Transformer substation-feeder line-transformer area voltage reactive power coordination control method and system

Abstract

The invention provides a transformer substation-feeder line-transformer area voltage reactive power coordination control method and system. The method comprises the following steps: acquiring real-time operation data of a power distribution network; substituting the real-time operation data into a pre-constructed voltage-control quantity sensitivity model, and solving by adopting an approximate dynamic programming method to obtain an optimal control strategy; wherein the voltage-control quantity sensitivity model is constructed by taking the optimal control effect and the minimum control cost as targets and taking the voltage regulation capability of the distributed adjustable resources under the transformer substation as a constraint. According to the method, the approximate dynamic programming method is adopted for solving based on the voltage-controlled variable sensitivity model, the optimal control strategy can be obtained through the online learning algorithm only by dynamically collecting operation data of the voltage-controlled variable sensitivity model, and accurate parameter information independent of a system model is achieved.

Description

Technical Field

[0001] The present invention relates to the field of voltage control, and in particular to a transformer substation-feeder-area voltage and reactive power coordinated control method and system. Background Art

[0002] With the large-scale development of distributed photovoltaics, the volatility and randomness of high-proportion distributed photovoltaic output have significantly increased, the grid load fluctuations have increased, showing a "duck curve" feature, and the difficulty of matching source and load supply and demand and balancing on-site has increased, resulting in prominent problems such as voltage / power over-limits at multiple voltage levels in the distribution network. The voltage is high during the day and low at night, and the voltage regulation is more difficult, posing a great challenge to the safe operation of the distribution network.

[0003] At present, some studies have adopted a hierarchical and partitioned operation control framework, and proposed safety control strategies from different levels such as the substation autonomy layer, feeder coordination layer, and global optimization layer. However, a high proportion of distributed photovoltaics will have an impact on each level, and there is coupling between the levels. Each level has the problem of insufficient voltage regulation capability, and it is necessary to carry out three-level coordinated control of the substation-feeder-substation. The three-level coordinated control of the substation-feeder-substation needs to rely on a complete and accurate grid topology and model parameters, which brings the risk of inaccuracy or even unavailability to the three-level coordinated control strategy of the substation-feeder-substation. Summary of the invention

[0004] In order to solve the problem that the existing technology needs to rely on complete and accurate power grid topology and model parameters when performing three-level coordinated control of substation-feeder-substation, the present invention proposes a substation-feeder-substation voltage and reactive power coordinated control method, including:

[0005] Obtain real-time operation data of the distribution network;

[0006] The real-time operation data is brought into a pre-built voltage-control quantity sensitivity model, and an approximate dynamic programming method is used to solve the model to obtain an optimal control strategy;

[0007] The voltage-control quantity sensitivity model is constructed with the goal of optimal control effect and minimum control cost and with the voltage regulation capability of the distributed adjustable resources under the substation as a constraint.

[0008] Optionally, the construction of the voltage-control amount sensitivity model includes:

[0009] Adopting the quadratic regulator theory, the objective function is constructed with the optimal control effect and the minimum control cost as the goal;

[0010] Setting operating constraints for the objective function;

[0011] The operation constraints include: physical constraints of distributed photovoltaics, physical constraints of energy storage, physical constraints of reactive power compensation on the load side, and network constraints.

[0012] Optionally, the objective function is as shown below:

[0013]

[0014] Where J is the target value to be optimized that comprehensively considers the state variable deviation and control cost, x is the state variable, and x T is the transposed matrix of the state variables, Q is the weight matrix of the state variable deviation, u is the control strategy matrix, and u T is the control strategy matrix transpose, R is the weight matrix of control cost, and t is the time.

[0015] Optionally, the physical constraints of the distributed photovoltaic are as follows:

[0016]

[0017] In the formula, is the predicted active power of the PV of node i at time t when it works at the maximum power point; are the injected active power and reactive power of the PV of node i at time t, respectively; is the installed capacity of the PV of node i; It is the decision variable of whether the PV of node i participates in the scheduling operation at time t, where 1 means participation and 0 means no participation.

[0018] Optionally, the physical constraints of the energy storage are as follows:

[0019]

[0020] Where W i ES (t+1) is the energy storage state of node i at time t+1, W i ES (t) is the energy storage state of node i at time t; is the energy storage loss ratio of node i; and are the charging and discharging efficiencies of energy storage at node i respectively; and are the charging power and discharging power of the energy storage at node i at time t respectively; and are the maximum charging power and maximum discharging power of energy storage at node i respectively; is a 0 or 1 variable indicating whether the energy storage at node i is in a charging state or a discharging state at time t. 1 indicates that it is in a charging state or a discharging state, and 0 indicates that it is not in a charging state or a discharging state. and are the minimum and maximum values ​​of the energy storage state of node i respectively; W i ES (0) is the energy storage state of node i at the initial time 0; W i ES (T) is the energy storage state of node i at the final time T; Δt is the duration from time t to time t+1.

[0021] Optionally, the physical constraint of reactive power compensation on the load side is as follows:

[0022]

[0023] In the formula, They are the upper and lower limits of SVC reactive power respectively; is the reactive power of the SVC at node i at time t.

[0024] Optionally, the network constraint is as shown in the following formula:

[0025] Δx L =-(f L ' xL ) -1 (Δw L -f L ' y Δy)

[0026] In the formula, Δx L is the amplitude change of the state variable load node voltage, f L ' xL 、f L ' y Δw is the sensitivity of the node voltage to the change in active power injected into the node and the change in transformer ratio, L is the change in active power injected into the node, and Δy is the change in transformer ratio.

[0027] Optionally, the real-time operation data is brought into a pre-built voltage-control quantity sensitivity model, and an approximate dynamic programming method is used to solve the model to obtain an optimal control strategy, including:

[0028] Step 1: Let the number of iterations k = 0, and the optimal control strategy K obtained by the disturbance e, the system state variable x, the weight matrix R of the control cost, and the kth iteration k , construct control strategy u k , record the parameters of the state variable x at each moment until the recording termination condition is met;

[0029] Step 2: Solve the solution P of the algebraic Riccati equation in the kth iteration based on the parameters of the state variable x at each time k and L k , where L k The input matrix and P k Determined intermediate variables;

[0030] Step 3: Based on the solution P of the algebraic Riccati equation in the kth iteration k and L k Update the control strategy and get the control strategy u k+1 , calculate the absolute value of the difference between the solution of the algebraic Riccati equation in the kth iteration and the solution of the algebraic Riccati equation in the k-1th iteration, and determine whether the absolute value of the difference is less than a set threshold. If so, proceed to step 5, otherwise proceed to step 4;

[0031] Step 4: Set k=k+1 and return to step 1;

[0032] Step 5: Let u = -K k x is used as the optimal control strategy for the system.

[0033] Optionally, the control strategy u k As shown below:

[0034]

[0035] In the formula, u k is the control strategy for the kth iteration, K k is the optimal control strategy obtained in the kth iteration, k is the number of iterations, e is the disturbance to the system, x is the state variable, L k-1 is an intermediate variable determined by the input matrix and the solution of the algebraic Riccati equation in k-1 iterations, and R is the weight matrix of the control cost.

[0036] Optionally, the parameters of the state variable x at each moment are as follows:

[0037]

[0038] In the formula, δ xx ,I xx and I xu are the first intermediate variable, the second intermediate variable and the third intermediate variable respectively, [t j ,t j +δt], j = 1, 2, …, h are some non-overlapping time periods; is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h The transpose of u T is the control strategy matrix transpose; x T is the transposed matrix of the state variables; τ is the time period [t j ,t j +δt] within the sampling interval of the state variable x.

[0039] Optionally, the record termination condition is as follows:

[0040]

[0041] Where N is the number of state variables and M is the number of control variables.

[0042] On the other hand, the present invention also provides a substation-feeder-area voltage and reactive power coordinated control system, comprising:

[0043] Parameter acquisition module, used to obtain real-time operation data of the distribution network;

[0044] A solution module is used to bring the real-time operation data into a pre-built voltage-control quantity sensitivity model, and use an approximate dynamic programming method to solve it to obtain an optimal control strategy;

[0045] The voltage-control quantity sensitivity model is constructed with the goal of optimal control effect and minimum control cost and with the voltage regulation capability of the distributed adjustable resources under the substation as a constraint.

[0046] Optionally, it also includes model building modules for:

[0047] Adopting the quadratic regulator theory, the objective function is constructed with the optimal control effect and the minimum control cost as the goal;

[0048] Setting operating constraints for the objective function;

[0049] The operation constraints include: physical constraints of distributed photovoltaics, physical constraints of energy storage, physical constraints of reactive power compensation on the load side, and network constraints.

[0050] Optionally, the objective function is as shown below:

[0051]

[0052] Where J is the target value to be optimized that comprehensively considers the state variable deviation and control cost, x is the state variable, and x T is the transposed matrix of the state variables, Q is the weight matrix of the state variable deviations, u is the control strategy matrix, and R is the weight matrix of the control cost.

[0053] Optionally, the physical constraints of the distributed photovoltaic are as follows:

[0054]

[0055] In the formula, is the predicted active power of the PV of node i at time t when it works at the maximum power point; are the injected active power and reactive power of the PV of node i at time t, respectively; is the installed capacity of the PV of node i; It is the decision variable of whether the PV of node i participates in the scheduling operation at time t, where 1 means participation and 0 means no participation.

[0056] Optionally, the physical constraints of the energy storage are as follows:

[0057]

[0058] Where W i ES (t+1) is the energy storage state of node i at time t+1, W i ES (t) is the energy storage state of node i at time t; is the energy storage loss ratio of node i; and are the charging and discharging efficiencies of energy storage at node i respectively; and are the charging power and discharging power of the energy storage at node i at time t respectively; and are the maximum charging power and maximum discharging power of energy storage at node i respectively; is a 0 or 1 variable indicating whether the energy storage at node i is in a charging state or a discharging state at time t. 1 indicates that it is in a charging state or a discharging state, and 0 indicates that it is not in a charging state or a discharging state. and are the minimum and maximum values ​​of the energy storage state of node i respectively; W i ES (0) is the energy storage state of node i at the initial time 0; W iES (T) is the energy storage state of node i at the final time T; Δt is the duration from time t to time t+1.

[0059] Optionally, the physical constraint of reactive power compensation on the load side is as follows:

[0060]

[0061] In the formula, They are the upper and lower limits of SVC reactive power respectively; is the reactive power of the SVC at node i at time t.

[0062] Optionally, the network constraint is as shown in the following formula:

[0063] Δx L =-(f L ' xL ) -1 (Δw L -f L ' y Δy)

[0064] In the formula, Δx L is the amplitude change of the state variable load node voltage, f L ' xL 、f L ' y Δw is the sensitivity of the node voltage to the change in active power injected into the node and the change in transformer ratio, L is the change in active power injected into the node, and Δy is the change in transformer ratio.

[0065] Optionally, the solution module is specifically used for:

[0066] Step 1: Let the number of iterations k = 0, and construct the control strategy u based on the disturbance e, system state variable x, control cost weight matrix R, and the optimal control strategy Kk obtained in the kth iteration. k , record the parameters of the state variable x at each moment until the recording termination condition is met;

[0067] Step 2: Solve the solution P of the algebraic Riccati equation in the kth iteration based on the parameters of the state variable x at each time k and L k , where L k The input matrix and P k Determined intermediate variables;

[0068] Step 3: Based on the solution P of the algebraic Riccati equation in the kth iteration k and L k Update the control strategy and get the control strategy u k+1, calculate the absolute value of the difference between the solution of the algebraic Riccati equation in the kth iteration and the solution of the algebraic Riccati equation in the k-1th iteration, and determine whether the absolute value of the difference is less than a set threshold. If so, proceed to step 5, otherwise proceed to step 4;

[0069] Step 4: Set k=k+1 and return to step 1;

[0070] Step 5: Let u = -K k x is used as the optimal control strategy for the system.

[0071] Optionally, the control strategy u k As shown below:

[0072]

[0073] In the formula, u k is the control strategy for the kth iteration, K k is the optimal control strategy obtained in the kth iteration, k is the number of iterations, e is the disturbance to the system, x is the state variable, L k-1 is an intermediate variable determined by the input matrix and the solution of the algebraic Riccati equation in k-1 iterations, and R is the weight matrix of the control cost.

[0074] Optionally, the parameters of the state variable x at each moment are as follows:

[0075]

[0076]

[0077] In the formula, δ xx ,I xx and I xu are the first intermediate variable, the second intermediate variable and the third intermediate variable respectively, [t j ,t j +δt], j = 1, 2, …, h are some non-overlapping time periods; is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h is the transpose of; u is the optimal strategy; x T is the transposed matrix of the state variables; τ is the time period [t j ,t j +δt] within the sampling interval of the state variable x.

[0078] Optionally, the record termination condition is as follows:

[0079]

[0080] Where N is the number of state variables and M is the number of control variables.

[0081] In another aspect, the present application further provides a computing device, comprising: at least one processor and a memory;

[0082] The memory is used to store one or more programs;

[0083] When the one or more programs are executed by the at least one processor, a substation-feeder-area voltage-reactive power coordinated control method as described above is implemented.

[0084] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, a substation-feeder-area voltage and reactive power coordinated control method as described above is implemented.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] The present invention provides a method for coordinated control of voltage and reactive power of a substation-feeder-area, including obtaining real-time operation data of a distribution network; bringing the real-time operation data into a pre-constructed voltage-control quantity sensitivity model, and solving it using an approximate dynamic programming method to obtain an optimal control strategy; wherein the voltage-control quantity sensitivity model is constructed with the optimal control effect and the minimum control cost as the goal, and the voltage regulation capability of distributed adjustable resources under the substation as a constraint. The present invention adopts an approximate dynamic programming method to solve the voltage-control quantity sensitivity model, and only needs to dynamically collect its operation data to obtain the optimal control strategy through an online learning algorithm, thereby realizing accurate parameter information that is independent of the system model. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A flow chart of a transformer substation-feeder-area voltage-reactive power coordinated control method of the present invention;

[0088] Figure 2 A schematic diagram of a computer device according to the present invention. DETAILED DESCRIPTION

[0089] The substation-feeder-substation networks and distributed photovoltaics at all levels have been made observable and measurable, and their real-time measurement data can be obtained. By formulating a three-level coordinated control strategy for the substation-feeder-substation through a real-time measurement data-driven method, the problems of parameter uncertainty and inaccuracy can be solved.

[0090] To this end, the present invention proposes a substation-feeder-substation voltage-reactive coordinated control method. First, a sensitivity analysis method is used to establish a voltage-control quantity sensitivity model of a multi-voltage level distribution network, and then a three-level coordinated voltage safety optimal control model of the substation-feeder-substation is established. The approximate dynamic programming method is used to solve the voltage safety optimal control model of a high-proportion distributed photovoltaic distribution network with uncertainties and inaccuracies in parameters, which helps to improve the overall voltage safety level of each level of the substation-feeder-substation.

[0091] Embodiment 1:

[0092] A method for coordinated control of voltage and reactive power of a substation, feeder and substation area, such as Figure 1 As shown, including:

[0093] Step S1: Obtaining real-time operation data of the distribution network;

[0094] Step S2: bringing the real-time operation data into a pre-built voltage-control variable sensitivity model, solving it using an approximate dynamic programming method, and obtaining an optimal control strategy;

[0095] The voltage-control quantity sensitivity model is constructed with the goal of optimal control effect and minimum control cost and with the voltage regulation capability of the distributed adjustable resources under the substation as a constraint.

[0096] The present invention provides a transformer substation-feeder-area voltage and reactive power coordinated control method, the method comprising the following steps:

[0097] Before step S1, the construction of the voltage-control amount sensitivity model involved in the present invention is further introduced:

[0098] Step 1): Establish a voltage-control quantity sensitivity model for a multi-voltage distribution network, and solve the nonlinear network flow constraint problem caused by the multi-voltage distribution network equation through the voltage-control quantity sensitivity model. The control quantity in the present invention includes the main transformer tap and reactive power compensation of the substation, as well as the active and reactive power of distributed adjustable resources such as distributed photovoltaic, energy storage and load-side adjustable equipment. Specifically including steps 11)-13);

[0099] Step 11) The power flow equation in the distribution network can be described as:

[0100]

[0101] In the formula, w s is the injected power of the power supply node (including the balance node and PV node); f s (x,y) is the power flow equation between the injected power of the power node and x and y, f L (x,y) is the injected power of the load node and the power flow equation between x and y; w L is the power injected into the load node for nodes with reactive power compensation devices; x is the state variable, i.e., the node voltage modulus and phase angle; y is the transformer ratio.

[0102] Linearizing the above equation, we get

[0103]

[0104] In the formula, Δw s The change in power injected into the power node (including the balance node and PV node), Δw L is the change in power injected into the load node, f′ sx is the sensitivity matrix of the power injected into the power node to the node voltage amplitude, f′ sy is the sensitivity matrix of the power injected into the power node to the transformer ratio, f′ Lx is the sensitivity matrix of the load node injected power to the node voltage amplitude, f′ Ly is the sensitivity matrix of the load node injected power to the transformer ratio, f′ sxs is the sensitivity matrix of the power injected into the power node to the voltage amplitude of the power node, f′ sxL is the sensitivity matrix of the power injected into the power source node to the voltage amplitude of the load node, f′ Lxs is the sensitivity matrix of the load node injection power to the power node voltage amplitude, f′ LxL is the sensitivity matrix of the load node injection power to the load node voltage amplitude, Δx is the change of the state variable, Δy is the change of the transformer ratio, Δx s is the change in the voltage amplitude of the power node, Δx L is the change in voltage amplitude at the load node.

[0105] The coefficients are sensitivity matrices, whose elements represent the sensitivity of power injection at the source node and load node to the node voltage and transformer ratio, respectively.

[0106] Since the voltages of the balancing node and the PV node are known, we have Δx s =0, then the above expression is

[0107]

[0108] Step 12) Impact of substation adjustment on node voltage

[0109] Assume that only the main transformer ratio is adjusted without adjusting the output of reactive power compensation, because the node power injection remains unchanged, there is Δw L =0, then

[0110]

[0111] It can be solved

[0112] Δx L =-(f′ LxL ) -1 f′ Ly Δy (5)

[0113] The above formula shows the effect of changes in transformation ratio on node voltage.

[0114] Considering the influence of reactive power compensation regulation and main transformer ratio adjustment at the same time, we have

[0115] Δx L =-(f′ LxL ) -1 (Δw L -f′ Ly Δy) (6)

[0116] Step 13) Impact of distributed adjustable resource active and reactive power regulation on node voltage

[0117] Assuming that the transformation ratio remains unchanged, that is, Δy = 0, we have

[0118] Δx L =-(f′ LxL ) -1 Δw L (7)

[0119] Step 2): Establish a multi-voltage level voltage security optimal control model for high-proportion distributed photovoltaic access, specifically including steps 21)-22);

[0120] The voltage security optimal control modeling adopts the Linear Quadratic Regulator (LQR) theory. The model takes the optimal control effect and the minimum control cost as the goal, and considers the voltage regulation capabilities of distributed adjustable resources such as distributed photovoltaic, energy storage and load-side adjustable equipment under the substation, specifically the active and reactive regulation capabilities of photovoltaic inverters, energy storage charging and discharging control capabilities, energy storage reactive control capabilities, load-side reactive compensation, etc. to achieve coordinated control of voltage regulation resources.

[0121] Step 21) Establish the objective function as follows:

[0122]

[0123] Where x is the state variable, expressed as follows:

[0124]

[0125] Where: N is the number of state variables; ΔV is the network node voltage deviation matrix of the distribution network containing distributed adjustable resources. If the balancing node is the first network node, the voltage deviation of node 1 is constant to 0; V n It is a matrix of voltage ratings of the network nodes of a distribution network containing distributed adjustable resources, usually with a per-unit value.

[0126] u is the control strategy matrix, which can be expressed as follows:

[0127]

[0128] Where: M controls the number of variables. m When it is the active power control increment of distributed photovoltaic and energy storage, it is expressed as follows:

[0129] u m =ΔP m (11)

[0130] In the formula, ΔP m It is the active power control increment.

[0131] When u m When it is the reactive control increment of distributed photovoltaic, energy storage and reactive compensation on the load side, it is expressed as follows

[0132] u m =ΔQ m (12)

[0133] Where ΔQ m It is the reactive power control increment.

[0134] J comprehensively considers the state variable deviation and control cost and is the target value to be optimized; Q ≥ 0 and R > 0 are the weight matrices of the state variable deviation and control cost, respectively.

[0135] Step 22) Establish the operation constraints as follows:

[0136] It includes physical constraints of distributed photovoltaic, energy storage, reactive power compensation on the load side, and network constraints represented by sensitivity models, which are described as follows:

[0137] a. Physical constraints of distributed photovoltaics

[0138]

[0139] In the formula, is the predicted power of the PV of node i at time t when working at the maximum power point, is the injected active power and reactive power of the node PV at time t, is the installed capacity of PV at the node, and is the decision variable for whether to dispatch the active power of PV generation i, where 1 indicates dispatch and 0 indicates no dispatch. It is the decision variable of whether the PV of node i participates in the scheduling operation at time t, where 1 means participation and 0 means no participation.

[0140] b. Physical constraints of energy storage:

[0141]

[0142] Where W i ES (t+1) is the energy storage state of node i at time t+1; W i ES (t) is the energy storage state of node i at time t; is the loss ratio of energy storage, and are charge and discharge efficiency respectively; and are the maximum charging and discharging power of node energy storage, is a 0 or 1 variable indicating whether the i-th group of energy storage is scheduled for discharge or charging, and are the minimum and maximum values ​​of the energy storage state respectively.

[0143] c. Physical constraints on reactive power compensation on the load side

[0144]

[0145] In the formula, They are the upper and lower limits of SVC reactive power respectively; is the reactive power of the SVC at node i at time t.

[0146] d. Network constraints

[0147] As shown in the voltage-control variable sensitivity model shown in equations (1) to (7).

[0148] Step S1: Obtaining real-time operation data of the distribution network;

[0149] Step S2: Bring the real-time operation data into the pre-built voltage-control quantity sensitivity model, and use the approximate dynamic programming method to solve it to obtain the optimal control strategy, including:

[0150] Step 1: Let the number of iterations k = 0, and the optimal control strategy K obtained by the disturbance e, the system state variable x, the weight matrix R of the control cost, and the kth iterationk , construct control strategy u k , record the parameters of the state variable x at each moment until the recording termination condition is met;

[0151] Step 2: Solve the solution P of the algebraic Riccati equation in the kth iteration based on the parameters of the state variable x at each time k and L k ;

[0152] Step 3: Based on the solution P of the algebraic Riccati equation in the kth iteration k and L k Update the control strategy and get the control strategy u k+1 , calculate the absolute value of the difference between the solution of the algebraic Riccati equation in the kth iteration and the solution of the algebraic Riccati equation in the k-1th iteration, and determine whether the absolute value of the difference is less than a set threshold. If so, proceed to step 5, otherwise proceed to step 4;

[0153] Step 4: Set k=k+1 and return to step 1;

[0154] Step 5: Let u = -K k x is used as the optimal control strategy for the system.

[0155] Optionally, the control strategy u k As shown below:

[0156]

[0157] Where, L k =B T P k , for k∈Z + , L k is an intermediate variable, which is determined by the input matrix and the solution of the algebraic Riccati equation in the k iteration. B is the input matrix of the linearized continuous time-invariant system model of the substation-feeder-area multi-voltage level distribution network at a certain operating point. P k is the solution of the algebraic Riccati equation in the kth iteration, k is the number of iterations, Z + is a set of positive integers, e is the disturbance of the system, x is the system state variable, L k-1 is the optimal cost function starting from state x{k}, and R is the weight matrix controlling the cost.

[0158] The parameters of the state variable x at each moment are shown as follows:

[0159]

[0160] In the formula, δ xx ,I xx and I xuare the first intermediate variable, the second intermediate variable and the third intermediate variable respectively, [t j ,t j +δt], j = 1, 2, …, h are some non-overlapping time periods; is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h is the transpose of; u is the optimal strategy; x T is the transpose of the state variable x; τ is the time period [t j ,t j +δt] within the sampling interval of the state variable x.

[0161] The recording termination conditions are as follows:

[0162]

[0163] Where N is the number of state variables and M is the number of control variables.

[0164] The specific steps of step S2 are as follows:

[0165] An algorithm for solving the optimal control strategy for multi-voltage level voltage security with high proportion of distributed photovoltaic access based on approximate dynamic programming is proposed, which specifically includes steps 31)-32);

[0166] Approximate dynamic programming (ADP) is a heuristic method derived from the learning behavior of biological systems. This method does not rely on the precise parameter information of the system model, but only needs to dynamically collect its operating data to obtain the optimal control strategy through the online learning algorithm.

[0167] The linearized continuous time-invariant system model of the substation-feeder-area multi-voltage distribution network at a certain operating point is expressed as follows

[0168]

[0169] In the formula, is the differential of x; A is the system matrix; B is the input matrix. The system can be stabilized by the following state feedback control strategy:

[0170] u=-Kx (6)

[0171] According to the Bellman optimization principle, the optimal control strategy of the system can be expressed as follows

[0172] K=R -1 B T P (7)

[0173] K is the optimal control strategy, and P is the unique positive solution of the following Algebraic Riccati Equation (ARE):

[0174] A T P+PA-PBR -1 B T P+Q=0 (8)

[0175] Step 31) defines the control strategy in the iteration process as follows:

[0176] K in formula (7) can be solved by the following iterative algorithm, and the control strategy in the iterative process is:

[0177]

[0178] Where, L k =B T P k for k∈Z + , B is the input matrix, P k is the solution of the algebraic Riccati equation in the kth iteration, k is the number of iterations, e is the perturbation to the system, and u k is the control vector, K k The optimal control strategy K obtained in the kth iteration, x is the system state variable.

[0179] Step 32) The optimal control strategy iterative solution steps are as follows:

[0180] a. Let k = 0, and use the control strategy u defined by formula (16) k , record δ according to formula (10)-(12) xx , I xx and I xu until δ xx , I xx and I xu The definition is as follows:

[0181]

[0182] In the formula, is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h The transpose of .

[0183]

[0184]

[0185] In the formula, δ xx ,I xx and I xu are the first intermediate variable, the second intermediate variable and the third intermediate variable respectively, [t j ,t j +δt], j = 1, 2, …, h are some non-overlapping time periods; is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h is the transpose of; u is the optimal strategy; x T is the transpose of the state variable x; τ is the time period [t j ,t j +δt] within the sampling interval of the state variable x.

[0186] b. Solve for P using the following formula k , L k ;

[0187]

[0188] In the formula, vec(L k ) is the matrix L k The column vectorization operation is to rearrange the matrix into one column in the order of columns. vec(Q) is the column vectorization operation of the matrix Q. n is the identity matrix with n rows and n columns, is the transpose of the optimal control strategy K obtained in the kth iteration.

[0189] c. Update the control strategy u according to formula (16) k+1 ;

[0190] d. Let k = k + 1, and repeat steps a to c until ||P k -P k-1 ||≤ε, ε is the accuracy, where ε>0 is the pre-set accuracy requirement;

[0191] e. Let u=-K k x is used as the optimal control strategy for the system.

[0192] In real-time operation, the above solution process is executed every five minutes (settable), that is, the voltage security optimal control strategy of the distribution network containing distributed adjustable resources is updated every five minutes.

[0193] Compared with the prior art, the technical effects of the present invention are:

[0194] A substation-feeder-area voltage-reactive coordinated control method is proposed. The method constructs a voltage-control quantity sensitivity model of a multi-voltage distribution network and a multi-voltage level voltage safety optimal control model with a high proportion of distributed photovoltaic access, and proposes a multi-voltage level voltage safety optimal control strategy solution algorithm with a high proportion of distributed photovoltaic access based on approximate dynamic programming. The method can achieve accurate parameter information that is independent of the system model, and only needs to dynamically collect its operating data to obtain the optimal control strategy through an online learning algorithm.

[0195] (A) The voltage-control variable sensitivity model of the multi-voltage distribution network proposed in the embodiment of the present invention can solve the nonlinear network power flow constraint problem brought by the multi-voltage distribution network equation and make it a linear constraint, thereby facilitating the establishment of its optimal control model and solution.

[0196] (B) An embodiment of the present invention proposes a multi-voltage level voltage safety optimal control model for high-proportion distributed photovoltaic access. The model aims at optimal control effect and minimum control cost, and takes into account the voltage control and regulation capabilities of distributed resources such as distributed photovoltaics, energy storage, and load-side adjustable equipment under the substation, specifically the active and reactive regulation capabilities of photovoltaic inverters, energy storage charging and discharging control capabilities, energy storage reactive control capabilities, load-side reactive compensation, etc. to achieve coordinated control of voltage regulation resources.

[0197] (C) An algorithm for solving the optimal control strategy for voltage safety of multiple voltage levels with a high proportion of distributed photovoltaic access based on approximate dynamic programming is proposed in an embodiment of the present invention. It can achieve accurate parameter information that is independent of the system model and can obtain the optimal control strategy through an online learning algorithm by only dynamically collecting its operating data.

[0198] By implementing this method, the voltage coordination control level of distributed photovoltaic access to multi-voltage distribution networks can be improved. In the context of national energy transformation and accelerated development of new energy, this method can effectively support the national new energy development goals. During the planning period, the new energy generation capacity will increase to 600 billion kWh and the photovoltaic power generation increment will reach 350 billion kWh. This method is applied to distribution networks, and the application scale of each distribution network is about 1 million yuan. Based on the number of distribution networks of 10,000 scales nationwide, the implementation value of this method can reach 10 billion yuan.

[0199] Embodiment 2:

[0200] The present invention based on the same inventive concept also provides a substation-feeder-area voltage-reactive power coordinated control system, comprising:

[0201] Parameter acquisition module, used to obtain real-time operation data of the distribution network;

[0202] A solution module is used to bring the real-time operation data into a pre-built voltage-control quantity sensitivity model, and use an approximate dynamic programming method to solve it to obtain an optimal control strategy;

[0203] The voltage-control quantity sensitivity model is constructed with the goal of optimal control effect and minimum control cost and with the voltage regulation capability of the distributed adjustable resources under the substation as a constraint.

[0204] Optionally, it also includes model building modules for:

[0205] Adopting the quadratic regulator theory, the objective function is constructed with the optimal control effect and the minimum control cost as the goal;

[0206] Setting operating constraints for the objective function;

[0207] The operating constraints include: physical constraints of distributed photovoltaics, physical constraints of energy storage, physical constraints of reactive power compensation on the load side, and network constraints.

[0208] Optionally, the objective function is as shown below:

[0209]

[0210] Where J is the target value to be optimized that comprehensively considers the state variable deviation and control cost, x is the state variable matrix of the distribution network containing distributed adjustable resources, Q is the weight matrix of the state variable deviation, u is the control strategy matrix, and R is the weight matrix of the control cost.

[0211] Optionally, the physical constraints of the distributed photovoltaic are as follows:

[0212]

[0213] In the formula, is the predicted active power of the PV of node i at time t when it works at the maximum power point; are the injected active power and reactive power of the PV of node i at time t, respectively; is the installed capacity of the PV of node i; It is the decision variable of whether the PV of node i participates in the scheduling operation at time t, where 1 means participation and 0 means no participation.

[0214] Optionally, the physical constraints of the energy storage are as follows:

[0215]

[0216] Where W i ES (t+1) is the energy storage state of node i at time t+1, W i ES (t) is the energy storage state of node i at time t; is the energy storage loss ratio of node i; and are the charging and discharging efficiencies of energy storage at node i respectively; and are the charging power and discharging power of the energy storage at node i at time t respectively; and are the maximum charging power and maximum discharging power of energy storage at node i respectively; is a 0 or 1 variable indicating whether the energy storage at node i is in a charging state or a discharging state at time t. 1 indicates that it is in a charging state or a discharging state, and 0 indicates that it is not in a charging state or a discharging state. and are the minimum and maximum values ​​of the energy storage state of node i respectively; W i ES (0) is the energy storage state of node i at the initial time 0; W i ES (T) is the energy storage state of node i at the final time T; Δt is the duration from time t to time t+1.

[0217] Optionally, the physical constraint of reactive power compensation on the load side is as follows:

[0218]

[0219] In the formula, They are the upper and lower limits of SVC reactive power respectively; is the reactive power of the SVC at node i at time t.

[0220] Optionally, the network constraint is as shown in the following formula:

[0221] Δx L =-(f L ' xL ) -1 (Δw L -f L ' y Δy)

[0222] In the formula, Δx L is the amplitude change of the state variable load node voltage, f L ' xL 、f L ' y Δw is the sensitivity of the node voltage to the change in active power injected into the node and the change in transformer ratio, L is the change in active power injected into the node, and Δy is the change in transformer ratio.

[0223] Optionally, the solution module is specifically used for:

[0224] Step 1: Let the number of iterations k = 0, and the optimal control strategy K obtained by the disturbance e, the system state variable x, the weight matrix R of the control cost, and the kth iteration k , construct control strategy u k , record the parameters of the state variable x at each moment until the recording termination condition is met;

[0225] Step 2: Solve the solution P of the algebraic Riccati equation in the kth iteration based on the parameters of the state variable x at each time k and L k ;

[0226] Step 3: Based on the solution P of the algebraic Riccati equation in the kth iterationk and L k Update the control strategy and get the control strategy u k+1 , calculate the absolute value of the difference between the solution of the algebraic Riccati equation in the kth iteration and the solution of the algebraic Riccati equation in the k-1th iteration, and determine whether the absolute value of the difference is less than a set threshold. If so, proceed to step 5, otherwise proceed to step 4;

[0227] Step 4: Set k=k+1 and return to step 1;

[0228] Step 5: Let u = -K k x is used as the optimal control strategy for the system.

[0229] Optionally, the control strategy u k As shown below:

[0230]

[0231] Where, L k =B T P k , for k∈Z + ,L k is an intermediate variable determined by the input matrix and the solution of the algebraic Riccati equation in k iterations, B is the input matrix of the linearized continuous time-invariant system model of the substation-feeder-area multi-voltage level distribution network at a certain operating point, B T is the transpose of B, P k is the solution of the algebraic Riccati equation in the kth iteration, k is the number of iterations, Z + is a set of positive integers, e is the disturbance of the system, x is the system state variable, L k-1 is the optimal cost function starting from state x{k}, and R is the weight matrix controlling the cost.

[0232] Optionally, the parameters of the state variable x at each moment are as follows:

[0233]

[0234] In the formula, δ xx ,I xx and I xu are the first intermediate variable, the second intermediate variable and the third intermediate variable respectively, [t j ,t j +δt], j = 1, 2, …, h are some non-overlapping time periods; is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h is the transpose of ; u is the optimal strategy.

[0235] Optionally, the record termination condition is as follows:

[0236]

[0237] Where N is the number of state variables and M is the number of control variables.

[0238] Embodiment 3:

[0239] like Figure 2 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0240] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a substation-feeder-area voltage and reactive power coordinated control method in the above embodiment.

[0241] Embodiment 4:

[0242] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a substation-feeder-area voltage reactive coordination control method in the above embodiment.

[0243] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0244] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0245] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0246] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0247] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for coordinated control of voltage and reactive power of a substation, feeder and substation area, characterized in that: include: Obtain real-time operation data of the distribution network; The real-time operation data is brought into a pre-built voltage-control quantity sensitivity model, and an approximate dynamic programming method is used to solve the model to obtain an optimal control strategy; The voltage-control quantity sensitivity model is constructed with the goal of optimal control effect and minimum control cost and with the voltage regulation capability of the distributed adjustable resources under the substation as a constraint.

2. The method according to claim 1, characterized in that The construction of the voltage-control quantity sensitivity model includes: Adopting the quadratic regulator theory, the objective function is constructed with the optimal control effect and the minimum control cost as the goal; Setting operating constraints for the objective function; The operation constraints include: physical constraints of distributed photovoltaics, physical constraints of energy storage, physical constraints of reactive power compensation on the load side, and network constraints.

3. The method according to claim 2, characterized in that The objective function is shown in the following formula: Where J is the target value to be optimized that comprehensively considers the state variable deviation and control cost, x is the state variable, and x T is the transposed matrix of the state variables, Q is the weight matrix of the state variable deviation, u is the control strategy matrix, and u T is the control strategy matrix transpose, R is the weight matrix of control cost, and t is the time.

4. The method according to claim 2, characterized in that The physical constraints of distributed photovoltaics are as follows: In the formula, is the predicted active power of the PV of node i at time t when it works at the maximum power point; are the injected active power and reactive power of the PV of node i at time t, respectively; is the installed capacity of the PV of node i; It is the decision variable of whether the PV of node i participates in the scheduling operation at time t, where 1 means participation and 0 means no participation.

5. The method according to claim 2, characterized in that The physical constraints of the energy storage are as follows: Where W i ES (t+1) is the energy storage state of node i at time t+1, W i ES (t) is the energy storage state of node i at time t; is the energy storage loss ratio of node i; and are the charging and discharging efficiencies of energy storage at node i respectively; and are the charging power and discharging power of the energy storage at node i at time t respectively; and are the maximum charging power and maximum discharging power of energy storage at node i respectively; is a 0 or 1 variable indicating whether the energy storage at node i is in a charging state or a discharging state at time t. and are the minimum and maximum values ​​of the energy storage state of node i respectively; W i ES (0) is the energy storage state of node i at the initial time 0; W i ES (T) is the energy storage state of node i at the final time T; Δt is the duration from time t to time t+1.

6. The method according to claim 2, characterized in that The physical constraints of the load-side reactive power compensation are as follows: In the formula, They are the upper and lower limits of SVC reactive power respectively; is the reactive power of the SVC at node i at time t.

7. The method according to claim 2, characterized in that The network constraints are as follows: Δx L =-(f L ' xL ) -1 (Δw L -f L ' y Δy) In the formula, Δx L is the amplitude change of the state variable load node voltage, f L ' xL 、f L ' y Δw is the sensitivity of the node voltage to the change in active power injected into the node and the change in transformer ratio, L is the change in active power injected into the node, and Δy is the change in transformer ratio.

8. The method according to claim 1, characterized in that The real-time operation data is brought into a pre-built voltage-control quantity sensitivity model, and an approximate dynamic programming method is used to solve the model to obtain an optimal control strategy, including: Step 1: Let the number of iterations k = 0, and the optimal control strategy K obtained by the disturbance e, state variable x, control cost weight matrix R, and kth iteration k , construct control strategy u k , record the parameters of the state variable x at each moment until the recording termination condition is met; Step 2: Solve the solution P of the algebraic Riccati equation in the kth iteration based on the parameters of the state variable x at each time k and L k , where L k The input matrix and P k Determined intermediate variables; Step 3: Based on the solution P of the algebraic Riccati equation in the kth iteration k and L k Update the control strategy and get the control strategy u k+1 , calculate the absolute value of the difference between the solution of the algebraic Riccati equation in the kth iteration and the solution of the algebraic Riccati equation in the k-1th iteration, and determine whether the absolute value of the difference is less than a set threshold. If so, proceed to step 5, otherwise proceed to step 4; Step 4: Set k=k+1 and return to step 1; Step 5: Let u = -K k x is used as the optimal control strategy for the system.

9. The method according to claim 8, characterized in that The control strategy is shown below: In the formula, u k is the control strategy for the kth iteration, K k is the optimal control strategy obtained in the kth iteration, k is the number of iterations, e is the disturbance to the system, x is the state variable, L k-1 is an intermediate variable determined by the input matrix and the solution of the algebraic Riccati equation in k-1 iterations, and R is the weight matrix of the control cost.

10. The method according to claim 8, characterized in that The parameters of the state variable x at each moment are shown as follows: In the formula, δ xx ,I xx and I xu are the first intermediate variable, the second intermediate variable and the third intermediate variable respectively, [t j ,t j +δt], j = 1, 2, …, h are some non-overlapping time periods; is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h The transpose of u T is the control strategy matrix transpose; x T is the transpose of the state variable x; τ is the time period [t j ,t j +δt] within the sampling interval of the state variable x.

11. The method according to claim 10, characterized in that The recording termination conditions are as follows: Where N is the number of state variables and M is the number of control variables.

12. A substation-feeder-area voltage and reactive power coordinated control system, characterized in that: include: Parameter acquisition module, used to obtain real-time operation data of the distribution network; A solution module is used to bring the real-time operation data into a pre-built voltage-control quantity sensitivity model, and use an approximate dynamic programming method to solve it to obtain an optimal control strategy; The voltage-control quantity sensitivity model is constructed with the goal of optimal control effect and minimum control cost and with the voltage regulation capability of the distributed adjustable resources under the substation as a constraint.

13. The system of claim 12, wherein: Also included are model building modules for: Adopting the quadratic regulator theory, the objective function is constructed with the optimal control effect and the minimum control cost as the goal; Setting operating constraints for the objective function; The operation constraints include: physical constraints of distributed photovoltaics, physical constraints of energy storage, physical constraints of reactive power compensation on the load side, and network constraints.

14. The system of claim 13, wherein: The objective function is shown in the following formula: Where J is the target value to be optimized that comprehensively considers the state variable deviation and control cost, x is the state variable, and x T is the transposed matrix of the state variables, Q is the weight matrix of the state variable deviation, u T is the transpose of the control strategy matrix, and R is the weight matrix of the control cost.

15. The system of claim 13, wherein: The physical constraints of distributed photovoltaics are as follows: In the formula, is the predicted active power of the PV of node i at time t when it works at the maximum power point; are the injected active power and reactive power of the PV of node i at time t, respectively; is the installed capacity of the PV of node i; It is the decision variable of whether the PV of node i participates in the scheduling operation at time t, where 1 means participation and 0 means no participation.

16. The system of claim 13, wherein: The physical constraints of the energy storage are as follows: Where W i ES (t+1) is the energy storage state of node i at time t+1, W i ES (t) is the energy storage state of node i at time t; is the energy storage loss ratio of node i; and are the charging and discharging efficiencies of energy storage at node i respectively; and are the charging power and discharging power of the energy storage at node i at time t respectively; and are the maximum charging power and maximum discharging power of energy storage at node i respectively; is a 0 or 1 variable indicating whether the energy storage at node i is in a charging state or a discharging state at time t. and are the minimum and maximum values ​​of the energy storage state of node i respectively; W i ES (0) is the energy storage state of node i at the initial time 0; W i ES (T) is the energy storage state of node i at the final time T; Δt is the duration from time t to time t+1.

17. The system of claim 13, wherein: The physical constraints of the load-side reactive power compensation are as follows: In the formula, They are the upper and lower limits of SVC reactive power respectively; is the reactive power of the SVC at node i at time t.

18. The system of claim 13, wherein: The network constraints are as follows: Δx L =-(f L ' xL ) -1 (Δw L -f L ' y Δy) In the formula, Δx L is the amplitude change of the state variable load node voltage, f L ' xL 、f L ' y Δw is the sensitivity of the node voltage to the change in active power injected into the node and the change in transformer ratio, L is the change in active power injected into the node, and Δy is the change in transformer ratio.

19. The system of claim 12, wherein: The solution module is specifically used for: Step 1: Let the number of iterations k = 0, and the optimal control strategy K obtained by the disturbance e, state variable x, control cost weight matrix R, and kth iteration k , construct control strategy u k , record the parameters of the state variable x at each moment until the recording termination condition is met; Step 2: Solve the solution P of the algebraic Riccati equation in the kth iteration based on the parameters of the state variable x at each time k and L k , where L k The input matrix and P k Determined intermediate variables; Step 3: Based on the solution P of the algebraic Riccati equation in the kth iteration k and L k Update the control strategy and get the control strategy u k+1 , calculate the absolute value of the difference between the solution of the algebraic Riccati equation in the kth iteration and the solution of the algebraic Riccati equation in the k-1th iteration, and determine whether the absolute value of the difference is less than a set threshold. If so, proceed to step 5, otherwise proceed to step 4; Step 4: Set k=k+1 and return to step 1; Step 5: Let u = -K k x is used as the optimal control strategy for the system.

20. The system of claim 19, wherein: The control strategy u k As shown below: In the formula, u k is the control strategy for the kth iteration, K k is the optimal control strategy obtained in the kth iteration, k is the number of iterations, e is the disturbance to the system, x is the state variable, L k-1 is an intermediate variable determined by the input matrix and the solution of the algebraic Riccati equation in k-1 iterations, and R is the weight matrix of the control cost.

21. The system of claim 19, wherein: The parameters of the state variable x at each moment are shown as follows: In the formula, δ xx ,I xx and I xu are the first intermediate variable, the second intermediate variable and the third intermediate variable respectively, [t j ,t j +δt], j = 1, 2, …, h are some non-overlapping time periods; is the vector of state variables x The transpose at time t1+δt, is the vector of state variables x The transpose at time t1, is the vector of state variables x The transpose at time t2+δt, is the vector of state variables x The transpose at time t2, is the vector of state variables x At time t h +δt transpose, is the vector of state variables x At time t h The transpose of u T is the control strategy matrix transpose; x T is the transposed matrix of the state variables; τ is the time period [t j ,t j +δt] within the sampling interval of the state variable x.

22. The system of claim 21, wherein: The recording termination conditions are as follows: Where N is the number of state variables and M is the number of control variables.

23. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a substation-feeder-area voltage-reactive power coordinated control method according to any one of claims 1 to 11 is implemented.

24. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a substation-feeder-area voltage and reactive power coordinated control method as described in any one of claims 1 to 11 is implemented.

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