Source load cluster division method for improving renewable energy consumption capability of power distribution network

Abstract

The invention provides a source load cluster division method for improving the renewable energy consumption capability of a power distribution network, and belongs to the field of power distribution network planning. The method comprises the following steps: firstly, analyzing a power distribution network cluster division principle, and secondly, establishing a cluster division performance index model; and finally, establishing a cluster division model, and improving the source-load matching level under the renewable energy distributed access background in the power distribution network. According to the invention, the on-site consumption capability of renewable energy sources can be improved.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network planning technology, and specifically relates to a source-load clustering method to improve the renewable energy absorption capacity of power distribution networks. Background Technology

[0002] The integration of distributed energy sources, exemplified by large-scale distributed photovoltaic (PV) power, into the distribution network has impacted the safe and stable operation of the network system, leading to issues such as voltage exceeding limits and power backflow across voltage levels. The core problem lies in the insufficient local absorption capacity under high-proportion renewable energy integration, posing a severe challenge to the coordinated planning and control of the distribution network. Distribution network planning based on clusters primarily involves the rational division of clusters and the implementation of coordinated control within each cluster. The cluster management model for renewable energy generation has its own advantages in addressing large-scale renewable energy integration and local absorption. The cluster model divides the target power grid into several sub-regions, each forming a cluster. These sub-regions, as a whole, receive a single command for control, facilitating scheduling and management. Within the cluster, nodes cooperate to achieve common goals, efficiently leveraging inter-node coordination capabilities. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a source-load clustering method to improve the renewable energy absorption capacity of distribution networks. First, the method analyzes the distribution network clustering principles and establishes a clustering performance index model. Then, it establishes a clustering model to improve the source-load matching level under the background of distributed access of renewable energy in the distribution network, thereby improving the local absorption capacity of renewable energy.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for source-load cluster partitioning to improve the renewable energy absorption capacity of a distribution network includes the following steps:

[0006] Step 1: Analyze the distribution network cluster division principles and determine the structural principle based on the electrical density between nodes and the functional principle based on the coordinated balance of source, load and storage power.

[0007] Step 2: Establish a cluster partitioning performance index system based on the structural and functional principles. The index system includes modularity index based on electrical distance, cluster energy storage supply and demand coordination index, load balance index, and cluster absorption capacity index.

[0008] Step 3: Construct a cluster partitioning optimization model based on the performance index system, and obtain the power distribution network source-load cluster partitioning scheme by solving the optimization model.

[0009] Beneficial effects:

[0010] 1. This invention considers the structural characteristics of the distribution network cluster and the power characteristics of the source, load and storage, and the comprehensive performance index of the distribution network cluster division reflects the electrical coupling of the nodes in the cluster and the power coordination capability of the source, load and storage.

[0011] 2. The cluster partitioning result of this invention, which aims to improve the absorption capacity, can effectively avoid the problem of insufficient utilization of load-storage regulation capacity within the cluster, thereby improving the renewable energy absorption capacity of the cluster and thus improving the carbon reduction capacity of the system.

[0012] In summary, this invention can improve the electrical coupling of nodes within a cluster and the power coordination capability of source, load, and storage, while also enhancing the cluster's renewable energy absorption capacity, thereby improving the system's carbon reduction capability. Attached Figure Description

[0013] Figure 1 This is a flowchart of a source-load cluster partitioning method for improving the renewable energy absorption capacity of a power distribution network, according to the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0015] like Figure 1 As shown, this invention proposes a source-load cluster partitioning method to improve the renewable energy absorption capacity of the distribution network, including the following steps:

[0016] Step 1: Analyze the principles for dividing the power distribution network clusters;

[0017] Step 2: Establish cluster partitioning performance indicators, including modularity indicators based on electrical distance, cluster energy storage supply and demand coordination indicators, load balance indicators, and cluster absorption capacity indicators;

[0018] Step 3: Establish a cluster partitioning optimization model to improve the source-load matching level under the background of distributed access of renewable energy in the distribution network and improve the local consumption capacity of renewable energy.

[0019] Specifically, step 1 includes:

[0020] Step 1-1: Analyze the cluster partitioning principles:

[0021] Based on the logical division of distribution networks, the nodes are grouped into different sets according to two principles: structural principle, which prioritizes the electrical tightness between nodes, and functional principle, which prioritizes the coordinated balance of power generation, load, and storage. At the structural level, nodes within the same cluster are closely connected, while those between different clusters are loosely connected. At the functional level, the coordinated balance of power generation, load, and storage within a cluster is better, which can fully utilize the cluster's absorption capacity and regulation characteristics.

[0022] Within the cluster, centralized control distributes system operation information to each node, while between clusters, distributed control facilitates the exchange of cluster data. Cluster partitioning enables autonomy within the cluster and coordination between clusters, ensuring effective collaboration and laying the foundation for subsequent voltage regulation and other tasks in the distribution network.

[0023] Specifically, step 2 includes:

[0024] Step 2-1: Establish a modularity index based on electrical distance:

[0025] Modularity is an indicator used to evaluate the strength of network structure. Its value is related to network structure and edge weights. A higher modularity index indicates that nodes in the same area are more similar in function or nature, and that the coupling between different areas is weaker. In distribution networks, the network edge weights of modularity are represented by electrical distance, that is, the strength of electrical coupling characterizes the degree of coupling between areas. Therefore, a modularity index based on electrical distance can be used to measure the structural strength of cluster partitioning.

[0026] In a power distribution network, electrical distance is defined by the sensitivity between injected power and voltage between nodes, and can be obtained from the Jacobian matrix of the power flow calculation equations:

[0027] (1)

[0028] In the formula, Inject active power changes into the nodes. Inject reactive power changes into the nodes; by the block matrix , , , The Jacobian matrix represents the relationship between changes in active or reactive power and voltage phase angle or amplitude. and These represent the node phase angle and amplitude change, respectively. Therefore, the modularity index function based on electrical distance, representing the electrical tightness of nodes within a cluster, is:

[0029] (2)

[0030] (3)

[0031] In the formula, d represents the electrical distance between node i and node j; d represents the electrical distance between two nodes in the distribution network. Modularity; It is the sum of all edge weights of the distribution network; Let be the edge weight between node i and node j. This refers to the maximum electrical distance between two nodes in a power distribution network. , Let represent the sum of the edge weights connected to nodes i and j, respectively. Determine whether node i and node j are in the same cluster. A value of 0 indicates that the two nodes are not in the same cluster, while a value of 1 indicates that they are in the same cluster.

[0032] Step 2-2: Establish cluster energy storage supply and demand coordination indicators:

[0033] In distribution networks with a high proportion of distributed photovoltaic (PV) grid integration, to maintain the safe and stable operation of node voltages, distributed energy storage is often used to generate active power when the node voltage is below the lower limit, and to absorb active power when the node voltage is above the upper limit. To reflect the supply and demand coordination between distributed energy storage and net load in cluster partitioning, and to demonstrate the coordination between the adjustable resources of source, load, and storage in each cluster and the system's operational needs, clusters are partitioned based on the output of distributed energy storage and the net power of the distribution network:

[0034] (4)

[0035] In the formula, Let be the charging and discharging power of the distributed energy storage at time t. and Let be the load power and photovoltaic power at time t, respectively. Cluster at time t The difference between energy storage capacity and net power.

[0036] (5)

[0037] (6)

[0038] (7)

[0039] In the formula, Within period T The maximum value; For cluster Energy storage supply and demand coordination indicators; This is an indicator of the overall energy storage supply and demand coordination in the distribution network. T is the dispatch period, t is the time within the dispatch period; N c This represents the total number of clusters. Let x be the x-th cluster in the cluster set.

[0040] The cluster energy storage supply-demand coordination index comprehensively considers the power regulation capabilities of various adjustable resources within the system, ensuring coordinated allocation of load, distributed photovoltaic (PV), and distributed energy storage power within each cluster. When the spatiotemporal matching between distributed PV output and load demand is poor, the distributed energy storage capacity of the cluster should be appropriately increased to enhance system coordination and stability. By increasing energy storage capacity, the cluster energy storage supply-demand coordination index can be improved, fully reflecting the degree of coordination between the output power of distributed PV and distributed energy storage and the load's energy demand.

[0041] Steps 2-3: Establish load balancing indicators:

[0042] To improve grid stability, it is crucial to ensure that the load of each cluster can meet the supply and demand balance.

[0043] (8)

[0044] In the formula, Load power balance index for each model; Let i be the load requirement of the i-th node in the j-th cluster; Let be the power generation capacity of the i-th node within the j-th cluster; Let be the number of topological connections between nodes with similar loads within the j-th cluster.

[0045] Steps 2-4: Establish cluster absorption capacity indicators:

[0046] Cluster absorption capacity is mainly reflected in the balance between source, load and storage. In high-voltage power distribution systems with a high proportion of renewable energy, each node may still have a situation where the power output exceeds the load demand after adjusting for load demand and energy storage. This part of the electricity is the surplus electricity, which is manifested as power transmission in the network.

[0047] Clusters often restore their power balance by cutting some of the active power output from renewable energy sources to address surplus electricity, which limits the integration of renewable energy. By using cluster power dispatch as a means and optimizing load-storage coordination, the goal is to maximize the absorption of renewable energy within the cluster, and to divide the cluster based on surplus electricity.

[0048] Time t cluster The maximum total power that renewable energy sources can generate is , Let be the maximum renewable energy output of node i within the cluster at time t. To maximize the absorption of renewable energy, fully utilize the cluster's absorption capacity, and reduce power backflow across voltage levels, power regulation is implemented through responsive loads and energy storage devices, with the objective function being to maximize the total absorption capacity of all clusters.

[0049] (9)

[0050] In the formula, Cluster at time t The absorption capacity; c is the cluster set in the system; T is the set of each moment in the power scheduling period.

[0051] To more intuitively reflect the strength of the cluster's absorption capacity, we define the cluster's surplus power at time t. The surplus power is:

[0052] (10)

[0053] Furthermore, based on surplus power, a cluster absorption capacity index expressed in terms of surplus electricity is defined. :

[0054] (11)

[0055] Specifically, step 3 includes:

[0056] Step 3-1: Construct the objective function:

[0057] Objective function 1: Cluster absorption capacity:

[0058] (12)

[0059] In the formula, This is the maximum index function for the cluster's absorption capacity; This is an indicator of the cluster's absorption capacity.

[0060] Objective function 2: Investment cost:

[0061] (13)

[0062] In the formula: , These are the installed numbers of distributed power sources and energy storage, respectively. , These are the installation costs for distributed power sources and energy storage, respectively. The objective function is to minimize investment cost; n is the number of samples of distributed power sources and energy storage, which does not exceed the number of installations.

[0063] Step 3-2: Construct constraints:

[0064] (1) Electrical distance module constraints:

[0065] (14)

[0066] (2) Distribution network power flow constraints:

[0067] (15)

[0068] In the formula, , The active and reactive power injected into node i at time t; , Let be the voltage amplitudes at nodes i and j at time t; , For the admittance between nodes i and j, Let be the phase angle difference at time t.

[0069] (3) Node voltage constraints:

[0070] (16)

[0071] In the formula, Let be the voltage amplitude at node i at time t; , Let be the maximum and minimum voltage amplitudes at node i.

[0072] (4) Power balance constraints:

[0073] (17)

[0074] In the formula, , , These are the number of branches connecting the bus and the cluster, the number of clusters, and the number of nodes within the cluster, respectively. , , , , These represent the active power of branch l connected to the upper-level power grid at time t, the photovoltaic power at node i, the charging and discharging power of distributed energy storage, the load power, and the network loss of line ij.

[0075] (5) Distributed energy storage charging and discharging power and state of charge constraints:

[0076] (18)

[0077] In the formula, The maximum charging and discharging power of the distributed energy storage at node i; Let i be the state of charge of the energy stored at node i at time t. , These are the upper and lower limits of the energy storage's state of charge.

[0078] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A source-load cluster division method for improving renewable energy consumption capacity of a power distribution network, characterized in that, The method comprises the following steps: Step 1, analyzing power distribution network cluster division principles, determining a structural principle mainly based on electrical compactness between nodes and a functional principle mainly based on power coordination and balance of sources, loads and storages; Step 2, establishing a cluster division performance index system according to the structural principle and the functional principle, the index system comprising a modularity index based on electrical distance, a cluster storage energy supply and demand coordination index, a load balance index and a cluster consumption capacity index; Step 3, constructing a cluster division optimization model based on the performance index system, and obtaining a power distribution network source and load cluster division scheme by solving the optimization model.

2. The method of claim 1, wherein the method is characterized by, The structural principle requires that nodes in the same cluster are electrically closely connected and nodes in different clusters are loosely connected; and the functional principle requires that power of sources, loads and storages in the cluster is coordinately balanced, so that the cluster consumption capacity and regulation characteristics are fully utilized.

3. The method of claim 1, wherein the method is characterized by, The modularity index based on electrical distance defines electrical distance through the sensitivity between injected power and voltage between nodes, and constructs a modularity function using the electrical distance, the modularity function being used to measure the structural strength of cluster division.

4. The method of claim 1, wherein the method is characterized by, The cluster storage energy supply and demand coordination index is established by calculating the supply and demand relationship between distributed storage output and net power of the power distribution network, wherein the net power of the power distribution network is the difference between load power and photovoltaic power, and the coordination index reflects the coordinated allocation degree of power of loads, distributed photovoltaic and distributed storage in the cluster.

5. The method of claim 1, wherein the method is characterized by, The load balance index is established by calculating the balance degree of load power and generated power of each cluster, and is used to ensure that the load of each cluster meets the supply and demand balance.

6. The method of claim 1, wherein the method is characterized by, The cluster consumption capacity index is established by calculating the difference between the renewable energy source available power in the cluster and the actual consumption power after load and storage adjustment, the difference being surplus power, and the surplus power being used to evaluate the strength of the cluster consumption capacity.

7. The method of claim 1, wherein the method is characterized by, The optimization model in the step 3 comprises a first objective function with the maximum cluster consumption capacity as the target and a second objective function with the minimum investment cost as the target. 8.The method of claim 1, wherein, The constraint conditions in the step 3 comprise electrical distance module constraints, power distribution network power flow constraints, node voltage constraints, power balance constraints and distributed storage charging and discharging power and state of charge constraints.

9. The method of claim 8, wherein the method is characterized by, The electrical distance module constraint requires that the modularity index is greater than a set threshold.

10. The method of claim 8, wherein the method is characterized by, The power balance constraint comprises the balance relationship between branch power connected to the upper-level power grid, node photovoltaic power, distributed storage charging and discharging power, load power and line network loss.

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