Distribution microgrid cooperative reactive voltage optimization control method and device

Abstract

The invention provides a distribution microgrid cooperative reactive voltage optimization control method and device, and belongs to the field of power system operation regulation and control. The method comprises the following steps: establishing a distribution microgrid collaborative reactive voltage optimization model; after a constraint condition of the optimization model is relaxed, the optimization model is converted into a main problem of reactive voltage optimization of the power distribution network and a sub-problem of reactive voltage optimization of the micro-grid; by introducing slack variables, feasibility conversion is carried out on the sub-problems, an approximate projection function and an optimal cut plane of the sub-problems of reactive voltage optimization of the micro-grid about boundary parameters are established, and then the main problem of reactive voltage optimization of the power distribution network and the sub-problems of reactive voltage optimization of the micro-grid are solved. Therefore, power distribution and micro-grid cooperative reactive voltage optimization control is realized. Through application of a space decomposition strategy and a multi-parameter space projection algorithm, cooperative reactive power optimization control under a distributed renewable resource access situation is realized while internal privacy information of different main bodies of the power distribution microgrid is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of power system operation and regulation, and specifically relates to a distribution microgrid coordinated reactive voltage optimization control method and device. Background Art

[0002] A large number of distributed renewable energy, energy storage and other flexible resources are connected to distribution networks and microgrids, and the regulation of distribution networks and microgrids has become increasingly complex. If there is a lack of coordination between distribution and microgrids, node voltage limit problems may occur in extreme scenarios.

[0003] In recent years, with the widespread access of new energy, the coupling of transmission and distribution networks has been enhanced, the flow operation mode has changed, and problems such as flow return and voltage over-limit have frequently occurred. In order to give full play to the flexibility of the microgrid side to adapt to the widespread access of distributed new energy, the coordinated operation of the distribution network and the microgrid is crucial. The coordinated reactive power optimization of the transmission and distribution network is essentially a multi-period dynamic optimization problem covering multiple dispatching time scales such as day-ahead and intraday. In recent years, with the large-scale access of distributed resources to the power grid, the power system faces the following key challenges:

[0004] i) Large-scale power grids and a large number of distributed resources bring heavy computational burdens. At the same time, intraday rolling scheduling places high demands on the computational performance of distributed algorithms. Therefore, the problem of coordinated regulation of distribution and microgrids in multiple time periods urgently requires methods with fast convergence and good scalability.

[0005] ii) As the penetration rate of renewable energy continues to increase, problems such as voltage exceeding the limit and new energy power abandonment have become more serious. The traditional distribution network and microgrid split scheduling model faces limitations, and it is necessary to introduce efficient collaborative control algorithms to improve the safety and economy of power grid operation.

[0006] iii) As more and more energy storage systems and thermal loads are connected to the power system, the operation of the power grid presents a strong time coupling characteristic, which not only further increases the computational burden but also impairs the convergence performance of traditional distributed algorithms.

[0007] Therefore, it is urgent to break through the dispatching mode in which distribution and microgrids are separated from each other, so as to eliminate voltage safety hazards, reasonably distribute reactive power flows, and achieve optimal operation of grid costs. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the existing technology and propose a distribution microgrid coordinated reactive power voltage optimization control method and device. The present invention uses a spatial decomposition strategy and a multi-parameter spatial projection algorithm to protect the privacy information of different entities in the distribution microgrid while realizing the coordinated reactive power optimization control under the distributed renewable resource access scenario, effectively improving the efficiency of line optimization scheduling.

[0009] The first embodiment of the present invention provides a method for optimizing reactive power and voltage in a distribution microgrid, including:

[0010] Establish a coordinated reactive power and voltage optimization model for distribution microgrids;

[0011] By relaxing the constraints of the optimization model, the optimization model is converted into a main problem of reactive power voltage optimization of the distribution network and a sub-problem of reactive power voltage optimization of the microgrid;

[0012] By introducing slack variables, the sub-problem of reactive power and voltage optimization of the microgrid is transformed into a feasible one;

[0013] Based on the result of the feasibility conversion, an approximate projection function and an optimal cutting plane of the sub-problem of the microgrid reactive voltage optimization with respect to boundary parameters are established;

[0014] Based on the approximate projection function and the optimal cutting plane, the main problem of the reactive voltage optimization of the distribution network and the sub-problem of the reactive voltage optimization of the microgrid are solved to achieve coordinated reactive voltage optimization control of the distribution and microgrid.

[0015] In a specific embodiment of the present invention, the objective function expression of the distribution microgrid coordinated reactive power voltage optimization model is as follows:

[0016] min(C Ploss +C Pen-Volt +C DG-Cur )(1)

[0017] in,

[0018]

[0019] In the formula, C Ploss is the penalty function of network loss, C Pen-Volt is the penalty function for voltage exceeding the limit, C DG-Cur is the penalty function of the new energy reduction value; σ V and σ DG are penalty coefficients; v ref is the square of the node voltage setting value; is the specified value of the output of new energy unit i; v i,t is the square of the voltage amplitude of node i in time period t, l ij,t is the square of the current amplitude of branch ij in time period t, r ji is the resistance of branch j; is the output value of renewable energy unit i in period t; B is the set of network nodes, and DG is the set of renewable energy units.

[0020] In a specific embodiment of the present invention, the constraints of the distribution microgrid coordinated reactive power voltage optimization model include:

[0021] Branch flow equation constraints:

[0022]

[0023] Where P ij,t and Q ij,t are respectively the active power flow and reactive power flow of branch ij at time period t; v i,t is the square of the voltage amplitude of node i in time period t; l ij,t is the square of the current amplitude of branch ij in time period t; L is the set of lines;

[0024] Node injection balance constraints:

[0025]

[0026] In the formula, and are respectively the active processing and reactive output of the mth renewable energy unit in time period t; and are the charging power and discharging power of the nth energy storage in time period t respectively; Q ESS,n,t and Q SVC,n,t are the reactive power outputs of the nth energy storage and static VAR compensator in time period t respectively; and are respectively the active power demand and reactive power demand of the mth load in time period t; r ji With x ji are the resistance and reactance of branch ji respectively;

[0027] Branch voltage drop constraint:

[0028]

[0029] Voltage safety constraints:

[0030]

[0031] In the formula, and v i are the upper and lower limits of the square of the voltage safety amplitude at node i respectively;

[0032] Output constraints of new energy units:

[0033]

[0034] In the formula, and are the upper and lower limits of the active power of the new energy unit i, and are the upper and lower limits of reactive power generation of new energy unit i respectively;

[0035] Energy storage operation constraints:

[0036]

[0037]

[0038] In the formula, E ESS,n,t is the energy level of the nth energy storage in time period t; E ESS,n,0 ,E ESS,n and are the initial energy, the minimum energy level allowed and the maximum energy level allowed of the nth energy storage, respectively; and are the charging efficiency and discharging efficiency of the nth energy storage, respectively; and are the maximum charging power and maximum discharging power of the nth energy storage respectively; S ESS,n is the apparent capacity of the nth energy storage;

[0039] Reactive power compensation device operation constraints:

[0040]

[0041] In the formula, Q SVC,n and are the lower and upper limits of reactive power output of the nth static VAR compensator respectively;

[0042] Line capacity constraints:

[0043]

[0044] In the formula, It is the upper limit of the square of the current amplitude of branch ij.

[0045] In a specific embodiment of the present invention, the conversion of the optimization model into the main problem of reactive power voltage optimization of the distribution network and the sub-problem of reactive power voltage optimization of the microgrid includes:

[0046] 1) Relax the constraints of the coordinated reactive power and voltage optimization model of the distribution microgrid;

[0047] Among them, equation (5) is convexly relaxed and becomes:

[0048]

[0049] 2) Based on the results of step 1), the distribution microgrid coordinated reactive power and voltage optimization model is converted into a second-order cone programming problem;

[0050] Among them, the converted distribution microgrid coordinated reactive power and voltage optimization model is written as follows:

[0051]

[0052] In the formula, x D With x M(n) are the variables of the distribution network and microgrid n, respectively, D (x D ) and f M(n) (x M(n) ) are the optimization targets of the distribution network and microgrid n respectively, IM is the number set of microgrids, X D With X M(n) are the constraint sets of the distribution network and microgrid n respectively, constraint A n x D =B n x M(n) is the boundary coupling constraint between the distribution network and the microgrid n, that is, the active power, reactive power and the square of the voltage amplitude at the boundary node are matched;

[0053] 3) Based on the results of step 2), the main problem of reactive power and voltage optimization of distribution network and the sub-problem of reactive power and voltage optimization of microgrid are established;

[0054] Among them, based on formula (20), the main problem of reactive power and voltage optimization of distribution network is constructed:

[0055]

[0056] Construct the sub-problems of microgrid reactive power and voltage optimization:

[0057]

[0058] In the formula, B is the boundary variable given by the distribution network; n x M(n) is the boundary variable connecting the microgrid n with the upper distribution network.

[0059] In a specific embodiment of the present invention, the feasibility conversion of the sub-problem of the microgrid reactive power voltage optimization by introducing slack variables includes:

[0060] Introducing slack variables α into microgrid n n With β n , through the penalty coefficient The two slack variable penalties are introduced into the sub-problem corresponding to microgrid n:

[0061]

[0062] At the optimal solution of the optimization problem (23), the constraint The dual multiplier of n , constraint α n The dual multiplier ≥0 is denoted by Constrained β n The dual multiplier ≥0 is denoted by

[0063] According to the KKT conditions:

[0064]

[0065] in:

[0066]

[0067] Simplify formula (25):

[0068]

[0069] In a specific embodiment of the present invention, the establishment of the sub-problem of reactive power voltage optimization of the microgrid with respect to the approximate projection function and the optimal cutting plane of the boundary parameters includes:

[0070] 1) Given boundary variables The reactive power and voltage optimization problem of the microgrid n with relaxed boundary constraints can be written as follows:

[0071]

[0072] In the formula, the constraint Contains internal constraints and boundary constraints of microgrid n;

[0073] The optimal solution of equation (27) is The dual multiplier at the optimal solution is Then the optimal objective of microgrid n with respect to boundary variables The sensitivity is:

[0074]

[0075] The expansion point of the partial differential of formula (28) is

[0076] 2) Project the optimization problem (27) onto the boundary variable A n x D In terms of space, the optimal objective of (27) is considered to be mapped to the boundary variable A n x D The function of is called the projection function;

[0077] Then equation (27) is n xD The approximate projection function of the space is:

[0078]

[0079] 3) Discard the second-order expansion term in equation (28) and obtain the optimal cutting plane:

[0080]

[0081] Among them, the first-order expansion of formula (29) is the optimal cutting plane, denoted as OC n (A n x D ).

[0082] In a specific embodiment of the present invention, solving the main problem of reactive voltage optimization of the distribution network and the sub-problem of reactive voltage optimization of the microgrid includes:

[0083] 1) Initialize the number of iterations k = 1;

[0084] 2) The distribution network calculates the optimization problem equation (21) and obtains the optimal solution x D ; x D Denote the optimal solution x of the kth iteration D (k) , then x D (k) The corresponding boundary variable value is A n x D (k) is transmitted to the corresponding microgrids; when k=1, A n x D (k) The value of is obtained through initialization;

[0085] 3) Judgment:

[0086] 3-1) If k = 1, the microgrid performs sub-problem optimization of fixed boundary variables as shown in equation (30) to obtain the optimized variable x M(n) , then go to step 4);

[0087] 3-2) If k>1, each microgrid compares the boundary variable A transmitted by the distribution network n x D (k) A from the previous iteration n x D (k-1) Are they equal, where:

[0088] If they are equal, then go to step 5); otherwise, the microgrid performs sub-problem optimization of fixed boundary variables as shown in formula (30) to obtain the optimized variable x M(n), then go to step 4);

[0089]

[0090] 4) Microgrid will A n x D (k) As the target expansion point of the current partial differential, Substitute into equation (28) and equation (29) to generate A n x D (k) The approximate projection function and the optimal cutting plane at are denoted as and and transmitted to the distribution network, where p n The total number of optimal cutting planes generated for the microgrid n iterations;

[0091] 5) The distribution network is optimized by combining all the optimal cutting planes collected, as shown in the following formula:

[0092]

[0093] In the formula, o n represents the optimal target value of microgrid n, which is an intermediate variable; IM represents the set of microgrids;

[0094] Let the number of iterations k increase by 1, and denote the optimal solution of equation (31) as x D (k) , o n (k) ;

[0095] 6) Judgment:

[0096] 6-1) If k = 1, return to step 2) and continue iterating;

[0097] 6-2) If k>1, determine whether ||x is satisfied D (k) -x D (k-1) || 2 ≤ε, ε is the preset positive threshold, where:

[0098] If it is satisfied, the solution converges. The optimal solution of the main problem corresponding to the distribution network and the sub-problems corresponding to each microgrid in the most recent iteration is the global optimal solution. D With x M(n) Extract the active output and reactive output of the new energy unit Energy storage charging and discharging power And the output value Q of the reactive power compensation device SVC,n,t , realize the optimal control of reactive power and voltage in the distribution microgrid;

[0099] If it is not satisfied, the solution does not converge, and return to step 2) to continue iterating.

[0100] The second embodiment of the present invention provides a distribution microgrid coordinated reactive voltage optimization control device, comprising:

[0101] Optimization model building module, used to establish a coordinated reactive power and voltage optimization model for distribution microgrids;

[0102] An optimization model conversion module, used to convert the optimization model into a main problem of reactive power voltage optimization of distribution network and a sub-problem of reactive power voltage optimization of microgrid by relaxing the constraint conditions of the optimization model;

[0103] A sub-problem conversion module, used for performing feasibility conversion on the sub-problem of reactive power voltage optimization of the microgrid by introducing slack variables;

[0104] An optimization variable expansion module, used to establish an approximate projection function and an optimal cutting plane of the sub-problem of reactive power voltage optimization of the microgrid with respect to boundary parameters based on the result of the feasibility conversion;

[0105] A solution module is used to solve the main problem of reactive voltage optimization of the distribution network and the sub-problem of reactive voltage optimization of the microgrid based on the approximate projection function and the optimal cutting plane, so as to realize the coordinated reactive voltage optimization control of the distribution and microgrid.

[0106] A third aspect of the present invention provides an electronic device, including:

[0107] at least one processor; and a memory communicatively coupled to the at least one processor;

[0108] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above-mentioned distribution microgrid coordinated reactive voltage optimization control method.

[0109] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned distribution microgrid coordinated reactive voltage optimization control method.

[0110] The characteristics and beneficial effects of the present invention are:

[0111] 1. The present invention is reliable, fast and easy to use.

[0112] 2. The present invention can effectively solve the problems of unreasonable reactive power flow and unsafe voltage fluctuation faced by the split dispatching of distribution and microgrid in traditional dispatching scenarios. It can cope with the situation where distributed renewable energy, energy storage and other equipment are connected to the distribution network in large quantities, thereby improving the safe operation level of the distribution network and the absorption capacity of new energy.

[0113] 3. The present invention adopts a multi-parameter spatial projection decomposition algorithm in the spatial dimension, which has the characteristics of superlinear convergence, no parameter adjustment and good scalability.

[0114] 4. The present invention has a certain robustness to communication conditions, and its high computing performance can effectively reduce the occupation of the communication bandwidth of the power grid system, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 The present invention is an overall flow chart of a method for coordinated reactive power voltage optimization control of a distribution microgrid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0116] The present invention proposes a distribution microgrid coordinated reactive power voltage optimization control method and device, which are further described in detail below with reference to the accompanying drawings and specific embodiments.

[0117] The first embodiment of the present invention provides a method for optimizing reactive power and voltage in a distribution microgrid, including:

[0118] Establish a coordinated reactive power and voltage optimization model for distribution microgrids;

[0119] By relaxing the constraints of the optimization model, the optimization model is converted into a main problem of reactive voltage optimization of the distribution network and a sub-problem of reactive voltage optimization of the microgrid; by introducing relaxation variables, the sub-problem of reactive voltage optimization of the microgrid is converted into a feasibility;

[0120] Based on the result of the feasibility conversion, an approximate projection function and an optimal cutting plane of the sub-problem of the microgrid reactive voltage optimization with respect to boundary parameters are established;

[0121] Based on the approximate projection function and the optimal cutting plane, the main problem of the reactive voltage optimization of the distribution network and the sub-problem of the reactive voltage optimization of the microgrid are solved to achieve coordinated reactive voltage optimization control of the distribution and microgrid.

[0122] In a specific embodiment of the present invention, the overall process of the method for optimizing the control of the coordinated reactive power and voltage of a distribution microgrid is as follows: Figure 1 As shown, the following steps are included:

[0123] 1) Establish a coordinated reactive power and voltage optimization model for distribution microgrids; the specific steps are as follows:

[0124] 1-1) Establish the objective function of the coordinated reactive power and voltage optimization model of the distribution microgrid.

[0125] In this embodiment, a distribution-microgrid collaborative architecture in which a distribution network is connected to several microgrids is considered. The objective function of the distribution-microgrid collaborative reactive voltage optimization model is to minimize the total network loss of the system, the deviation of the voltage from the set value, and the sum of the reduction of new energy. The expression is as follows:

[0126] min(C Ploss +C Pen-Volt +C DG-Cur )(1)

[0127] in,

[0128]

[0129] In the formula, C Ploss is the penalty function of network loss, C Pen-Volt is the penalty function for voltage exceeding the limit, C DG-Cur is the penalty function of the new energy reduction value. V and σ DG are penalty coefficients; v ref is the square of the node voltage setting value. is the specified value of the output of new energy unit i. i,t is the square of the voltage amplitude of node i in time period t, l ij,t is the square of the current amplitude of branch ij in time period t, r ji is the resistance of branch ji. is the output value of renewable energy unit i in period t. B is the set of network nodes. DG is the set of renewable energy units.

[0130] 1-2) Establish the constraint conditions of the coordinated reactive power and voltage optimization model of the distribution microgrid.

[0131] In this embodiment, for radial distribution networks and microgrids, the branch optimal power flow model usually has better numerical characteristics. This model makes full use of the properties of radial power grids. Since there is no loop, there is no need for phase angle constraints, and the phase angle is no longer introduced as a variable in the model. On the other hand, the model introduces variables about the amplitude of branch current, and the charging susceptance of the branch is ignored here, so that the current at the beginning and end of the same branch will be the same.

[0132] Specifically, the constraints include:

[0133] Branch flow equation constraints:

[0134]

[0135] Where P ij,t and Qij,t are respectively the active power flow and reactive power flow of branch ij at time period t; v i,t is the square of the voltage amplitude of node i in time period t; l ij,t is the square of the current amplitude of branch ij in time period t; L is the collection of lines.

[0136] Node injection balance constraints:

[0137]

[0138] In the formula, and are respectively the active processing and reactive output of the mth renewable energy unit in time period t; and are the charging power and discharging power of the nth energy storage in time period t respectively; Q ESS,n,t and Q SVC,n,t are the reactive power outputs of the nth energy storage and static VAR compensator in time period t respectively; and are respectively the active power demand and reactive power demand of the mth load in time period t; r ji With x ji are the resistance and reactance of branch ji respectively.

[0139] Branch voltage drop constraint:

[0140]

[0141] Where P ij,t and Q ij,t are respectively the active power flow and reactive power flow of branch ij at time period t; v i,t is the square of the voltage amplitude of node i in time period t; l ij,t is the square of the current amplitude of branch ij in time period t; L is the set of lines. ji With x ji are the resistance and reactance of branch ji respectively.

[0142] Voltage safety constraints:

[0143]

[0144] In the formula, and v i are the upper and lower limits of the square of the voltage safety amplitude at node i respectively.

[0145] Output constraints of new energy units:

[0146]

[0147] In the formula, and are the upper and lower limits of the active power of the new energy unit i, and They are respectively the upper and lower limits of the reactive power generated by the new energy unit i.

[0148] Energy storage operation constraints:

[0149]

[0150] In the formula, E ESS,n,t is the energy level of the nth energy storage in time period t; E ESS,n,0 ,E ESS,n and are the initial energy, the minimum energy level allowed and the maximum energy level allowed of the nth energy storage, respectively; and are the charging efficiency and discharging efficiency of the nth energy storage, respectively; and are the maximum charging power and maximum discharging power of the nth energy storage respectively; S ESS,n is the apparent capacity of the nth energy storage.

[0151] Reactive power compensation device operation constraints:

[0152]

[0153] In the formula, Q SVC,n and are the lower and upper limits of the reactive power output of the nth static VAR compensator respectively.

[0154] Line capacity constraints:

[0155]

[0156] In the formula, It is the upper limit of the square of the current amplitude of branch ij.

[0157] 2) By relaxing the constraints of the model established in step 1), the model established in step 1) is converted into the main problem of reactive power voltage optimization of the distribution network and the sub-problem of reactive power voltage optimization of the microgrid; the specific steps are as follows:

[0158] 2-1) Relax the constraints of the model established in step 1).

[0159] In this embodiment, the non-convex constraint in the model established in step 1) is equation (5), and equation (5) can be convexly relaxed to become:

[0160]

[0161] Because v i With lij As the square of the voltage and current amplitude, it naturally has the property of being non-negative, so the constraint of equation (19) is a standard rotated second-order cone constraint, which is a convex constraint.

[0162] 2-2) Based on the result of step 2-1), the model established in step 1) is converted into a second-order cone programming problem.

[0163] In this embodiment, by replacing equation (5) with equation (19), a convexly relaxed distribution microgrid coordinated reactive power and voltage optimization model can be obtained. The relaxed model is a second-order cone programming problem.

[0164] Then the distribution microgrid coordinated reactive power and voltage optimization model constructed in step 1) can be written as follows:

[0165]

[0166] In the formula, x D With x M(n) are the variables of the distribution network and microgrid n, respectively, D (x D ) and f M(n) (x M(n) ) are the optimization targets of the distribution network and microgrid n respectively, IM is the number set of microgrids, X D With X M(n) are the constraint sets of the distribution network and microgrid n respectively, constraint A n x D =B n x M(n) is the boundary coupling constraint between the distribution network and the microgrid n, that is, the active power, reactive power and the square of the voltage amplitude at the boundary node are matched.

[0167] 2-3) Based on the results of step 2-2), the main problem of reactive power voltage optimization of distribution network and the sub-problem of reactive power voltage optimization of microgrid are established.

[0168] Specifically, based on formula (20), the main problem of reactive power and voltage optimization of distribution network is constructed:

[0169]

[0170] For each microgrid, the subproblem is a bounded reactive power voltage optimization problem:

[0171]

[0172] In the formula, B is the boundary variable given by the distribution network and does not participate in the optimization. n x M(n)is the boundary variable connecting microgrid n with the upper distribution network, and participates in the optimization of the sub-problem. When the distribution network transmits boundary information with each microgrid, the distribution network does not transmit the complete Instead of passing it to each microgrid, the mapped boundary variables As a whole, the boundary variables in the subsequent analysis of each microgrid are Rather than

[0173] Furthermore, in a specific embodiment of the present invention, the concept of feasibility conversion of microgrid sub-problems is introduced. The necessity and process of feasibility conversion are briefly described below.

[0174] In the microgrid subproblem of the form (22), the microgrid boundary variables and the upper distribution network boundary variables are connected by This equality constraint is linked together. During the iteration process, As the constraint parameter of the subproblem, it may happen that the subproblem has no solution due to unreasonable values, so the subproblem cannot return the corresponding lower-level optimal solution. Therefore, this embodiment adopts the form of introducing slack variables and penalty terms to perform feasibility conversion, ensuring that The subproblem can still be solved when the value is unreasonable.

[0175] 3) Based on the results of step 2), the feasibility transformation of the sub-problem of microgrid reactive power and voltage optimization is performed by introducing slack variables.

[0176] This embodiment introduces the concept of feasibility conversion by using a multi-parameter spatial projection decomposition algorithm, which specifically includes:

[0177] Two slack variables α are introduced for the microgrid n n With β n , and through the penalty coefficient These two slack variable penalties are introduced into the sub-problems corresponding to microgrid n:

[0178]

[0179] In this embodiment, the slack variable α is introduced n With β n After that, it does not affect the optimal solution of the original distribution microgrid coordinated reactive power and voltage optimization model (20) before the boundary constraint relaxation. To this end, an intuitive solution is to set the penalty coefficient Set it large enough to avoid the boundary constraints from being relaxed. However, this is only theoretically feasible. If it is set too large, it will cause serious numerical problems. Therefore, it is necessary to penalize the relaxation term appropriately.

[0180] At the optimal solution of the optimization problem (23), the constraint The dual multiplier of n , constraint α n The dual multiplier ≥0 is denoted by Constrained β n The dual multiplier ≥0 is denoted by

[0181] According to the KKT condition (Karashi-Kuhn-Tucker condition):

[0182]

[0183] In order to ensure the optimal solution μ of the dual problem after adding the penalty term after relaxing the boundary constraints n If no changes occur, you need to ensure that:

[0184]

[0185] Formula (25) can be simplified to:

[0186]

[0187] Therefore, as long as the penalty coefficient is guaranteed The optimal solution of the original distribution microgrid coordinated reactive power voltage optimization model (20) is constrained by A. n x D =B n x M(n) If the absolute value of the dual multiplier of , then relaxing the bound constraints will not change the optimal solution.

[0188] In actual calculation, the boundary constraint A n x D =B n x M(n) The dual multiplier of cannot be known in advance, but an estimate of the dual multiplier of the constraint can be obtained through historical operation data. In order to leave a certain margin, the penalty term coefficient The value of is set to be an order of magnitude higher than the dual multiplier estimate, which generally does not cause numerical problems.

[0189] 4) Based on the results of step 3), the optimization variables of the coordinated reactive power voltage optimization model of the distribution microgrid are expanded, and the approximate projection function of the boundary parameters and the optimal cutting plane of the sub-problem of reactive power voltage optimization of the microgrid are established.

[0190] Specifically, if the variable x of microgrid n is M(n) Expand, so that the expanded variable x M(n) Contains the original variable x M(n) and the slack variable α n With β n, and make appropriate transformations to the original constraint set and boundary constraint matrix. It can be found that the boundary constraint after relaxation, Equation (23) can also be written in the abstract form of the equation before relaxation. This shows that the feasibility conversion of the microgrid optimization problem does not affect the form of the original abstract model. For the sake of simplicity of expression, unless otherwise emphasized, this embodiment continues to use the original abstract model formula (20) by default, and assumes that each microgrid has undergone feasibility conversion, and there will be no situation where the microgrid is infeasible due to inappropriate boundary variable values ​​transmitted by the distribution network. In this embodiment, after the microgrid subproblem is expanded, the abstract model formula (20) is still used to express the microgrid subproblem.

[0191] In a specific embodiment of the present invention, the multi-parameter spatial projection decomposition algorithm introduces an approximate projection function of the lower-level microgrid sub-problem with respect to the boundary parameters and an optimal cutting plane, including:

[0192] 4-1) Analysis of optimality conditions for microgrid sub-problems.

[0193] Given boundary variables The reactive power and voltage optimization problem of the microgrid n with relaxed boundary constraints can be written as follows:

[0194]

[0195] In the formula, the constraint Contains the internal constraints and boundary constraints of microgrid n.

[0196] Assume that the optimal solution of equation (27) is The dual multiplier at the optimal solution is According to the physical meaning of the dual multiplier, the optimal objective of the microgrid n is about the boundary variable The sensitivity is:

[0197]

[0198] The expansion point of the partial differential of formula (28) is

[0199] 4-2) Derivation of boundary parameter space projection function.

[0200] Project the optimization problem (27) onto the boundary variable A n x D In terms of space, the optimal objective of (27) is considered to be mapped to the boundary variable A n x D The function of is called the projection function.

[0201] Then equation (27) is n x D The approximate projection function of the space is:

[0202]

[0203] 4-3) Generate optimal cuts for subproblems.

[0204] Considering that the optimal cutting plane calculated previously is essentially the first-order expansion of the projection function with respect to the boundary variables, discarding the second-order expansion term in equation (28) can give the optimal cutting plane:

[0205]

[0206] In this embodiment, the first-order expansion of equation (29) is called the optimal cutting plane, denoted as OC n (A n x D ). According to the continuous convexity of the microgrid optimal power flow problem, the projection function is also a convex function. Therefore, the optimal cutting plane is the strict lower bound of the projection function. This property can be used to form the basic iterative steps of the distribution-microgrid collaborative optimization.

[0207] However, it is worth noting that the optimal cutting plane is a strict lower bound of the projection function, but the approximate projection function cannot be guaranteed to be a strict lower bound of the projection function. Therefore, the approximate projection function can only be used as an accelerated convergence correction term in each iteration in the distribution network problem, and cannot be retained in each iteration like the optimal cutting plane.

[0208] 5) Based on the results of step 4), the multi-parameter spatial projection decomposition algorithm is used to solve the main problem of reactive power voltage optimization of distribution network and the sub-problem of reactive power voltage optimization of microgrid.

[0209] In this embodiment, an approximate projection function correction term is added on the basis of the generalized Benders decomposition to form an iterative optimization step, which specifically includes:

[0210] 5-1) Initialize the number of iterations k = 1.

[0211] 5-2) The distribution network calculates the optimization problem equation (21) and obtains the optimal solution x D ; x D Denote the optimal solution x of the kth iteration D (k) , then x D (k) The corresponding boundary variable value is A n x D (k) is transmitted to the corresponding microgrids. When k=1, A n x D (k) The value of is obtained through initialization.

[0212] 5-3) Judgment:

[0213] 5-3-1) If k = 1, the microgrid directly performs sub-problem optimization of fixed boundary variables as shown in formula (30) to obtain the optimized variable x M(n) , then go to step 5-4);

[0214] 5-3-2) If k>1, each microgrid compares the boundary variable A transmitted by the distribution network n x D (k) Compared with A in the previous iteration n x D (k-1) Are they equal, where:

[0215] If they are equal, go directly to step 5-5); otherwise, the microgrid performs sub-problem optimization of fixed boundary variables as shown in formula (30) to obtain the optimized variable x M(n) , then go to step 5-4).

[0216]

[0217] 5-4) Microgrid will A n x D (k) As the target expansion point of the current partial differential (i.e. ) into equation (28) and equation (29), generating A n x D (k) The approximate projection function and the optimal cutting plane at are denoted as and and transmitted to the distribution network, where p n is the total number of optimal cutting planes generated for the microgrid in n iterations.

[0218] 5-5) The distribution network is optimized by combining all the optimal cutting planes collected, as shown in the following formula:

[0219]

[0220] In the formula, o n It represents the optimal target value of microgrid n and is an intermediate variable. IM represents the set of microgrids.

[0221] Let the number of iterations k increase by 1, and denote the optimal solution of equation (31) as x D (k) , o n (k) .

[0222] 5-6) Judgment:

[0223] 5-6-1) If k = 1, return to step 5-2) and continue iterating;

[0224] 5-6-2) If k>1, determine whether ||x is satisfied D (k) -x D (k-1) || 2 ≤ε, ε is a small positive threshold (such as 1e-3), where:

[0225] If it is satisfied, the solution converges, and the optimal solution of the main problem corresponding to the distribution network and the sub-problems corresponding to each microgrid in the most recent iteration is the global optimal solution. D With x M(n) Extract the active output and reactive output of the new energy unit Energy storage charging and discharging power And the output value Q of the reactive power compensation device SVC,n,t , to achieve optimal control of reactive power and voltage in coordinated distribution microgrids.

[0226] If it is not satisfied, the solution does not converge, and the process returns to step 5-2) and continues iterating.

[0227] In the above iterative steps of this embodiment, the most recently generated optimal cutting plane is replaced by an approximate projection function in the distribution network coordination problem equation (31) to improve the convergence speed.

[0228] Furthermore, the information exchanged between the distribution microgrids during the iteration process is analyzed here. In each iteration, the distribution network sends active power, reactive power, and voltage amplitude information to the microgrid boundary, and the microgrid returns an approximate projection function of its cost on these boundary information. Since only boundary physical information and boundary price information are exchanged, the sharing of boundary physical information and boundary price information is also a necessary condition for coordination between distribution microgrids. In addition, considering that the boundary physical information and boundary price information at the optimal solution are the same for any party to the coordination, that is, one party to the coordination can obtain this information of the other party by calculating its own boundary information, the boundary physical information and boundary price information must be shared with each other in various coordination forms, and thus cannot be considered as the privacy of any party. From this perspective, the information privacy between the distribution microgrids is guaranteed during the implementation of the algorithm in this embodiment.

[0229] To implement the above embodiment, a second aspect of the present invention provides a microgrid coordinated reactive power voltage optimization control device, comprising:

[0230] Optimization model building module, used to establish a coordinated reactive power and voltage optimization model for distribution microgrids;

[0231] An optimization model conversion module, used to convert the optimization model into a main problem of reactive power voltage optimization of distribution network and a sub-problem of reactive power voltage optimization of microgrid by relaxing the constraint conditions of the optimization model;

[0232] A sub-problem conversion module, used for performing feasibility conversion on the sub-problem of reactive power voltage optimization of the microgrid by introducing slack variables;

[0233] An optimization variable expansion module, used to establish an approximate projection function and an optimal cutting plane of the sub-problem of reactive power voltage optimization of the microgrid with respect to boundary parameters based on the result of the feasibility conversion;

[0234] A solution module is used to solve the main problem of reactive voltage optimization of the distribution network and the sub-problem of reactive voltage optimization of the microgrid based on the approximate projection function and the optimal cutting plane, so as to realize the coordinated reactive voltage optimization control of the distribution and microgrid.

[0235] It should be noted that the above explanation of the embodiment of a distribution microgrid coordinated reactive voltage optimization control method is also applicable to a distribution microgrid coordinated reactive voltage optimization control device of the present embodiment, which will not be repeated here. A distribution microgrid coordinated reactive voltage optimization control device proposed in an embodiment of the present invention establishes a distribution microgrid coordinated reactive voltage optimization model; by relaxing the constraints of the optimization model, the optimization model is converted into the main problem of distribution network reactive voltage optimization and the sub-problem of microgrid reactive voltage optimization; by introducing relaxation variables, the sub-problem of microgrid reactive voltage optimization is feasibility converted; based on the result of the feasibility conversion, the approximate projection function and the optimal cutting plane of the sub-problem of microgrid reactive voltage optimization with respect to the boundary parameters are established; based on the approximate projection function and the optimal cutting plane, the main problem of distribution network reactive voltage optimization and the sub-problem of microgrid reactive voltage optimization are solved to realize the distribution microgrid coordinated reactive voltage optimization control. Through the application of spatial decomposition strategy and multi-parameter spatial projection algorithm, while ensuring the internal privacy information of different entities in the distribution and microgrid, collaborative reactive power optimization control is realized under the access scenario of distributed renewable resources, effectively improving the efficiency of line optimization scheduling.

[0236] To implement the above embodiment, a third aspect of the present invention provides an electronic device, including:

[0237] at least one processor; and a memory communicatively coupled to the at least one processor;

[0238] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above-mentioned distribution microgrid coordinated reactive voltage optimization control method.

[0239] To implement the above-mentioned embodiment, the fourth aspect of the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned distribution microgrid coordinated reactive voltage optimization control method.

[0240] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0241] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes a distribution microgrid coordinated reactive voltage optimization control method of the above embodiment.

[0242] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0243] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0244] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0245] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0246] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0247] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0248] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0249] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0250] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing reactive power and voltage in a microgrid, characterized in that: include: Establish a coordinated reactive power and voltage optimization model for distribution microgrids; By relaxing the constraints of the optimization model, the optimization model is converted into a main problem of reactive power voltage optimization of the distribution network and a sub-problem of reactive power voltage optimization of the microgrid; By introducing slack variables, the sub-problem of reactive power and voltage optimization of the microgrid is transformed into a feasible one; Based on the result of the feasibility conversion, an approximate projection function and an optimal cutting plane of the sub-problem of the microgrid reactive voltage optimization with respect to boundary parameters are established; Based on the approximate projection function and the optimal cutting plane, the main problem of the reactive voltage optimization of the distribution network and the sub-problem of the reactive voltage optimization of the microgrid are solved to achieve coordinated reactive voltage optimization control of the distribution and microgrid.

2. The method according to claim 1, characterized in that: The objective function expression of the distribution microgrid coordinated reactive power and voltage optimization model is as follows: min(C Ploss +C Pen-Volt +C DG-Cur )(1) in, In the formula, C Ploss is the penalty function of network loss, C Pen-Volt is the penalty function for voltage exceeding the limit, C DG-Cur is the penalty function of the new energy reduction value; σ V and σ DG are penalty coefficients; v ref is the square of the node voltage setting value; is the specified value of the output of new energy unit i; v i,t is the square of the voltage amplitude of node i in time period t, l ij,t is the square of the current amplitude of branch ij in time period t, r ji is the resistance of branch j; is the output value of renewable energy unit i in period t; B is the set of network nodes, and DG is the set of renewable energy units.

3. The method according to claim 2, characterized in that: The constraints of the distribution microgrid coordinated reactive power and voltage optimization model include: Branch flow equation constraints: Where P ij,t and Q ij,t are respectively the active power flow and reactive power flow of branch ij at time period t; v i,t is the square of the voltage amplitude of node i in time period t; l ij,t is the square of the current amplitude of branch ij in time period t; L is the set of lines; Node injection balance constraints: In the formula, and are respectively the active processing and reactive output of the mth renewable energy unit in time period t; and are the charging power and discharging power of the nth energy storage in time period t respectively; Q ESS,n,t and Q SVC,n,t are the reactive power outputs of the nth energy storage and static VAR compensator in time period t respectively; and are respectively the active power demand and reactive power demand of the mth load in time period t; r ji With x ji are the resistance and reactance of branch ji respectively; Branch voltage drop constraint: Voltage safety constraints: In the formula, and v i are the upper and lower limits of the square of the voltage safety amplitude at node i respectively; Output constraints of new energy units: In the formula, and P i DG are the upper and lower limits of the active power of the new energy unit i, and Q i DG are the upper and lower limits of reactive power generation of new energy unit i respectively; Energy storage operation constraints: In the formula, E ESS,n,t is the energy level of the nth energy storage in time period t; E ESS,n,0 ,E ESS,n and are the initial energy, the minimum energy level allowed and the maximum energy level allowed of the nth energy storage, respectively; and are the charging efficiency and discharging efficiency of the nth energy storage, respectively; and are the maximum charging power and maximum discharging power of the nth energy storage respectively; S ESS,n is the apparent capacity of the nth energy storage; Reactive power compensation device operation constraints: In the formula, Q SVC,n and are the lower and upper limits of reactive power output of the nth static VAR compensator respectively; Line capacity constraints: In the formula, It is the upper limit of the square of the current amplitude of branch ij.

4. The method according to claim 3, characterized in that: The said conversion of the said optimization model into the main problem of reactive power voltage optimization of distribution network and the sub-problem of reactive power voltage optimization of microgrid includes: 1) Relax the constraints of the coordinated reactive power and voltage optimization model of the distribution microgrid; Among them, equation (5) is convexly relaxed and becomes: 2) Based on the results of step 1), the distribution microgrid coordinated reactive power and voltage optimization model is converted into a second-order cone programming problem; Among them, the converted distribution microgrid coordinated reactive power and voltage optimization model is written as follows: In the formula, x D With x M(n) are the variables of the distribution network and microgrid n, respectively, D (x D ) and f M(n) (x M(n) ) are the optimization targets of the distribution network and microgrid n respectively, IM is the number set of microgrids, X D With X M(n) are the constraint sets of the distribution network and microgrid n respectively, constraint A n x D =B n x M(n) is the boundary coupling constraint between the distribution network and the microgrid n, that is, the active power, reactive power and the square of the voltage amplitude at the boundary node are matched; 3) Based on the results of step 2), the main problem of reactive power and voltage optimization of distribution network and the sub-problem of reactive power and voltage optimization of microgrid are established; Among them, based on formula (20), the main problem of reactive power and voltage optimization of distribution network is constructed: Construct the sub-problems of microgrid reactive power and voltage optimization: In the formula, B is the boundary variable given by the distribution network; n x M(n) is the boundary variable connecting the microgrid n with the upper distribution network.

5. The method according to claim 4, characterized in that: The feasibility conversion of the sub-problem of the microgrid reactive power voltage optimization is performed by introducing slack variables, including: Introducing slack variables α into microgrid n n With β n , through the penalty coefficient The two slack variable penalties are introduced into the sub-problem corresponding to microgrid n: At the optimal solution of the optimization problem (23), the constraint The dual multiplier of n , constraint α n The dual multiplier ≥0 is denoted by Constrained β n The dual multiplier ≥0 is denoted by According to the KKT conditions: in: Simplify formula (25):

6. The method according to claim 5, characterized in that: The sub-problem of establishing the microgrid reactive voltage optimization with respect to the approximate projection function and the optimal cutting plane of the boundary parameters includes: 1) Given boundary variables The reactive power and voltage optimization problem of the microgrid n with relaxed boundary constraints can be written as follows: In the formula, the constraint Contains internal constraints and boundary constraints of microgrid n; The optimal solution of equation (27) is The dual multiplier at the optimal solution is Then the optimal objective of microgrid n with respect to boundary variables The sensitivity is: The expansion point of the partial differential of formula (28) is 2) Project the optimization problem (27) onto the boundary variable A n x D In terms of space, the optimal objective of (27) is considered to be mapped to the boundary variable A n x D The function of is called the projection function; Then equation (27) is n x D The approximate projection function of the space is: 3) Discard the second-order expansion term in equation (28) and obtain the optimal cutting plane: Among them, the first-order expansion of formula (29) is the optimal cutting plane, denoted as OC n (A n x D ).

7. The method according to claim 6, characterized in that: The solving of the main problem of reactive power voltage optimization of the distribution network and the sub-problem of reactive power voltage optimization of the microgrid includes: 1) Initialize the number of iterations k = 1; 2) The distribution network calculates the optimization problem equation (21) and obtains the optimal solution x D ; x D Denote the optimal solution x of the kth iteration D (k) , then x D (k) The corresponding boundary variable value is A n x D (k) is transmitted to the corresponding microgrids; when k=1, A n x D (k) The value of is obtained through initialization; 3) Judgment: 3-1) If k = 1, the microgrid performs sub-problem optimization of fixed boundary variables as shown in equation (30) to obtain the optimized variable x M(n) , then go to step 4); 3-2) If k>1, each microgrid compares the boundary variable A transmitted by the distribution network n x D (k) A from the previous iteration n x D (k-1) Are they equal, where: If they are equal, then go to step 5); otherwise, the microgrid performs sub-problem optimization of fixed boundary variables as shown in formula (30) to obtain the optimized variable x M(n) , then go to step 4); 4) Microgrid will A n x D (k) As the target expansion point of the current partial differential, Substitute into equation (28) and equation (29) to generate A n x D (k) The approximate projection function and the optimal cutting plane at are denoted as and and transmitted to the distribution network, where p n The total number of optimal cutting planes generated for the microgrid n iterations; 5) The distribution network is optimized by combining all the optimal cutting planes collected, as shown in the following formula: In the formula, o n represents the optimal target value of microgrid n, which is an intermediate variable; IM represents the set of microgrids; Let the number of iterations k increase by 1, and denote the optimal solution of equation (31) as x D (k) , o n (k) ; 6) Judgment: 6-1) If k = 1, return to step 2) and continue iterating; 6-2) If k>1, determine whether ε is the preset positive threshold, where: If it is satisfied, the solution converges. The optimal solution of the main problem corresponding to the distribution network and the sub-problems corresponding to each microgrid in the most recent iteration is the global optimal solution. D With x M(n) Extract the active output and reactive output of the new energy unit Energy storage charging and discharging power And the output value Q of the reactive power compensation device SVC,n,t , realize the optimal control of reactive power and voltage in the distribution microgrid; If it is not satisfied, the solution does not converge, and return to step 2) to continue iterating.

8. A microgrid coordinated reactive voltage optimization control device, characterized in that: include: Optimization model building module, used to establish a coordinated reactive power and voltage optimization model for distribution microgrids; An optimization model conversion module, used to convert the optimization model into a main problem of reactive power voltage optimization of distribution network and a sub-problem of reactive power voltage optimization of microgrid by relaxing the constraint conditions of the optimization model; A sub-problem conversion module, used for performing feasibility conversion on the sub-problem of reactive power voltage optimization of the microgrid by introducing slack variables; An optimization variable expansion module, used to establish an approximate projection function and an optimal cutting plane of the sub-problem of reactive power voltage optimization of the microgrid with respect to boundary parameters based on the result of the feasibility conversion; A solution module is used to solve the main problem of reactive voltage optimization of the distribution network and the sub-problem of reactive voltage optimization of the microgrid based on the approximate projection function and the optimal cutting plane, so as to realize the coordinated reactive voltage optimization control of the distribution and microgrid.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method according to any one of claims 1 to 7.

Images