The extension variable is represented by a number of extension variables,
Figure FDA0002642459020000032
representing the voltage reference value of node m in region b,
Figure FDA0002642459020000033
representing the voltage estimate at node m in region b at time t + at, lambda represents a weighting factor,
Figure FDA0002642459020000034
and
Figure FDA0002642459020000035
respectively representing the boundary active and reactive power transferred by the upstream zone a at times t-deltat,
Figure FDA0002642459020000036
representing the voltage and payload reference vectors in region b,
Figure FDA0002642459020000037
representing the voltage and incoming power estimate vectors in region b at time t + deltat,
Figure FDA0002642459020000038
and
Figure FDA0002642459020000039
respectively representing the node voltage and the inflow power estimate in region b at time t + deltat,
Figure FDA00026424590200000310
and
Figure FDA00026424590200000311
respectively, the node voltage and the payload reference value in region b.
6. The method for partitioned cooperative adaptive voltage control according to claim 1, wherein the downstream region data-driven intelligent soft-switch adaptive voltage control model in step 5) is represented as:
Figure FDA00026424590200000312
in the formula (I), the compound is shown in the specification,
Figure FDA00026424590200000313
representing the estimated values of voltage and power flowing in the region b at time t + Deltat, Yb[t]And
Figure FDA00026424590200000314
a measured value and an estimated value vector X respectively representing the voltage and the inflow power in the t-time region bb[t]And Xb[t-Δt]Respectively represents the output vector phi of the intelligent soft switch in the t moment and t-delta t moment region bb[t]And phib[t-Δt]And respectively representing a pseudo Jacobian matrix of the region b at the time t and the time t-delta t, and mu represents a weight coefficient.
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