A Voltage Control Method for Cluster Division of a Photovoltaic Energy Storage Distribution Network

Abstract

The present invention relates to a cluster division voltage control method for a photovoltaic energy storage distribution network, and belongs to the technical field of distribution networks. According to the distribution network model, the method first uses the Louvain algorithm to perform initial cluster division; then adjusts cluster allocation according to real-time data to optimize voltage management; firstly, the voltage over-limit problem is handled by the reactive power regulation capability of the photovoltaic system, and when the photovoltaic regulation capability is insufficient, chemical energy storage equipment is used to regulate the voltage. The present invention optimizes the voltage by utilizing key nodes with large reactive power regulation potential, and is supplemented by energy storage equipment when necessary, thereby improving the efficiency and reliability of the distribution network operation.

Description

Technical Field

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

[0002] Due to the characteristics of low capacity, high quantity, and dispersed layout of distributed generators (DGs), local power surplus and power reverse feeding phenomena often occur, which pose a so-called curse of dimensionality to traditional centralized voltage control systems. Compared with traditional centralized control, distributed control methods exhibit higher flexibility and computational efficiency. Current cluster division control technologies still face 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 have limited capabilities in solving local power surplus and voltage control.

[0003] During the process of cluster division, there are mainly three types of methods. One is to perform clustering using electrical distance, the second is to plan the power grid clusters based on optimization theory, and the third is to divide the community according to the graph theory based on the network. There are limitations and dependent factors in these two methods, which may affect the final division result. First, when using the clustering analysis method to divide clusters, it is usually necessary to preset the clustering center and the number of clusters in advance, which may cause the result to be affected by certain subjective settings. Second, when using an optimization algorithm for cluster division, different coding methods may lead to significant differences in the division results. Finally, when using a complex community algorithm for cluster division, if the considered factors are not comprehensive enough, it may also have an adverse impact on the cluster division effect.

[0004] Therefore, there is an urgent need for a new control strategy to solve these problems, especially in cluster division and inter-cluster coordination. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies of the prior art and provide a voltage control method for cluster division of a distribution network with photovoltaic energy storage. This 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 in-cluster voltage management, and the reactive regulation capabilities of the photovoltaic set and the energy storage set are used for voltage regulation. In addition, this 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 voltage control method for cluster division of a photovoltaic energy storage distribution network, comprising the following steps:

[0008] Step (1), based on the distribution network model, use the Louvain algorithm based on the improved weight matrix to divide the distribution network into clusters, and obtain the active cluster and reactive cluster division results of the distribution network;

[0009] Step (2), based on the current cluster division results, construct active and reactive regulation sets to achieve in-cluster voltage management; relying on the key nodes of the photovoltaic set, use the photovoltaic set to eliminate voltage over-limit problems; when the reactive regulation capacity of the photovoltaic system is exhausted, switch to chemical energy storage equipment for voltage adjustment to ensure stable voltage within the cluster, and output the in-cluster voltage regulation results;

[0010] Step (3), according to the in-cluster voltage regulation results, use the adjustable set to perform inter-cluster voltage regulation 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 assignment optimization:

[0014] First, for each node i in the power grid, try to assign node i to the active cluster or reactive cluster to which its adjacent node belongs one by one;

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

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

[0017] ρ QU =ρ QUa +ρ QUb ;

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

[0019] The reactive modularity function is expressed as:

[0020]

[0021] Where, A QUij represents the improved reactive power weight matrix, k i is the sum of the weights of all edges connected to node i, k j is the sum of the weights of all edges 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 power aggregation degree index is expressed as:

[0023]

[0024] Where, c is the label 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 power 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 adjustable capacity of photovoltaic, α QUij is the reactive power voltage support ability; η QUij is the improved edge weight, which replaces the original edge weight matrix with the average value of different node sensitivities. This matrix is expressed as:

[0028]

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

[0030] The reactive power voltage support ability α of adjusting the reactive power of node i to node j QUij is expressed as:

[0031]

[0032] Where, Q QUi is the adjustable reactive power capacity of the photovoltaic inverter of node i;

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

[0034] ρ PU = ρ PUa + ρ PUb ;

[0035] Where ρ PUa is the active modularity function, and ρ PUb is the active aggregation index;

[0036] The active modularity function is expressed as:

[0037]

[0038] Where A PUij represents the improved active weight matrix;

[0039] The active aggregation index is expressed as:

[0040]

[0041] Where 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, and S Puji is the active voltage sensitivity factor of j to node j;

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

[0043] A PUij = S PUij + α PUij ;

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

[0045] The influence of the active power adjustment of node i on the active consumption capacity α PUij of node j is expressed as:

[0046]

[0047] Where P PUi represents the active power that can be provided by photovoltaic power 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 locally optimal cluster division has been reached;

[0049] (1 - 4) Power grid compression: In the distribution network model, all nodes belonging to the same cluster are merged into a new node, and this new node 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: Consider the compressed distribution network model as a new optimization problem, and restart steps (1 - 2) to (1 - 4) until the modularity of the entire distribution network model no longer changes, indicating that the cluster division reaches the global optimum; at this time, calculate the modularity to divide the corresponding clusters.

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

[0052] (2 - 1) Construct an intra - cluster regulation set within the cluster according to the current cluster division:

[0053] Let the distribution network obtained in step (1) be divided into M reactive power clusters and N active power clusters;

[0054] In the Mth i reactive power cluster or the Nth i active power cluster of the divided distribution network model, denote the set of all photovoltaic nodes as the photovoltaic set H, and the set of all energy storage nodes as C;

[0055] Within the photovoltaic set, denote the photovoltaic nodes with remaining adjustable reactive power capacity as the adjustable photovoltaic node set H1, and the photovoltaic nodes with zero adjustable reactive power capacity as the non - adjustable photovoltaic node set H2;

[0056] Within the energy storage set, denote the energy storage devices with regulation margin as the adjustable energy storage set C1, and the energy storage devices with no remaining regulation capacity as the non - adjustable energy storage set C2;

[0057] Therefore, four sets are defined:

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

[0059] Set Ⅱ: Insufficient coordination ability, that is, the combination of H2 + C2;

[0060] Set Ⅲ: Voltage violation set, the set of voltages exceeding the limit;

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

[0062] When the sets are constructed, first use the photovoltaic set to perform corresponding voltage control within the cluster;

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

[0064] In the already divided clusters, Set Ⅰ is to implement the voltage regulation mechanism, and in this regulation mechanism, photovoltaic is preferentially used for voltage regulation; during this regulation process, photovoltaic nodes with larger adjustable margins are preferentially used. Therefore, it is necessary to sort the photovoltaic regulation capabilities, and the node ranked first after each sorting is the key node;

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

[0066]

[0067] where ω ij represents the node observability, i is the PV label number in the current cluster set, j is the node number where the PV actually locates, N is the total number of nodes in this cluster, and x is the number of PVs in set Ⅰ within the current cluster;

[0068] Considering the influence degree of the reactive power adjustable amount of different distributed PVs on the voltages of other nodes, the node controllability index is expressed as:

[0069]

[0070] where γ ij represents the node controllability index, ι is the PV label number with reactive power regulation ability in set Ⅰ within the current cluster, j is the node number where the PV locates within the cluster, m is the total number of PVs with reactive power regulation ability in this cluster, Q i is the adjustable active power of node i; n is the number of PVs on the nodes in the distribution network;

[0071] The comprehensive evaluation index for key node selection is expressed as:

[0072]

[0073] where K1 is the weight coefficient; ω ij is the node observability index; γ ij is the node controllability index;

[0074] Firstly, for each node in the network, calculate its comprehensive evaluation index Then, sort the values of all nodes in descending order; secondly, during the sorting process, the nodes at the top of the list, that is, those with the highest values, are identified as key nodes;

[0075] For the over-limit nodes within the cluster, they will be classified into set Ⅲ; calculate the reactive power adjustment amount required for the over-limit node voltage to return to normal, and this adjustment amount is expressed as:

[0076]

[0077] where ΔU1 is the difference between the voltage of the over-limit node and the voltage limit value, S QUab is the reactive power-voltage sensitivity between the over-limit node a to be adjusted and the key node b for adjustment;

[0078] (2 - 3) The PV set performs voltage control on the out - of - limit set:

[0079] In the out - of - limit voltage node set, select the node with the largest voltage magnitude and record its voltage magnitude as V max , ΔV max is the amount by which the node voltage exceeds the specified upper limit value;

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

[0081] According to the reactive power - voltage sensitivity, calculate the PV max reactive power output Q required to adjust V back to the normal range i C1 will not intervene in voltage regulation when the adjustable reactive power capacity Qa in H1 has not been exhausted; C When the adjustable reactive power capacity Qa in set Ⅰ is greater than or equal to Qc, then perform reactive power compensation on V according to Qc

[0082] and then perform a power flow calculation within the cluster. If there are still out - of - limit voltage nodes after the power flow calculation, repeat the above process to update the distribution network network state; max When the adjustable reactive power capacity Qa in set Ⅰ is less than Qc, then call the PVs with adjustable capabilities in set Ⅰ to compensate V

[0083] and then divide the PV node into set II. In the updated adjustable PV node cluster, find the PV corresponding to the maximum reactive power sensitivity and continue the reactive power compensation process from step (2 - 2) to step (2 - 3); max When all PVs in the system are classified into set II, the energy storage devices within the cluster will be started to adjust the system voltage; when the voltages of all voltage nodes within the cluster are within the adjustable range or there are no adjustable PVs within the cluster, then the voltage control process within the cluster ends; for the active power voltage control within the cluster, since the control rules are the same as those for reactive power control;

[0084] When the available reactive power adjustment capacity of the system PVs is exhausted, chemical energy storage devices will be used for out - of - limit voltage regulation; when the available reactive power adjustment capacities of all PV devices in the system are exhausted, energy storage devices need to be connected for voltage regulation to maintain system stability.

[0085] (2 - 4) When the available reactive power adjustment capacity of the system PVs is exhausted, chemical energy storage devices will be used for out - of - limit voltage regulation; when the available reactive power adjustment capacities of all PV devices in the system are exhausted, energy storage devices need to be connected for voltage regulation to maintain system stability.

[0086] Furthermore, K1 = 1.

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

[0088]

[0089] In the formula, ΔU1 is the difference between the voltage of the over-limit node and the voltage limit value, S QUab is the reactive power-voltage sensitivity between the over-limit node a to be adjusted and the key node b for adjustment;

[0090] Further, in step (2-4), during the adjustment process of the energy storage, its initial state needs to be set. On this basis, the energy storage device determines whether it needs to discharge currently and whether the relevant parameters meet the requirements after execution by monitoring the system state, so as 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), initialize the distribution network model to ensure that the monitoring and control process starts from a known state;

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

[0093]

[0094] In the formula, P Dmax is the maximum output of the distributed power source, P Lmax is the maximum equivalent power consumption at the same moment, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e is the actual operation limit of the transformer or line;

[0095] (2-4-3) Control logic judgment: By comparing the voltage u of the highest voltage point max and the maximum reverse load rate LF max with the preset threshold values of the highest voltage point voltage threshold U max , the lowest voltage point voltage threshold U min , the maximum reverse load rate threshold LFB max and the minimum reverse load rate threshold LFB min , judge whether it is necessary to start the energy storage device currently. If it exceeds the threshold, the energy storage device will be started;

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

[0097] (2-4-5) Execution and feedback: According to the determination result of (2-4-4), control the energy storage device to perform charging or discharging operations, and continuously monitor the system parameters. When the adjustment capacity is not zero, continue to adjust the power output to adapt to the change of voltage demand in the cluster; if all nodes in the current cluster meet the voltage demand, first traverse all the energy storage device capacity information, put the devices with zero adjustment capacity into C2, and allocate the C1 with remaining adjustment capacity to the voltage over-limit outside the cluster for capacity allocation to perform inter-cluster voltage regulation.

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

[0099]

[0100] In the formula, P Dmax is the maximum output of the distributed power source, P Lmax is the maximum equivalent power consumption load at the same time, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e is the actual operation limit of the transformer or line.

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

[0102] When there is no voltage over-limit situation after the internal adjustment of the cluster is completed, the monitoring system will automatically monitor the voltage level of the entire distribution network;

[0103] If voltage over-limit of any node is detected outside the cluster, the monitoring system will first evaluate whether there is any remaining adjustable capacity in set Ⅰ; if there is, allocate the set Ⅰ of the current cluster to adjust the voltage of the nodes outside the cluster with these capacities to 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 enter the continuous monitoring state, and track the voltage over-limit situation in real time to ensure the continuous and stable operation of the power grid.

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

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

[0106]

[0107] In the formula: denotes taking the partial derivative of with respect to voltage, denotes the partial derivative of voltage with respect to reactive power.

[0108] Set Ⅱ (set with insufficient coordination ability): This set includes those PV and energy storage devices with limited or exhausted adjustable ability in voltage regulation. Specifically, it may include those PV power generation units that cannot be adjusted or have reached the upper limit of the adjustment capacity (non-adjustable PV node set H2), and those energy storage devices that have exhausted their energy storage or are close to full load (non-adjustable energy storage set C2). In 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 have voltages within the safe range. These nodes do not require additional voltage regulation and thus are not prioritized for regulation 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 grid state changes and causes the voltage states of these nodes to become abnormal.

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

[0111] In the present invention (2 - 3), when the adjustable reactive power capacity Qa in Set Ⅰ is greater than or equal to Qc, then reactive power compensation is performed on V according to Qc max , and then a power flow calculation within the cluster is performed. If there are still voltage - over - limit nodes after the power flow calculation, the above process is repeated to update the distribution network state; when updating, usually the distribution network state parameters are 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 min;In these cases, the system will also take the same measures beyond the threshold range. This means that even if the voltage or load is exactly equal to the threshold, the system will prophylactically charge or discharge the energy storage device to maintain the stability and safety of the power grid.

[0113] When U min <u max <U max and LFB min <LF max <LFB min , if both the voltage and the load are within the safe range and no threshold has been reached, no charging or discharging operation of the energy storage device is performed. In this case, the system maintains the status quo and does not perform additional energy transfer operations to maintain the stability and efficiency of the system.

[0114] In step (3) of the present invention, one situation is: If there is, allocate the capacities of set Ⅰ of the current cluster 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 enter the continuous monitoring state, and track the voltage over-limit situation 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 power regulation resources proposed in the present invention) are insufficient, it indicates that the distribution network may have entered the emergency state of the power system and the conventional regulation means have failed. At this time, this cluster control method fails and it is necessary to rely on relay protection and automatic devices to quickly and selectively cut off some power system components with faults.

[0115] The present invention selects chemical energy storage devices mainly for the following reasons:

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

[0117] 2. Mature technology: Chemical battery technology is relatively mature, there are various product options in 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 capacities as needed, and enhance their ability to regulate the power grid by adding battery units.

[0119] 4. Quick response: Chemical energy storage devices can respond to the power grid regulation requirements at the millisecond level and quickly adjust the output to respond to the rapid changes in power grid load and power supply.

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

[0121] (1) Data acquisition and processing:

[0122] 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 methods such as interpolation filling, model prediction, or direct deletion according to the situation to ensure the integrity of the data. Then, statistical methods are used to identify outliers in the current and voltage data, and deletion, correction, or retention and marking are selected according to their influence degree. Finally, methods such as smoothing filtering, noise reduction algorithms, or noise model modeling are used to reduce the interference of noise on data quality. After that, the cleaned 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 the photovoltaic power generation system, the expected output power is calculated using the optical power calculation formula based on real-time irradiance and temperature data, and then the voltage and current output by the photovoltaic power generation system are calculated using the output power.

[0125] For the energy storage system, first analyze its current state of charge and historical charge and discharge patterns. Based on this data, a long short-term neural network prediction model based on time series is used to calculate the expected power output, as well as the voltage and current levels that may be reached within a specific future period.

[0126] These power, voltage, and current data are substituted into the distribution network model, and the initial power generation (for the photovoltaic power generation system) or load (for the energy storage system, depending on whether it is in a discharge or charge state) for power flow calculation is set;

[0127] In addition, the voltage and load requirements of other parts of the power grid are set, thus forming a complete initial state; after the initial state is set, the nodal admittance matrix can be formed to construct a complete distribution network model;

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

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

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

[0131] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0132] In the present invention, the improved Louvain algorithm with a weight matrix is used to perform clustering division on the distribution network with access, considering the influence of active and reactive power changes on other nodes of the power grid and the coupling degree of nodes within the cluster. The improvements shown in Table 1 are achieved in the IEEE 69-node system. The optimal modularity of the 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 all optimized to a certain extent. By selecting the key nodes of the cluster, this method effectively reduces the number of control nodes. Even when the measurement equipment is limited, it can still significantly improve the efficiency of voltage control, providing an efficient solution for optimizing voltage management and enhancing the overall stability of the distributed photovoltaic distribution network.

[0133] Table 1 Comparison of different control methods

[0134] Control Mode 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 composition diagram of the clustering division voltage control framework of the distribution network with photovoltaic energy storage in the present invention;

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

[0137] Figure 3 It is a general flow chart of the clustering division voltage control method of the distribution network with photovoltaic energy storage in the present invention;

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

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

[0140] Figure 6It is the flowchart 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 the voltage calculation result diagram after the photovoltaic energy storage is connected to the IEEE69-node distribution network shown in the application example of the cluster division voltage control of the photovoltaic energy storage distribution network of the present invention;

[0142] Figure 8 It is the voltage control result diagram of the IEEE69-node distribution network using the cluster division voltage control method shown in the application example of the cluster division voltage control of the photovoltaic energy storage distribution network of the present invention;

[0143] Figure 9 It is the voltage control result diagram of the IEEE69-node distribution network using the centralized voltage control method shown in the application example of the cluster division voltage control of the photovoltaic energy storage distribution network of the present invention;

[0144] Figure 10 It is the cluster division result of the IEEE69-node distribution network using the Louvain algorithm based on the improved weight matrix shown in the application example of the cluster division voltage control of the photovoltaic energy storage distribution network of the present invention. Detailed implementation manners

[0145] The present invention will be further described in detail below in conjunction with the embodiments.

[0146] Those skilled in the art will understand that the following embodiments are only used to illustrate the present invention and should not be construed as limiting the scope of the present invention. For those not specified in the embodiments regarding specific technologies or conditions, they shall be carried out according to the technologies or conditions described in the literature in this field or according to the product specifications. For those materials or equipment not specified with the manufacturer, they are all conventional products that can be obtained by purchase.

[0147] Embodiment 1

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

[0149] Step (1), based on the distribution network model, use the Louvain algorithm based on the improved weight matrix to perform cluster division on the distribution network to obtain the active power cluster and reactive power cluster division results of the distribution network;

[0150] Step (2), based on the current cluster division result, construct active and reactive power regulation sets to achieve in-cluster voltage management; rely on the key nodes of the photovoltaic set to use the photovoltaic set to eliminate voltage over-limit problems; when the reactive power regulation capacity of the photovoltaic system is exhausted, switch to chemical energy storage devices for voltage adjustment to ensure the in-cluster voltage stability, and output the in-cluster voltage regulation results;

[0151] Step (3): Based on the voltage regulation results within the clusters, use the adjustable set to perform inter-cluster voltage regulation so as to maintain the stability of the voltage of the entire distribution network model.

[0152] Embodiment 2

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

[0154] Step (1): Based on the distribution network model, use the Louvain algorithm based on an improved weight matrix to perform cluster division on the distribution network, and obtain the active power cluster and reactive power cluster division results of the distribution network;

[0155] Step (2): Based on the current cluster division results, construct active and reactive power regulation sets to achieve in-cluster voltage management; relying on the key nodes of the photovoltaic set, use the photovoltaic set to eliminate voltage over-limit problems; when the reactive power regulation capacity of the photovoltaic system is exhausted, switch to chemical energy storage equipment for voltage adjustment to ensure the stability of the voltage within the cluster, and output the voltage regulation results within the cluster;

[0156] Step (3): Based on the voltage regulation results within the clusters, use the adjustable set to perform inter-cluster voltage regulation so as to maintain the stability of the voltage of the entire distribution network model.

[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 assignment optimization:

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

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

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

[0163] ρ QU =ρ QUa +ρ QUb ;

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

[0165] The reactive power modularity function is expressed as:

[0166]

[0167] In the formula, A QUij represents the improved reactive power weight matrix, and k i is the sum of the weights of all edges connected to node i, and k j is the sum of the weights of all edges 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 power aggregation index is expressed as:

[0169]

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

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

[0172] A QUij = η QUij + α QUij ;

[0173] In the formula: A QUij is the edge weight of node i to node j considering the adjustable capacity of photovoltaic, α QUij is the reactive power voltage support ability; η QUij is the improved edge weight, which replaces the original edge weight matrix with the average value of different node sensitivities. This matrix is expressed as:

[0174]

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

[0176] The reactive power voltage support ability α for adjusting the reactive power of node i to node j QUij is expressed as:

[0177]

[0178] In the formula, Q QUi is the adjustable reactive power 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, and ρ PUb 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, and S Puji is the active voltage sensitivity factor of j to node j for 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 PV consumption capacity, and α PUij is the active consumption capacity;

[0191] The influence of the active power adjustment of node i on the active consumption capacity α of node j PUij is expressed as:

[0192]

[0193] In the formula, P PUi represents the active power that can be provided by the PV in node i;

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

[0195] (1-4) Power 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 weights of the connection lines within the cluster are merged into the self-loop of the new node, while the weights of the connection lines between clusters become the connection weights between the new nodes;

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

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

[0198] (2-1) Construct an in-cluster regulation set within the cluster according to the current cluster division:

[0199] Let the distribution network obtained in step (1) be divided into M reactive power clusters and N active power clusters;

[0200] In the Mth i reactive power cluster or the Nth i active power cluster of the divided distribution network model, denote all photovoltaic node sets as the photovoltaic set H, and all energy storage node sets as C;

[0201] Within the photovoltaic set, denote the photovoltaic with remaining adjustable reactive power capacity as the adjustable photovoltaic node set H1, and the photovoltaic with zero adjustable reactive power capacity as the non-adjustable photovoltaic node set H2;

[0202] Within the energy storage set, denote the energy storage device with adjustment margin as the adjustable energy storage set C1, and the energy storage device with no remaining adjustment capacity as the non-adjustable energy storage set C2;

[0203] Therefore, four sets are defined:

[0204] Set Ⅰ: Sufficient coordination ability, that is, the combination of H1 + C1;

[0205] Set Ⅱ: Insufficient coordination ability, that is, the combination of H2 + C2;

[0206] Set Ⅲ: Voltage over-limit set, the set of voltages exceeding the limit;

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

[0208] When the set construction is completed, first use the photovoltaic set to perform corresponding voltage control within the cluster;

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

[0210] In the already partitioned clusters, Set Ⅰ implements a voltage regulation mechanism, in which PV is preferentially used for voltage regulation; during this regulation process, PV nodes with a large adjustable margin are preferentially used. Therefore, it is necessary to sort the PV regulation capabilities. After each sorting, the node ranked first is the key node;

[0211] The selection of PV key nodes is based on the voltage-reactive power sensitivity matrix. The node observability index is expressed as:

[0212]

[0213] where ω ij represents the node observability, i is the PV label in the set within the current cluster, j is the actual node number where the PV is located, N is the total number of nodes in this cluster, and x is the number of PVs in Set Ⅰ within the current cluster;

[0214] Considering the influence degree of the reactive power adjustable amount of different distributed PVs on the voltages of other nodes, the node controllability index is expressed as:

[0215]

[0216] where γ ij represents the node controllability index, ι is the PV label with reactive power regulation ability in Set Ⅰ within the current cluster, j is the actual node number where the PV is located in the cluster, m is the total number of PVs with reactive power regulation ability in this cluster, Q i is the adjustable active power of node i; n is the number of PVs at the nodes in the distribution network;

[0217] The comprehensive evaluation index for key node selection is expressed as:

[0218]

[0219] where K1 is the weight coefficient; ω ij is the node observability index; γ ij is the node controllability index;

[0220] First, for each node in the network, calculate its comprehensive evaluation index Then sort the values of all nodes in descending order; secondly, during the sorting process, the nodes at the top of the list, that is, those with the highest values, are identified as key nodes;

[0221] The out-of-limit nodes within the cluster will be classified into Set Ⅲ; calculate the reactive power adjustment amount required for the out-of-limit node voltages to return to normal;

[0222] (2-3) The PV set performs voltage control on the out-of-limit set:

[0223] In the out-of-limit voltage node set, select the node with the largest voltage magnitude and record its voltage magnitude as V max , ΔV max is the amount by which the node voltage exceeds the specified upper limit value;

[0224] According to the reactive power-voltage sensitivity matrix, in H1, find the PV with the largest reactive power-voltage sensitivity value according to observability and controllability i As the PV key node, its sensitivity value is S QUmax ;

[0225] According to the reactive power-voltage sensitivity, calculate the PV max required to adjust V back to the normal range i reactive power output Q C , C1 will not intervene in voltage regulation when the adjustable reactive power capacity Qa in H1 is not exhausted;

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

[0227] When the adjustable reactive power capacity Qa in set Ⅰ is less than Qc, then call the PVs with adjustable ability in set Ⅰ to compensate for V max , and then divide the PV node into set II. In the updated adjustable PV node cluster, find the PV corresponding to the maximum reactive power sensitivity and continue the reactive power compensation process of the above steps (2-2) to (2-3);

[0228] When all PVs in the system are included in set II, the energy storage devices within the cluster will be activated to adjust the system voltage; when the voltages of all voltage nodes within the cluster are within the adjustable range or there are no adjustable PVs within the cluster, the voltage control process within the cluster ends; for the active voltage control within the cluster, since the control rules are the same as those for reactive power control;

[0229] (2-4) When the available reactive power adjustment capacity of the system PVs is exhausted, chemical energy storage devices will be used for out-of-limit voltage regulation; when the available reactive power adjustment capacities of all PV devices in the system are exhausted, energy storage devices need to be connected for voltage regulation to maintain system stability.

[0230] K1 = 1.

[0231] In step (2-4), during the process of energy storage regulation, its initial state needs to be set. Based on this, the energy storage device determines whether current discharge is required and whether relevant parameters meet the requirements after execution by monitoring the system state, so as to ensure the voltage stability of the current system. The specific steps are as follows:

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

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

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

[0235] (2-4-4) Power output optimization: In the case of u max >U max or LF max >LFB max , it is determined that there is a power limit situation. The system allocates the power limit to the energy storage device for charging until the capacity of the energy storage device is full; in the case of u max <U min and LF max <LFB min , the system executes energy storage discharge and sets the maximum discharge ratio to 15% to ensure that voltage over-limit is not caused;

[0236] (2-4-5) Execution and feedback: According to the judgment result of (2-4-4), control the energy storage device to perform charging or discharging operations, and continuously monitor the system parameters. In the case that the adjustment capacity is not 0, continue to adjust the power output to adapt to the change of voltage demand in the cluster; if all nodes in the current cluster meet the voltage demand, first traverse all the energy storage device capacity information, put the devices with an adjustment capacity of 0 into C2, and allocate the capacity of C1 with remaining adjustment capacity to the nodes outside the cluster with voltage over-limit for inter-cluster voltage regulation.

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

[0238] When there is no voltage over-limit situation after the internal adjustment of the cluster is completed, the monitoring system will automatically monitor the voltage level of the entire distribution network;

[0239] If a voltage over-limit of any node is detected outside the cluster, the monitoring system will first evaluate whether there is any remaining adjustable capacity in Set Ⅰ; if there is, allocate the capacity in Set Ⅰ of the current cluster 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 an appropriate range, the cluster will stop the voltage regulation action and enter the 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] As Figure 1-7 shown, a voltage control method for cluster division of a distribution network with photovoltaic energy storage includes the following steps:

[0242] Step (1), based on the distribution network model, use the Louvain algorithm based on the improved weight matrix to divide the distribution network into clusters, and obtain the active power cluster and reactive power cluster division results of the distribution network;

[0243] Step (2), based on the current cluster division results, construct active and reactive power regulation sets to achieve voltage management within the cluster; relying on the key nodes of the photovoltaic set, use the photovoltaic set to eliminate voltage over-limit problems; when the reactive power regulation capacity of the photovoltaic system is exhausted, switch to chemical energy storage equipment for voltage adjustment to ensure the voltage stability within the cluster, and output the voltage regulation results within the cluster;

[0244] Step (3), according to the voltage regulation results within the cluster, use the adjustable set to perform voltage regulation between clusters so that the voltage of the entire distribution network model is maintained stable.

[0245] Furthermore, it may include the following:

[0246] Collect and process the distribution network data to set the initial state and construct 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, try to allocate node i to the active power cluster or reactive power cluster to which its adjacent nodes belong one by one;

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

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

[0252] ρ QU = ρ QUa + ρ QUb ;

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

[0254] The reactive power modularity function is expressed as:

[0255]

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

[0257] The reactive power aggregation index is expressed as:

[0258]

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

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

[0261] A QUij = η QUij + α QUij ;

[0262] In the formula: A QUij is the edge weight of node i to node j considering the adjustable capacity of photovoltaic, α QUij is the reactive power voltage support ability; η QUij is the improved edge weight, which replaces the original edge weight matrix with the average value of different node sensitivities. This matrix is expressed as:

[0263]

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

[0265] Among them, the reactive power sensitivity matrix can be expressed as:

[0266]

[0267] In the formula: represents taking the partial derivative with respect to the voltage, represents the partial derivative of voltage with respect to reactive power.

[0268] The ability α of adjusting the reactive power of node i to support the reactive power - voltage of node j QUij is expressed as:

[0269]

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

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

[0272] ρ PU =ρ PUa +ρ PUb ;

[0273] In the formula, ρ PUa is the active modularity function, and ρ PUb is the active aggregation degree 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 degree index is expressed as:

[0278]

[0279] In the formula, c is the label of the current cluster, S PUij is the active power - voltage sensitivity factor of node i to node j, b is the number of total clusters, and S Puji is the active power - voltage sensitivity factor of node j to 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 considering the local PV consumption capacity for node j, and α PUij is the active consumption capacity;

[0283] The influence of the active power adjustment of node i on the active consumption capacity α of node j PUij is expressed as:

[0284]

[0285] In the formula, P PUi represents the active power that the PV in node i can provide;

[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 reached;

[0287] (1 - 4) Power 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: Consider the compressed distribution network model as a new optimization problem, and restart steps (1 - 2) to (1 - 4) until the modularity of the entire distribution network model no longer changes, indicating that the cluster division reaches the global optimum; at this time, the corresponding clusters are obtained by calculating the modularity.

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

[0290] (2 - 1) Construct the in - cluster regulation set within the cluster according to the current cluster division:

[0291] Let the distribution network obtained in step (1) be divided into M reactive power clusters and N active power clusters;

[0292] In the M i th reactive power cluster or the N i th active power cluster of the divided distribution network model, denote the set of all PV nodes as the PV set H, the set of all energy storage nodes as C, and the set of nodes with normal voltage; all nodes whose voltage limits do not exceed the normal nodes are denoted as the PV set

[0293] Inside the photovoltaic set, the photovoltaics with surplus adjustable reactive power capacity are denoted as the adjustable photovoltaic node set H1, and the photovoltaics with zero adjustable reactive power capacity are denoted as the non-adjustable photovoltaic node set H2;

[0294] Inside the energy storage set, the energy storage devices with adjustment margin are denoted as the adjustable energy storage set C1, and the energy storage devices with no remaining adjustment capacity are denoted as the non-adjustable energy storage set C2;

[0295] Therefore, four sets are defined:

[0296] Set Ⅰ: Sufficient coordination ability, that is, the combination of H1 + C1;

[0297] Set Ⅱ: Insufficient coordination ability, that is, the combination of H2 + C2;

[0298] Set Ⅲ: Voltage over-limit set, the set of voltages exceeding the limit;

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

[0300] When the sets are constructed, first use the photovoltaic set to perform corresponding voltage control within the cluster;

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

[0302] In the already divided cluster, Set Ⅰ is to implement the voltage regulation mechanism, and in this regulation mechanism, photovoltaics will be preferentially used for voltage regulation; during this regulation process, the photovoltaic nodes with larger adjustable margins will be preferentially used. Therefore, it is necessary to sort the photovoltaic regulation capabilities. Each time after sorting, the one ranked first is the key node;

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

[0304]

[0305] In the formula, ω ij represents the node observability, i is the photovoltaic label in the set within the current cluster, j is the actual node number where the photovoltaic is located, N is the total number of nodes in this cluster, and x is the number of photovoltaics in Set Ⅰ within the current cluster;

[0306] Considering the influence degree of the reactive power adjustable amount of different distributed photovoltaics on the voltages of other nodes, the node controllability index is expressed as:

[0307]

[0308] In the formula, γ ijIndicates the node controllability index. ι is the label of the PV with reactive power regulation ability in set Ⅰ within the current cluster, j is the node number where the PV is actually located in the cluster, m is the total number of PVs with reactive power regulation ability in this cluster, and Q i is the adjustable active power of node i; n is the number of PVs at nodes in the distribution network;

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

[0310]

[0311] In the formula, K1 is the weight coefficient; ω ij is the node observability index; γ ij Node controllability index;

[0312] First, for each node in the network, we calculate its comprehensive evaluation index After the calculation is completed, the values of all nodes are sorted in descending order. Secondly, during the sorting process, the nodes at the top of the list, that is, those with the highest values, are identified as key nodes.

[0313] The out-of-limit nodes within the cluster will be classified into set Ⅲ; calculate the reactive power adjustment amount required for the out-of-limit node voltage to return to normal, and this adjustment amount is expressed as:

[0314]

[0315] In the formula, ΔU1 is the difference between the voltage of the out-of-limit node and the voltage limit value, and S QUab is the reactive power-voltage sensitivity between the out-of-limit node a to be adjusted and the key node b for adjustment;

[0316] (2 - 3) The PV set conducts voltage control on the out-of-limit set:

[0317] In the out-of-limit voltage node set, take the node with the largest voltage amplitude and record its voltage amplitude as V max , and ΔV max is the amount by which the node voltage exceeds the specified upper limit value;

[0318] According to the reactive power-voltage sensitivity matrix, in H1, find the PV with the largest reactive power-voltage sensitivity value according to observability and controllability, which is PV i as the PV key node, and 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] Wherein, P Dmax is the maximum output of the distributed power source, and P Lmax is the maximum equivalent power consumption load at the same time, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e is the actual operation limit of the transformer or line;

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

[0331] (2-4-4) Power output optimization: When u max >U max or LF max >LFB max , it is determined that there is a power limit situation. The system distributes the power limit to the energy storage device for charging until the capacity of the energy storage device is full; When u max <U min and LF max <LFB min , the system performs energy storage discharge and sets the maximum discharge ratio to 15% to ensure that voltage over-limit is not caused;

[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 those beyond the threshold range. This means that even if the voltage or load is exactly equal to the threshold, the system will prophylactically charge or discharge the energy storage device to maintain the stability and safety of the power grid.

[0333] When U min <u max <U max and LFB min <LF max <LFB minWhen 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 without performing additional energy transfer operations to maintain the stability and efficiency of the system.

[0334] (2-4-5) Execution and feedback: According to the determination result of (2-4-4), control the energy storage device to perform charging or discharging operations, and continuously monitor the system parameters. When the adjustment capacity is not zero, continue to adjust the power output to adapt to the change of voltage demand within the cluster; if all nodes in the current cluster meet the voltage demand, first traverse all the energy storage device capacity information, put the devices with zero adjustment capacity into C2, and allocate the adjustment capacity of C1 with remaining adjustment capacity to the nodes outside the cluster with voltage over-limit for inter-cluster voltage regulation.

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

[0336] When there is no voltage over-limit situation after the internal adjustment of the cluster, the monitoring system will automatically monitor the voltage level of the entire distribution network;

[0337] If any node's voltage is detected to be over-limit outside the cluster, the monitoring system will first evaluate whether there is any remaining adjustable capacity in set Ⅰ; if there is, allocate the adjustable capacity of the current cluster's set Ⅰ 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 enter the continuous monitoring state to track 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 this system (such as the active and reactive power regulation resources proposed in the present invention) are insufficient, it indicates that the distribution network may have entered the emergency state of the power system and the conventional regulation means have failed. At this time, this cluster control method fails and it is necessary to quickly and selectively cut off some faulty power system components with the help of relay protection and automatic devices.

[0339] The present invention takes the IEEE69-node distribution network as an example to verify the effectiveness of the proposed voltage control method for clustering the distribution network with photovoltaic energy storage. The system base capacity S base = 10 MVA, the system base voltage U base= 12.66 kV. The system contains 69 nodes, and the PV access nodes are 15, 20, 25, 34, 45, 49, 57, 61, 65, 67. The access capacities are 0.8, 1.2, 0.8, 0.53, 0.46, 0.32, 0.8, 0.53, 1.2, 0.8 MVA respectively. The set minimum power factor is 0.95; energy storage batteries are installed at nodes 20 and 65, with an installed capacity of 0.15 MW, and the state of charge of the energy storage battery is [0.15, 0.85]; the set normal voltage level is [0.9, 1.07]. Select the day with the maximum light intensity in July. The daily load curves of residential and commercial users in the distribution network conform to the residential load, commercial load, and industrial load in July. To illustrate the flexibility and rapidity of the voltage control strategy based on cluster division, the present invention uses a centralized control method without cluster division for global voltage control and compares and analyzes the simulation results of the two control methods. When PV and energy storage are connected to the distribution network, a centralized control method is used for voltage control. The voltage fluctuation situation of the system after connecting distributed PV throughout the day is as Figure 7 shown. Due to the volatility of distributed PV output, voltage violations occur at some moments; the voltage fluctuation situation after centralized control throughout the day is as Figure 8 shown, and the voltage fluctuation situation throughout the day after the control strategy of the present invention is as Figure 9 shown. Although both 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 beneficial to the stable operation of the power system.

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

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

[0342] Cluster Number Reactive Power Cluster Node Number Active Power 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 reactive power cluster division results of different algorithms

[0344]

[0345]

[0346] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments, and what is described in the above-mentioned embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A voltage control method for cluster division of a photovoltaic energy storage distribution network, characterized in that It includes the following steps: Step (1), based on the distribution network model, use the Louvain algorithm based on the improved weight matrix to cluster the distribution network, and obtain the active and reactive cluster division results of the distribution network; Step (2), based on the current cluster division results, construct active and reactive regulation sets to achieve in-cluster voltage management; relying on the key nodes of the photovoltaic set, use the photovoltaic set to eliminate voltage over-limit problems; when the reactive regulation capacity of the photovoltaic system is exhausted, switch to chemical energy storage equipment for voltage adjustment to ensure in-cluster voltage stability, and output the in-cluster voltage regulation results; Step (3), according to the in-cluster voltage regulation results, use the adjustable set to perform inter-cluster voltage regulation to keep the voltage of the entire distribution network model stable; The specific steps of Step (2) are as follows: (2-1) Construct an in-cluster regulation set within the cluster according to the current cluster division: Let the distribution network obtained in Step (1) be divided into M reactive clusters and N active clusters; In the Mth i reactive power cluster or the Nth i active power cluster of the completed distribution network model, denote the set of all photovoltaic nodes as the photovoltaic set H, and denote the set of all energy storage nodes as C; Within the photovoltaic set, the photovoltaic with remaining adjustable reactive capacity is denoted as the adjustable photovoltaic node set H1, and the photovoltaic with zero adjustable reactive capacity is denoted as the non-adjustable photovoltaic node set H2; Within the energy storage set, the energy storage device with adjustment margin is denoted as the adjustable energy storage set C1, and the energy storage device without remaining adjustment capacity is denoted as the non-adjustable energy storage set C2; Therefore, four sets are defined: Set Ⅰ: Sufficient coordination ability, that is, the combination of H1 + C1; Set Ⅱ: Insufficient coordination ability, that is, the combination of H2 + C2; Set Ⅲ: Voltage over-limit set, the set of voltages exceeding the limit; Set Ⅳ: Normal load set, the voltage does not exceed the limit; When the set construction is completed, first use the photovoltaic set to perform corresponding voltage control within the cluster; (2-2) Determine the key nodes within the set for voltage control: In the already divided cluster, Set Ⅰ is to implement the voltage regulation mechanism, and in this regulation mechanism, the photovoltaic will be preferentially used for voltage regulation; during this regulation process, the photovoltaic node with a larger adjustable margin will be preferentially used. Therefore, it is necessary to sort the photovoltaic regulation capabilities, and the one ranked first after each sorting is the key node; The selection of photovoltaic key nodes is based on the voltage-reactive power sensitivity matrix, and the node observability index is expressed as: where ω ij represents the node observability, i is the photovoltaic label aggregated within the current cluster, j is the node number where the photovoltaic actually locates, N is the total number of nodes in this cluster, and x is the number of photovoltaics in Set Ⅰ within the current cluster; Considering the influence degree of the reactive adjustable amount of different distributed photovoltaics on the voltages of other nodes, the node controllability index is expressed as: Where, γ ij represents the node controllability index, ι is the label of the PV with reactive power regulation ability in set Ⅰ within the current cluster, j is the number of the actual node where the PV is located in the cluster, m is the total number of PVs with reactive power regulation ability in this cluster, Q i is the adjustable active power of node i; n is the number of PVs at 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; γ ij is the node controllability index; First, for each node in the network, calculate its comprehensive evaluation index Then, for all nodes values are sorted in descending order; Secondly, during the sorting process, the nodes at the top of the list, i.e., those with the highest values, are identified as key nodes The over-limit nodes within the cluster will be classified into Set Ⅲ; calculate the reactive adjustment amount required for the over-limit node voltage to return to normal, and this adjustment amount is expressed as: where ΔU1 is the difference between the voltage of the over-limit node and the voltage limit value, and S QUab is the reactive power-voltage sensitivity between the over-limit node a to be adjusted and the key node b for adjustment; (2-3) The photovoltaic set performs voltage control on the over-limit set: Among the set of over-limit voltage nodes, select the node with the largest voltage magnitude and record its voltage magnitude as V max , ΔV max is the amount by which the node voltage exceeds the specified upper limit value; According to the reactive power-voltage sensitivity matrix, in H1, find the PV with the largest reactive power-voltage sensitivity value based on observability and controllability i As the key PV node, its sensitivity value is S QUmax ; According to the reactive power voltage sensitivity, calculate the PV max reactive power output Q i required to adjust V C back to the normal range. C1 will not intervene in voltage regulation when H1 has not exhausted the adjustable reactive power capacity Qa; When the adjustable reactive power capacity Qa in set Ⅰ is greater than or equal to Qc, then perform reactive power compensation on V according to Qc, and then perform a power flow calculation within the cluster once. If there are still overvoltage nodes after the power flow calculation, repeat the above process to update the network state of the distribution network; max When the adjustable reactive power capacity Qa in set Ⅰ is greater than or equal to Qc, then perform reactive power compensation on V according to Qc, and then perform a power flow calculation within the cluster once. If there are still overvoltage nodes after the power flow calculation, repeat the above process to update the network state of the distribution network; When the adjustable reactive power capacity Qa in set Ⅰ is less than Qc, the photovoltaic with adjustable capacity in set Ⅰ is called to compensate for V max Then, the photovoltaic node is divided into set II. In the updated adjustable photovoltaic node cluster, the photovoltaic corresponding to the maximum reactive power sensitivity is found to continue the reactive power compensation process from step (2-2) to step (2-3) above; When all the photovoltaics in the system are classified into Set Ⅱ, the energy storage devices within the cluster will be started to regulate the system voltage; when the voltages of all voltage nodes within the cluster are within the adjustable range or there is no adjustable photovoltaic within the cluster, the in-cluster voltage control process ends; for the in-cluster voltage control of the active cluster, since the control rules are the same as those for reactive control; (2-4) When the available reactive power regulation capacity of the system's photovoltaic power generation is exhausted, chemical energy storage devices will be used for over-limit voltage regulation; when the available reactive power regulation capacities of all photovoltaic devices in the system are exhausted, energy storage devices need to be connected for voltage regulation to maintain system stability; In step (2-4), during the regulation process of the energy storage, its initial state needs to be set. Based on this, the energy storage device monitors the system state to determine whether current discharge is required and whether relevant parameters meet the requirements after execution, so as to ensure the voltage stability of the current system. The specific steps are as follows: (2-4-1) Initial state setting: At the start of step (1), initialize the distribution network model to ensure that the monitoring and control process starts from a known state; (2-4-2) System parameter monitoring: Real-time monitoring of the voltage u at the highest voltage point 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 sources, P Lmax is the maximum equivalent power consumption at the same time, that is, the maximum load minus the maximum output of other power sources except distributed power sources; S e is the actual operation limit of transformers or lines; (2-4-3) Control logic judgment: By comparing the voltage u of the highest voltage point max and the maximum reverse load rate LF max with the preset threshold voltage threshold U of the highest voltage point max , the voltage threshold U of the lowest voltage point min , the maximum reverse load rate threshold LFB max and the minimum reverse load rate threshold LFB min , it is judged whether the energy storage device needs to be started currently. If the threshold is exceeded, the energy storage device will be started; (2-4-4) Power output optimization: When u max >U max or LF max >LFB max , it is determined that there is a power rationing situation. The system allocates the rationed power to the energy storage device for charging until the capacity of the energy storage device is fully charged; when u max <U min and LF max <LFB min , the system performs energy storage discharge and sets the maximum discharge ratio to 15% to ensure that voltage over-limit is not caused. (2-4-5) Execution and feedback: According to the determination result of (2-4-4), control the energy storage device to perform charging or discharging operations, and continuously monitor system parameters. When the regulation capacity is not zero, continue to adjust the power output to adapt to the changes in voltage demand within the cluster; if all nodes in the current cluster meet the voltage demand, first traverse all the capacity information of the energy storage devices, put the devices with zero regulation capacity into C2, and allocate the C1 with remaining regulation capacity to the clusters outside the cluster with voltage over-limit for capacity allocation to perform inter-cluster voltage regulation.

2. The voltage control method for cluster division of a photovoltaic energy storage distribution network according to claim 1, wherein 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, try to allocate node i to the active or reactive power cluster to which its adjacent node belongs one by one; In each attempt, calculate the change ρ in the comprehensive evaluation index of the system before and after the allocation; Record the adjacent node with the largest ρ value; If ρ>0, then allocate node i to the active or reactive power cluster where the adjacent node is located; If there is no improvement, keep node i unchanged in its original active or reactive power cluster; Among them, the reactive power comprehensive evaluation index ρ QU is as follows: ρ QU = ρ QUa + ρ QUb ; where ρ QUa is the reactive power modularity function, and ρ QUb is the reactive power aggregation index; The reactive modularity function is expressed as: Wherein, A QUij represents the improved reactive power weight matrix, and k i is the sum of the weights of all edges connected to node i, and k j is the sum of the weights of all edges 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: where c is the label of the current cluster, b is the total number of clusters, n is the total number of nodes, and S QUij is the voltage sensitivity factor of node i to node j; The improved reactive edge weight matrix is expressed as: A QUij = η QUij + α QUij ; Where: A QUij is the edge weight from node i considering the adjustable capacity of photovoltaic to node j, and α QUij is the reactive power voltage support ability; η QUij is the improved edge weight, which replaces the original edge weight matrix with the average value of different node sensitivities. This matrix is expressed as: where S QUij is the reactive power voltage sensitivity factor of node i to node j, and S QUji is the reactive power voltage sensitivity factor of node j to node i; Reactive power voltage support ability α of adjusting node i for node j QUij It is expressed as: where Q QUi is the adjustable reactive power capacity of the PV inverter at node i; The comprehensive active modularity evaluation index is expressed as: ρ PU = ρ PUa + ρ PUb ; where ρ PUa is the active modularity function, and ρ PUb is the active aggregation degree index; The active modularity function is expressed as: where A PUij represents the improved active power weight matrix; The active aggregation index is expressed as: where 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 i to node j; The improved active edge weight matrix is expressed as: A PUij = S PUij + α PUij ; Where A PUij is the active power edge weight matrix from node i to node j considering the local PV accommodation capacity, and α PUij is the active power accommodation capacity; Active power adjustment of node i on active power absorption capacity α of node j 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 locally optimal cluster division has been reached; (1-4) Power grid compression: In the distribution network model, merge all nodes belonging to the same cluster into a new node, and this new node 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: Regard the compressed distribution network model as a new optimization problem, and restart steps (1-2) to (1-4) until the modularity of the entire distribution network model no longer changes, and consider that the cluster division reaches the global optimum; at this time, calculate the corresponding clusters according to the modularity obtained.

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

5. The voltage control method for cluster division of a photovoltaic energy storage distribution network according to claim 1, wherein Step (2-4-2), the maximum reverse load rate is: Wherein, P Dmax is the maximum output of the distributed power source, and P Lmax is the maximum equivalent power consumption at the same moment, that is, the maximum load minus the maximum output of other power sources except the distributed power source; S e is the actual operation limit of the transformer or line.

6. The voltage control method for cluster division of a photovoltaic energy storage distribution network according to claim 1, wherein The specific steps of step (3) are as follows: When there is no voltage over-limit situation after the adjustment within the cluster is completed, the monitoring system will automatically monitor the voltage level of the entire distribution network; If a voltage over-limit of any node is detected outside the cluster, the monitoring system will first evaluate whether there is any remaining adjustable capacity in set Ⅰ; if there is, allocate the set Ⅰ of the current cluster to 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 the continuous monitoring state to track the voltage over-limit situation in real time to ensure the continuous and stable operation of the power grid.

7. The voltage control method for cluster division of a photovoltaic energy storage distribution network according to claim 6, characterized in that, If a voltage over-limit of any node is detected outside the cluster, the monitoring system will first evaluate whether there is any remaining adjustable capacity in set Ⅰ; if not, it is necessary to use relay protection and automatic devices to quickly cut off some of the faulty power system components.

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