Cluster division voltage control method containing photovoltaic energy storage power distribution network

Abstract

The invention relates to a cluster division voltage control method containing a photovoltaic energy storage power distribution network, and belongs to the technical field of power distribution networks. According to a power distribution network model, firstly, a Louvain algorithm is used for carrying out initial division of a cluster; then cluster distribution is adjusted according to real-time data, and voltage management is optimized; firstly, the voltage out-of-limit problem is solved through the reactive power regulation capability of the photovoltaic system, and when the photovoltaic regulation capability is insufficient, chemical energy storage equipment is used for voltage regulation. According to the method, the voltage is optimized by using the key node with relatively large reactive power regulation potential, and the energy storage equipment is supplemented when necessary, so that the operation efficiency and reliability of the power distribution network are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution networks, and in particular relates to a cluster division voltage control method for a photovoltaic energy storage distribution network. Background Art

[0002] Distributed Generators (DGs) often lead to local power surplus and power reverse due to their low capacity, high number and dispersed layout characteristics, which constitute the so-called dimensionality curse for traditional centralized voltage control systems. Compared with traditional centralized control, distributed control methods show higher flexibility and computational efficiency. Current cluster partitioning control technology still faces several challenges when dealing with the coordinated control of distributed photovoltaic and energy storage devices, especially in ensuring efficient energy utilization and grid stability. Although current technologies can promote the integration of distributed resources to a certain extent, they are limited in their ability to solve local power surplus and voltage control.

[0003] In the process of clustering, there are mainly three methods: one is clustering based on electrical distance, the second is grid cluster planning based on optimization theory, and the third is community division based on the network based on graph theory. Both methods have their limitations and dependent factors, which may affect the final division results. First, when using cluster analysis to divide clusters, it is usually necessary to pre-set the cluster center and the number of clusters, which makes the results may be affected by certain subjective settings. Secondly, when using optimization algorithms for clustering, different encoding methods may lead to significant differences in the division results. Finally, when using complex community algorithms for clustering, if the factors considered are not comprehensive enough, it may also have an adverse effect on the clustering effect.

[0004] Therefore, a new control strategy is urgently needed to address these problems, especially in terms of cluster partitioning and inter-cluster coordination. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the prior art and to provide a cluster division voltage control method for a photovoltaic energy storage distribution network. The method first uses the Louvain algorithm based on an improved weight matrix to perform cluster division of the power grid, effectively dividing the distribution network into active and reactive clusters to optimize the management efficiency of the power grid. On this basis, active and reactive regulation sets are constructed to implement voltage management within the cluster, and the reactive regulation capabilities of the photovoltaic set and the energy storage set are used to regulate the voltage. In addition, the method also includes inter-cluster voltage regulation and compression and iterative optimization of the power grid model to ensure the stability of the voltage of the entire distribution network model.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A cluster partitioning voltage control method for a photovoltaic energy storage distribution network includes the following steps:

[0008] Step (1), according to the distribution network model, the distribution network is clustered by using the Louvain algorithm based on the improved weight matrix to obtain the active cluster and reactive cluster division results of the distribution network;

[0009] Step (2): Based on the current cluster division results, active and reactive regulation sets are constructed to achieve voltage management within the cluster; relying on the key nodes of the photovoltaic set, the photovoltaic set is used to eliminate the voltage over-limit problem; when the reactive regulation capacity of the photovoltaic system is exhausted, the chemical energy storage device is used to adjust the voltage to ensure the voltage stability within the cluster, and the voltage regulation result within the cluster is output;

[0010] Step (3), based on the voltage regulation result within the cluster, the voltage between clusters is regulated using the adjustable set so that the voltage of the entire distribution network model remains stable.

[0011] Further, the specific steps of step (1) are as follows:

[0012] (1-1) Initial cluster definition: In the distribution network model, each node is regarded as an independent cluster, and the total number of clusters is equal to the number of nodes;

[0013] (1-2) Cluster allocation optimization:

[0014] First, for each node i in the power grid, we need to try to assign node i to the active cluster or reactive cluster to which its adjacent nodes belong.

[0015] In each attempt, calculate the change ρ of the system comprehensive evaluation index before and after the allocation; record the adjacent node with the largest ρ value; if ρ>0, allocate node i to the active or reactive cluster where the adjacent node is located; if there is no improvement, keep node i in the original active or reactive cluster unchanged;

[0016] Among them, the reactive power comprehensive evaluation index ρ QU for:

[0017] ρ QU =ρ QUa +ρ QUb ;

[0018] In the formula, ρ QUa is the reactive modularity function, ρ QUb It is the reactive power aggregation index;

[0019] The reactive modularity function is expressed as:

[0020]

[0021] In the formula, A QUij represents the improved reactive weight matrix, k i is the sum of the edge weights connected to node i, k j is the sum of the edge weights connected to node j, is the sum of the weights of all edges in the network. If nodes i and j are in the same cluster, δ(i, j) = 1, otherwise δ(i, j) = 0;

[0022] The reactive aggregation index is expressed as:

[0023]

[0024] In the formula, c is the number of the current cluster, b is the total number of clusters, n is the total number of nodes, S QUij is the voltage sensitivity factor of node i to node j;

[0025] The improved reactive edge weight matrix is ​​expressed as:

[0026] A QUij =η QUij +α QUij ;

[0027] Where: A QUij is the edge weight of node i to node j considering the PV adjustable capacity, α QUij is the reactive voltage support capability; η QUij To improve the edge weight, the original edge weight matrix is ​​replaced by the mean of different node sensitivities, which is expressed as:

[0028]

[0029] In the formula, S QUij is the reactive voltage sensitivity factor of node i to node j, S QUji is the reactive voltage sensitivity factor of node j to node i;

[0030] Adjust the reactive power of node i to support the reactive voltage of node j α QUij It is expressed as:

[0031]

[0032] In the formula, Q QUi The adjustable reactive capacity of the photovoltaic inverter of node i;

[0033] The active comprehensive modularity evaluation index is expressed as:

[0034] ρ PU =ρ PUa +ρ PUb ;

[0035] In the formula, ρ PUa is the active modularity function, ρ PUb It is the active aggregation index;

[0036] The active modularity function is expressed as:

[0037]

[0038] In the formula, A PUij represents the improved active weight matrix;

[0039] The active aggregation index is expressed as:

[0040]

[0041] In the formula, c is the label of the current cluster, S PUij is the active voltage sensitivity factor of node i to node j, b is the total number of clusters, S Puji is the active voltage sensitivity factor of node j for node i;

[0042] The improved active edge weight matrix is ​​expressed as:

[0043] A PUij =S PUij +α PUij ;

[0044] In the formula, A PUij is the active edge weight matrix of node i to node j considering the local photovoltaic consumption capacity, α PUij is the active power consumption capacity;

[0045] Active power adjustment of node i affects active power consumption capacity of node j α PUij It is expressed as:

[0046]

[0047] Where P PUi represents the active power that can be provided by the photovoltaic in node i;

[0048] (1-3) Iterative optimization process: Repeat the cluster allocation optimization process in step (1-2) until the clusters to which all nodes belong no longer change, indicating that the local optimal cluster division has been achieved;

[0049] (1-4) Grid compression: In the distribution network model, all nodes belonging to the same cluster are merged into a new node, which represents a cluster. The connection line weights within the cluster are merged into the self-loop of the new node, while the connection line weights between clusters become the connection weights between the new nodes.

[0050] (1-5) Iterative compression and optimization: The compressed distribution network model is regarded as a new optimization problem, and steps (1-2) to (1-4) are restarted until the modularity of the entire distribution network model no longer changes, and the cluster division is considered to have reached the global optimum; at this time, the corresponding clusters are divided according to the calculated modularity.

[0051] Further, the specific steps of step (2) are as follows:

[0052] (2-1) Construct an intra-cluster adjustment set within the cluster based on the current cluster division:

[0053] Let step (1) obtain that the distribution network is divided into M reactive clusters and N active clusters;

[0054] In the Mth i Reactive cluster or Nth i Within the active cluster, the set of all photovoltaic nodes is recorded as the photovoltaic set H, and the set of all energy storage nodes is recorded as C;

[0055] Within the photovoltaic set, the photovoltaics with surplus adjustable reactive capacity are recorded as the adjustable photovoltaic node set H1, and the photovoltaics with 0 adjustable reactive capacity are recorded as the non-adjustable photovoltaic node set H2;

[0056] Within the energy storage set, the energy storage devices with adjustment margin are recorded as the adjustable energy storage set C1, and the energy storage devices without adjustment margin are recorded as the non-adjustable energy storage set C2;

[0057] So we define four sets:

[0058] Set I: sufficient coordination ability, that is, the combination of H1+C1;

[0059] Set II: insufficient coordination ability, i.e., the combination of H2+C2;

[0060] Set III: Voltage over-limit set, the set where the voltage exceeds the limit;

[0061] Set IV: Normal load set, the voltage does not exceed the limit;

[0062] When the cluster is built, the photovoltaic cluster is first used to control the corresponding voltage in the cluster;

[0063] (2-2) Determine the key nodes in the set for voltage control:

[0064] In the divided clusters, cluster I is to implement the voltage regulation mechanism, in which photovoltaics will be used first for voltage regulation. In this regulation process, photovoltaic nodes with large adjustable margins will be used first. For this purpose, the photovoltaic regulation capabilities need to be ranked. The key nodes are ranked first after each ranking.

[0065] The selection of photovoltaic key nodes is based on the voltage-reactive sensitivity matrix, and the node observability index is expressed as:

[0066]

[0067] In the formula, ω ij Indicates node observability, i is the PV number of the current cluster, j is the node number where the PV is actually located, N is the total number of nodes in the cluster, and x is the number of PVs in the current cluster set I;

[0068] Considering the influence of different distributed photovoltaic reactive adjustable quantities on other node voltages, the node controllability index is expressed as:

[0069]

[0070] In the formula, γ ij represents the node controllability index, ι is the number of the photovoltaic with reactive power regulation capability in the current cluster set I, j is the node number where the photovoltaic is actually located in the cluster, m is the total number of photovoltaics with reactive power regulation capability in this cluster, Q i is the adjustable active power of node i; n is the number of photovoltaic nodes in the distribution network;

[0071] Comprehensive evaluation index for key node selection It is expressed as:

[0072]

[0073] Where K1 is the weight coefficient; ω ij is the node observability index; ij Node controllability indicators;

[0074] First, for each node in the network, calculate its comprehensive evaluation index Then all nodes The values ​​are sorted from largest to smallest; second, during the sorting process, the nodes at the top of the list, i.e., those with the highest The nodes with the highest value are identified as key nodes;

[0075] The nodes that exceed the limit in the cluster will be classified into set III. The reactive power adjustment required to restore the voltage of the node that exceeds the limit to normal is calculated. The adjustment is expressed as:

[0076]

[0077] Where ΔU1 is the difference between the voltage of the over-limit node and the voltage limit, S QUab is the reactive voltage sensitivity between the over-limit node a that needs to be regulated and the key node b to be regulated;

[0078] (2-3) The photovoltaic group controls the voltage of the over-limit group:

[0079] In the set of nodes that exceed the voltage limit, select the node with the largest voltage amplitude and record its voltage amplitude as V max , ΔV max The amount by which the node voltage exceeds the specified upper limit;

[0080] According to the reactive voltage sensitivity matrix, in H1, find the photovoltaic PV with the largest reactive voltage sensitivity value based on observability and controllability. i As a key photovoltaic node, its sensitivity value is S QUmax ;

[0081] According to the reactive voltage sensitivity, calculate V max Adjust the PV back to the normal range i Reactive output Q C , C1 will not intervene in voltage regulation when H1 has not exhausted its adjustable reactive capacity Qa;

[0082] When the adjustable reactive capacity Qa in set I is greater than or equal to Qc, the V max Perform reactive power compensation, and then perform a power flow calculation within the cluster. If there are still over-limit voltage nodes after the power flow calculation, repeat the above process to update the distribution network status;

[0083] When the adjustable reactive capacity Qa in set I is less than Qc, the photovoltaic power with regulation capability in set I is called to max Compensation is performed, and then the photovoltaic node is divided into set II. In the updated adjustable photovoltaic node cluster, the photovoltaic corresponding to the maximum reactive sensitivity is found to continue the reactive compensation of the process from step (2-2) to step (2-3) above;

[0084] When all photovoltaics in the system are classified into set II, the energy storage equipment in the cluster will be started to adjust the system voltage; when the voltage of all voltage nodes in the cluster is within the adjustable range or there is no adjustable photovoltaic in the cluster, the voltage control process in the cluster ends; for the voltage control in the active cluster, the control rules are the same as those for reactive power control;

[0085] (2-4) When the available reactive power regulation capacity of the photovoltaic system is exhausted, chemical energy storage equipment will be used to regulate the voltage beyond the limit; when the available reactive power regulation capacity of all photovoltaic devices in the system is exhausted, energy storage equipment needs to be connected to regulate the voltage to maintain system stability.

[0086] Furthermore, K1=1.

[0087] Further, in step (2-2), the reactive adjustment amount required for the voltage of the over-limit node to return to normal is calculated, and the adjustment amount is expressed as:

[0088]

[0089] Where ΔU1 is the difference between the voltage of the over-limit node and the voltage limit, S QUab is the reactive voltage sensitivity between the over-limit node a that needs to be regulated and the key node b to be regulated;

[0090] Furthermore, in step (2-4), during the energy storage adjustment process, its initial state needs to be set. On this basis, the energy storage device monitors the system state to determine whether discharge is currently required and whether the relevant parameters meet the requirements after execution to ensure the voltage stability of the current system. The specific steps are as follows:

[0091] (2-4-1) Initial state setting: At the beginning of step (1), the distribution network model is initialized to ensure that the monitoring and control process starts from a known state;

[0092] (2-4-2) System parameter monitoring: real-time monitoring of the highest voltage point voltage u in the cluster max And the maximum reverse load rate LF max ; Among them, the maximum reverse load rate is:

[0093]

[0094] Where P Dmax is the maximum output of distributed power generation, P Lmax is the maximum equivalent power load at the same moment, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e Actual operating limits of transformer or line;

[0095] (2-4-3) Control logic judgment: By comparing the voltage u at the highest voltage point max and maximum reverse load rate LF max The voltage threshold U max , the lowest voltage point voltage threshold U min , Maximum reverse load rate threshold LFB max and minimum reverse load rate threshold LFB min , determine whether the energy storage device needs to be started at present. If it exceeds the threshold, the energy storage device will be started;

[0096] (2-4-4) Power output optimization: max >U max or LF max >LFB maxIn the case of u, it is determined that there is a power limit, and the system allocates the limited power to the energy storage device for charging until the energy storage device is fully charged; max min And LF max <LFB min In the case of , the system performs energy storage discharge and sets the maximum discharge ratio to 15% to ensure that the voltage does not exceed the limit;

[0097] (2-4-5) Execution and feedback: Based on the judgment result of (2-4-4), the energy storage device is controlled to perform charging or discharging operations, and the system parameters are continuously monitored. When the adjustment capacity is not 0, the power output is continuously adjusted to adapt to the changes in the voltage demand within the cluster. If all nodes in the current cluster meet the voltage requirements, first traverse the capacity information of all energy storage devices and put the devices with an adjustment capacity of 0 into C2. The capacity of C1 that still has adjustment capacity is allocated to the voltage outside the cluster where there is a voltage limit violation, so as to adjust the voltage between clusters.

[0098] Further, in step (2-4-2), the maximum reverse load rate is:

[0099]

[0100] Where P Dmax is the maximum output of distributed power generation, P Lmax is the maximum equivalent power load at the same moment, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e This is the actual operating limit of the transformer or line.

[0101] Further, the specific steps of step (3) are as follows:

[0102] When the internal regulation of the cluster is completed and there is no voltage exceeding the limit, the monitoring system will automatically monitor the voltage level of the entire distribution network;

[0103] If the voltage of any node outside the cluster is detected to be over-limit, the monitoring system will first evaluate whether there is any remaining adjustable capacity in Set I; if so, Set I of the current cluster will deploy this capacity to adjust the voltage of the nodes outside the cluster and restore it to the normal range; once the voltage of the entire network is stabilized within the appropriate range, the cluster will stop the voltage regulation action and switch to a continuous monitoring state, tracking the voltage over-limit situation in real time to ensure the continuous and stable operation of the power grid.

[0104] Furthermore, if the voltage exceeding the limit is detected at any node outside the cluster, the monitoring system will first evaluate whether there is any remaining adjustable capacity in set I; if not, it will be necessary to use relay protection and automatic devices to quickly cut off some of the faulty power system components.

[0105] ​In the present invention, the reactive sensitivity matrix can be expressed as:

[0106]

[0107] Where: It means to find the partial derivative of voltage, represents the partial derivative of voltage with respect to reactive power.

[0108] Set II (set with insufficient coordination capacity): This set includes photovoltaic and energy storage devices that have limited or exhausted adjustable capacity in voltage regulation. Specifically, it may include photovoltaic power generation units that cannot be adjusted or have reached the upper limit of the adjustment capacity (non-adjustable photovoltaic node set H2), and energy storage devices that have exhausted their energy storage or are close to full load (non-adjustable energy storage set C2). In terms of voltage management, the devices in set II may not be able to provide further voltage regulation support.

[0109] Set IV (Normal Load Set): Set IV refers to the set of nodes in the power grid that are operating normally and whose voltage is within the safe range. These nodes do not require additional voltage regulation and are therefore not prioritized in the current voltage management strategy. The normal load set only requires routine monitoring and does not involve emergency or active voltage regulation measures unless the power grid state changes, causing the voltage state of these nodes to become abnormal.

[0110] In the present invention (2-3), according to the reactive voltage sensitivity matrix, in H1, the photovoltaic PV with the largest reactive voltage sensitivity value is found according to observability and controllability. i As a key photovoltaic node, its sensitivity value is S QUmax ; The specific method is: first, the observability and controllability of each photovoltaic node are calculated, and then based on these observability and controllability indicators, combined with reactive voltage sensitivity analysis, the photovoltaic node with the largest reactive voltage sensitivity value is identified.

[0111] In the present invention (2-3), when the adjustable reactive capacity Qa in set I is greater than or equal to Qc, the V max Reactive power compensation is performed, and then a power flow calculation is performed within the cluster. If there are still over-limit voltage nodes after the power flow calculation, the above process is repeated to update the distribution network status; when updating, the distribution network status parameters are usually updated.

[0112] In the present invention (2-4-4), when u max =U max or LF max =LFB max and when u max =U min And LF max =LFB minIn these cases, the system will also take the same measures as when the threshold value is exceeded. This means that even if the voltage or load is exactly equal to the threshold, the system will preventively charge or discharge the energy storage device to maintain the stability and safety of the power grid.

[0113] When U min max max And LFB min <LF max <LFB min When the voltage and load are both within the safe range and no threshold is reached, no charging or discharging operation of any energy storage device is performed. In this case, the system maintains the status quo and no additional energy transfer operation is performed to maintain system stability and efficiency.

[0114] In step (3) of the present invention, one situation is: if there is, the current cluster set I will deploy these capacities to adjust the voltage of the nodes outside the cluster and restore it to the normal range; once the voltage of the entire network is stabilized within the appropriate range, the cluster will stop the voltage regulation action and switch to a continuous monitoring state to track the voltage limit in real time to ensure the continuous and stable operation of the power grid. Another situation is: if the adjustable resources in the system (such as the active and reactive regulation resources proposed in the present invention) are insufficient, it means that the distribution network may have entered an emergency state of the power system and the conventional regulation means have failed. At this time, the cluster control method fails and needs to use relay protection and automatic devices to quickly and selectively cut off some of the faulty power system components.

[0115] The present invention selects chemical energy storage equipment mainly because of the following reasons:

[0116] 1. Energy density and efficiency: Chemical batteries generally provide higher energy density and conversion efficiency, and are suitable for application scenarios that require frequent charging and discharging, such as grid voltage adjustment.

[0117] 2. Mature technology: Chemical battery technology is relatively mature, there are a variety of product options on the market, and the technical stability and economy have been widely verified.

[0118] 3. Flexibility and scalability: Chemical energy storage systems can easily expand their capacity as needed, enhancing their ability to regulate the grid by adding battery cells.

[0119] 4. Rapid response: Chemical energy storage devices can respond to grid control needs in milliseconds and quickly adjust output to respond to rapid changes in grid load and power supply.

[0120] The present invention collects and processes distribution network data to set the initial state and constructs a distribution network model accordingly, and the process adopts existing technology. For example:

[0121] ​​(1) Data collection and processing:

[0122] The voltage and current data (including the amplitude, frequency, and phase of current and voltage) of the battery energy storage system and the photovoltaic power generation system are collected in real time, and then the collected data is cleaned; the cleaning process of power production data mainly includes: missing value processing, outlier identification and processing, and noise reduction. First, the missing values ​​in the data are processed by interpolation filling, model prediction, or direct deletion according to the situation to ensure the integrity of the data. Next, statistical methods are used to identify outliers in the current and voltage data, and they are deleted, corrected, or retained and marked according to the degree of their influence. Finally, the noise in the data is treated by smoothing filtering, noise reduction algorithms, or noise modeling to reduce the interference of noise on data quality. Afterwards, the clear data is converted into the format required for analysis; the phasor data is converted into scalar data with timestamps; the corresponding scalar data with timestamps needs to be brought in for subsequent voltage control;

[0123] (2) Initial condition setting:

[0124] For photovoltaic power generation systems, the expected output power is calculated using the optical power calculation formula based on real-time irradiance and temperature data, and the output power is then used to calculate the voltage and current output by the photovoltaic power generation system;

[0125] For energy storage systems, the current state of charge and historical charging and discharging patterns are first analyzed. Based on this data, a time series-based long-term and short-term neural network forecasting model is used to calculate the expected power output, as well as the voltage and current levels that may be achieved in a specific period of time in the future.

[0126] Substitute these power, voltage, and current data into the distribution network model, and set the initial power generation (for photovoltaic power generation systems) or load (for energy storage systems, whether they are in a discharging or charging state) for the power flow calculation;

[0127] In addition, the voltage and load requirements of other parts of the power grid are set, so that a complete initial state is formed; after the initial state is set, the node admittance matrix is ​​formed to construct the complete distribution network model;

[0128] (3) Construction of distribution network model:

[0129] The model of the distribution network is established based on a detailed analysis of the power grid topology to obtain the node admittance matrix Y g , which consists of nodes and branches of the power system; here, nodes represent various electrical facilities in the distribution network model, such as loads, transformers, photovoltaic power sources, and energy storage elements, while branches connect these facilities. To construct the distribution network node admittance matrix Y gAccording to the previously set initial state, the real-time power data of the photovoltaic system and the energy storage system and the corresponding load data are brought into the corresponding nodes. According to the distribution network model, the corresponding distribution network cluster division and corresponding regulation control can be carried out.

[0130] In the present invention, the energy storage system is Energy Storage System, referred to as ESS); the distributed generation cluster is Distributed Generator Cluster, referred to as DGC.

[0131] Compared with the prior art, the present invention has the following beneficial effects:

[0132] In the present invention, the Louvain algorithm with an improved weight matrix is ​​used to perform cluster division for the access distribution network, taking into account the impact of active and reactive power changes on other nodes in the power grid and the coupling degree of nodes in the cluster. In the IEEE 69 node system, the improvement shown in Table 1 is achieved, and the optimal division modularity of reactive and active clusters is increased to 12.3% and 18.8% respectively. At the same time, compared with the centralized control method, the network loss, active power reduction, and reactive power absorption are optimized to a certain extent. This method effectively reduces the number of control nodes by selecting the key nodes of the cluster. When the measurement equipment is limited, the efficiency of voltage control can still be significantly improved, providing a distributed photovoltaic distribution network with an efficient solution for optimizing voltage management and improving overall stability.

[0133] Table 1 Comparison of different control methods

[0134] Control method Reactive power absorption(MW) Active power reduction (MW) Network loss (MW) Centralized control 0.1413 0.2634 0.3936 Cluster control 0.119 0.3621 0.1254 BRIEF DESCRIPTION OF THE DRAWINGS

[0135] Figure 1 It is a diagram of the voltage control framework composition of the cluster division of the photovoltaic energy storage distribution network of the present invention;

[0136] Figure 2 It is a cluster division voltage control framework diagram of the photovoltaic energy storage distribution network of the present invention;

[0137] Figure 3 It is an overall flow chart of the cluster division voltage control method of the photovoltaic energy storage distribution network of the present invention;

[0138] Figure 4 It is a cluster division flow chart of Louvain algorithm based on improved weight matrix of the present invention;

[0139] Figure 5 It is a schematic diagram of the photovoltaic energy storage voltage regulation in the cluster division voltage control cluster containing the photovoltaic energy storage distribution network of the present invention;

[0140] Figure 6It is a flow chart of the energy storage voltage control method in the cluster division voltage control method of the photovoltaic energy storage distribution network of the present invention.

[0141] Figure 7 It is a voltage calculation result diagram after the IEEE69 node distribution network is connected to the photovoltaic energy storage, which is shown in the cluster partition voltage control application example of the photovoltaic energy storage distribution network of the present invention;

[0142] Figure 8 The voltage control result diagram of the IEEE 69 node distribution network using the cluster partitioning voltage control method is shown in the application example of the cluster partitioning voltage control of the photovoltaic energy storage distribution network of the present invention;

[0143] Fig. 9 This is a voltage control result diagram of the IEEE 69 node distribution network using a centralized voltage control method, as shown in the application example of the cluster division voltage control of the photovoltaic energy storage distribution network of the present invention;

[0144] Fig.10 This is the cluster division result of the IEEE69 node distribution network using the Louvain algorithm based on the improved weight matrix shown in the cluster division voltage control application example of the photovoltaic energy storage distribution network of the present invention. DETAILED DESCRIPTION

[0145] The present invention is further described in detail below in conjunction with embodiments.

[0146] Those skilled in the art will appreciate that the following examples are only used to illustrate the present invention and should not be considered to limit the scope of the present invention. If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the art or the product specifications are used. If the manufacturer of the materials or equipment used is not specified, they are all conventional products that can be purchased.

[0147] Example 1

[0148] A cluster partitioning voltage control method for a photovoltaic energy storage distribution network includes the following steps:

[0149] Step (1), according to the distribution network model, the distribution network is clustered by using the Louvain algorithm based on the improved weight matrix to obtain the active cluster and reactive cluster division results of the distribution network;

[0150] Step (2): Based on the current cluster division results, active and reactive regulation sets are constructed to achieve voltage management within the cluster; relying on the key nodes of the photovoltaic set, the photovoltaic set is used to eliminate the voltage over-limit problem; when the reactive regulation capacity of the photovoltaic system is exhausted, the chemical energy storage device is used to adjust the voltage to ensure the voltage stability within the cluster, and the voltage regulation result within the cluster is output;

[0151] Step (3), based on the voltage regulation result within the cluster, the voltage between clusters is regulated using the adjustable set so that the voltage of the entire distribution network model remains stable.

[0152] Example 2

[0153] like Figure 1-7 As shown, a cluster partitioning voltage control method for a photovoltaic energy storage distribution network includes the following steps:

[0154] Step (1), according to the distribution network model, the distribution network is clustered by using the Louvain algorithm based on the improved weight matrix to obtain the active cluster and reactive cluster division results of the distribution network;

[0155] Step (2): Based on the current cluster division results, active and reactive regulation sets are constructed to achieve voltage management within the cluster; relying on the key nodes of the photovoltaic set, the photovoltaic set is used to eliminate the voltage over-limit problem; when the reactive regulation capacity of the photovoltaic system is exhausted, the chemical energy storage device is used to adjust the voltage to ensure the voltage stability within the cluster, and the voltage regulation result within the cluster is output;

[0156] Step (3), based on the voltage regulation result within the cluster, the voltage between clusters is regulated using the adjustable set so that the voltage of the entire distribution network model remains stable.

[0157] The specific steps of step (1) are as follows:

[0158] (1-1) Initial cluster definition: In the distribution network model, each node is regarded as an independent cluster, and the total number of clusters is equal to the number of nodes;

[0159] (1-2) Cluster allocation optimization:

[0160] First, for each node i in the power grid, we need to try to assign node i to the active cluster or reactive cluster to which its adjacent nodes belong.

[0161] In each attempt, calculate the change ρ of the system comprehensive evaluation index before and after the allocation; record the adjacent node with the largest ρ value; if ρ>0, allocate node i to the active or reactive cluster where the adjacent node is located; if there is no improvement, keep node i in the original active or reactive cluster unchanged;

[0162] Among them, the reactive power comprehensive evaluation index ρ QU for:

[0163] ρ QU =ρ QUa +ρ QUb ;

[0164] In the formula, ρ QUa is the reactive modularity function, ρQUb It is the reactive power aggregation index;

[0165] The reactive modularity function is expressed as:

[0166]

[0167] In the formula, A QUij represents the improved reactive weight matrix, k i is the sum of the edge weights connected to node i, k j is the sum of the edge weights connected to node j, is the sum of the weights of all edges in the network. If nodes i and j are in the same cluster, δ(i, j) = 1, otherwise δ(i, j) = 0;

[0168] The reactive aggregation index is expressed as:

[0169]

[0170] In the formula, c is the number of the current cluster, b is the total number of clusters, n is the total number of nodes, S QUij is the voltage sensitivity factor of node i to node j;

[0171] The improved reactive edge weight matrix is ​​expressed as:

[0172] A QUij =η QUij +α QUij ;

[0173] Where: A QUij is the edge weight of node i to node j considering the PV adjustable capacity, α QUij is the reactive voltage support capability; η QUij To improve the edge weight, the original edge weight matrix is ​​replaced by the mean of different node sensitivities, which is expressed as:

[0174]

[0175] In the formula, S QUij is the reactive voltage sensitivity factor of node i to node j, S QUji is the reactive voltage sensitivity factor of node j to node i;

[0176] Adjust the reactive power of node i to support the reactive voltage of node j α QUij It is expressed as:

[0177]

[0178] In the formula, Q QUi The adjustable reactive capacity of the photovoltaic inverter of node i;

[0179] The active comprehensive modularity evaluation index is expressed as:

[0180] ρ PU =ρ PUa +ρ PUb ;

[0181] In the formula, ρ PUa is the active modularity function, ρ PUb It is the active aggregation index;

[0182] The active modularity function is expressed as:

[0183]

[0184] In the formula, A PUij represents the improved active weight matrix;

[0185] The active aggregation index is expressed as:

[0186]

[0187] In the formula, c is the label of the current cluster, S PUij is the active voltage sensitivity factor of node i to node j, b is the total number of clusters, S Puji is the active voltage sensitivity factor of node j for node i;

[0188] The improved active edge weight matrix is ​​expressed as:

[0189] A PUij =S PUij +α PUij ;

[0190] In the formula, A PUij is the active edge weight matrix of node i to node j considering the local photovoltaic consumption capacity, α PUij is the active power consumption capacity;

[0191] Active power adjustment of node i affects active power consumption capacity of node j α PUij It is expressed as:

[0192]

[0193] Where P PUi represents the active power that can be provided by the photovoltaic in node i;

[0194] (1-3) Iterative optimization process: Repeat the cluster allocation optimization process in step (1-2) until the clusters to which all nodes belong no longer change, indicating that the local optimal cluster division has been achieved;

[0195] (1-4) Grid compression: In the distribution network model, all nodes belonging to the same cluster are merged into a new node, which represents a cluster. The connection line weights within the cluster are merged into the self-loop of the new node, while the connection line weights between clusters become the connection weights between the new nodes.

[0196] (1-5) Iterative compression and optimization: The compressed distribution network model is regarded as a new optimization problem, and steps (1-2) to (1-4) are restarted until the modularity of the entire distribution network model no longer changes, and the cluster division is considered to have reached the global optimum; at this time, the corresponding clusters are divided according to the calculated modularity.

[0197] The specific steps of step (2) are as follows:

[0198] (2-1) Construct an intra-cluster adjustment set within the cluster based on the current cluster division:

[0199] Let step (1) obtain that the distribution network is divided into M reactive clusters and N active clusters;

[0200] In the Mth i Reactive cluster or Nth i Within the active cluster, the set of all photovoltaic nodes is recorded as the photovoltaic set H, and the set of all energy storage nodes is recorded as C;

[0201] Within the photovoltaic set, the photovoltaics with surplus adjustable reactive capacity are recorded as the adjustable photovoltaic node set H1, and the photovoltaics with 0 adjustable reactive capacity are recorded as the non-adjustable photovoltaic node set H2;

[0202] Within the energy storage set, the energy storage devices with adjustment margin are recorded as the adjustable energy storage set C1, and the energy storage devices without adjustment margin are recorded as the non-adjustable energy storage set C2;

[0203] So we define four sets:

[0204] Set I: sufficient coordination ability, i.e., the combination of H1+C 1;

[0205] Set II: insufficient coordination ability, i.e., the combination of H2+C2;

[0206] Set III: Voltage over-limit set, the set where the voltage exceeds the limit;

[0207] Set IV: Normal load set, the voltage does not exceed the limit;

[0208] When the cluster is built, the photovoltaic cluster is first used to control the voltage in the cluster;

[0209] (2-2) Determine the key nodes in the set for voltage control:

[0210] In the divided clusters, cluster I is to implement the voltage regulation mechanism, in which photovoltaics will be used first for voltage regulation. In this regulation process, photovoltaic nodes with large adjustable margins will be used first. For this purpose, the photovoltaic regulation capabilities need to be ranked. The key nodes are ranked first after each ranking.

[0211] The selection of photovoltaic key nodes is based on the voltage-reactive sensitivity matrix, and the node observability index is expressed as:

[0212]

[0213] In the formula, ω ij Indicates node observability, i is the PV number of the current cluster, j is the node number where the PV is actually located, N is the total number of nodes in the cluster, and x is the number of PVs in the current cluster set I;

[0214] Considering the influence of different distributed photovoltaic reactive adjustable quantities on other node voltages, the node controllability index is expressed as:

[0215]

[0216] In the formula, γ ij represents the node controllability index, ι is the number of the photovoltaic with reactive power regulation capability in the current cluster set I, j is the node number where the photovoltaic is actually located in the cluster, m is the total number of photovoltaics with reactive power regulation capability in this cluster, Q i is the adjustable active power of node i; n is the number of photovoltaic nodes in the distribution network;

[0217] Comprehensive evaluation index for key node selection It is expressed as:

[0218]

[0219] Where K1 is the weight coefficient; ω ij is the node observability index; ij Node controllability indicators;

[0220] First, for each node in the network, calculate its comprehensive evaluation index Then all nodes The values ​​are sorted from largest to smallest; second, during the sorting process, the nodes at the top of the list, i.e., those with the highest The nodes with the highest value are identified as key nodes;

[0221] The nodes that exceed the limit in the cluster will be classified into set III; the reactive power adjustment required to restore the voltage of the node that exceeds the limit to normal is calculated;

[0222] (2-3) The photovoltaic group controls the voltage of the over-limit group:

[0223] In the set of nodes that exceed the voltage limit, select the node with the largest voltage amplitude and record its voltage amplitude as V max , ΔV max The amount by which the node voltage exceeds the specified upper limit;

[0224] According to the reactive voltage sensitivity matrix, in H1, find the photovoltaic PV with the largest reactive voltage sensitivity value based on observability and controllability. i As a key photovoltaic node, its sensitivity value is S QUmax ;

[0225] According to the reactive voltage sensitivity, calculate V max Adjust the PV back to the normal range i Reactive output Q C , C1 will not intervene in voltage regulation when H1 has not exhausted its adjustable reactive capacity Qa;

[0226] When the adjustable reactive capacity Qa in set I is greater than or equal to Qc, the V max Perform reactive power compensation, and then perform a power flow calculation within the cluster. If there are still over-limit voltage nodes after the power flow calculation, repeat the above process to update the distribution network status;

[0227] When the adjustable reactive capacity Qa in set I is less than Qc, the photovoltaic power with regulation capability in set I is called to max Compensation is performed, and then the photovoltaic node is divided into set II. In the updated adjustable photovoltaic node cluster, the photovoltaic corresponding to the maximum reactive sensitivity is found to continue the reactive compensation of the process from step (2-2) to step (2-3) above;

[0228] When all photovoltaics in the system are classified into set II, the energy storage equipment in the cluster will be started to adjust the system voltage; when the voltage of all voltage nodes in the cluster is within the adjustable range or there is no adjustable photovoltaic in the cluster, the voltage control process in the cluster ends; for the voltage control in the active cluster, the control rules are the same as those for reactive power control;

[0229] (2-4) When the available reactive power regulation capacity of the photovoltaic system is exhausted, chemical energy storage equipment will be used to regulate the voltage beyond the limit; when the available reactive power regulation capacity of all photovoltaic devices in the system is exhausted, energy storage equipment needs to be connected to regulate the voltage to maintain system stability.

[0230] K1=1.

[0231] In step (2-4), during the energy storage adjustment process, its initial state needs to be set. On this basis, the energy storage device monitors the system state to determine whether discharge is currently required and whether the relevant parameters meet the requirements after execution to ensure the voltage stability of the current system. The specific steps are as follows:

[0232] (2-4-1) Initial state setting: At the beginning of step (1), the distribution network model is initialized to ensure that the monitoring and control process starts from a known state;

[0233] (2-4-2) System parameter monitoring: real-time monitoring of the highest voltage point voltage u in the cluster max And the maximum reverse load rate LF max ;

[0234] (2-4-3) Control logic judgment: By comparing the voltage u at the highest voltage point max and maximum reverse load rate LF max The voltage threshold U max , the lowest voltage point voltage threshold U min , Maximum reverse load rate threshold LFB max and minimum reverse load rate threshold LFB min , determine whether the energy storage device needs to be started at present. If it exceeds the threshold, the energy storage device will be started;

[0235] (2-4-4) Power output optimization: max >U max or LF max >LFB max In the case of u, it is determined that there is a power limit, and the system allocates the limited power to the energy storage device for charging until the energy storage device is fully charged; max min And LF max <LFB min In the case of , the system performs energy storage discharge and sets the maximum discharge ratio to 15% to ensure that the voltage does not exceed the limit;

[0236] (2-4-5) Execution and feedback: Based on the judgment result of (2-4-4), the energy storage device is controlled to perform charging or discharging operations, and the system parameters are continuously monitored. When the adjustment capacity is not 0, the power output is continuously adjusted to adapt to the changes in the voltage demand within the cluster. If all nodes in the current cluster meet the voltage requirements, first traverse the capacity information of all energy storage devices and put the devices with an adjustment capacity of 0 into C2. The capacity of C1 that still has adjustment capacity is allocated to the voltage outside the cluster where there is a voltage limit violation, so as to adjust the voltage between clusters.

[0237] The specific steps of step (3) are as follows: ​

[0238] When the internal regulation of the cluster is completed and there is no voltage exceeding the limit, the monitoring system will automatically monitor the voltage level of the entire distribution network;

[0239] If the voltage of any node outside the cluster is detected to be over-limit, the monitoring system will first evaluate whether there is any remaining adjustable capacity in Set I; if so, Set I of the current cluster will deploy this capacity to adjust the voltage of the nodes outside the cluster and restore it to the normal range; once the voltage of the entire network is stabilized within the appropriate range, the cluster will stop the voltage regulation action and switch to a continuous monitoring state, tracking the voltage over-limit situation in real time to ensure the continuous and stable operation of the power grid.

[0240] Application Example 1

[0241] like Figure 1-7 As shown, a cluster partitioning voltage control method for a photovoltaic energy storage distribution network includes the following steps:

[0242] Step (1), according to the distribution network model, the distribution network is clustered by using the Louvain algorithm based on the improved weight matrix to obtain the active cluster and reactive cluster division results of the distribution network;

[0243] Step (2): Based on the current cluster division results, active and reactive regulation sets are constructed to achieve voltage management within the cluster; relying on the key nodes of the photovoltaic set, the photovoltaic set is used to eliminate the voltage over-limit problem; when the reactive regulation capacity of the photovoltaic system is exhausted, the chemical energy storage device is used to adjust the voltage to ensure the voltage stability within the cluster, and the voltage regulation result within the cluster is output;

[0244] Step (3), based on the voltage regulation result within the cluster, the voltage between clusters is regulated using the adjustable set so that the voltage of the entire distribution network model remains stable.

[0245] Further, it may include the following:

[0246] The distribution network data is collected and processed to set the initial state and build the distribution network model accordingly.

[0247] (1-1) Initial cluster definition: In the distribution network model, each node is regarded as an independent cluster, and the total number of clusters is equal to the number of nodes;

[0248] (1-2) Cluster allocation optimization:

[0249] First, for each node i in the power grid, we need to try to assign node i to the active cluster or reactive cluster to which its adjacent nodes belong.

[0250] In each attempt, calculate the change ρ of the system comprehensive evaluation index before and after the allocation; record the adjacent node with the largest ρ value; if ρ>0, allocate node i to the active or reactive cluster where the adjacent node is located; if there is no improvement, keep node i in the original active or reactive cluster unchanged;

[0251] Among them, the reactive power comprehensive evaluation index ρ QU for:

[0252] ρ QU =ρ QUa +ρ QUb ;

[0253] In the formula, ρ QUa is the reactive modularity function, ρ QUb It is the reactive power aggregation index;

[0254] The reactive modularity function is expressed as:

[0255]

[0256] In the formula, A QUij represents the improved reactive weight matrix, k i is the sum of the edge weights connected to node i, k j is the sum of the edge weights connected to node j, is the sum of the weights of all edges in the network. If nodes i and j are in the same cluster, δ(i, j) = 1, otherwise δ(i, j) = 0;

[0257] The reactive aggregation index is expressed as:

[0258]

[0259] In the formula, c is the number of the current cluster, b is the total number of clusters, n is the total number of nodes, S QUij is the voltage sensitivity factor of node i to node j;

[0260] The improved reactive edge weight matrix is ​​expressed as:

[0261] A QUij =η QUij +α QUij ;

[0262] Where: A QUij is the edge weight of node i to node j considering the PV adjustable capacity, α QUij is the reactive voltage support capability; η QUij To improve the edge weight, the original edge weight matrix is ​​replaced by the mean of different node sensitivities, which is expressed as:

[0263]

[0264] In the formula, S QUij is the reactive voltage sensitivity factor of node i to node j, S QUji is the reactive voltage sensitivity factor of node j to node i;

[0265] Wherein, the reactive sensitivity matrix can be expressed as:

[0266]

[0267] Where: It means to find the partial derivative of voltage, represents the partial derivative of voltage with respect to reactive power.

[0268] Adjust the reactive power of node i to support the reactive voltage of node j α QUij It is expressed as:

[0269]

[0270] In the formula, Q QUi The adjustable reactive capacity of the photovoltaic inverter of node i;

[0271] The active comprehensive modularity evaluation index is expressed as:

[0272] ρ PU =ρ PUa +ρ PUb ;

[0273] In the formula, ρ PUa is the active modularity function, ρ PUb It is the active aggregation index;

[0274] The active modularity function is expressed as:

[0275]

[0276] In the formula, A PUij represents the improved active weight matrix;

[0277] The active aggregation index is expressed as:

[0278]

[0279] In the formula, c is the label of the current cluster, S PUij is the active voltage sensitivity factor of node i to node j, b is the total number of clusters, S Puji is the active voltage sensitivity factor of node j for node i;

[0280] The improved active edge weight matrix is ​​expressed as:

[0281] A PUij =S PUij +α PUij ;

[0282] In the formula, A PUij is the active edge weight matrix of node i to node j considering the local photovoltaic consumption capacity, α PUij is the active power consumption capacity;

[0283] Active power adjustment of node i affects active power consumption capacity of node j α PUij It is expressed as:

[0284]

[0285] Where P PUi represents the active power that can be provided by the photovoltaic in node i;

[0286] (1-3) Iterative optimization process: Repeat the cluster allocation optimization process in step (1-2) until the clusters to which all nodes belong no longer change, indicating that the local optimal cluster division has been achieved;

[0287] (1-4) Grid compression: In the distribution network model, all nodes belonging to the same cluster are merged into a new node, which represents a cluster. The connection line weights within the cluster are merged into the self-loop of the new node, while the connection line weights between clusters become the connection weights between the new nodes.

[0288] (1-5) Iterative compression and optimization: The compressed distribution network model is regarded as a new optimization problem, and steps (1-2) to (1-4) are restarted until the modularity of the entire distribution network model no longer changes, and the cluster division is considered to have reached the global optimum; at this time, the corresponding clusters are divided according to the calculated modularity.

[0289] According to the distribution network model, the Louvain algorithm based on the improved weight matrix is ​​used to cluster the distribution network to obtain the active and reactive cluster division results of the distribution network. The steps are as follows:

[0290] (2-1) Construct an intra-cluster adjustment set within the cluster based on the current cluster division:

[0291] Let step (1) obtain that the distribution network is divided into M reactive clusters and N active clusters;

[0292] In the Mth i Reactive cluster or Nth i Within the active cluster, all photovoltaic nodes are recorded as the photovoltaic set H, all energy storage nodes are recorded as C, and all nodes with normal voltage are recorded as the photovoltaic set.

[0293] Within the photovoltaic set, the photovoltaics with surplus adjustable reactive capacity are recorded as the adjustable photovoltaic node set H1, and the photovoltaics with 0 adjustable reactive capacity are recorded as the non-adjustable photovoltaic node set H2;

[0294] Within the energy storage set, the energy storage devices with adjustment margin are recorded as the adjustable energy storage set C1, and the energy storage devices without adjustment margin are recorded as the non-adjustable energy storage set C2;

[0295] So we define four sets:

[0296] Set I: sufficient coordination ability, i.e., the combination of H1+C 1;

[0297] Set II: insufficient coordination ability, i.e., the combination of H2+C2;

[0298] Set III: Voltage over-limit set, the set where the voltage exceeds the limit;

[0299] Set IV: Normal load set, the voltage does not exceed the limit;

[0300] When the cluster is built, the photovoltaic cluster is first used to control the voltage in the cluster;

[0301] (2-2) Determine the key nodes in the set for voltage control:

[0302] In the divided clusters, cluster I is to implement the voltage regulation mechanism, in which photovoltaics will be used first for voltage regulation. In this regulation process, photovoltaic nodes with large adjustable margins will be used first. For this purpose, the photovoltaic regulation capabilities need to be ranked. The key nodes are ranked first after each ranking.

[0303] The selection of photovoltaic key nodes is based on the voltage-reactive sensitivity matrix, and the node observability index is expressed as:

[0304]

[0305] In the formula, ω ij Indicates node observability, i is the PV number of the current cluster, j is the node number where the PV is actually located, N is the total number of nodes in the cluster, and x is the number of PVs in the current cluster set I;

[0306] Considering the influence of different distributed photovoltaic reactive adjustable quantities on other node voltages, the node controllability index is expressed as:

[0307]

[0308] In the formula, γ ijrepresents the node controllability index, ι is the number of the photovoltaic with reactive power regulation capability in the current cluster set I, j is the node number where the photovoltaic is actually located in the cluster, m is the total number of photovoltaics with reactive power regulation capability in this cluster, Q i is the adjustable active power of node i; n is the number of photovoltaic nodes in the distribution network;

[0309] The comprehensive evaluation index for key node selection is expressed as:

[0310]

[0311] Where K1 is the weight coefficient; ω ij is the node observability index; ij Node controllability indicators;

[0312] First, for each node in the network, we calculate its comprehensive evaluation index After the calculation is completed, all nodes The values ​​are sorted from largest to smallest. Second, during the sorting process, the nodes at the top of the list, i.e., those with the highest The nodes with the highest value are identified as key nodes.

[0313] The nodes that exceed the limit in the cluster will be classified into set III. The reactive power adjustment required to restore the voltage of the node that exceeds the limit to normal is calculated. The adjustment is expressed as:

[0314]

[0315] Where ΔU1 is the difference between the voltage of the over-limit node and the voltage limit, S QUab is the reactive voltage sensitivity between the over-limit node a that needs to be regulated and the key node b to be regulated;

[0316] (2-3) The photovoltaic group controls the voltage of the over-limit group:

[0317] In the set of nodes that exceed the voltage limit, select the node with the largest voltage amplitude and record its voltage amplitude as V max , ΔV max The amount by which the node voltage exceeds the specified upper limit;

[0318] According to the reactive voltage sensitivity matrix, in H1, find the photovoltaic PV with the largest reactive voltage sensitivity value based on observability and controllability. i As a key photovoltaic node, its sensitivity value is S QUmax ;

[0319] Firstly, the observability and controllability of each PV node are calculated. Then, based on these observability and controllability indicators and combined with reactive voltage sensitivity analysis, the PV node with the largest reactive voltage sensitivity value is identified.

[0320] According to the reactive voltage sensitivity, calculate V max Adjust the PV back to the normal range i Reactive output Q C , C1 will not intervene in voltage regulation when H1 has not exhausted its adjustable reactive capacity Qa;

[0321] When the adjustable reactive capacity Qa in set I is greater than or equal to Qc, the V max Perform reactive power compensation, and then perform a power flow calculation within the cluster. If there are still over-limit voltage nodes after the power flow calculation, repeat the above process to update the distribution network status;

[0322] When the adjustable reactive capacity Qa in set I is less than Qc, the photovoltaic power with regulation capability in set I is called to max Compensation is performed, and then the photovoltaic node is divided into set II. In the updated adjustable photovoltaic node cluster, the photovoltaic corresponding to the maximum reactive sensitivity is found to continue the reactive compensation of the process from step (2-2) to step (2-3) above;

[0323] When all photovoltaics in the system are classified into set II, the energy storage equipment in the cluster will be started to adjust the system voltage; when the voltage of all voltage nodes in the cluster is within the adjustable range or there is no adjustable photovoltaic in the cluster, the voltage control process in the cluster ends; for the voltage control in the active cluster, the control rules are the same as those for reactive power control;

[0324] (2-4) When the available reactive power regulation capacity of the photovoltaic system is exhausted, chemical energy storage equipment will be used to regulate the voltage beyond the limit; when the available reactive power regulation capacity of all photovoltaic devices in the system is exhausted, energy storage equipment needs to be connected to regulate the voltage to maintain system stability.

[0325] The steps of using photovoltaic and energy storage facilities to adjust the voltage within the cluster according to the current distribution network daily operation results include:

[0326] (2-4-1) Initial state setting: At the beginning of step (1), the distribution network model is initialized to ensure that the monitoring and control process starts from a known state;

[0327] (2-4-2) System parameter monitoring: real-time monitoring of the highest voltage point voltage u in the cluster max And the maximum reverse load rate LF max ; Among them, the maximum reverse load rate is:

[0328]

[0329] Where P Dmax is the maximum output of distributed power generation, P Lmax is the maximum equivalent power load at the same moment, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e Actual operating limits of transformer or line;

[0330] (2-4-3) Control logic judgment: By comparing the voltage u at the highest voltage point max and maximum reverse load rate LF max The voltage threshold U max , the lowest voltage point voltage threshold U min , Maximum reverse load rate threshold LFB max and minimum reverse load rate threshold LFB min , determine whether the energy storage device needs to be started at present. If it exceeds the threshold, the energy storage device will be started;

[0331] (2-4-4) Power output optimization: max >U max or LF max >LFB max In the case of u, it is determined that there is a power limit, and the system allocates the limited power to the energy storage device for charging until the energy storage device is fully charged; max min And LF max <LFB min In the case of , the system performs energy storage discharge and sets the maximum discharge ratio to 15% to ensure that the voltage does not exceed the limit;

[0332] When u max =U max or LF max =LFB max and when u max =U min And LF max =LFB min In these cases, the system will also take the same measures as when the threshold value is exceeded. This means that even if the voltage or load is exactly equal to the threshold, the system will preventively charge or discharge the energy storage device to maintain the stability and safety of the power grid.

[0333] When U min max max And LFB min <LF max <LFB min ​​​When the voltage and load are both within the safe range and no threshold is reached, no charging or discharging operation of any energy storage device is performed. In this case, the system maintains the status quo and no additional energy transfer operation is performed to maintain system stability and efficiency.

[0334] (2-4-5) Execution and feedback: Based on the judgment result of (2-4-4), the energy storage device is controlled to perform charging or discharging operations, and the system parameters are continuously monitored. When the adjustment capacity is not 0, the power output is continuously adjusted to adapt to the changes in the voltage demand within the cluster. If all nodes in the current cluster meet the voltage requirements, first traverse the capacity information of all energy storage devices and put the devices with an adjustment capacity of 0 into C2. The capacity of C1 that still has adjustment capacity is allocated to the voltage outside the cluster where there is a voltage limit violation, so as to adjust the voltage between clusters.

[0335] The specific steps of step (3) are as follows:

[0336] When the internal regulation of the cluster is completed and there is no voltage exceeding the limit, the monitoring system will automatically monitor the voltage level of the entire distribution network;

[0337] If the voltage of any node outside the cluster is detected to be over-limit, the monitoring system will first evaluate whether there is any remaining adjustable capacity in Set I; if so, Set I of the current cluster will deploy this capacity to adjust the voltage of the nodes outside the cluster and restore it to the normal range; once the voltage of the entire network is stabilized within the appropriate range, the cluster will stop the voltage regulation action and switch to a continuous monitoring state, tracking the voltage over-limit situation in real time to ensure the continuous and stable operation of the power grid.

[0338] If the adjustable resources in the system (such as the active and reactive regulation resources proposed in the present invention) are insufficient, it means that the distribution network may have entered an emergency state of the power system and conventional regulation means have failed. At this time, the cluster control method fails and it is necessary to use relay protection and automatic devices to quickly and selectively cut off some of the faulty power system components.

[0339] This paper takes the IEEE69 node distribution network as an example to verify the effectiveness of the proposed voltage control method for cluster partitioning of the photovoltaic energy storage distribution network. base =10MVA, system reference voltage U base=12.66kV. The system contains 69 nodes, and the photovoltaic access nodes are 15, 20, 25, 34, 45, 49, 57, 61, 65, and 67, with access capacities of 0.8, 1.2, 0.8, 0.53, 0.46, 0.32, 0.8, 0.53, 1.2, and 0.8MVA, respectively. The minimum power factor is set to 0.95; the energy storage battery is installed at node 20 and node 65, with an installed capacity of 0.15MW, and the energy storage battery charge state is [0.15, 0.85]; the normal voltage level is set to [0.9, 1.07]. The day with the highest light intensity in July was selected, and the daily load curve of the distribution network, including residents and businesses, conforms to the residential load, commercial load, and industrial load in July. In order to illustrate the flexibility and rapidity of the voltage control strategy based on cluster division, the present invention adopts a centralized control method without cluster division for global voltage control and compares and analyzes the simulation results of the two control methods. When photovoltaic energy storage is connected to the distribution network, a centralized control method is used to control the voltage. After the distributed photovoltaic is connected throughout the day, the system voltage fluctuation is as follows: Figure 7 As shown in the figure, due to the volatility of distributed photovoltaic output, the voltage exceeds the limit at some times; after centralized control throughout the day, the voltage fluctuation is as follows Figure 8 As shown, after the control strategy of the present invention is adopted, the voltage fluctuation throughout the day is as follows Fig. 9 Although both the centralized control and the control of the present invention have the effect of maintaining normal voltage, the voltage fluctuation in the control strategy of the present invention is smaller, which is conducive to the stable operation of the power system.

[0340] In order to verify the superiority of the clustering method based on the improved Louvain algorithm proposed in the present invention, the FastNewman clustering algorithm, the electrical distance clustering algorithm and the algorithm proposed in the present invention are respectively applied to cluster the distribution network system. The modularity comparison is shown in Table 2: The modularity can measure whether the clustering result is reasonable. From the clustering results in Table 3, it can be seen that for the Fast Newman clustering algorithm, the present invention reduces the problem of a small number of individual cluster nodes to a certain extent by considering the relationship between the coupling degree, and improves the accuracy of clustering. The results after clustering by the method proposed in the present invention are shown in Figure 2. Fig.10 As shown, the optimal number of reactive power clusters for the entire system is 6, and the maximum modularity is 0.755. The optimal number of active power clusters for the entire system is 5, and the maximum modularity is 0.81.

[0341] Table 2 Key nodes of reactive power cluster and active power cluster

[0342] Cluster ID Reactive cluster node number Active cluster node number Cluster I 45 45 Cluster II 49 34 Cluster III 34 67 Cluster IV 67 65 Cluster V 20 20 Cluster VI 65 none

[0343] Table 3 Comparison of different reactive power cluster division results

[0344]

[0345]

[0346] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A cluster partitioning voltage control method for a photovoltaic energy storage distribution network, characterized in that: The steps include: Step (1), according to the distribution network model, the distribution network is clustered by using the Louvain algorithm based on the improved weight matrix to obtain the active cluster and reactive cluster division results of the distribution network; Step (2): Based on the current cluster division results, active and reactive regulation sets are constructed to achieve voltage management within the cluster; relying on the key nodes of the photovoltaic set, the photovoltaic set is used to eliminate the voltage over-limit problem; when the reactive regulation capacity of the photovoltaic system is exhausted, the chemical energy storage device is used to adjust the voltage to ensure the voltage stability within the cluster, and the voltage regulation result within the cluster is output; Step (3), based on the voltage regulation result within the cluster, the voltage between clusters is regulated using the adjustable set so that the voltage of the entire distribution network model remains stable.

2. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 1 is characterized in that: The specific steps of step (1) are as follows: (1-1) Initial cluster definition: In the distribution network model, each node is regarded as an independent cluster, and the total number of clusters is equal to the number of nodes; (1-2) Cluster allocation optimization: First, for each node i in the power grid, we need to try to assign node i to the active cluster or reactive cluster to which its adjacent nodes belong. In each attempt, the change ρ of the system comprehensive evaluation index before and after the allocation is calculated; Record the adjacent node with the largest ρ value; If ρ>0, node i is assigned to the active or reactive cluster where the neighboring node is located; If there is no improvement, keep node i in the original active or reactive cluster unchanged; Among them, the reactive power comprehensive evaluation index ρ QU for: r QU =ρ QUa +r QUb ; In the formula, ρ QUa is the reactive modularity function, ρ QUb It is the reactive power aggregation index; The reactive modularity function is expressed as: In the formula, A QUij represents the improved reactive weight matrix, k i is the sum of the edge weights connected to node i, k j is the sum of the edge weights connected to node j, is the sum of the weights of all edges in the network. If nodes i and j are in the same cluster, δ(i, j) = 1, otherwise δ(i, j) = 0; The reactive aggregation index is expressed as: In the formula, c is the number of the current cluster, b is the total number of clusters, n is the total number of nodes, S QUij is the voltage sensitivity factor of node i to node j; The improved reactive edge weight matrix is ​​expressed as: A QUij =the QUij +a QUij ; Where: A QUij is the edge weight of node i to node j considering the PV adjustable capacity, α QUij is the reactive voltage support capability; η QUij To improve the edge weight, the original edge weight matrix is ​​replaced by the mean of different node sensitivities, which is expressed as: In the formula, S QUij is the reactive voltage sensitivity factor of node i to node j, S QUji is the reactive voltage sensitivity factor of node j to node i; Adjust the reactive power of node i to support the reactive voltage of node j α QUij It is expressed as: In the formula, Q QUi The adjustable reactive capacity of the photovoltaic inverter of node i; The active comprehensive modularity evaluation index is expressed as: r PU =ρ PUa +r PUb ; In the formula, ρ PUa is the active modularity function, ρ PUb It is the active aggregation index; The active modularity function is expressed as: In the formula, A PUij represents the improved active weight matrix; The active aggregation index is expressed as: In the formula, c is the label of the current cluster, S PUij is the active voltage sensitivity factor of node i to node j, b is the total number of clusters, S Puji is the active voltage sensitivity factor of node j for node i; The improved active edge weight matrix is ​​expressed as: A PUij =S PUij +a PUij ; In the formula, A PUij is the active edge weight matrix of node i to node j considering the local photovoltaic consumption capacity, α PUij is the active power consumption capacity; Active power adjustment of node i affects active power consumption capacity of node j α PUij It is expressed as: Where P PUi represents the active power that can be provided by the photovoltaic in node i; (1-3) Iterative optimization process: Repeat the cluster allocation optimization process in step (1-2) until the clusters to which all nodes belong no longer change, indicating that the local optimal cluster division has been achieved; (1-4) Grid compression: In the distribution network model, all nodes belonging to the same cluster are merged into a new node, which represents a cluster. The connection line weights within the cluster are merged into the self-loop of the new node, while the connection line weights between clusters become the connection weights between the new nodes. (1-5) Iterative compression and optimization: The compressed distribution network model is regarded as a new optimization problem, and steps (1-2) to (1-4) are restarted until the modularity of the entire distribution network model no longer changes, and the cluster division is considered to have reached the global optimum; at this time, the corresponding clusters are divided according to the calculated modularity.

3. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 1 is characterized in that: The specific steps of step (2) are as follows: (2-1) Construct an intra-cluster adjustment set within the cluster based on the current cluster division: Let step (1) obtain that the distribution network is divided into M reactive clusters and N active clusters; In the Mth i Reactive cluster or Nth i Within the active cluster, the set of all photovoltaic nodes is recorded as the photovoltaic set H, and the set of all energy storage nodes is recorded as C; Within the photovoltaic set, the photovoltaics with surplus adjustable reactive capacity are recorded as the adjustable photovoltaic node set H1, and the photovoltaics with 0 adjustable reactive capacity are recorded as the non-adjustable photovoltaic node set H2; Within the energy storage set, the energy storage devices with adjustment margin are recorded as the adjustable energy storage set C1, and the energy storage devices without adjustment margin are recorded as the non-adjustable energy storage set C2; So we define four sets: Set I: sufficient coordination ability, that is, the combination of H1+C1; Set II: insufficient coordination ability, i.e., the combination of H2+C2; Set III: Voltage over-limit set, the set where the voltage exceeds the limit; Set IV: Normal load set, the voltage does not exceed the limit; When the cluster is built, the photovoltaic cluster is first used to control the voltage in the cluster; (2-2) Determine the key nodes in the set for voltage control: In the divided clusters, cluster I is to implement the voltage regulation mechanism, in which photovoltaics will be used first for voltage regulation. In this regulation process, photovoltaic nodes with large adjustable margins will be used first. For this purpose, the photovoltaic regulation capabilities need to be ranked. The key nodes are ranked first after each ranking. The selection of photovoltaic key nodes is based on the voltage-reactive sensitivity matrix, and the node observability index is expressed as: In the formula, ω ij Indicates node observability, i is the PV number of the current cluster, j is the node number where the PV is actually located, N is the total number of nodes in the cluster, and x is the number of PVs in the current cluster set I; Considering the influence of different distributed photovoltaic reactive adjustable quantities on other node voltages, the node controllability index is expressed as: In the formula, γ ij represents the node controllability index, ι is the number of the photovoltaic with reactive power regulation capability in the current cluster set I, j is the node number where the photovoltaic is actually located in the cluster, m is the total number of photovoltaics with reactive power regulation capability in this cluster, Q i is the adjustable active power of node i; n is the number of photovoltaic nodes in the distribution network; Comprehensive evaluation index for key node selection It is expressed as: Where K1 is the weight coefficient; ω ij is the node observability index; ij Node controllability indicators; First, for each node in the network, calculate its comprehensive evaluation index Then all nodes The values ​​are sorted from largest to smallest; second, during the sorting process, the nodes at the top of the list, i.e., those with the highest Nodes with high values ​​are identified as key nodes; The nodes that exceed the limit in the cluster will be classified into set III. The reactive power adjustment required to restore the voltage of the node that exceeds the limit to normal is calculated. The adjustment is expressed as: Where ΔU1 is the difference between the voltage of the over-limit node and the voltage limit, S QUab is the reactive voltage sensitivity between the over-limit node a that needs to be regulated and the key node b to be regulated; (2-3) The photovoltaic group controls the voltage of the over-limit group: In the set of nodes that exceed the voltage limit, select the node with the largest voltage amplitude and record its voltage amplitude as V max , ΔV max The amount by which the node voltage exceeds the specified upper limit; According to the reactive voltage sensitivity matrix, in H1, find the photovoltaic PV with the largest reactive voltage sensitivity value based on observability and controllability. i As a key photovoltaic node, its sensitivity value is S QUmax ; According to the reactive voltage sensitivity, calculate V max Adjust the PV back to the normal range i Reactive output Q C , C1 will not intervene in voltage regulation when H1 has not exhausted its adjustable reactive capacity Qa; When the adjustable reactive capacity Qa in set I is greater than or equal to Qc, the V max Perform reactive power compensation, and then perform a power flow calculation within the cluster. If there are still over-limit voltage nodes after the power flow calculation, repeat the above process to update the distribution network status; When the adjustable reactive capacity Qa in set I is less than Qc, the photovoltaic power with regulation capability in set I is called to max Compensation is performed, and then the photovoltaic node is divided into set II. In the updated adjustable photovoltaic node cluster, the photovoltaic corresponding to the maximum reactive sensitivity is found to continue the reactive compensation of the process from step (2-2) to step (2-3) above; When all photovoltaics in the system are classified into set II, the energy storage equipment in the cluster will be started to adjust the system voltage; when the voltage of all voltage nodes in the cluster is within the adjustable range or there is no adjustable photovoltaic in the cluster, the voltage control process in the cluster ends; for the voltage control in the active cluster, the control rules are the same as those for reactive power control; (2-4) When the available reactive power regulation capacity of the photovoltaic system is exhausted, chemical energy storage equipment will be used to regulate the voltage beyond the limit; when the available reactive power regulation capacity of all photovoltaic devices in the system is exhausted, energy storage equipment needs to be connected to regulate the voltage to maintain system stability.

4. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 3 is characterized in that: K1＝1。 5. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 3 is characterized in that: In step (2-2), the reactive power adjustment required for the voltage of the over-limit node to return to normal is calculated, and the adjustment is expressed as: Where ΔU1 is the difference between the voltage of the over-limit node and the voltage limit, S QUab It is the reactive voltage sensitivity between the over-limit node a that needs to be adjusted and the key node b to be adjusted.

6. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 3 is characterized in that: In step (2-4), during the energy storage adjustment process, its initial state needs to be set. On this basis, the energy storage device monitors the system state to determine whether discharge is currently required and whether the relevant parameters meet the requirements after execution to ensure the voltage stability of the current system. The specific steps are as follows: (2-4-1) Initial state setting: At the beginning of step (1), the distribution network model is initialized to ensure that the monitoring and control process starts from a known state; (2-4-2) System parameter monitoring: real-time monitoring of the highest voltage point voltage u in the cluster max And the maximum reverse load rate LF max ; Among them, the maximum reverse load rate is: Where P Dmax is the maximum output of distributed power generation, P Lmax is the maximum equivalent power load at the same moment, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e Actual operating limits of transformer or line; (2-4-3) Control logic judgment: By comparing the voltage u at the highest voltage point max and maximum reverse load rate LF max The voltage threshold U max , the lowest voltage point voltage threshold U min , Maximum reverse load rate threshold LFB max and minimum reverse load rate threshold LFB min , determine whether the energy storage device needs to be started at present. If it exceeds the threshold, the energy storage device will be started; (2-4-4) Power output optimization: max >U max or LF max >LFB max In the case of u, it is determined that there is a power limit, and the system allocates the limited power to the energy storage device for charging until the energy storage device is fully charged; max min And LF max <LFB min In the case of , the system performs energy storage discharge and sets the maximum discharge ratio to 15% to ensure that the voltage does not exceed the limit;​ (2-4-5) Execution and feedback: Based on the judgment result of (2-4-4), the energy storage device is controlled to perform charging or discharging operations, and the system parameters are continuously monitored. When the adjustment capacity is not 0, the power output is continuously adjusted to adapt to the changes in the voltage demand within the cluster. If all nodes in the current cluster meet the voltage requirements, first traverse the capacity information of all energy storage devices and put the devices with an adjustment capacity of 0 into C2. The capacity of C1 that still has adjustment capacity is allocated to the voltage outside the cluster where there is a voltage limit violation, so as to adjust the voltage between clusters.

7. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 6 is characterized in that: Step (2-4-2), the maximum reverse load rate is: Where P Dmax is the maximum output of distributed power generation, P Lmax is the maximum equivalent power load at the same moment, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e This is the actual operating limit of the transformer or line.

8. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 1 is characterized in that: The specific steps of step (3) are as follows: When the internal regulation of the cluster is completed and there is no voltage exceeding the limit, the monitoring system will automatically monitor the voltage level of the entire distribution network; If the voltage of any node outside the cluster is detected to be over-limit, the monitoring system will first evaluate whether there is any remaining adjustable capacity in Set I; if so, Set I of the current cluster will deploy this capacity to adjust the voltage of the nodes outside the cluster and restore it to the normal range; once the voltage of the entire network is stabilized within the appropriate range, the cluster will stop the voltage regulation action and switch to a continuous monitoring state, tracking the voltage over-limit situation in real time to ensure the continuous and stable operation of the power grid.

9. The cluster division voltage control method of the photovoltaic energy storage distribution network according to claim 8 is characterized in that: If the voltage of any node outside the cluster is detected to be out of limit, the monitoring system will first evaluate whether there is any remaining adjustable capacity in set I; if not, it will use relay protection and automatic devices to quickly cut off some of the faulty power system components.

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