Power distribution network-microgrid collaborative optimization scheduling method and device based on space-time decomposition

Abstract

The invention provides a time-space decomposition-based power distribution network-microgrid collaborative optimization scheduling method and device, and belongs to the field of power system operation optimization. The method comprises the following steps: establishing a multi-period active and reactive joint scheduling model of an active power distribution network and a grid-connected micro-grid, and converting the multi-period active and reactive joint scheduling model into a double-layer Riemann nonlinear programming optimal scheduling problem in a continuous domain; processing the optimal scheduling problem based on a decomposition strategy of a manifold model, and decomposing the optimal scheduling problem into a relaxation sub-problem, a box-type sub-problem and a spherical sub-problem corresponding to an active power distribution microgrid and a grid-connected microgrid respectively; and based on a manifold decomposition result, obtaining an optimization result of the scheduling model by using an enhanced ADMM algorithm of Riemannian manifold and a layered space-time decomposition algorithm of the double-layer relaxation sub-problem. According to the invention, under the cooperative operation scene of the active power distribution network and the multi-microgrid, the refined scheduling and active cooperative control of the source network load storage can be realized, and the method has a high application value.

Description

Technical Field

[0001] This disclosure pertains to the field of power system operation optimization, and specifically relates to a method and apparatus for coordinated optimization scheduling of distribution networks and microgrids based on spatiotemporal decomposition. Background Technology

[0002] With the large-scale integration of distributed energy resources into distribution systems, the operational characteristics of active distribution networks are becoming increasingly complex. Grid-connected microgrids, as components of active distribution networks, can achieve optimized scheduling of distributed power sources, energy storage systems, and adjustable loads through local energy management systems, thereby improving energy utilization efficiency and enhancing the operational flexibility and resilience of the power system. Solving the coordinated operation of active distribution networks and multiple microgrids can be reduced to a two-level mixed-integer nonlinear programming problem, which presents the following two challenges:

[0003] (1) The difficulty of solving mixed-integer nonlinear programming problems increases exponentially with the model size. Active distribution networks and grid-connected microgrids contain both continuous adjustable resources and discrete control equipment. Both participate in multi-time active and reactive power joint optimization, which makes the model exhibit strong nonlinearity and mixed-integer characteristics. The two-layer structure combined with the mixed modeling of discrete and continuous variables makes it difficult for traditional optimization algorithms to solve directly and efficiently, especially to guarantee convergence in a distributed environment.

[0004] (2) The spatiotemporal coupling between the active distribution network and the grid-connected microgrid is strong, resulting in high computational complexity. The limitations on the number of actions and time intervals of discrete devices, as well as the state of charge constraints of energy storage devices, tightly couple the decision variables of the entire optimization cycle in the time dimension; constraints such as power balance between the main grid and the active distribution network, and between the active distribution network and each grid-connected microgrid, form complex spatial coupling relationships. These multiple coupling constraints not only greatly increase the dimensionality of the problem, but also significantly reduce the convergence performance of distributed algorithms.

[0005] In summary, existing methods for coordinated optimization of active distribution networks and multiple microgrids suffer from shortcomings in both solution efficiency and convergence reliability. This makes it difficult for active distribution networks to issue stable and feasible dispatch commands in a timely manner, especially in multi-time-period, large-scale, and multi-regional coordinated operation scenarios. Problems such as delayed dispatch commands, voltage exceeding limits, or frequent equipment operations may occur, thus affecting the safety and economy of the power system. With the continuous increase in the penetration rate of distributed renewable energy and the increasing spatiotemporal coupling complexity of power systems, there is an urgent need for an optimization method capable of handling discrete variable constraints in the continuous domain, achieving spatiotemporal decoupling, and possessing distributed parallel capabilities. This method would improve the scalability and engineering practicality of the algorithm while ensuring computational accuracy and convergence. Summary of the Invention

[0006] The purpose of this disclosure is to overcome the shortcomings of existing technologies by proposing a distribution network-microgrid collaborative optimization scheduling method and device based on spatiotemporal decomposition. This disclosure transforms a complex mixed-integer nonlinear programming problem into a manifold optimization problem in a continuous domain, significantly improving solution efficiency and convergence reliability through efficient decomposition and coordination strategies. In the collaborative operation scenario of active distribution networks and multiple microgrids, it avoids the risk of data privacy leakage, ensures the real-time nature and global coordination of scheduling decisions, and effectively addresses the operation scenarios of large-scale discrete equipment and renewable energy power generation equipment, achieving refined scheduling and active collaborative control of power generation, grid, load, and storage, thus possessing high application value.

[0007] The first aspect of this disclosure proposes a distribution network-microgrid collaborative optimization scheduling method based on spatiotemporal decomposition, including:

[0008] A multi-period active and reactive power joint scheduling model for active distribution networks and grid-connected microgrids is established. The scheduling model is a two-level mixed integer nonlinear programming model.

[0009] The scheduling model is transformed into a two-level Riemannian nonlinear programming optimal scheduling problem in a continuous domain.

[0010] The optimal scheduling problem is handled by a decomposition strategy based on the manifold model, which decomposes the optimal scheduling problem into relaxation subproblems, box subproblems, and spherical subproblems corresponding to active distribution and grid-connected microgrids, respectively.

[0011] Based on the results of manifold decomposition, the optimization results of the scheduling model are obtained by using the enhanced ADMM algorithm of Riemannian manifold and the hierarchical spatiotemporal decomposition algorithm of the two-layer slack subproblem.

[0012] In a specific embodiment of this disclosure, the multi-time active and reactive power joint dispatch model expression of the active distribution network and grid-connected microgrid is as follows:

[0013] (1)

[0014] st (2)

[0015] (3)

[0016] (4)

[0017] (5)

[0018] (6)

[0019] in, and These are the target terms related to continuous and discrete variables in an active distribution network, respectively. and These are the objective terms related to continuous and discrete variables in a grid-connected microgrid, respectively. For time period t, there are continuous variables within the active distribution network, including: generator output power, energy storage system charging and discharging power, static var compensator injected power, and grid inverter injected power. Let t be the discrete variables within the active distribution network during time period t, including: tap position of on-load transformers, capacitor switch status, and energy storage charging and discharging actions, with dimension t. , representing the number of discrete control devices in the active distribution network; For time period t, the first The continuous variables within a grid-connected microgrid include: generator output power, energy storage system charging and discharging power, and grid inverter injected power. For time period t, the first Discrete variables within a grid-connected microgrid include: on-load transformer tap position, capacitor switching status, and energy storage charging and discharging actions, with dimensions of [missing information]. , representing the number of discrete control devices within the microgrid; To optimize the number of time periods, A collection of grid-connected microgrids;

[0020] Constraints (2) and (3) are the operational constraints of the active distribution network. For the continuous feasible region constraints of active distribution network operation, Constraints on the operational actions of discrete devices in an active distribution network; The spatial boundary variables of the active distribution network during time period t include: active and reactive power injection, voltage amplitude and phase, and transformer tap position at the boundary connected to the main grid, and active and reactive power injection, voltage amplitude and phase, and transformer tap position at the boundary connected to the grid-connected microgrid. For time period t and the first A grid-connected microgrid corresponding to The subvectors of; 0 is the zero vector; constraints (4) and (5) are the first... Operational constraints of a grid-connected microgrid To ensure operational consistency between grid-connected microgrids and active distribution networks, Constraints on the operation of discrete devices in grid-connected microgrids; For time period t, the first The spatial boundary variables of a grid-connected microgrid include: active and reactive power exchange at the boundary with the active distribution network, voltage amplitude and phase, and transformer tap position; constraint equation (6) is the spatial boundary consistency constraint between the active distribution network and the grid-connected microgrid.

[0021] In a specific embodiment of this disclosure, transforming the scheduling model into a two-level Riemannian nonlinear programming optimal scheduling problem in a continuous domain includes:

[0022] 1) Using the l2-box method, the discrete variables of the active distribution network are equivalently represented as high-dimensional box constraints S as shown in equation (7). b and( ) 3D spherical S p The intersecting form equivalently represents the discrete variables within the grid-connected microgrid as a high-dimensional box constraint S, as shown in equation (8). b and( ) 3D spherical S p Forms of intersection:

[0023] (7)

[0024] (8)

[0025] in, It is a set of values ​​all equal to 1. 3D column vector; It is the first A grid-connected microgrid A column vector of dimensions, all with values ​​of 1;

[0026] 2) The equivalent expressions for the discrete variables of the active distribution network are established as follows:

[0027] (9)

[0028] (10)

[0029] (11)

[0030] in, For continuous variables in the active distribution network slack subproblem; For the box-type slack variables of the active distribution network, constrained in the high-dimensional box-type constraint S b middle; For the spherical relaxation variables of the active distribution network, constrained on the sphere S p superior;

[0031] Establish the first The equivalent expression for the discrete variables of a grid-connected microgrid is as follows:

[0032] (12)

[0033] (13)

[0034] (14)

[0035] in, For continuous variables in the slack subtraction problem of grid-connected microgrids; For box-type slack variables of grid-connected microgrids, constrained in a high-dimensional box S b middle; For grid-connected microgrids, spherical relaxation variables are constrained on the sphere S. p superior;

[0036] 3) Based on the results of steps 1) and 2), the scheduling model is transformed into the following form:

[0037] (15)

[0038] Feasible region of active distribution network:

[0039] (16)

[0040] (17)

[0041] No. Feasible region of a microgrid:

[0042] (18)

[0043] (19)

[0044] Spatial boundary consistency constraints between active distribution networks and microgrids:

[0045] (20)

[0046] Equivalent representation of discrete variables in an active distribution network:

[0047] (twenty one)

[0048] (twenty two)

[0049] (twenty three)

[0050] No. Equivalent representation of discrete variables in a grid-connected microgrid:

[0051] (twenty four)

[0052] (25)

[0053] (26)

[0054] Equations (15)-(26) constitute a continuous optimization model.

[0055] In one specific embodiment of this disclosure, it further includes:

[0056] The decomposition strategy of the manifold model is applied to the active distribution network objective term in the continuous optimization model, resulting in the following relaxation subproblem, box subproblem, and spherical subproblem; wherein each subproblem is coordinated by the enhanced ADMM algorithm based on Riemannian manifold, and the number of algorithm iterations is counted as k;

[0057] The relaxon problem:

[0058] (27)

[0059] st (28)

[0060] (29)

[0061] in, This is a penalty function for non-integer values; The penalty coefficient for positive values; Box duality constraints for the l2-box method; For the spherical dual constraint of the l2-box method; and It is the dual multiplier for the (k-1)th iteration; In equation (23) The penalty function; In equation (23) The penalty parameter;

[0062] Box problem:

[0063] (30)

[0064] st (31)

[0065] (32)

[0066] in, Box duality constraints for the l2-box method;

[0067] spherical problem:

[0068] (33)

[0069] st (34)

[0070] in, For the spherical dual constraint of the l2-box method.

[0071] In one specific embodiment of this disclosure, it further includes:

[0072] The decomposition strategy of the manifold model is applied to the target term of the grid-connected microgrid in the continuous optimization model, resulting in the following relaxation subproblem, box subproblem, and spherical subproblem; wherein each subproblem is coordinated by the enhanced ADMM algorithm based on Riemannian manifold, and the number of algorithm iterations is counted as k;

[0073] The relaxon problem:

[0074] (35)

[0075] st (36)

[0076] (37)

[0077] in, This is a penalty function for non-integer values; Box duality constraints for the l2-box method; For the spherical dual constraint of the l2-box method; and It is the dual multiplier for the (k-1)th iteration; In equation (26) The penalty function; In equation (26) The penalty parameter;

[0078] Box problem:

[0079] (38)

[0080] st (39)

[0081] (40)

[0082] in, Box duality constraints for the l2-box method;

[0083] spherical problem:

[0084] (41)

[0085] st (42)

[0086] in, For the spherical dual constraint of the l2-box method.

[0087] In one specific embodiment of this disclosure, obtaining the optimization result of the scheduling model includes:

[0088] 1) Initialize parameters;

[0089] Set the number of iterations during initialization ,Will , , , , , Used as initial values ​​and for warm restarts based on historical values; set , , , , , , , , , The value of the convergence tolerance is selected. ;

[0090] Then, let the number of iterations... ;

[0091] 2) A hierarchical spatiotemporal decomposition algorithm based on accelerated ADMM is used to process the multi-time active and reactive power joint scheduling model. The hierarchical spatiotemporal decomposition scheme, implemented in a distributed parallel manner, effectively decomposes the two-level relaxation subproblem into multiple small-scale subproblems, obtaining... , , , , and ,in , where w is the number of iterations in the hierarchical spatiotemporal decomposition algorithm, and the update scheme steps of the hierarchical spatiotemporal decomposition algorithm are as follows:

[0092] 2-1) After performing a spatiotemporal decomposition, equation (27) can be restated into the following form:

[0093] (43)

[0094] st (44)

[0095] (45)

[0096] (46)

[0097] (47)

[0098] in, Representing subproblems The corresponding scalar, vector, or matrix; Represents a set consisting of variables / vectors; Spatial coupling constraints for active distribution networks; For time coupling constraints of active distribution networks; and For the repeated time domain between active distribution network sub-problems; , , and All are in the ADMM algorithm for manifold enhancement. Obtained in the next iteration; and Subproblems , and Coupled variables; and The multipliers correspond to the subproblems respectively. , and Consistency constraints between them; and These are the diagonal matrices of penalty parameters related to spatial decomposition and temporal decomposition, respectively; and Representing subproblems and Sub-problems and Global variables between;

[0099] 2-2) The prediction-correction acceleration introduced by Nesterov gradient descent method is applied to the first... In the next iteration, the equations (50) and (51) are used to determine the... and The expression is as follows:

[0100] (48)

[0101] (49)

[0102] (50)

[0103] (51)

[0104] 3) The active distribution network obtains the box subproblem of equation (30) and the spherical subproblem of equation (33) by using the Gurobi and manifold trust region algorithms in parallel. , ,in ;

[0105] 4) Grid-connected microgrids obtain the box subproblem of equation (38) and the spherical subproblem of equation (41) by using the Gurobi and manifold trust region algorithms in parallel. , ,in ;

[0106] 5) Active distribution networks use equations (52) and (53) to update the dual multipliers and ; Grid-connected microgrids use equations (54) and (55) to update the multipliers and ;

[0107] (52)

[0108] (53)

[0109] (54)

[0110] (55)

[0111] 6) Update the penalty parameters of the active distribution network using equations (56) and (57). and The penalty parameters of the grid-connected microgrid are updated using equations (58) and (59). and ;

[0112] (56)

[0113] (57)

[0114] (58)

[0115] (59)

[0116] in, For non-integer solutions of box-constrained subproblems in active distribution networks, the penalty weight constant is denoted as . For non-integer solutions of the spherical manifold problem in an active distribution network, the penalty weight constant is given. For the non-integer solution penalty weight constant of the box-constrained subproblem in a grid-connected microgrid; For the non-integer solution penalty weight constant of the spherical manifold problem in a grid-connected microgrid; The upper limit of the penalty parameter for discrete variables in the active distribution network; The upper limit of the penalty parameter for discrete variables in grid-connected microgrids;

[0117] 7) Determine if equation (60) is satisfied: if yes, the iteration ends and proceed to step 8); otherwise, increment the iteration count k by 1 and return to step 2).

[0118] (60)

[0119] 8) Output active distribution network results , , ,in Output results for grid-connected microgrids , ,in The coordinated operation optimization of the active distribution network and the grid-connected microgrid has been completed.

[0120] A second aspect of this disclosure proposes a distribution network-microgrid collaborative optimization scheduling device based on spatiotemporal decomposition, comprising:

[0121] The scheduling model construction module is used to establish a multi-time active and reactive power joint scheduling model for active distribution networks and grid-connected microgrids. The scheduling model is a two-level mixed integer nonlinear programming model.

[0122] The model transformation module is used to transform the scheduling model into a two-level Riemann nonlinear programming optimal scheduling problem in a continuous domain.

[0123] The manifold decomposition module is used to process the optimal scheduling problem based on the decomposition strategy of the manifold model, and decomposes the optimal scheduling problem into relaxation subproblems, box subproblems and spherical subproblems corresponding to active distribution and grid-connected microgrids, respectively.

[0124] The optimization scheduling module is used to obtain the optimization results of the scheduling model based on the results of manifold decomposition, using the enhanced ADMM algorithm of Riemannian manifold and the hierarchical spatiotemporal decomposition algorithm of two-layer slack subproblems.

[0125] A third aspect of this disclosure provides an electronic device, comprising:

[0126] At least one processor; and a memory communicatively connected to said at least one processor;

[0127] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-described spatiotemporal decomposition-based distribution network-microgrid collaborative optimization scheduling method.

[0128] A fourth aspect of this disclosure provides a computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned spatiotemporal decomposition-based distribution network-microgrid collaborative optimization scheduling method.

[0129] The features and beneficial effects of this disclosure are as follows:

[0130] 1. This disclosure features high efficiency, good scalability, and parallelization. It employs the l2-box method to transform the originally difficult-to-solve two-level mixed-integer nonlinear programming problem involving discrete variables into a two-level Riemann nonlinear programming problem in a continuous domain.

[0131] 2. This disclosure proposes a manifold-based model decomposition strategy, which systematically decomposes the original problem into relaxed subproblems, box subproblems, and spherical subproblems. This achieves the separation of continuous variables and manifold constraints, significantly reduces the solution complexity of each subproblem, and achieves a good balance between subproblem solution efficiency and global coordination efficiency.

[0132] 3. This disclosure presents a distributed coordination algorithm that guarantees the convergence of the solution process and the quality of the solution. For the decomposed subproblems, an enhanced manifold alternating direction multiplier method (ADMM) is developed for coordination. By introducing a penalty term for non-integer solutions, the optimization process can be effectively driven to converge to feasible integer solutions, while ensuring the reliability and convergence of the two-level optimization results.

[0133] 4. This disclosure integrates efficient subproblem-solving methods and a parallel framework, significantly improving computational efficiency and scalability. It employs the Riemann trust region method to solve spherical subproblems and leverages the spatiotemporal structural characteristics of power systems to decompose relaxed subproblems into smaller, fully parallelizable problems. This allows the method to fully utilize distributed computing resources, efficiently handle the strong spatiotemporal coupling characteristics of large-scale systems, and achieves computational efficiency significantly higher than traditional commercial solvers and other distributed methods.

[0134] 5. This disclosure can be widely applied to active distribution networks, grid-connected microgrids, and collaborative optimization scheduling of power generation, grid, load, and storage. By deploying the scheme disclosed herein in dispatch control centers, energy management systems, or distributed control platforms, autonomous coordination and distributed parallel optimization of grid-connected microgrids can be achieved, providing rapid and reliable optimization decision support for active distribution networks, and significantly improving the real-time dispatch capability and operational stability of the power system under conditions of high renewable energy penetration, load fluctuations, and complex equipment status. Attached Figure Description

[0135] Figure 1 This is an overall flowchart of a distribution network-microgrid collaborative optimization scheduling method based on spatiotemporal decomposition in an embodiment of this disclosure. Detailed Implementation

[0136] This disclosure proposes a method and apparatus for coordinated optimization scheduling of distribution networks and microgrids based on spatiotemporal decomposition. The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0137] The first aspect of this disclosure proposes a distribution network-microgrid collaborative optimization scheduling method based on spatiotemporal decomposition, comprising:

[0138] A multi-period active and reactive power joint scheduling model for active distribution networks and grid-connected microgrids is established. The scheduling model is a two-level mixed integer nonlinear programming model.

[0139] The scheduling model is transformed into a two-level Riemannian nonlinear programming optimal scheduling problem in a continuous domain.

[0140] The optimal scheduling problem is addressed by a decomposition strategy based on the manifold model, which decomposes the optimal scheduling problem into relaxation subproblems, box subproblems, and spherical subproblems corresponding to active distribution networks and grid-connected microgrids, respectively.

[0141] Based on the results of manifold decomposition, the optimization results of the scheduling model are obtained by using the enhanced ADMM algorithm of Riemannian manifold and the hierarchical spatiotemporal decomposition algorithm of the two-layer slack subproblem.

[0142] In a specific embodiment of this disclosure, the overall process of the distribution network-microgrid collaborative optimization scheduling method based on spatiotemporal decomposition is as follows: Figure 1 As shown, it includes the following steps:

[0143] 1) Establish a multi-period active and reactive power joint dispatch model for active distribution networks and grid-connected microgrids.

[0144] In one embodiment of this disclosure, the active distribution network and grid-connected microgrid multi-time active and reactive power joint dispatch model is a two-level mixed integer nonlinear programming model, expressed as follows:

[0145] (1)

[0146] st (2)

[0147] (3)

[0148] (4)

[0149] (5)

[0150] (6)

[0151] in, and These are the target terms related to continuous and discrete variables in an active distribution network, respectively. and These are the objective terms related to continuous and discrete variables in a grid-connected microgrid, respectively. For time period t, there are continuous variables within the active distribution network, including: generator output power, energy storage system charging and discharging power, static var compensator injected power, and grid inverter injected power. Let t be the discrete variables within the active distribution network during time period t, including: tap position of on-load transformers, capacitor switch status, and energy storage charging and discharging actions, with dimension t. , representing the number of discrete control devices in the active distribution network; For time period t, the first The continuous variables within a grid-connected microgrid include: generator output power, energy storage system charging and discharging power, and grid inverter injected power. For time period t, the first Discrete variables within a grid-connected microgrid include: on-load transformer tap position, capacitor switching status, and energy storage charging and discharging actions, with dimensions of [missing information]. , representing the number of discrete control devices within the microgrid. To optimize the number of time periods, It is a collection of grid-connected microgrids.

[0152] Constraints (2) and (3) are the operational constraints of the active distribution network. For the continuous feasible region constraints of active distribution network operation, Constraints on the operational actions of discrete devices in an active distribution network; The spatial boundary variables of the active distribution network during time period t include: active and reactive power injection, voltage amplitude and phase, and transformer tap position at the boundary connected to the main grid, and active and reactive power injection, voltage amplitude and phase, and transformer tap position at the boundary connected to the grid-connected microgrid. For time period t and the first A grid-connected microgrid corresponding to 0 is a subvector; 0 is the zero vector. Constraints (4) and (5) are the first subvectors. Operational constraints of a grid-connected microgrid To ensure operational consistency between grid-connected microgrids and active distribution networks, For the operational constraints of discrete devices in grid-connected microgrids. For time period t, the first The spatial boundary variables of a grid-connected microgrid include: active and reactive power exchange at the boundary with the active distribution network, voltage amplitude and phase, and transformer tap position; constraint equation (6) is the spatial boundary consistency constraint between the active distribution network and the grid-connected microgrid.

[0153] Specifically, the objective function of the multi-time active and reactive power joint dispatch model is to minimize the sum of electricity purchase cost, network loss, renewable energy curtailment penalty, energy storage loss, and discrete adjustable equipment operating cost. For operational constraints, a three-phase linearized power flow model is used to characterize the active distribution network and grid-connected microgrids, while considering the following constraints: operational constraints of renewable energy, energy storage, static var compensators (SVCs), on-load tap changers, and capacitors. The following system security constraints are also considered: boundary power limits between the main grid and the active distribution network, and between the active distribution network and the grid-connected microgrids.

[0154] 2) Transform the multi-time active and reactive power joint scheduling model established in step 1) into a continuous optimization model.

[0155] In this embodiment, the multi-time active and reactive power joint scheduling model established in step 1) is transformed into a two-level Riemann nonlinear programming optimal scheduling problem in the continuous domain. The specific steps are as follows:

[0156] 2-1) Using the l2-box method, the discrete variables of the active distribution network are equivalently represented as high-dimensional box constraints S as shown in equation (7). b and( ) 3D spherical S p The intersecting form equivalently represents the discrete variables within the grid-connected microgrid as a high-dimensional box constraint S, as shown in equation (8). b and( ) 3D spherical S p Forms of intersection:

[0157] (7)

[0158] (8)

[0159] in, It is a set of values ​​all equal to 1. 3D column vector; It is the first A grid-connected microgrid A column vector of dimensions, all with a value of 1.

[0160] 2-2) The equivalent expression for the discrete variables of the active distribution network is established as follows:

[0161] (9)

[0162] (10)

[0163] (11)

[0164] in, For continuous variables in the active distribution network slack subproblem; For the box-type slack variables of the active distribution network, constrained in the high-dimensional box-type constraint S b middle; For the spherical relaxation variables of the active distribution network, constrained on the sphere S p superior.

[0165] Establish the first The equivalent expression for the discrete variables of a grid-connected microgrid is as follows:

[0166] (12)

[0167] (13)

[0168] (14)

[0169] in, For continuous variables in the slack subtraction problem of grid-connected microgrids; For box-type slack variables of grid-connected microgrids, constrained in a high-dimensional box S b middle; For grid-connected microgrids, spherical relaxation variables are constrained on the sphere S. p superior.

[0170] 2-3) Based on the results of steps 2-1) and 2-2), the model established in step 1) is transformed into the following form:

[0171] (15)

[0172] Feasible region of active distribution network:

[0173] (16)

[0174] (17)

[0175] No. Feasible region of a microgrid:

[0176] (18)

[0177] (19)

[0178] Spatial boundary consistency constraints between active distribution networks and microgrids:

[0179] (20)

[0180] Equivalent representation of discrete variables in an active distribution network:

[0181] (twenty one)

[0182] (twenty two)

[0183] (twenty three)

[0184] No. Equivalent representation of discrete variables in a grid-connected microgrid:

[0185] (twenty four)

[0186] (25)

[0187] (26)

[0188] 3) Based on the manifold model decomposition strategy, process the continuous optimization model obtained in step 2), decompose the model into subproblems that can be solved independently and are easy to coordinate. The specific steps are as follows:

[0189] 3-1) Applying the decomposition strategy of the manifold model to the active distribution network objective term in the continuous optimization model obtained in step 2-3) yields the following relaxation subproblem, box subproblem, and spherical subproblem. Each subproblem is coordinated by the enhanced ADMM algorithm based on Riemannian manifolds, with k iterations.

[0190] The relaxon problem:

[0191] (27)

[0192] st (28)

[0193] (29)

[0194] in, This is a penalty function for non-integer values; The penalty coefficient for positive values; Box duality constraints for the l2-box method; For the spherical dual constraint of the l2-box method; and It is the dual multiplier for the (k-1)th iteration; In equation (23) The penalty function; In equation (23) The penalty parameter.

[0195] Box problem:

[0196] (30)

[0197] st (31)

[0198] (32)

[0199] in, Box duality constraints for the l2-box method.

[0200] spherical problem:

[0201] (33)

[0202] st (34)

[0203] in, For the spherical dual constraint of the l2-box method.

[0204] 3-2) Applying the decomposition strategy of the manifold model to the target term of the grid-connected microgrid in the continuous optimization model obtained in step 2-3) yields the following relaxation subproblem, box subproblem, and spherical subproblem. Each subproblem is coordinated by the enhanced ADMM algorithm based on Riemannian manifolds, with k iterations.

[0205] The relaxon problem:

[0206] (35)

[0207] st (36)

[0208] (37)

[0209] in, This is a penalty function for non-integer values; Box duality constraints for the l2-box method; For the spherical dual constraint of the l2-box method; and It is the dual multiplier for the (k-1)th iteration; In equation (26) The penalty function; In equation (26) The penalty parameter.

[0210] Box problem:

[0211] (38)

[0212] st (39)

[0213] (40)

[0214] in, Box duality constraints for the l2-box method.

[0215] spherical problem:

[0216] (41)

[0217] st (42)

[0218] in, For the spherical dual constraint of the l2-box method.

[0219] 4) Based on the results of step 3), the spherical manifold optimization method is used to solve the multi-time active and reactive power joint scheduling model.

[0220] In one embodiment of this disclosure, by combining the enhanced ADMM algorithm based on Riemannian manifolds with the hierarchical spatiotemporal decomposition algorithm of the two-layer slack subproblem, a spherical manifold optimization method for a multi-time-period active and reactive power joint scheduling model can be obtained. The specific steps are as follows:

[0221] 4-1) Initialize parameters.

[0222] Set the number of iterations during initialization ,Will , , , , , Used as initial values ​​and for warm restarts based on historical values. , , , , , , , , , The value of the convergence tolerance is selected. .

[0223] Then, let the number of iterations... .

[0224] 4-2) A hierarchical spatiotemporal decomposition algorithm based on accelerated ADMM is used to process the multi-time active and reactive power joint scheduling model. The hierarchical spatiotemporal decomposition scheme, implemented in a distributed parallel manner, decomposes the two-level relaxation subproblem into multiple small-scale subproblems, obtaining... , , , , and ,in , where w is the number of iterations in the hierarchical spatiotemporal decomposition algorithm, and the update scheme steps of the hierarchical spatiotemporal decomposition algorithm are as follows:

[0225] 4-2-1) Subproblem restatement.

[0226] After performing a spatiotemporal decomposition on equation (27), it can be restated into the following form:

[0227] (43)

[0228] st (44)

[0229] (45)

[0230] (46)

[0231] (47)

[0232] in, Representing subproblems The corresponding scalar, vector, or matrix; It represents a set consisting of variables / vectors. Spatial coupling constraints for active distribution networks; For time coupling constraints of active distribution networks; and For the repeated time domain between active distribution network sub-problems; , , and All are in the ADMM algorithm for manifold enhancement. Obtained in the next iteration. and Subproblems , and Coupled variables. and The multipliers correspond to the subproblems respectively. , and Consistency constraints between them. and These are the diagonal matrices of penalty parameters related to spatial and temporal decomposition, respectively. The subproblems to the left of the superscripts (·,·) are responsible for determining the values ​​of the coupling variables. and Representing subproblems and Sub-problems and Global variables between them.

[0233] 4-2-2) The prediction-correction acceleration introduced by Nesterov gradient descent method is applied in the first... In the next iteration, the equations (50) and (51) are used to determine the... and The expression is as follows:

[0234] (48)

[0235] (49)

[0236] (50)

[0237] (51)

[0238] 4-3) The active distribution network obtains the box subproblem of equation (30) and the spherical subproblem of equation (33) by using the Gurobi and manifold trust region algorithms in parallel. , ,in .

[0239] 4-4) Grid-connected microgrids obtain the box subproblem of equation (38) and the spherical subproblem of equation (41) by using the Gurobi and manifold trust region algorithms in parallel. , ,in .

[0240] 4-5) Active distribution networks use formulas (52) and (53) to update the dual multipliers and ; Grid-connected microgrids use equations (54) and (55) to update the multipliers and .

[0241] (52)

[0242] (53)

[0243] (54)

[0244] (55)

[0245] 4-6) Update the penalty parameters of the active distribution network using equations (56) and (57). and The penalty parameters of the grid-connected microgrid are updated using equations (58) and (59). and .

[0246] (56)

[0247] (57)

[0248] (58)

[0249] (59)

[0250] in, For non-integer solutions of box-constrained subproblems in active distribution networks, the penalty weight constant is denoted as . For non-integer solutions of the spherical manifold problem in an active distribution network, the penalty weight constant is given. For the non-integer solution penalty weight constant of the box-constrained subproblem in a grid-connected microgrid; For the non-integer solution penalty weight constant of the spherical manifold problem in a grid-connected microgrid; The upper limit of the penalty parameter for discrete variables in the active distribution network; This is the upper limit of the penalty parameter for discrete variables in grid-connected microgrids.

[0251] 4-7) Determine if equation (60) is satisfied: if yes, the iteration ends and proceed to step 4-8; otherwise, increment the iteration number k by 1 and return to step 4-2.

[0252] (60)

[0253] 4-8) Output active distribution network results , , ,in Output results for grid-connected microgrids , ,in The coordinated operation optimization of the active distribution network and the grid-connected microgrid has been completed.

[0254] The optimization results obtained using the method described in this embodiment can be distributed by the power system dispatcher to various new energy power plants, energy storage power stations, and other equipment to control the relevant equipment and ensure the safe and economical operation of the entire network.

[0255] To implement the above embodiments, a second aspect of this disclosure proposes a distribution network-microgrid collaborative optimization scheduling device based on spatiotemporal decomposition, comprising:

[0256] The scheduling model construction module is used to establish a multi-time active and reactive power joint scheduling model for active distribution networks and grid-connected microgrids. The scheduling model is a two-level mixed integer nonlinear programming model.

[0257] The model transformation module is used to transform the scheduling model into a two-level Riemann nonlinear programming optimal scheduling problem in a continuous domain.

[0258] The manifold decomposition module is used to process the optimal scheduling problem based on the decomposition strategy of the manifold model, and decomposes the optimal scheduling problem into relaxation subproblems, box subproblems and spherical subproblems corresponding to active distribution and grid-connected microgrids, respectively.

[0259] The optimization scheduling module is used to obtain the optimization results of the scheduling model based on the results of manifold decomposition, using the enhanced ADMM algorithm of Riemannian manifold and the hierarchical spatiotemporal decomposition algorithm of two-layer slack subproblems.

[0260] It should be noted that the foregoing explanation of an embodiment of a distribution network-microgrid collaborative optimization scheduling method based on spatiotemporal decomposition also applies to a distribution network-microgrid collaborative optimization scheduling device based on spatiotemporal decomposition in this embodiment, and will not be repeated here. According to the distribution network-microgrid collaborative optimization scheduling device based on spatiotemporal decomposition proposed in this disclosure, a multi-period active and reactive power joint scheduling model for active distribution networks and grid-connected microgrids is established. The scheduling model is a two-level mixed integer nonlinear programming model. The scheduling model is transformed into a two-level Riemann nonlinear programming optimal scheduling problem in a continuous domain. The optimal scheduling problem is processed based on a manifold model decomposition strategy, decomposing the optimal scheduling problem into slack subproblems, box subproblems, and spherical subproblems corresponding to active distribution networks and grid-connected microgrids, respectively. Based on the manifold decomposition results, the optimized result of the scheduling model is obtained using the enhanced ADMM algorithm of Riemann manifolds and the hierarchical spatiotemporal decomposition algorithm for the two-level slack subproblems. This enables coordinated operation of active distribution networks and multiple microgrids, avoiding data privacy risks, ensuring real-time scheduling decisions and global coordination, and effectively addressing the operation scenarios of large-scale discrete equipment and renewable energy power generation equipment. It also achieves refined scheduling and proactive coordinated control of power generation, grid, load and storage, and has high application value.

[0261] To implement the above embodiments, a third aspect of this disclosure provides an electronic device, comprising:

[0262] At least one processor; and a memory communicatively connected to said at least one processor;

[0263] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-described spatiotemporal decomposition-based distribution network-microgrid collaborative optimization scheduling method.

[0264] To implement the above embodiments, a fourth aspect of this disclosure proposes a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for coordinated optimization scheduling of distribution networks and microgrids based on spatiotemporal decomposition.

[0265] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0266] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a spatiotemporal decomposition-based distribution network-microgrid collaborative optimization scheduling method according to the above embodiments.

[0267] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone 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 remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0268] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0269] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0270] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0271] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0272] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0273] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0274] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0275] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for coordinated optimization scheduling of distribution networks and microgrids based on spatiotemporal decomposition, characterized in that, include: A multi-period active and reactive power joint scheduling model for active distribution networks and grid-connected microgrids is established. The scheduling model is a two-level mixed integer nonlinear programming model. The scheduling model is transformed into a two-level Riemannian nonlinear programming optimal scheduling problem in a continuous domain. The optimal scheduling problem is handled by a decomposition strategy based on the manifold model, which decomposes the optimal scheduling problem into relaxation subproblems, box subproblems, and spherical subproblems corresponding to active distribution and grid-connected microgrids, respectively. Based on the results of manifold decomposition, the optimization results of the scheduling model are obtained by using the enhanced ADMM algorithm of Riemannian manifold and the hierarchical spatiotemporal decomposition algorithm of the two-layer slack subproblem.

2. The method according to claim 1, characterized in that, The expression for the multi-time active and reactive power joint dispatch model of the active distribution network and grid-connected microgrid is as follows: （1） s.t. (2) （3） （4） （5） （6） in, and These are the target terms related to continuous and discrete variables in an active distribution network, respectively. and These are the objective terms related to continuous and discrete variables in a grid-connected microgrid, respectively. For time period t, there are continuous variables within the active distribution network, including: generator output power, energy storage system charging and discharging power, static var compensator injected power, and grid inverter injected power. Let t be the discrete variables within the active distribution network during time period t, including: tap position of on-load transformers, capacitor switch status, and energy storage charging and discharging actions, with dimension t. , representing the number of discrete control devices in the active distribution network; For time period t, the first The continuous variables within a grid-connected microgrid include: generator output power, energy storage system charging and discharging power, and grid inverter injected power. For time period t, the first Discrete variables within a grid-connected microgrid include: on-load transformer tap position, capacitor switching status, and energy storage charging and discharging actions, with dimensions of [missing information]. , representing the number of discrete control devices within the microgrid; To optimize the number of time periods, A collection of grid-connected microgrids; Constraints (2) and (3) are the operational constraints of the active distribution network. For the continuous feasible region constraints of active distribution network operation, Constraints on the operational actions of discrete devices in an active distribution network; The spatial boundary variables of the active distribution network during time period t include: active and reactive power injection, voltage amplitude and phase, and transformer tap position at the boundary connected to the main grid, and active and reactive power injection, voltage amplitude and phase, and transformer tap position at the boundary connected to the grid-connected microgrid. For time period t and the first A grid-connected microgrid corresponding to The subvectors of; 0 is the zero vector; constraints (4) and (5) are the first... Operational constraints of a grid-connected microgrid To ensure operational consistency between grid-connected microgrids and active distribution networks, Constraints on the operation of discrete devices in grid-connected microgrids; For time period t, the first The spatial boundary variables of a grid-connected microgrid include: active and reactive power exchange at the boundary with the active distribution network, voltage amplitude and phase, and transformer tap position; constraint equation (6) is the spatial boundary consistency constraint between the active distribution network and the grid-connected microgrid.

3. The method according to claim 2, characterized in that, The step of transforming the scheduling model into a two-level Riemannian nonlinear programming optimal scheduling problem in a continuous domain includes: 1) Using the l2-box method, the discrete variables of the active distribution network are equivalently represented as high-dimensional box constraints S as shown in equation (7). b and( ) 3D spherical S p The intersecting form equivalently represents the discrete variables within the grid-connected microgrid as a high-dimensional box constraint S, as shown in equation (8). b and( ) 3D spherical S p Forms of intersection: （7） （8） in, It is a set of values ​​all equal to 1. 3D column vector; It is the first A grid-connected microgrid A column vector of dimensions, all with values ​​of 1; 2) The equivalent expressions for the discrete variables of the active distribution network are established as follows: （9） （10） （11） in, For continuous variables in the active distribution network slack subproblem; For the box-type slack variables of the active distribution network, constrained in the high-dimensional box-type constraint S b middle; For the spherical relaxation variables of the active distribution network, constrained on the sphere S p superior; Establish the first The equivalent expression for the discrete variables of a grid-connected microgrid is as follows: （12） （13） （14） in, For continuous variables in the slack subtraction problem of grid-connected microgrids; For box-type slack variables of grid-connected microgrids, constrained in a high-dimensional box S b middle; For grid-connected microgrids, spherical relaxation variables are constrained on the sphere S. p superior; 3) Based on the results of steps 1) and 2), the scheduling model is transformed into the following form: （15） Feasible region of active distribution network: （16） （17） No. Feasible region of a microgrid: （18） （19） Spatial boundary consistency constraints between active distribution networks and microgrids: （20） Equivalent representation of discrete variables in an active distribution network: （21） （22） （23） No. Equivalent representation of discrete variables in a grid-connected microgrid: （24） （25） （26） Equations (15)-(26) constitute a continuous optimization model.

4. The method according to claim 3, characterized in that, Also includes: The decomposition strategy of the manifold model is applied to the active distribution network objective term in the continuous optimization model, resulting in the following relaxation subproblem, box subproblem, and spherical subproblem; wherein each subproblem is coordinated by the enhanced ADMM algorithm based on Riemannian manifold, and the number of algorithm iterations is counted as k; The relaxon problem: （27） s.t. (28) （29） in, This is a penalty function for non-integer values; The penalty coefficient for positive values; Box duality constraints for the l2-box method; For the spherical dual constraint of the l2-box method; and It is the dual multiplier for the (k-1)th iteration; In equation (23) The penalty function; In equation (23) The penalty parameter; Box problem: （30） s.t. (31) （32） in, Box duality constraints for the l2-box method; spherical problem: （33） s.t. (34) in, For the spherical dual constraint of the l2-box method.

5. The method according to claim 4, characterized in that, Also includes: The decomposition strategy of the manifold model is applied to the target term of the grid-connected microgrid in the continuous optimization model, resulting in the following relaxation subproblem, box subproblem, and spherical subproblem; wherein each subproblem is coordinated by the enhanced ADMM algorithm based on Riemannian manifold, and the number of algorithm iterations is counted as k; The relaxon problem: （35） s.t. (36) （37） in, This is a penalty function for non-integer values; Box duality constraints for the l2-box method; For the spherical dual constraint of the l2-box method; and It is the dual multiplier for the (k-1)th iteration; In equation (26) The penalty function; In equation (26) The penalty parameter; Box problem: （38） s.t. (39) （40） in, Box duality constraints for the l2-box method; spherical problem: （41） s.t. (42) in, For the spherical dual constraint of the l2-box method.

6. The method according to claim 5, characterized in that, The optimization result of the scheduling model is obtained, including: 1) Initialize parameters; Set the number of iterations during initialization ,Will , , , , , Used as initial values ​​and for warm restarts based on historical values; set , , , , , , , , , The value of the convergence tolerance is selected. ; Then, let the number of iterations... ; 2) A hierarchical spatiotemporal decomposition algorithm based on accelerated ADMM is used to process the multi-time active and reactive power joint scheduling model. The hierarchical spatiotemporal decomposition scheme, implemented in a distributed parallel manner, effectively decomposes the two-level relaxation subproblem into multiple small-scale subproblems, obtaining... , , , , and ,in , where w is the number of iterations in the hierarchical spatiotemporal decomposition algorithm, and the update scheme steps of the hierarchical spatiotemporal decomposition algorithm are as follows: 2-1) After performing a spatiotemporal decomposition, equation (27) can be restated into the following form: （43） s.t. (44) （45） （46） （47） in, Representing subproblems The corresponding scalar, vector, or matrix; Represents a set consisting of variables / vectors; Spatial coupling constraints for active distribution networks; For time coupling constraints of active distribution networks; and For the repeated time domain between active distribution network sub-problems; , , and All are in the ADMM algorithm for manifold enhancement. Obtained in the next iteration; and Subproblems , and Coupled variables; and The multipliers correspond to the subproblems respectively. , and Consistency constraints between them; and These are the diagonal matrices of penalty parameters related to spatial decomposition and temporal decomposition, respectively; and Representing subproblems and Sub-problems and Global variables between; 2-2) The prediction-correction acceleration introduced by Nesterov gradient descent method is applied to the first... In the next iteration, the equations (50) and (51) are used to determine the... and The expression is as follows: （48） （49） （50） （51） 3) The active distribution network obtains the box subproblem of equation (30) and the spherical subproblem of equation (33) by using the Gurobi and manifold trust region algorithms in parallel. , ,in ; 4) Grid-connected microgrids obtain the box subproblem of equation (38) and the spherical subproblem of equation (41) by using the Gurobi and manifold trust region algorithms in parallel. , ,in ; 5) Active distribution networks use equations (52) and (53) to update the dual multipliers and ; Grid-connected microgrids use equations (54) and (55) to update the multipliers and ; （52） （53） （54） （55） 6) Update the penalty parameters of the active distribution network using equations (56) and (57). and The penalty parameters of the grid-connected microgrid are updated using equations (58) and (59). and ; （56） （57） （58） （59） in, For non-integer solutions of box-constrained subproblems in active distribution networks, the penalty weight constant is denoted as . For non-integer solutions of the spherical manifold problem in an active distribution network, the penalty weight constant is given. For the non-integer solution penalty weight constant of the box-constrained subproblem in a grid-connected microgrid; For the non-integer solution penalty weight constant of the spherical manifold problem in a grid-connected microgrid; The upper limit of the penalty parameter for discrete variables in the active distribution network; The upper limit of the penalty parameter for discrete variables in grid-connected microgrids; 7) Determine if equation (60) is satisfied: if yes, the iteration ends and proceed to step 8); otherwise, increment the iteration count k by 1 and return to step 2). （60） 8) Output active distribution network results , , ,in Output results for grid-connected microgrids , ,in The coordinated operation optimization of the active distribution network and the grid-connected microgrid has been completed.

7. A distribution network-microgrid collaborative optimization scheduling device based on spatiotemporal decomposition, characterized in that, include: The scheduling model construction module is used to establish a multi-time active and reactive power joint scheduling model for active distribution networks and grid-connected microgrids. The scheduling model is a two-level mixed integer nonlinear programming model. The model transformation module is used to transform the scheduling model into a two-level Riemann nonlinear programming optimal scheduling problem in a continuous domain. The manifold decomposition module is used to process the optimal scheduling problem based on the decomposition strategy of the manifold model, and decomposes the optimal scheduling problem into relaxation subproblems, box subproblems and spherical subproblems corresponding to active distribution and grid-connected microgrids, respectively. The optimization scheduling module is used to obtain the optimization results of the scheduling model based on the results of manifold decomposition, using the enhanced ADMM algorithm of Riemannian manifold and the hierarchical spatiotemporal decomposition algorithm of two-layer slack subproblems.

8. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-6.

Images