Regional division and voltage fluctuation suppression method and device for micro-grid containing renewable energy sources

Abstract

The invention discloses a renewable energy-containing micro-grid region division and voltage fluctuation suppression method and device, and the method comprises the steps: constructing an optimization model composed of a micro-grid topology flexible division module, a micro-grid network construction network type function connection module, and an optimal power flow operation module, and solving the optimization model to obtain a micro-grid division result; calculating a voltage fluctuation index through a microgrid group voltage fluctuation index module, verifying whether the voltage fluctuation index meets the requirement of a voltage fluctuation index constraint module, if the constraint requirement is not met, generating a corresponding linear feasible cut by using a linear feasible cut generation algorithm module, merging the linear feasible cut into an optimization model, and solving again; a new micro-grid division result is obtained; and running the renewable energy power distribution network based on the new micro-grid division result, and enabling each node in the renewable energy power distribution network to obtain voltage fluctuation so as to improve the adaptive capacity of the renewable energy power distribution network to renewable energy. The device comprises a processor and a memory. According to the invention, voltage fluctuation caused by output uncertainty of renewable energy sources is reduced, so that the adaptive capacity of the power distribution network to the renewable energy sources is improved.

Description

Technical Field

[0001] This invention relates to the field of flexible operation technology of distribution networks (microgrids), and in particular to a method and apparatus for regional division and voltage fluctuation suppression of microgrids containing renewable energy. Background Technology

[0002] In recent years, the installed capacity of renewable energy sources such as solar, wind, and biomass energy has shown a trend of rapid growth.

[0003] However, the rapid integration of a large amount of distributed renewable energy has also brought significant challenges. The inherent randomness and intermittency of photovoltaic and wind power generation lead to a significant increase in the strong fluctuations and uncertainties in the output of distributed power sources in the short term, making voltage fluctuation problems at various nodes in the distribution network particularly prominent. According to statistics, in some regional power grids, the power fluctuation of distributed renewable energy can even reach 20% to 40% of the installed capacity in a short period of time. These fluctuations propagate and amplify through the grid topology, and in severe cases, may lead to voltage exceeding limits, power quality degradation, equipment damage, or even local network outages. Therefore, how to effectively mitigate the voltage fluctuation risks brought about by the high proportion of distributed renewable energy integration has become an important problem that urgently needs to be solved in the field of active distribution network operation and control.

[0004] The flexible construction and dynamic topology reconfiguration method of microgrid clusters is a novel proactive distribution network operation and management strategy suitable for solving the aforementioned problems. Microgrids, with their relatively small scale, high controllability, and high autonomy, can flexibly adjust their internal and external topology to effectively isolate and disperse regional renewable energy fluctuation risks, reducing the impact of voltage fluctuations. Simultaneously, by actively dividing microgrid clusters, the internal power flow and network structure of the power grid can be dynamically optimized, effectively reducing power flow congestion and voltage limit exceedance issues during grid operation. Microgrid clusters formed based on this proactive and flexible division method not only significantly improve the distribution network's ability to accommodate high proportions of distributed renewable energy and enhance operational reliability, but also provide solid technical support for the friendly integration and utilization of larger-scale renewable energy sources in the future. Summary of the Invention

[0005] This invention provides a method and apparatus for dividing microgrid areas and suppressing voltage fluctuations in high-renewable-energy distribution networks. This invention flexibly divides microgrid groups in high-renewable-energy distribution networks, adjusts power flow distribution, and reduces voltage fluctuations caused by the uncertainty of renewable energy output, thereby improving the distribution network's adaptability to renewable energy. See the description below for details:

[0006] A first aspect: a method for regional division and voltage fluctuation suppression in a microgrid containing renewable energy, the method comprising:

[0007] An optimization model is constructed, consisting of a microgrid topology flexible partitioning module, a microgrid networking and structure functional connection module, and an optimal power flow operation module. The microgrid partitioning result is obtained by solving the optimization model.

[0008] The voltage fluctuation index is calculated through the microgrid group voltage fluctuation index module, and it is verified whether the requirements of the voltage fluctuation index constraint module are met. If the constraint requirements are not met, a corresponding linear feasible cut is generated using the linear feasible cut generation algorithm module. The linear feasible cut is then incorporated into the optimization model and solved again to obtain a new microgrid partitioning result.

[0009] The operation of the renewable energy distribution network is based on the new microgrid partitioning results. Each node in the renewable energy distribution network receives voltage fluctuations, which are used to improve the adaptability of the renewable energy distribution network to renewable energy.

[0010] The microgrid networking functional connection module is as follows:

[0011] Constraints are constructed based on a multi-commodity network flow model to ensure that each microgrid contains at least one node with a network-type functional device.

[0012]

[0013] Constraints ensure that each non-potential root node is connected to at least one potential root node. This represents the flow of goods received by node i from the Kth potential root node, while and Let i and j represent the virtual goods traffic flowing out from the Kth potential root node, through line (i,j), and into node i, respectively. This indicates that the flow direction on line (i,j) is from i to j. The first equation indicates the flow direction on line (i,j) is from j to i; the second equation describes the virtual goods flow. With branch state α ij The relationship between ε and ε ensures that non-zero virtual traffic can only appear on closed lines; ε is a very small positive number that ensures that each non-potential root node has a non-zero commodity flow inflow.

[0014] The microgrid group voltage fluctuation index module is used to describe the potential voltage fluctuation magnitude of each node; the method adopts matrix transformation-based index calculation under a multi-microgrid topology.

[0015] The index based on matrix transformation is calculated as follows:

[0016] For the k-th subnet Let the conductivity matrix and susceptance matrix be respectively denoted as... and They are all functions of the 0 / 1 variable α:

[0017]

[0018] in, r ij Let (i,j) be the resistance value of the line. x ij Let (i,j) be the reactance value of the line. For set The correlation matrix corresponding to the nodes in the matrix;

[0019] Corresponding corrected resistance matrix and the modified reactance matrix Each is a matrix and The inverse matrix, i.e.:

[0020]

[0021] For each node in the k-th subnet of the m subnets, i.e. the node set Voltage fluctuation index vector value Represented as:

[0022]

[0023] in, For a set of nodes An indicator vector containing RDG;

[0024] The voltage fluctuation index vector of the k-th subnet is represented as: The voltage fluctuation index vector I(α) for the entire distribution network is then:

[0025]

[0026] in, These are the voltage fluctuation indices of each node in the first subnet and the voltage fluctuation indices of each node in the m-th subnet, respectively.

[0027] The linear feasible cut generation algorithm module is as follows:

[0028] ||α-α (ι) ||1≥ε

[0029] Where ||·||1 represents the 1-norm of the vector, α (ι) Let α be the solution obtained in the ι-th iteration, α be the variable to be solved in this iteration, and ε be a small positive number, until a solution that satisfies the constraints is obtained.

[0030] In a second aspect, a device for dividing and suppressing voltage fluctuations in a microgrid containing renewable energy, the device comprising: a processor and a memory, the memory storing program instructions, the processor calling the program instructions stored in the memory to cause the device to perform the method described in any one of the first aspects.

[0031] Third aspect, a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method described in any one of the first aspects.

[0032] The beneficial effects of the technical solution provided by this invention are:

[0033] 1) Global Optimality: This method adopts the MINLP (Mixed Integer Nonlinear Programming) model to uniformly model and jointly optimize various decision variables such as the multi-microgrid partitioning and power flow allocation of the distribution network; it approximates the global optimal solution in a finite number of steps through an iterative solution method, ensuring that voltage fluctuations are suppressed while satisfying system safety and operational constraints; at the same time, this method has good mathematical solvability and computational convergence, and is suitable for the dispatching and operation of actual distribution systems.

[0034] 2) Flexibility in microgrid partitioning: This method introduces a virtual network flow model to model the partitioning structure of multiple microgrids, enabling the network topology to be flexibly reconfigured according to operating conditions at different times / scenarios. This modeling method is highly versatile and can adapt to distribution networks of any topology shape and operating state, including various actual operating conditions such as node offline, line faults, and equipment maintenance. In addition, the proposed method does not require pre-setting microgrid boundaries, can realize dynamic partitioning and reconfiguration, and supports the deep integration of source-load coordination and local autonomous operation.

[0035] 3) Adaptability to voltage fluctuations: To address the voltage fluctuation problem caused by high-penetration renewable energy, this method effectively reduces voltage fluctuation risks by dividing the microgrid (group) topology and optimizing the output paths of distributed power sources. This method is particularly suitable for areas with strong wind and solar resource volatility, and can significantly improve the system's capacity to support high-proportion renewable energy access, thereby enhancing the sustainability of the distribution system operation. Attached Figure Description

[0036] Figure 1 The resulting diagram of microgrid partitioning to reduce voltage fluctuations;

[0037] Figure 2 This is a schematic diagram showing the voltage fluctuation index values ​​of each node under three network structures.

[0038] Figure 3A schematic diagram of voltage fluctuations under the output fluctuations of RDG (Renewable Energy Distributed Generation) for the initial network topology;

[0039] Figure 4 A schematic diagram of voltage fluctuations under RDG output fluctuations in a DNR (Distribution Network Reconfiguration) network topology.

[0040] Figure 5 Network topology partitioning for microgrids: Schematic diagram of voltage fluctuations under RDG output fluctuations.

[0041] Figure 6 This is a flowchart of a method for dividing microgrid areas and suppressing voltage fluctuations in a microgrid containing renewable energy. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0043] The objective of this invention is to reduce voltage fluctuations caused by the superposition of distributed renewable energy sources in the distribution network by flexibly dividing the network into microgrids within a distribution network containing a high proportion of renewable energy. This approach also alters the power flow distribution to minimize these voltage fluctuations. The method considers a voltage fluctuation index based on the distribution network topology. This index describes the potential voltage fluctuations at each node caused by the output volatility of renewable energy. A mathematical model for microgrid division is established using this index, and the magnitude of the index is constrained to find the optimal microgrid topology with low voltage fluctuations. Since this voltage fluctuation index is directly related to the network topology, which is represented by 0 / 1 variables in the mathematical optimization model, a challenge of this method is that the mathematical model is a mixed-integer nonlinear programming model, which cannot be directly solved using common solvers.

[0044] To address this challenge, this invention proposes an iterative algorithm for generating linear feasible cuts (a technical term in this field). This algorithm decomposes a mixed-integer nonlinear programming model into a series of mixed-integer linear programming models to solve, thereby obtaining the optimal topology that satisfies the given conditions. This method aims to achieve the following key objectives:

[0045] (1) Construct a voltage fluctuation index adapted to multiple microgrid clusters. A voltage fluctuation description method closely related to the microgrid cluster topology is proposed to quantify the impact of distributed renewable energy output fluctuations on each node of the network and accurately describe the potential voltage fluctuation risks of each node.

[0046] (2) Construct a flexible microgrid group partitioning model. Based on the voltage fluctuation index and combined with the actual topological characteristics of the microgrid group, the network switching state is flexibly represented by 0 / 1 variables, which clearly reflects the role of network-type functional equipment in the microgrid partitioning process, and forms a mathematical model for optimizing the microgrid group topology.

[0047] (3) An iterative algorithm for generating linear cuts is proposed. To address the challenge that partitioning models are mixed-integer nonlinear programming problems that are difficult to solve directly, an iterative method for generating linear cuts is proposed. This method decomposes the complex model into several mixed-integer quadratic programming subproblems and approximates the optimal solution through repeated iterations, thereby improving computational efficiency and practicality.

[0048] Example 1

[0049] This invention provides a method for regional division and voltage fluctuation suppression of microgrids containing renewable energy, comprising: a flexible microgrid topology division module, a microgrid network structure functional connection module, a microgrid group voltage fluctuation index module, an optimal power flow operation module, a voltage fluctuation index constraint module, and a linear feasible cut generation algorithm module. The flexible microgrid topology division module, the microgrid network structure functional connection module, the optimal power flow operation module, and the voltage fluctuation index constraint module together constitute a mixed integer nonlinear programming model, and the linear feasible cut generation algorithm module is the module for solving this model.

[0050] The steps of this embodiment of the invention are as follows:

[0051] Step 1: The microgrid topology flexible partitioning module, the microgrid networking and structure functional connection module, and the optimal power flow operation module together constitute an optimization model. Solving this model yields the microgrid partitioning results.

[0052] Step 2: Calculate the voltage fluctuation index using the microgrid group voltage fluctuation index module and verify whether it meets the requirements of the voltage fluctuation index constraint module. If it does not meet the requirements, proceed to Step 3. If it meets the requirements, the microgrid division result at this time is the final result, and the process ends.

[0053] Step 3: Use the linear feasible cut generation algorithm module to generate a corresponding linear feasible cut, and merge this linear feasible cut into the optimization model composed of the microgrid topology flexible partitioning module, microgrid network structure functional connection module, and optimal power flow operation module in Step 1 to solve it again and obtain a new microgrid partitioning result;

[0054] Step 4: Based on the microgrid partitioning results, the renewable energy distribution network is operated, resulting in lower voltage fluctuations at each node within the renewable energy distribution network and improving the network's adaptability to renewable energy.

[0055] In summary, the embodiments of the present invention flexibly divide microgrid groups in a high-renewable-energy distribution network through the above steps, adjust the power flow distribution, and reduce voltage fluctuations caused by the uncertainty of renewable energy output, thereby improving the adaptability of the distribution network to renewable energy.

[0056] Example 2

[0057] The purpose of this invention is to utilize the topological flexibility of the distribution network to construct microgrids for distribution networks with a high proportion of high renewable energy, so as to achieve the lowest network loss operation of the system and reduce the overall voltage fluctuation range within the distribution network.

[0058] in:

[0059] I. Flexible Module Division of Microgrid Topology

[0060] Considering the overall structure of the power distribution network is as follows There are N nodes and L lines containing switches in total. Let | represent the set of all nodes, where | ε represents the set of all lines, where |ε| = L.

[0061] In practice, a distribution network / microgrid must satisfy a radial constraint, namely, the following two conditions: a) each of the m subgrids (microgrids) is a connected subgraph; b) there are Nm closed branches within the distribution network. For a distribution network containing multiple microgrids, each subgrid (microgrid) is a spanning tree, and all subgrids together constitute a spanning forest. Therefore, a normally operating distribution network without microgrids is a spanning tree. By disconnecting several line switches on this spanning tree, several sub-spanning trees can be constructed, each of which is a subgrid (microgrid), and all sub-spanning trees constitute a spanning forest.

[0062] When partitioning a distribution network into microgrids, a virtual spanning tree is first constructed. Then, further partitioning occurs on this virtual spanning tree, creating the actual spanning forest (multiple microgrids) topology. The constraints for constructing a spanning forest in a distribution network are as follows:

[0063]

[0064] Constraints (1)-(4) are used to construct a virtual spanning tree using virtual commodity flow modeling, denoted by a 0 / 1 vector β, where β = [β ij ], The variable is 0 / 1, representing the connection state of each line in the virtual spanning tree (β). ij =1 indicates that branch (i,j) is closed, β ij =0 indicates that branch (i,j) is disconnected, therefore β can be regarded as the distribution network diagram structure. The association vector of a certain spanning tree topology. Where, f = [f ij ], For virtual goods flow variables.

[0065] In constraint (1), i r As a virtual root node (any node in the graph can be selected as the root node), (i r (j,i) and (j,i) r ) represents the same line in the line set ε, f irj f jir Representing lines (i) r From node i on (j) r Flow to node j and flow from node j to node i r The virtual flow, where the constraint represents the flow from root node i r A total of N-1 units of goods flowed out.

[0066] In constraint (2), (i,j) and (j,i) represent the same line, f ij f ji These represent the virtual flows from node i to node j and from node j to node i on line (i,j), respectively. This constraint indicates that, except for the root node i... r In addition, each of the other nodes receives one unit of the product stream.

[0067] Constraint (3) requires that the flow of goods can only be transmitted on the virtual closed branches of the virtual spanning tree.

[0068] Constraint (4) determines that the number of closed branches in the virtual spanning tree must be N-1.

[0069] In constraint (5), the 0 / 1 vector α is defined as [α ij ], Used to represent the actual switching state of the circuit, where α ij =1 indicates that the switch on line (i,j) is closed, α ij =0 indicates that the circuit switch is open, which can represent a spanning forest. Constraint (5) is to disconnect several circuits based on the virtual spanning tree β to form the actual spanning forest topology.

[0070] II. Microgrid Networking Functional Connection Module

[0071] During microgrid operation, voltage sources such as nodes of grid-connected devices (e.g., diesel generators, grid-connected inverters, grid-connected energy storage systems) are essential to provide voltage and frequency regulation. Consider configuring several diesel generators in the distribution network to provide a voltage reference for the system when necessary; this set is denoted as the cluster. Combined with substation node set but This can be viewed as a set of potential root nodes for each subgrid (microgrid) during the microgrid construction process. To ensure the reliable operation of the microgrid, this invention proposes a novel model based on multi-commodity network flow to construct relevant constraints, ensuring that each microgrid contains at least one diesel generator or is connected to a substation.

[0072]

[0073] By using constraints (6)-(8), it can be ensured that each non-potential root node is connected to at least one potential root node. In other words, it is required that there is a virtual commodity flow between each non-potential root node and at least one potential root node, which is a sufficient condition to guarantee connectivity.

[0074] In constraint (6), This represents the flow of goods received by node i from the Kth potential root node, while and Let i and j represent the virtual goods traffic flowing out from the Kth potential root node, through line (i,j), and into node i, respectively. This indicates that the flow direction on line (i,j) is from i to j. This indicates that the flow direction on line (i,j) is from j to i.

[0075] Constraint (7) describes the flow of virtual goods. With branch state α ij The relationship between these factors ensures that non-zero virtual traffic can only appear on closed lines.

[0076] In constraint (8), ε is a very small positive number (e.g., ε = 1) to ensure that each non-potential root node has a non-zero commodity flow inflow, regardless of which potential root node it comes from.

[0077] III. Microgrid Group Voltage Fluctuation Index Module

[0078] Voltage fluctuation index is an indicator that describes the potential voltage fluctuation magnitude of each node. This module presents an index calculation method based on matrix transformation under a multi-microgrid topology.

[0079] The vector of the voltage fluctuation index is denoted as... in This represents the voltage fluctuation index value at node i. The voltage fluctuation index can be further expressed as... That is, a function of α.

[0080] Assume the distribution network is divided into m subnetworks (a subnetwork containing substation nodes cannot be called a microgrid). The node set of the k-th subnetwork is... The route set is Its graph structure can be represented as remember Where N k Let L be the number of nodes in the k-th subnet. k This represents the number of closed branches. Additionally, we introduce... Let represent the nodes in the k-th subnet that have the function of network formation (GFM). The set of nodes in the subnet excluding GFM nodes is denoted as .

[0081] Obviously, the diagram Correlation matrix The graph can be partitioned according to the above set of nodes. For example: Correlation matrix and They can be obtained from matrices respectively. Bank of China corresponds to the set of nodes. and The submatrix representation. Furthermore, the location of the RDG in the distribution network can be indicated by the indicator vector. It means that among them This indicates that RDG is installed on node i; otherwise... Similarly, e rdg Based on set and To divide, that is It refers to vector e rdg The middle is composed of a set of nodes The subvector formed by the indexed rows.

[0082] Furthermore, a voltage fluctuation index function with α as the variable can be established for the k-th subnet. For the k-th subnet... The modified conductivity matrix and susceptance matrix are denoted as follows: and They are all functions of the 0 / 1 variable α:

[0083]

[0084] in, r ij Let (i,j) be the resistance value of the line. x ij Let (i,j) be the reactance value of the line. For set The correlation matrix corresponds to the nodes in the network. Since each subnet is a spanning tree (radial graph structure), the matrix... and They are all invertible matrices.

[0085] Corresponding corrected resistance matrix and the modified reactance matrix Each is a matrix and The inverse matrix, i.e.:

[0086]

[0087] Therefore, for each node in the k-th subnet of the m subnets, i.e. the node set Voltage fluctuation index vector value It can be represented as:

[0088]

[0089] in, For a set of nodes The indicator vector containing RDG.

[0090] For nodes with GFM functionality Since its voltage is constant, its voltage fluctuation index is: Therefore, the voltage fluctuation index vector of the k-th subnet can be expressed as: The voltage fluctuation index vector I(α) for the entire distribution network is then:

[0091]

[0092] in, These are the voltage fluctuation indices of each node in the first subnet and the voltage fluctuation indices of each node in the m-th subnet, respectively.

[0093] IV. Optimal Power Flow Operation Module

[0094] The linear DistFlow model is used for modeling, which has high computational efficiency and good effectiveness. The power balance constraints based on the linear DistFlow model are as follows:

[0095]

[0096] The specific meanings of the symbols in the above constraints are shown in Table 1. The objective function (15) is to minimize the system network loss, r ij Let be the resistance value of line (i,j). Let be the squares of the active power flow and reactive power flow on line (i,j), respectively. Equations (16) and (17) represent the balance constraints for active and reactive power, respectively. In constraint (18), Represents the set of all nodes containing power supply nodes (where, The set consists of substations and diesel generators, which are nodes capable of flexibly adjusting power output in real time and providing reactive power to regulate voltage. Constraints (19)-(20) limit the amount of active and reactive power injected into the set containing power supply nodes. Constraint (19) restricts the set The power output range of the nodes. Constraint (20) indicates that the power output of renewable energy is the predicted value in the optimization model. Constraints (21) and (22) are relaxation forms of the linear DistFlow model using the Big M method. Constraints (23) and (24) are system security constraints, which limit the power flow of the line and the voltage range of the nodes, respectively.

[0097] Table 1. Symbol Meanings in the Power Flow Model

[0098]

[0099] V. Voltage Fluctuation Index Constraint Module

[0100]

[0101] In constraint (25), e i Let N be a column vector whose i-th element is 1 and all others are 0. This is the maximum voltage fluctuation index value set for node i in this optimization model.

[0102] VI. Linear Feasible Cut Generation Algorithm Module

[0103] Since the proposed flexible microgrid region partitioning and voltage fluctuation suppression optimization model (including: a flexible microgrid topology partitioning module, a microgrid network structure functional connection module, and an optimal power flow operation module) is a mixed-integer nonlinear programming (MINLP) problem, it is difficult to solve efficiently using existing solvers. Therefore, this invention proposes a linear feasible cut generation algorithm for iteratively solving this optimization model.

[0104] Specifically, the model contains quadratic terms in the objective function (15) and constraints (23). and Meanwhile, constraint (25) contains a nonlinear function I(α). If constraint (25) is temporarily removed, the remaining part will become a convex mixed integer quadratic programming (MIQP) problem, which can be solved directly by the solver. Therefore, the original problem can be decomposed, and the MIQP part can be taken as the master problem (MP). After obtaining the solution of MP using the solver, the constraint (25) can be used to check it. If the solution does not satisfy constraint (25), a linear feasible cut is generated and added to MP in the next iteration until the optimal solution of MP satisfies all the constraints of the original problem.

[0105] For the optimization algorithm proposed to solve the optimization model, let the optimal solution of MP in the ι-th iteration be (α) (ι) ,p g(ι) ,q g (ι) ,P (ι) Q (ι) ,v (ι) ),in This represents the actual switching state of line (i,j) during the ι-th iteration. Let i be the active power output of node i in the ι-th iteration. The reactive power output of node i in the ι-th iteration. Let represent the active power flow of line (i,j) in the ι-th iteration. Let (i,j) be the reactive power flow during the ι-th iteration of line (i,j). The square of the voltage amplitude at node i during the ι-th iteration.

[0106] Based on equations (13) and (14), the index I = I(α) can be calculated. (ι) ), and check whether it satisfies constraint (25). If it does not satisfy, generate the corresponding feasible cut and add it to MP in the (1+1)th iteration:

[0107] ||α-α (ι) ||1≥ε (26)

[0108] Where ε is a small positive number. Since the feasible cut (26) added in each iteration is linear, MP is always a MIQP problem in each iteration until a solution satisfying constraint (25) is found.

[0109] Example 3

[0110] The optimal implementation of this method relates to a microgrid area partitioning and voltage fluctuation suppression method with renewable energy, which aims to iteratively calculate the established MINLP model to achieve flexible microgrid partitioning and voltage fluctuation reduction.

[0111] I. Microgrid Division and Calculation of Voltage Fluctuation Indicators

[0112] After adopting the microgrid partitioning strategy, the maximum value of the voltage fluctuation index of all nodes is limited to [value missing]. Figure 1 The results demonstrate the effectiveness of using microgrid partitioning to reduce voltage fluctuations caused by renewable energy sources.

[0113] The entire power distribution network is divided into three subgrids, named: Substation-powered subgrid, Microgrid 1 (MG 1), and Microgrid 2 (MG 2). Excluding nodes with network-building functions, the Substation-powered subgrid contains one diesel generator and five RDGs; MG 1 contains two diesel generators and seven RDGs; and MG 2 contains six RDGs. These 18 RDGs are approximately evenly distributed across the three subgrids, effectively and significantly reducing the voltage fluctuation index values ​​of the nodes in each subgrid.

[0114] Distribution network reconfiguration (DNR) based methods have proven highly efficient in mitigating voltage fluctuations caused by renewable energy sources. To highlight the effectiveness of this method, the DNR method is implemented as a benchmark. See [link to implementation details]. Figure 2 When using the DNR strategy, the maximum value of the node voltage fluctuation index can only be limited to 23.

[0115] To distinguish them from the initial network topology, the switching states under the DNR strategy and the microgrid partitioning strategy are detailed in Table 2.

[0116] Table 2 Topology of the three types of networks

[0117]

[0118] Voltage fluctuations under the original network topology were also considered for comparison. Figure 2 The voltage fluctuation index values ​​of each node under three network structures are shown, namely solid line (original network), dashed line (DNR scheme) and dotted line (microgrid partitioning scheme).

[0119] In the original network and DNR scheme, the voltage fluctuation index of nodes farther from the substation is greater, especially the fluctuation of nodes 90 to 123. However, after adopting the microgrid partitioning method, the network is divided into three subgrids, and multiple RDGs are deployed in a decentralized and isolated manner, thereby significantly reducing the voltage fluctuation index values ​​of each node.

[0120] II. Monte Carlo Simulation of Voltage Fluctuations

[0121] Monte Carlo simulations were further employed to verify the effectiveness of the proposed method. To simulate power fluctuations and prediction errors in distributed renewable energy sources, simulations were performed on data belonging to the RDG set. For the i-th RDG, multiply its predicted output by a normally distributed random variable ξ with a mean of 1 and a standard deviation of 0.5. i ,Right now The results are used as the actual output power. The voltage distribution in each simulation scenario is obtained by solving the Matpower-based AC-OPF model.

[0122] Five thousand simulations were performed on each of the three network structures, and the results are as follows: Figure 3 (Original network) Figure 4 (DNR scheme) Figure 5 (Microgrid partitioning scheme) is shown in the three figures. In these three figures, the dashed and solid lines represent the theoretical voltage values ​​of each node when the RDG output is at the predicted value in the three network schemes, and the solid lines represent the actual voltage values ​​of each node when the RDG output deviates from the predicted value in the simulation.

[0123] Clearly, the voltage distribution under the original network topology exhibits a large fluctuation range, even showing significant voltage limit violations in some scenarios. While the DNR scheme reduces voltage fluctuations to some extent compared to the original network topology, it still exhibits a considerable fluctuation range and exceeds voltage limits. In contrast, the microgrid partitioning scheme achieves the smallest voltage fluctuation range, with its maximum fluctuation value not exceeding the safe voltage limit.

[0124] Table 3 Statistics on the Number of Voltage Exceedances

[0125] Initial network topology DNR strategy Microgrid partitioning strategy Exceeding the limit 120 / 5000 91 / 5000 0 / 5000 Maximum over-limit value 0.0299pu 0.0407pu —

[0126] Table 3 presents the statistical results of voltage limit violations. In the 5000 simulated scenarios, 120 voltage limit violations occurred under the original network topology, with a maximum exceedance of 0.0299 pu; the DNR scheme experienced 91 violations, with a maximum exceedance of 0.0407 pu. This indicates that the DNR-based method is still ineffective in mitigating voltage fluctuations in some scenarios. The microgrid partitioning scheme, however, did not experience any voltage limit violations in any scenario, demonstrating optimal performance in mitigating voltage fluctuations.

[0127] Therefore, the embodiments of the present invention can better improve the flexibility of system operation, and at the same time enhance the adaptability of each node in the system to voltage fluctuations under the access of a high proportion of renewable energy by utilizing network flexibility.

[0128] Example 4

[0129] A microgrid area partitioning and voltage fluctuation suppression device with renewable energy includes a processor and a memory. The memory stores program instructions, and the processor calls the program instructions stored in the memory to cause the device to execute the following method steps in Embodiment 1:

[0130] An optimization model is constructed, consisting of a microgrid topology flexible partitioning module, a microgrid networking and structure functional connection module, and an optimal power flow operation module. The microgrid partitioning result is obtained by solving the optimization model.

[0131] The voltage fluctuation index is calculated through the microgrid group voltage fluctuation index module, and it is verified whether the requirements of the voltage fluctuation index constraint module are met. If the constraint requirements are not met, a corresponding linear feasible cut is generated using the linear feasible cut generation algorithm module. The linear feasible cut is then incorporated into the optimization model and solved again to obtain a new microgrid partitioning result.

[0132] The operation of the renewable energy distribution network is based on the new microgrid partitioning results. Each node in the renewable energy distribution network receives voltage fluctuations, which are used to improve the adaptability of the renewable energy distribution network to renewable energy.

[0133] The microgrid networking functional connection module is as follows:

[0134] Constraints are constructed based on a multi-commodity network flow model to ensure that each microgrid contains at least one node with a network-type functional device:

[0135]

[0136] Constraints ensure that each non-potential root node is connected to at least one potential root node. This represents the flow of goods received by node i from the Kth potential root node, while and Let i and j represent the virtual goods traffic flowing out from the Kth potential root node, through line (i,j), and into node i, respectively. This indicates that the flow direction on line (i,j) is from i to j. The first equation indicates the flow direction on line (i,j) is from j to i; the second equation describes the virtual goods flow. With branch state α ij The relationship between ε and ε ensures that non-zero virtual traffic can only appear on closed lines; ε is a very small positive number that ensures that each non-potential root node has a non-zero commodity flow inflow.

[0137] The microgrid group voltage fluctuation index module is used to describe the potential voltage fluctuation magnitude of each node; the method adopts matrix transformation-based index calculation under a multi-microgrid topology.

[0138] The index based on matrix transformation is calculated as follows:

[0139] For the k-th subnet Let the conductivity matrix and susceptance matrix be respectively denoted as... and They are all functions of the 0 / 1 variable α:

[0140]

[0141] in, r ij Let (i,j) be the resistance value of the line. x ij Let (i,j) be the reactance value of the line. For set The correlation matrix corresponding to the nodes in the matrix;

[0142] Corresponding corrected resistance matrix and the modified reactance matrix Each is a matrix and The inverse matrix, i.e.:

[0143]

[0144] For each node in the k-th subnet of the m subnets, i.e. the node set Voltage fluctuation index vector value Represented as:

[0145]

[0146] in, For a set of nodes An indicator vector containing RDG;

[0147] The voltage fluctuation index vector of the k-th subnet is represented as: The voltage fluctuation index vector I(α) for the entire distribution network is then:

[0148]

[0149] in, These are the voltage fluctuation indices of each node in the first subnet and the voltage fluctuation indices of each node in the m-th subnet, respectively.

[0150] The linear feasible cut generation algorithm module is as follows:

[0151] ||α-α (ι) ||1≥ε

[0152] Where ||·||1 represents the 1-norm of the vector, α (ι) Let α be the solution obtained in the ι-th iteration, α be the variable to be solved in this iteration, and ε be a small positive number, until a solution that satisfies the constraints is obtained.

[0153] It should be noted that the device descriptions in the above embodiments correspond to the method descriptions in the embodiments, and the embodiments of the present invention will not be repeated here.

[0154] The execution entities of the aforementioned processor and memory can be devices with computing functions such as computers, microcontrollers, and single-chip microcomputers. In specific implementations, the embodiments of the present invention do not limit the execution entities and can select them according to the needs of actual applications.

[0155] Data signals are transmitted between the memory and the processor via a bus, which will not be elaborated upon in this embodiment of the invention.

[0156] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium, the storage medium including a stored program, which, when the program is running, controls the device where the storage medium is located to execute the method steps in the above embodiments.

[0157] The computer-readable storage medium includes, but is not limited to, flash memory, hard disk, solid-state drive, etc.

[0158] It should be noted that the description of the readable storage medium in the above embodiments corresponds to the description of the method in the embodiments, and the embodiments of the present invention will not be repeated here.

[0159] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated.

[0160] The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer-readable storage medium can be any usable medium accessible to a computer or a data storage device such as a server or data center that integrates one or more usable media. The usable medium can be a magnetic medium or a semiconductor medium, etc. Unless otherwise specified, the model numbers of the devices in this embodiment of the invention are not limited; any device capable of performing the above functions is acceptable.

[0161] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0162] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for regional division and voltage fluctuation suppression in a microgrid containing renewable energy, characterized in that, The method includes: An optimization model is constructed, consisting of a microgrid topology flexible partitioning module, a microgrid networking and structure functional connection module, and an optimal power flow operation module. The microgrid partitioning result is obtained by solving the optimization model. The voltage fluctuation index is calculated through the microgrid group voltage fluctuation index module, and it is verified whether the requirements of the voltage fluctuation index constraint module are met. If the constraint requirements are not met, a corresponding linear feasible cut is generated using the linear feasible cut generation algorithm module. The linear feasible cut is then incorporated into the optimization model and solved again to obtain a new microgrid partitioning result. The operation of the renewable energy distribution network is based on the new microgrid partitioning results. Each node in the renewable energy distribution network receives voltage fluctuations, which are used to improve the adaptability of the renewable energy distribution network to renewable energy.

2. The method for regional division and voltage fluctuation suppression of a microgrid containing renewable energy as described in claim 1, characterized in that, The microgrid networking functional connection module is as follows: Constraints are constructed based on a multi-commodity network flow model to ensure that each microgrid contains at least one node with a network-type functional device. Constraints ensure that each non-potential root node is connected to at least one potential root node. This represents the flow of goods received by node i from the Kth potential root node, while and Let i and j represent the virtual goods traffic flowing out from the Kth potential root node, through line (i,j), and into node i, respectively. This indicates that the flow direction on line (i,j) is from i to j. The first equation indicates the flow direction on line (i,j) is from j to i; the second equation describes the virtual goods flow. With branch state α ij The relationship between these factors ensures that non-zero virtual traffic can only appear on closed loops. ε is a very small positive number that ensures that every non-potential root node has a non-zero commodity flow inflow.

3. The method for regional division and voltage fluctuation suppression of a microgrid containing renewable energy as described in claim 1, characterized in that, The microgrid group voltage fluctuation index module is used to describe the potential voltage fluctuation magnitude of each node; the method adopts matrix transformation-based index calculation under a multi-microgrid topology.

4. The method for regional division and voltage fluctuation suppression of a microgrid containing renewable energy as described in claim 3, characterized in that, The index based on matrix transformation is calculated as follows: For the k-th subnet Let the conductivity matrix and susceptance matrix be respectively denoted as... and They are all functions of the 0 / 1 variable α: in, r ij Let (i,j) be the resistance value of the line. x ij Let (i,j) be the reactance value of the line. For set The correlation matrix corresponding to the nodes in the matrix; Corresponding corrected resistance matrix and the modified reactance matrix Each is a matrix and The inverse matrix, i.e.: For each node in the k-th subnet of the m subnets, i.e. the node set Voltage fluctuation index vector value Represented as: in, For a set of nodes An indicator vector containing RDG; The voltage fluctuation index vector of the k-th subnet is represented as: The voltage fluctuation index vector I(α) for the entire distribution network is then: in, These are the voltage fluctuation indices of each node in the first subnet and the voltage fluctuation indices of each node in the m-th subnet, respectively.

5. The method for regional division and voltage fluctuation suppression of a microgrid containing renewable energy according to claim 3, characterized in that, The linear feasible cut generation algorithm module is as follows: ||a-a (ι) ||1≥e Where ||·||1 represents the 1-norm of the vector, α (ι) Let α be the solution obtained in the ι-th iteration, α be the variable to be solved in this iteration, and ε be a small positive number, until a solution that satisfies the constraints is obtained.

6. A device for dividing microgrid areas and suppressing voltage fluctuations with renewable energy, characterized in that, The device includes a processor and a memory, the memory storing program instructions, the processor invoking the program instructions stored in the memory to cause the device to perform the method according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method described in any one of claims 1-5.

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