Power distribution network distributed resource cluster optimization regulation and control method, system and device considering power communication coupling and medium

Abstract

The invention relates to the technical field of distributed resource scheduling, and discloses a power distribution network distributed resource cluster optimization regulation and control method, system and device considering power communication coupling and a medium, and the method comprises the steps: obtaining the related data of a power distribution network and a communication network, and building a comprehensive division index set based on a target communication network; performing cluster division according to the index set to obtain a plurality of power distribution network distributed resource clusters; an optimization regulation and control model is constructed based on the division result, a target function is oriented to the minimum cluster operation cost, and power flow, communication operation and resource operation characteristic constraints are comprehensively considered; solving the model by adopting a time and event dual-drive solving strategy; a rapid solving method based on multi-graph convolution is utilized to extract a data space dependency relationship, a mapping mechanism from an operation state to an optimal strategy is constructed, and solving efficiency is improved; and performing optimization regulation and control on the cluster according to the solved optimal strategy to realize efficient and stable operation of the power distribution network under power communication coupling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed resource scheduling, and in particular to a power distribution network distributed resource cluster optimization control method, system, device and medium considering power communication coupling. BACKGROUND

[0002] With the rapid development of power systems, the penetration rate of distributed resources in power distribution networks is continuously increasing. The integration of flexible resources such as photovoltaic, energy storage and electric vehicles poses new challenges to power balance and optimization control of power distribution networks. These distributed resources have characteristics such as dispersion, heterogeneity and controllability, and although they can provide power regulation capability at multiple time scales for power distribution networks, their installation locations are dispersed and their operating characteristics are complex, making it difficult for traditional centralized scheduling methods to efficiently coordinate these resources. Therefore, how to reasonably divide resource clusters and achieve optimization control has become a hot research topic.

[0003] In the prior art, power distribution network cluster division methods mainly rely on physical indicators such as electrical connection strength, such as modular division methods based on electrical distance or power flow sensitivity. These methods can reflect the physical coupling relationship between nodes, but ignore the important role of communication networks in cluster coordination. With the deep integration of cyber-physical power systems, the coupling relationship between the physical electrical system of the power distribution network and the communication network is increasingly close, and only considering physical electrical indicators may result in cluster division results that cannot effectively balance communication traffic, thereby affecting the effectiveness of subsequent power optimization control.

[0004] In addition, existing power distribution network power optimization control models mostly focus on power flow constraints of physical power systems and operating characteristics of distributed resources, while ignoring the impact of communication networks on optimization results. At the same time, commercial solvers are usually used to solve the mixed integer linear programming optimization control model established, but when the scale of the power distribution network is large or the communication constraints are complex, the solving efficiency is low and even no solution may occur. This limitation makes it difficult for existing methods to meet the optimization scheduling requirements with high real-time requirements. SUMMARY

[0005] In view of the above existing problems, the present application is proposed.

[0006] Therefore, the present application provides a power distribution network distributed resource cluster optimization control method, system, device and medium considering power communication coupling, which can solve the problems of existing technologies such as only considering physical electrical indicators for cluster division, ignoring the impact of communication networks on optimization control, and low solving efficiency.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for optimized control of distributed resource clusters in a distribution network that considers power communication coupling, comprising:

[0009] Establish a comprehensive partitioning index set based on the target communication network, and perform cluster partitioning based on the comprehensive partitioning index set;

[0010] The comprehensive segmentation index set includes several cluster segmentation indexes;

[0011] The comprehensive classification index set includes at least modularity index based on electrical distance, cluster power balance index, and cluster communication network coordination index.

[0012] Based on the results of the cluster partitioning, an optimized control model for distributed resource clusters in the power distribution network is established.

[0013] The distribution network distributed resource cluster optimization and control model includes an objective function aimed at minimizing cluster operating costs and a set of constraints consisting of several different constraints.

[0014] The optimal control strategy for the distributed resource cluster of the distribution network is obtained by solving the distributed resource cluster optimization control model. Based on the optimal control strategy, the distributed resource cluster optimization control of the distribution network is carried out.

[0015] As a preferred embodiment of the distribution network distributed resource cluster optimization control method considering power communication coupling described in this invention, wherein solving the distribution network distributed resource cluster optimization control model includes:

[0016] Establish time-driven and event-driven solution strategies;

[0017] The time-driven solution strategy is used to solve the distribution network distributed resource cluster optimization control model under normal conditions, and obtain the cluster optimal optimization control strategy under normal conditions.

[0018] The event-driven solution strategy is used to solve the distribution network distributed resource cluster optimization and control model under abnormal conditions, and obtain the optimal cluster optimization and control strategy under abnormal conditions.

[0019] This preferred solution employs a dual solution strategy—time-driven and event-driven—to comprehensively and specifically address various situations during the optimization and control of distributed resource clusters in distribution networks. Under normal circumstances, the time-driven solution strategy ensures the distribution network operates continuously and stably according to the predetermined optimal strategy, effectively reducing cluster operating costs, improving resource utilization efficiency, and maintaining the efficient and economical operation of the distribution network. In the event of abnormal situations, the event-driven solution strategy can respond rapidly, adjust the control strategy in a timely manner, ensure the safe and reliable operation of the distribution network, minimize the impact of abnormal situations on the distribution network, and avoid serious consequences such as large-scale power outages caused by faults or emergencies. This dual-strategy solution method combines the stability requirements and emergency handling requirements of distribution network operation, improving the flexibility, reliability, and economy of the entire distributed resource cluster optimization and control of the distribution network. This enables the distribution network to better adapt to complex and ever-changing operating environments and achieve efficient and stable operation under power-communication coupling.

[0020] As a preferred embodiment of the distribution network distributed resource cluster optimization control method considering power communication coupling described in this invention, the distribution network distributed resource cluster optimization control model includes:

[0021] The objective function includes at least the cluster network loss cost, the operating cost of distributed resources within the cluster, and the cluster communication cost;

[0022] The set of constraints includes at least several physical power system power flow constraints, several communication network operation constraints, and several distributed resource operation characteristic constraints.

[0023] As a preferred embodiment of the distribution network distributed resource cluster optimization and control method considering power communication coupling described in this invention, the comprehensive partitioning index set further includes:

[0024] The modularity index based on electrical distance is used to evaluate the network structure strength of the physical electrical system. The larger the modularity index based on electrical distance, the more similar the functions or properties of the nodes in the divided clusters are, and the weaker the coupling between the clusters.

[0025] The cluster power balance index includes active power balance and reactive power balance, which are used to represent the degree of source-load matching within each cluster.

[0026] The coordination index of the cluster communication network is characterized by communication traffic balance and construction investment cost.

[0027] As a preferred embodiment of the power-communication coupling-considered distribution network distributed resource cluster optimization and control method of the present invention, the plurality of communication network operation constraints include connecting each distributed resource and node in the target communication network to a smart terminal, configuring an information network node for each partitioned cluster, and summing all the communication information that the cluster needs to transmit to the outside as the communication network operation constraints.

[0028] The distributed resources include photovoltaics, energy storage, and electric vehicles.

[0029] As a preferred embodiment of the distribution network distributed resource cluster optimization control method considering power communication coupling described in this invention, the step of solving the distribution network distributed resource cluster optimization control model further includes:

[0030] A fast solution method for distributed resource cluster optimization and control based on multi-graph convolution is established, using the operating status of the distribution network cluster as input data;

[0031] The input data includes the active and reactive power distribution of the distribution network, voltage distribution, access location and operating status of distributed resources, and access location and operating status of smart terminals.

[0032] By extracting spatial dependencies from the input data;

[0033] A mapping mechanism is constructed between the operating status of the distribution network cluster and the optimal power optimization strategy, so as to solve the distribution network distributed resource cluster optimization and control model considering power communication coupling.

[0034] As a preferred embodiment of the distribution network distributed resource cluster optimization control method considering power communication coupling described in this invention, the step of solving the distribution network distributed resource cluster optimization control model further includes:

[0035] Under normal circumstances, the dispatch management center sends an optimization instruction to the distribution network cluster every 15 minutes. The distribution network cluster receives and executes the optimization instruction to find the optimal optimization control strategy for the distributed resource cluster with a total period of T from the current moment.

[0036] The smart terminals installed on each node and distributed resource in the power distribution network cluster monitor abnormal events in the cluster in real time, including voltage exceeding limits, frequency fluctuations, and power flow overload.

[0037] When a smart terminal detects an abnormal event, it immediately reports the type of the abnormal event to the scheduling management center, which then confirms and executes the optimal control strategy for the distributed resource cluster with a total period of T from the current moment.

[0038] Secondly, the present invention provides a distributed resource cluster optimization and control system for distribution networks that considers power communication coupling, comprising:

[0039] The partitioning module is used to establish a comprehensive partitioning index set based on the target communication network, and to perform cluster partitioning based on the comprehensive partitioning index set;

[0040] The comprehensive segmentation index set includes several cluster segmentation indexes;

[0041] The comprehensive classification index set includes at least modularity index based on electrical distance, cluster power balance index, and cluster communication network coordination index.

[0042] The model building module is used to build a distributed resource cluster optimization and control model for the distribution network based on the results of the cluster partitioning.

[0043] The distribution network distributed resource cluster optimization and control model includes an objective function aimed at minimizing cluster operating costs and a set of constraints consisting of several different constraints.

[0044] The control module is used to solve the distribution network distributed resource cluster optimization control model to obtain the cluster optimal control strategy, and to perform distribution network distributed resource cluster optimization control based on the cluster optimal control strategy.

[0045] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0046] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0047] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes an optimized control method for distributed resource clusters in power distribution networks that considers the coupling between power and communication. It comprehensively considers the coupling relationship between the power system and the communication network, introducing a comprehensive set of partitioning indicators based on the target communication network during the cluster partitioning stage. This set covers multiple aspects, including modularity indicators based on electrical distance, cluster power balance indicators, and cluster communication network coordination indicators. This ensures that the cluster partitioning results not only reflect the network structure strength and power balance of the physical electrical system but also effectively balance communication traffic. This avoids the problem of existing methods relying solely on physical electrical indicators while neglecting the role of the communication network, thus laying a solid foundation for subsequent power optimization and control.

[0048] In constructing the optimized control model, the objective function includes the cluster network loss cost, the operating cost of distributed resources within the cluster, and the cluster communication cost. The set of constraints covers the power flow constraints of the physical power system, the operating constraints of the communication network, and the operating characteristics constraints of distributed resources. This comprehensive model construction approach fully considers the actual situation of power-communication coupling, making the optimized control strategy more aligned with practical needs and better able to achieve the goal of minimizing cluster operating costs.

[0049] In terms of model solving, this invention employs a dual solution strategy of time-driven and event-driven approaches, combined with a fast solution method for distributed resource cluster optimization and control based on multi-graph convolution. The time-driven strategy ensures the stable and efficient operation of the distribution network under normal conditions, while the event-driven strategy can respond quickly to abnormal events and adjust the control strategy in a timely manner, ensuring the safe and reliable operation of the distribution network. The multi-graph convolution solution method extracts spatial dependencies from the input data and constructs a mapping mechanism between the operating state of the distribution network cluster and the optimal power optimization strategy, effectively improving the solution efficiency and solving the problem of low efficiency or even no solution in existing commercial solvers when the distribution network scale is large or the communication constraints are complex.

[0050] In summary, this invention effectively solves the problems in existing technologies, such as considering only physical and electrical indicators for cluster partitioning, neglecting the impact of communication networks on optimization and control, and low solution efficiency. It improves the flexibility, reliability, and economy of distributed resource cluster optimization and control in distribution networks, enabling distribution networks to better adapt to complex and ever-changing operating environments and achieve efficient and stable operation under power-communication coupling. It has significant application value and promotion prospects. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 The present invention provides a flowchart of a method for optimizing and controlling a distributed resource cluster in a power distribution network that considers power communication coupling, as an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of the overall process of a distributed resource cluster optimization and control method for power distribution networks that considers power communication coupling, provided as an embodiment of the present invention.

[0054] Figure 3 This is an internal structure diagram of an electronic device for a power distribution network distributed resource cluster optimization control method considering power communication coupling, as provided in one embodiment of the present invention. Detailed Implementation

[0055] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0056] Example 1, referring to Figures 1-2 This is the first embodiment of the present invention, which provides a method for optimized control of distributed resource clusters in a distribution network considering power communication coupling, comprising:

[0057] In existing related technologies, cluster partitioning often only focuses on physical and electrical indicators, making it difficult to balance communication traffic and affecting the subsequent power optimization and control effects. Power optimization and control models mostly ignore the role of communication networks in the optimization results, and commercial solvers are inefficient or even unsolvable when faced with large-scale distribution networks or complex communication constraints.

[0058] This invention provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement this distribution network distributed resource cluster optimization and control method that considers power communication coupling, using multiple embodiments.

[0059] Figure 1 A flowchart of a method for optimized control of distributed resource clusters in distribution networks considering power communication coupling is shown, including:

[0060] S101, Establish a comprehensive partitioning index set based on the target communication network, and perform cluster partitioning based on the comprehensive partitioning index set, wherein:

[0061] It should be noted that with the rapid development of distributed resources and communication technologies, the increasingly complex interaction methods and control mechanisms of power distribution networks have led to deep coupling between physical electrical systems and communication networks. Traditional power distribution network cluster partitioning methods often consider the physical electrical relationships between clusters separately, ignoring the balance of communication traffic between clusters, making it difficult to adapt to power optimization models that take communication networks into account.

[0062] In one alternative implementation, the entire distribution network can be divided into several relatively independent yet coordinated clusters through distributed resource clustering. For example, graph theory algorithms and clustering algorithms can be used to cluster nodes and distributed resources in the distribution network based on a comprehensive set of partitioning indicators. In this way, nodes and resources with similar electrical characteristics, power balance, and communication needs can be grouped into the same cluster, resulting in closer connections within each cluster and relatively weaker coupling between clusters.

[0063] In this embodiment of the invention, the comprehensive partitioning index set includes several cluster partitioning indexes;

[0064] In one specific implementation, the comprehensive partitioning index set can include different index types to more comprehensively measure the rationality of cluster partitioning. For example, the modularity index based on electrical distance can measure the network structure strength of the physical electrical system, helping to determine the similarity of nodes within a cluster and the degree of coupling between clusters; the cluster power balance index, including active power balance and reactive power balance, can accurately reflect the source-load matching situation within each cluster; and the cluster communication network coordination index, characterized by communication traffic balance and construction investment costs, ensures the reasonable consideration of communication networks in cluster partitioning.

[0065] In one specific implementation, a communication reliability index can be introduced to evaluate the stability and reliability of information transmission within the communication network. Higher communication reliability means smoother information exchange between and within distribution networks, reducing control delays or errors caused by communication failures. Simultaneously, a distributed resource diversity index can be considered, reflecting the types and distribution of distributed resources within the cluster. Higher distributed resource diversity allows the cluster to be more flexible in responding to different operating conditions and demands, enabling better optimization and complementary utilization of resources.

[0066] In this embodiment of the invention, the comprehensive classification index set includes at least the modularity index based on electrical distance, the cluster power balance index, and the cluster communication network coordination index.

[0067] In this embodiment of the invention, the comprehensive classification index set further includes:

[0068] The modularity index based on electrical distance is used to evaluate the network structure strength of a physical electrical system. The larger the modularity index based on electrical distance, the more similar the functions or properties of the nodes in the divided clusters are, and the weaker the coupling between the clusters.

[0069] Cluster power balance metrics include active power balance and reactive power balance, which are used to represent the degree of source-load matching within each cluster.

[0070] The coordination index of trunked communication networks is characterized by communication traffic balance and construction investment cost.

[0071] Specifically, a modularity index based on electrical distance is first constructed. The modularity index is used to evaluate the network structure strength of the physical electrical system; a higher modularity indicates more similar functions or properties between nodes within the divided clusters, and weaker coupling between clusters. The active voltage sensitivity matrix reflects the change in node voltage when active power changes in the distribution network; therefore, the electrical distance between distribution network nodes is represented based on the node active voltage sensitivity matrix.

[0072] Active voltage sensitivity matrix S VP It is expressed as follows:

[0073]

[0074] ΔU=(N-HM -1 L) -1 ΔP=S VP ΔP (2)

[0075] In the formula: ΔP, ΔQ, Δδ, and ΔU are the changes in active power, reactive power, voltage phase angle, and voltage amplitude at the node, respectively; H, N, M, and L are Jacobi submatrices, corresponding to the partial derivatives of active and reactive power with respect to phase angle and voltage, respectively.

[0076] Furthermore, thus, the electrical distance d between node i and node j ij It can be represented as follows:

[0077]

[0078] In the formula: d ij d reflects the ratio of the voltage change at node j to that at node i when the active power at node j changes. ij The larger the value, the stronger the electrical coupling between the two nodes and the smaller the electrical distance. S represents the active voltage sensitivity matrix. VP The element in the i-th row and j-th column.

[0079] Furthermore, the modularity index κ1 based on electrical distance is expressed as follows:

[0080]

[0081] In the formula: N represents the number of nodes in the distribution network; For all edge weights w of the distribution network ij The sum of; θ(i,j) represents the sum of the edge weights connected to node i; θ(i,j) indicates whether node i and node j belong to the same cluster. If it is 1, it means they belong to the same cluster; otherwise, it is 0.

[0082] Furthermore, a cluster power balance index is constructed. This index includes active power balance and reactive power balance, representing the degree of source-load matching within each cluster. The active power supply-demand balance at node i is also considered. and reactive power supply and demand balance The power matching characteristics of a node can be represented as follows:

[0083]

[0084]

[0085] In the formula: and Let represent the active / reactive power and active / reactive power load demand that node i in cluster c can generate and provide at time t, respectively. Therefore, the power balance index... It can be defined as follows:

[0086]

[0087] Where: N c T and T represent the number of clusters and the total power optimization scheduling cycle of the distribution network, respectively; w P and w Q It is an adjustable weighting coefficient.

[0088] Furthermore, a coordination index for cluster communication networks can be constructed. When the communication network is used as one of the elements for cluster partitioning, the balance of communication traffic and the investment cost of building smart terminals used for communication can serve as important references for cluster partitioning. This ensures the balance of communication traffic between clusters and reduces the differences in construction investment costs between clusters, thus ensuring the rationality of the partitioning results.

[0089] Communication traffic balance at node i Construction investment cost C c,i,t It can be represented as follows:

[0090]

[0091] In the formula: l c,i,t This represents the communication traffic transmitted outward by node i in cluster c at time t; and N represents the construction cost of a single smart terminal in cluster c and the operating cost of a single unit of communication traffic emitted by the smart terminal, respectively; i,DER This indicates the number of distributed resources that node i has accessed. Each distributed resource and node needs to be connected to the smart terminal.

[0092] Furthermore, this leads to the coordination index of trunked communication networks. It can be defined as follows:

[0093]

[0094] In the formula: w f and w c It is an adjustable weighting coefficient.

[0095] In summary, by combining the proposed cluster partitioning indices to form a comprehensive partitioning index, a distributed resource cluster partitioning model for distribution networks considering communication networks is constructed as follows:

[0096] max k1κ1+k2κ2+k3κ3

[0097] stκ i ≥κ i,min i = 1, 2, 3 (12)

[0098] In the formula: k1, k2, and k3 are the weight coefficients of each indicator; κ i,min This represents the lower limit of the index for each cluster. Solving equation (12) will yield the cluster partitioning results.

[0099] It should be noted that establishing a comprehensive partitioning index set based on the target communication network, and then partitioning the clusters based on this set, provides a more reasonable cluster foundation for subsequent power optimization and control. Because both physical electrical and communication network indicators are considered, the nodes and resources within the partitioned clusters are closely connected, while the coupling between clusters is relatively weak. This helps balance communication traffic and improves the stability and reliability of the communication network. Consequently, during subsequent power optimization and control, each cluster can more efficiently perform internal power balancing and resource optimization, reducing unnecessary communication interactions and power transmission losses between clusters.

[0100] S102, Based on the results of cluster partitioning, establish a distributed resource cluster optimization and control model for the distribution network, wherein:

[0101] It should be noted that once the cluster partitioning results are obtained, a distributed resource cluster optimization and control model for the power distribution network can be established for each cluster. Traditional power optimization and control models mostly ignore the role of communication networks in the optimization results, while the model established in this invention fully considers the coupling relationship between power and communication to achieve more precise and effective power optimization and control.

[0102] In a specific implementation, the optimal control model for distributed resource clusters in a distribution network can be constructed in different ways to adapt to different application scenarios and needs. One approach is based on mathematical programming, which describes the optimal control problem of distributed resource clusters in a distribution network by establishing an objective function and constraints. The objective function can be set according to specific optimization objectives, such as minimizing system operating costs, maximizing the utilization rate of distributed resources, and improving the power supply reliability of the system. Constraints include physical and electrical constraints, communication constraints, and power balance constraints to ensure the feasibility and rationality of the model.

[0103] In one specific implementation, another approach is to employ intelligent algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization. These intelligent algorithms possess strong global search and adaptive capabilities, enabling them to find optimal solutions in complex solution spaces. By transforming the distributed resource cluster optimization and control problem of the distribution network into an optimization search problem, intelligent algorithms are used to iteratively search and gradually approach the optimal solution.

[0104] In one specific implementation, model predictive control can also be combined with this approach. Based on historical data and real-time status, the system's operating conditions over a future period can be predicted, and then optimization and control can be performed based on the prediction results. This method can proactively address system changes and improve the timeliness and accuracy of control.

[0105] In this embodiment of the invention, the distribution network distributed resource cluster optimization and control model includes an objective function aimed at minimizing the cluster operating cost and a set of constraints consisting of several different constraints.

[0106] In this embodiment of the invention, the distributed resource cluster optimization and control model for power distribution networks includes:

[0107] The objective function should include at least the cluster network loss cost, the operating cost of distributed resources within the cluster, and the cluster communication cost;

[0108] The set of constraints includes at least several physical power system power flow constraints, several communication network operation constraints, and several distributed resource operation characteristic constraints.

[0109] In an embodiment of the present invention, several communication network operation constraints include connecting each distributed resource and node in the target communication network to a smart terminal, configuring each partitioned cluster with an information network node, and summing all communication information that the cluster needs to transmit outward as communication network operation constraints.

[0110] Distributed resources include photovoltaics, energy storage, and electric vehicles.

[0111] Specifically, with the rapid development of power systems, the distributed resources included in distribution networks are becoming increasingly diversified. Common distributed resources include photovoltaics, energy storage, and electric vehicles. Rational utilization of these distributed resources can improve the overall operation of the distribution network. Furthermore, the increasingly complex interaction methods and control mechanisms of distribution networks make it difficult to guarantee the overall optimal power allocation scheme by considering only one aspect of the physical electrical system or communication network. In the context of the coupling of cyber-physical power and communication networks, this invention proposes a cluster optimization and control model for distributed resources in distribution networks that considers power-communication coupling.

[0112] First, based on the cluster partitioning results, power optimization scheduling is performed on a single cluster. Considering the economics of the partitioned clusters, a power optimization model for power communication coupling in distribution network clusters is established with the objective function of minimizing cluster operating costs. The operating cost f of cluster c is... c Including cluster network loss costs Operating costs of distributed resources within the cluster and cluster communication costs As shown below:

[0113]

[0114] In the formula: ω1, ω2, and ω3 are adjustable weighting coefficients; and These represent the unit network loss cost and total network loss of cluster c, respectively. G represents the number of nodes in cluster c; c,ij,t and θ c,ij,t U represents the conductance and phase angle of line ij at time t in cluster c, respectively; c,i,t It is the voltage amplitude of node i in cluster c at time t; These represent the unit operation and maintenance costs of photovoltaic, energy storage, and electric vehicle equipment at time t in cluster c, respectively. These represent the active power outputs of photovoltaic, energy storage, and electric vehicles at time t in cluster c, respectively.

[0115] Furthermore, the constraints include three parts: physical power system power flow constraints, communication network operation constraints, and distributed resource operation characteristic constraints.

[0116] First, the power flow distribution within the cluster is constrained using the Distflow power flow model as follows:

[0117]

[0118] In the formula: P c,ji,t and Q c,ji,t These are the active power and reactive power at the beginning of line ji in cluster c at time t, respectively. This represents the active power consumed by node i for communication at time t in cluster c; and This represents the reactive power output of photovoltaic and energy storage units in cluster c at time t; and Let L(j) represent the active and reactive power demands of node i at time t in cluster c; L(j) represents the set of nodes connected to node i; I c,ji,t R c,ji,t X c,ji,t These are the current, resistance, and reactance of branch ji, respectively.

[0119] In addition, to ensure the safe operation of the cluster, the following voltage safety constraints (20) and line thermal stability capacity limit constraints (21) should also be considered:

[0120] U c,i,min ≤U c,i,t ≤U c,i,max (20)

[0121]

[0122] In the formula: U c,i,min and U c,i,max These are the lower and upper limits of the allowed voltage fluctuation for node i in cluster c, respectively; S c,ij It is the upper limit of the apparent power capacity of line ij in cluster c.

[0123] Furthermore, construct the relevant constraints for the communication network, ensuring that each distributed resource and node is connected to a smart terminal, each partitioned cluster is configured with an information network node, and sum all the communication information that the cluster needs to transmit outwards:

[0124]

[0125]

[0126] l c,i,min ≤l c,i,t ≤l c,i,max (twenty four)

[0127] l c,ij,min ≤l c,ij,t ≤l c,ij,max (25)

[0128]

[0129] In the formula: This represents the conversion coefficient between communication traffic and communication active power in cluster c; and Let l represent the communication traffic sent by node i at time t in cluster c and the communication traffic sent by the distributed resource located at node i, respectively; c,ij,t This represents the communication traffic sent from node i to node j; l c,i,min and l c,i,max Let l represent the minimum and maximum communication traffic that node i can send at time t in cluster c, respectively; c,ij,min and l c,ij,max Represents the minimum and maximum communication traffic that can be transmitted through the communication link between node i and node j; and These represent the net communication traffic of the information network node at time t in cluster c, and the communication traffic transmitted from other clusters to cluster c, respectively. and These are the minimum and maximum net communication traffic that the information network nodes of cluster c can carry.

[0130] Equation (22) describes the relationship between electrical power and communication traffic; Equations (23)-(25) are the communication traffic balance constraints of a single node in cluster c; Equations (26)-(27) describe the communication traffic balance constraints of the information network nodes in cluster c.

[0131] Furthermore, distributed resources include photovoltaics, energy storage, and electric vehicles. Distributed photovoltaics can improve the operating status of the distribution network by regulating the output active and reactive power through inverters. The operating characteristic constraints can be described as follows:

[0132]

[0133] In the formula: It is the inverter capacity of the photovoltaic system located at node i in cluster c; and These represent the changes in active and reactive power output of the photovoltaic system at node i at time t in cluster c, respectively. This indicates the upper limit of the reduction in active power output of photovoltaic systems; and This indicates the upper and lower limits of the change in photovoltaic reactive power output.

[0134] Furthermore, energy storage devices interact with the power grid through charging or discharging to achieve power flow balancing, thereby improving the stability and reliability of the distribution network. The operational constraints can be described as follows:

[0135]

[0136] In the formula: It is the capacity of the inverter located at node i in cluster c; and These are the charging and discharging power of the energy storage; and These are the upper and lower limits of the energy storage charging and discharging power; and These represent the stored energy and the upper and lower limits of the stored energy, respectively. and Δt represents the charge / discharge efficiency of the energy storage; Δt represents the operating time period of the energy storage.

[0137] Furthermore, electric vehicles can participate in grid power regulation by dynamically adjusting charging time and charging power through reasonable control strategies. The operating characteristic constraints can be described as follows:

[0138]

[0139] In the formula: and Let be the storage energy of the electric vehicle located at node i in cluster c at time t, and its upper and lower limits, respectively. and This indicates the maximum energy storage that an electric vehicle is technically allowed to store, as well as the energy storage when the electric vehicle is connected to the grid and disconnected from the grid; and The maximum charging power and charging efficiency technically permissible for electric vehicles; and Indicates the grid connection and grid disconnection times of electric vehicles; and This represents the actual charging power of the electric vehicle at time t, as well as its maximum and minimum charging power. and These represent the remaining stored energy when the electric vehicle is connected to the grid and the minimum stored energy required when it is disconnected from the grid, respectively.

[0140] In summary, a distributed resource cluster optimization and control model for distribution networks that considers power communication coupling is constructed as follows:

[0141]

[0142] Although any commercial solver can solve the mixed-integer linear programming model shown in equation (45) and obtain the optimal operating strategy when the partitioned clusters simultaneously consider the physical power network and the communication network, the solution efficiency is low and there may be no solution.

[0143] It should be noted that establishing a distributed resource cluster optimization and control model for the distribution network based on the cluster partitioning results can achieve refined management and efficient allocation of distributed resources. By dividing the distribution network into multiple clusters, the coupling characteristics of power communication within each cluster can be fully considered, making the control strategy more closely aligned with actual operating conditions.

[0144] S103, Solve the distribution network distributed resource cluster optimization control model to obtain the optimal cluster optimization control strategy, and perform distribution network distributed resource cluster optimization control based on the optimal cluster optimization control strategy, wherein:

[0145] It should be noted that distribution networks inherently possess graph structure characteristics, and graph convolutional networks can effectively process such graph-structured data and extract key spatial dependencies. This invention proposes a fast solution method for power optimization and control of distributed resource clusters in distribution networks based on multi-graph convolution. The method uses the operating status of the distribution network cluster as input data, including the active and reactive power distribution, voltage distribution, access location and operating status of distributed resources, and access location and operating status of smart terminals. By extracting spatial dependencies from the input data, a mapping mechanism is constructed between the operating status of the distribution network cluster and the optimal power optimization strategy, enabling a fast and accurate solution to the distribution network distributed resource cluster optimization and control model considering power-communication coupling.

[0146] In a specific implementation, various solution strategies can be employed to solve the distributed resource cluster optimization and control model of a power distribution network. For example, heuristic algorithms such as genetic algorithms and particle swarm optimization (PSO) can be used. Genetic algorithms simulate biological evolution, using operations such as selection, crossover, and mutation to search for the optimal solution in the solution space. They can handle complex nonlinear optimization problems and possess strong global search capabilities. When solving the distributed resource cluster optimization and control model of a power distribution network, the model's decision variables can be encoded as chromosomes, and the optimal solution is gradually approximated through iterative population updates. PSO, on the other hand, simulates the collective behavior of flocks of birds or schools of fish, where each particle represents a potential solution and continuously searches the solution space. Particles update their velocity and position based on their own historical best position and the historical best position of the group, ultimately finding the optimal solution. This algorithm has advantages such as fast convergence speed and ease of implementation.

[0147] In one specific implementation, deep learning-based solution methods, such as deep reinforcement learning, can also be employed. Deep reinforcement learning combines the powerful representation capabilities of deep learning with the decision-making capabilities of reinforcement learning, continuously learning the optimal control strategy through the interaction between the agent and the environment. In the problem of optimal control of distributed resource clusters in power distribution networks, the operating state of the power distribution network can be considered as the environmental state, and the control strategy as the agent's action. A reward mechanism guides the agent to learn the optimal control strategy. During the interaction with the environment, the agent continuously adjusts its strategy to maximize long-term cumulative rewards.

[0148] In one specific implementation, a decomposition and coordination algorithm can be used to decompose the large-scale distributed resource cluster optimization and control model of the power distribution network into multiple smaller sub-problems. These sub-problems are solved separately, and then the solutions are integrated through a coordination mechanism to obtain an approximate optimal solution to the original problem. This method can effectively reduce the complexity of the solution and improve the efficiency. For example, the power distribution network can be divided according to geographical regions or electrical characteristics. Each region or characteristic-related part can be treated as a sub-problem for solution. Then, through information exchange and coordination, the solutions to the various sub-problems can complement each other to achieve an overall optimized control effect.

[0149] In this embodiment of the invention, solving the distributed resource cluster optimization and control model for the distribution network further includes:

[0150] A fast solution method for distributed resource cluster optimization and control based on multi-graph convolution is established, using the operating status of the distribution network cluster as input data;

[0151] Input data includes the active and reactive power distribution of the distribution network, voltage distribution, access location and operating status of distributed resources, and access location and operating status of smart terminals;

[0152] By extracting spatial dependencies from the input data;

[0153] A mapping mechanism is constructed between the operating status of the distribution network cluster and the optimal power optimization strategy, so as to solve the distribution network distributed resource cluster optimization and control model considering power communication coupling.

[0154] In embodiments of the present invention, such as Figure 2 As shown in the middle section, solving the distributed resource cluster optimization and control model for the distribution network also includes:

[0155] Under normal circumstances, the dispatch management center sends an optimization instruction to the distribution network cluster every 15 minutes. The distribution network cluster receives and executes the optimization instruction to find the optimal optimization control strategy for the distributed resource cluster with a total period of T from the current moment.

[0156] The smart terminals installed on each node and distributed resource in the distribution network cluster monitor abnormal events in the cluster in real time. Abnormal events include voltage over-limit, frequency fluctuation, and power flow overload.

[0157] When a smart terminal detects an abnormal event, it immediately reports the type of the abnormal event to the scheduling management center, which then confirms and executes the optimal control strategy for the distributed resource cluster with a total period of T from the current moment.

[0158] In summary, this invention discloses an optimized control method for distributed resource clusters in a distribution network considering power-communication coupling. It acquires relevant data from the distribution network and communication network, establishes a comprehensive partitioning index set based on the target communication network, partitions the clusters according to the index set, obtaining multiple distributed resource clusters in the distribution network, constructs an optimized control model based on the partitioning results, with the objective function guided by minimizing cluster operating costs, comprehensively considering constraints related to power flow, communication operation, and resource operation characteristics, and employs a time- and event-driven dual-drive solution strategy to solve the model. A fast solution method based on multi-graph convolution is used to extract data spatial dependencies and construct a mapping mechanism from operating state to optimal strategy, improving solution efficiency. The optimal strategy is then used to optimize and control the clusters, achieving efficient and stable operation of the distribution network under power-communication coupling.

[0159] Example 2, in a preferred embodiment, the specific steps for solving the distributed resource cluster optimization control model of the distribution network can be as follows:

[0160] First, an electrical connection matrix is ​​constructed using the impedance relationships between nodes c in the distribution network cluster. An electrical connection matrix is ​​used to effectively characterize the electrical connection strength and mutual influence between nodes. The value in the i-th row and j-th column The electrical connection strength between node i and node j is shown below:

[0161]

[0162] In the formula: It is a binary variable. This indicates that node i and node j are connected by a line; otherwise...

[0163] Secondly, a communication connection matrix is ​​constructed using the intelligent terminals configured on node c of the distribution network cluster. A communication connection matrix is ​​used to effectively characterize the existence and strength of communication links between nodes. The value in the i-th row and j-th column It is expressed as follows:

[0164]

[0165] In the formula: φ(i,j) is a binary variable. φ(i,j) = 1 indicates that node i and node j have a communication link; otherwise...

[0166] Finally, the active power flow between lines in the distribution network is also a key factor affecting power optimization decisions. A cluster power interaction matrix is ​​constructed using the power flow between lines in distribution network cluster c. To effectively characterize the power flow characteristics between nodes in a distribution network cluster, the value in the i-th row and j-th column... It is expressed as follows:

[0167]

[0168] In the formula: This represents the average active power flowing through line ij in cluster c over a total of T time periods; This represents the maximum active power flowing through line ij in cluster c over a total of T time periods.

[0169] Furthermore, before performing graph convolution operations, the distribution network cluster needs to be initialized as an undirected weighted graph G = (N, L, A), where N represents the node set of the distribution network cluster, L represents the set of power lines within the cluster, and A is the adjacency matrix. In this invention, the electrical connection matrix constructed above is used. Communication connection matrix and cluster power interaction matrix The adjacency matrix participates in the convolution operation. The convolution module consists of multiple graph convolutional network layers. Through multi-layer convolution operations, it gradually obtains the spatial dependencies between nodes in the distribution network cluster, thereby constructing a mapping mechanism from the operating state of the distribution network cluster to the optimal power optimization strategy. The graph convolution operation of the l-th layer can be represented as follows:

[0170]

[0171] In the formula: Where E N D is the identity matrix; D is a diagonal matrix, representing The degree matrix; The input feature matrix of the first graph convolutional layer is constructed from the operating status data of the power distribution network. F represents the dimension of the feature matrix, which is the dimension of the input operating status data. Let σ(·) represent the trainable weight matrix of the l-th graph convolutional layer; σ(·) represents the sigmoid activation function. It is the convolution output obtained by substituting the three constructed adjacency matrices into the graph convolution layer.

[0172] The outputs of the three convolutions are combined as follows:

[0173]

[0174] In the formula: and These are adjustable weighting coefficients; X c,l+1 It is the fused convolution result, which will be used as the input to the next graph convolutional layer.

[0175] The output of the last graph convolutional layer is used as the output of the convolutional module. After passing through a fully connected layer, the output of the convolutional module can be mapped to the optimal control strategy of the distributed resource cluster in the power distribution network.

[0176] Example 3, referring to Figure 3 This embodiment also provides a distributed resource cluster optimization and control system for distribution networks that considers power communication coupling, including:

[0177] The partitioning module is used to establish a comprehensive partitioning index set based on the target communication network, and to perform cluster partitioning based on the comprehensive partitioning index set;

[0178] The comprehensive classification index set includes several cluster classification indicators;

[0179] The comprehensive classification index set includes at least modularity index based on electrical distance, cluster power balance index, and cluster communication network coordination index.

[0180] The model building module is used to build a distributed resource cluster optimization and control model for the power distribution network based on the results of cluster partitioning.

[0181] The distribution network distributed resource cluster optimization and control model includes an objective function aimed at minimizing cluster operating costs and a set of constraints consisting of several different constraints.

[0182] The control module is used to solve the distribution network distributed resource cluster optimization control model, obtain the cluster optimal control strategy, and perform distribution network distributed resource cluster optimization control based on the cluster optimal control strategy.

[0183] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0184] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as follows: Figure 3As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a distributed resource cluster optimization and control method for power distribution networks that considers power communication coupling. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0185] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps:

[0186] Establish a comprehensive partitioning index set based on the target communication network, and perform cluster partitioning based on the comprehensive partitioning index set;

[0187] The comprehensive classification index set includes several cluster classification indicators;

[0188] The comprehensive classification index set includes at least modularity index based on electrical distance, cluster power balance index, and cluster communication network coordination index.

[0189] A distributed resource cluster optimization and control model for power distribution networks is established based on the results of cluster partitioning.

[0190] The distribution network distributed resource cluster optimization and control model includes an objective function aimed at minimizing cluster operating costs and a set of constraints consisting of several different constraints.

[0191] The optimal control model of distributed resource clusters in the distribution network is solved to obtain the optimal control strategy of the cluster, and the optimal control strategy of distributed resource clusters in the distribution network is carried out based on the optimal control strategy of the cluster.

[0192] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0193] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0194] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimized control of distributed resource clusters in distribution networks considering power-communication coupling, characterized in that, include: Establish a comprehensive partitioning index set based on the target communication network, and perform cluster partitioning based on the comprehensive partitioning index set; The comprehensive segmentation index set includes several cluster segmentation indexes; The comprehensive classification index set includes at least modularity index based on electrical distance, cluster power balance index, and cluster communication network coordination index. Based on the results of the cluster partitioning, an optimized control model for distributed resource clusters in the power distribution network is established. The distribution network distributed resource cluster optimization and control model includes an objective function aimed at minimizing cluster operating costs and a set of constraints consisting of several different constraints. The optimal control strategy for the distributed resource cluster of the distribution network is obtained by solving the distributed resource cluster optimization control model. Based on the optimal control strategy, the distributed resource cluster optimization control of the distribution network is carried out.

2. The method for optimized control of distributed resource clusters in a distribution network considering power communication coupling as described in claim 1, characterized in that, Solving the distributed resource cluster optimization and control model of the power distribution network includes: Establish time-driven and event-driven solution strategies; The time-driven solution strategy is used to solve the distribution network distributed resource cluster optimization control model under normal conditions, and obtain the cluster optimal optimization control strategy under normal conditions. The event-driven solution strategy is used to solve the distribution network distributed resource cluster optimization and control model under abnormal conditions, and obtain the optimal cluster optimization and control strategy under abnormal conditions.

3. The method for optimized control of distributed resource clusters in a distribution network considering power communication coupling as described in claim 2, characterized in that, The distributed resource cluster optimization and control model for the power distribution network includes: The objective function includes at least the cluster network loss cost, the operating cost of distributed resources within the cluster, and the cluster communication cost; The set of constraints includes at least several physical power system power flow constraints, several communication network operation constraints, and several distributed resource operation characteristic constraints.

4. The method for optimized control of distributed resource clusters in a distribution network considering power communication coupling as described in claim 3, characterized in that, The comprehensive classification index set also includes: The modularity index based on electrical distance is used to evaluate the network structure strength of the physical electrical system. The larger the modularity index based on electrical distance, the more similar the functions or properties of the nodes in the divided clusters are, and the weaker the coupling between the clusters. The cluster power balance index includes active power balance and reactive power balance, which are used to represent the degree of source-load matching within each cluster. The coordination index of the cluster communication network is characterized by communication traffic balance and construction investment cost.

5. The method for optimized control of distributed resource clusters in a distribution network considering power communication coupling as described in claim 4, characterized in that, The aforementioned communication network operation constraints include connecting each distributed resource and node in the target communication network to a smart terminal, configuring each partitioned cluster with an information network node, and summing all communication information that the cluster needs to transmit outward as communication network operation constraints. The distributed resources include photovoltaics, energy storage, and electric vehicles.

6. The method for optimized control of distributed resource clusters in a distribution network considering power communication coupling as described in claim 5, characterized in that, Solving the distributed resource cluster optimization and control model of the distribution network also includes: A fast solution method for distributed resource cluster optimization and control based on multi-graph convolution is established, using the operating status of the distribution network cluster as input data; The input data includes the active and reactive power distribution of the distribution network, voltage distribution, access location and operating status of distributed resources, and access location and operating status of smart terminals. By extracting spatial dependencies from the input data; A mapping mechanism is constructed between the operating status of the distribution network cluster and the optimal power optimization strategy, so as to solve the distribution network distributed resource cluster optimization and control model considering power communication coupling.

7. The method for optimized control of distributed resource clusters in a distribution network considering power communication coupling as described in claim 6, characterized in that, Solving the distributed resource cluster optimization and control model of the distribution network also includes: Under normal circumstances, the dispatch management center sends an optimization instruction to the distribution network cluster every 15 minutes. The distribution network cluster receives and executes the optimization instruction to find the optimal optimization control strategy for the distributed resource cluster with a total period of T from the current moment. The smart terminals installed on each node and distributed resource in the power distribution network cluster monitor abnormal events in the cluster in real time, including voltage exceeding limits, frequency fluctuations, and power flow overload. When a smart terminal detects an abnormal event, it immediately reports the type of the abnormal event to the scheduling management center, which then confirms and executes the optimal control strategy for the distributed resource cluster with a total period of T from the current moment.

8. A distributed resource cluster optimization and control system for a distribution network considering power communication coupling, using the method described in any one of claims 1 to 7, characterized in that, include: The partitioning module is used to establish a comprehensive partitioning index set based on the target communication network, and to perform cluster partitioning based on the comprehensive partitioning index set; The comprehensive segmentation index set includes several cluster segmentation indexes; The comprehensive classification index set includes at least modularity index based on electrical distance, cluster power balance index, and cluster communication network coordination index. The model building module is used to build a distributed resource cluster optimization and control model for the distribution network based on the results of the cluster partitioning. The distribution network distributed resource cluster optimization and control model includes an objective function aimed at minimizing cluster operating costs and a set of constraints consisting of several different constraints. The control module is used to solve the distribution network distributed resource cluster optimization control model to obtain the cluster optimal control strategy, and to perform distribution network distributed resource cluster optimization control based on the cluster optimal control strategy.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of any one of claims 1 to 7: a distributed resource cluster optimization and control method for power distribution networks that considers power communication coupling.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of any one of claims 1 to 7 of the method for optimizing and controlling distributed resource clusters in a power distribution network that considers power communication coupling.

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