Voltage Coordination Control Method for Large-Scale Distributed Photovoltaic-DC Hybrid Distribution Networks Based on Energy Storage

Abstract

This application relates to the field of power system technology, and in particular to a voltage coordination control method for large-scale distributed photovoltaic (DPV) AC / DC hybrid distribution networks based on energy storage. The method divides the distribution network into reactive power clusters and active power clusters using aggregation degree, achieving coordinated and optimized voltage regulation of active and reactive power. The reactive power clusters are used to control the power of photovoltaic inverters and / or voltage source converters according to different scenarios, while the active power clusters are allocated power considering the energy storage capacity and state of charge of the energy storage devices. This coordinates the voltage source converters and energy storage devices, thereby alleviating the voltage limit exceeding problem caused by large-scale DPV access to AC / DC hybrid distribution networks. The aim is to solve the problem of voltage limit exceeding in AC / DC hybrid distribution networks caused by large-scale distributed photovoltaic (DPV) access.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a voltage coordination control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage. Background Technology

[0002] With the large-scale and high-proportion decentralized integration of distributed photovoltaic (DPV) systems into AC / DC hybrid distribution networks, the operating characteristics of distribution networks have changed significantly, bringing a series of technical challenges. Among these challenges, the randomness, intermittency, and volatility of photovoltaic power generation, as well as the spatiotemporal uncertainty of load distribution, can lead to continuous or transient voltage deviations at local nodes in the distribution network. This can cause voltage over-limit issues, which in severe cases may trigger malfunctions of protection devices, degrade power quality, and even threaten the safe and stable operation of the system.

[0003] Traditional centralized voltage control methods based on master stations or regional control centers rely on comprehensive global information acquisition and complex optimization model calculations. However, when faced with a large number of widely distributed photovoltaic units, centralized control methods have inherent limitations in terms of communication transmission delay, data acquisition frequency, and calculation response speed. They are unable to achieve rapid tracking and real-time suppression of photovoltaic power fluctuations and lack the ability to cope with sudden voltage problems.

[0004] In view of this, this application proposes a voltage collaborative control method for large-scale distributed photovoltaic AC / DC hybrid distribution networks based on energy storage, aiming to overcome the problem of voltage over-limit phenomenon in AC / DC hybrid distribution networks caused by large-scale decentralized access of distributed photovoltaics. Summary of the Invention

[0005] The main objective of this application is to provide a voltage collaborative control method for large-scale distributed photovoltaic AC / DC hybrid distribution networks based on energy storage, aiming to solve the problem of voltage over-limit phenomena that easily occur in AC / DC hybrid distribution networks caused by the large-scale decentralized access of distributed photovoltaics.

[0006] To achieve the above objectives, this application provides a voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage, the method comprising:

[0007] S10, the AC / DC hybrid distribution network after regional decoupling is divided into reactive power clusters and active power clusters by the aggregation degree, wherein the aggregation degree includes the aggregation degree of active power clusters and the aggregation degree of reactive power clusters.

[0008] S20, the reactive power cluster is controlled by a photovoltaic inverter and / or a voltage source converter; and / or the active power cluster is controlled by a voltage source converter and an energy storage device, wherein the energy storage device performs power distribution based on the current energy storage power and the current state of charge.

[0009] Optionally, S10 includes:

[0010] S11, Each node in the AC / DC hybrid distribution network is regarded as an initial cluster, and the initial aggregation degree of the initial cluster is calculated based on the electrical characteristic quantities of the node;

[0011] S12, randomly select a target node i, calculate the aggregation degree difference between the target node i and the adjacent node j, if the aggregation degree difference is positive, then merge the target node i and the adjacent node j, traverse each node in the AC / DC hybrid distribution network to form a new cluster;

[0012] S13, detect whether there is a single-node cluster in the new cluster. If so, iteratively calculate the modularity improvement rate of the single-node cluster joining the neighboring cluster. When the modularity improvement rate of three consecutive iterations is less than the improvement rate threshold or the number of iterations reaches the preset value, output the reactive cluster and active cluster divided in the current iteration as the final cluster division scheme.

[0013] Optionally, the calculation expression for the active cluster aggregation degree includes:

[0014]

[0015] In the formula, D is the sum of the mean of the active power sensitivity matrix and the active power support capacity of node j. PUij and D PUji P represents the active voltage sensitivity factor of node i to node j and node j to node i, respectively; ESSi For adjustable active power capacity of energy storage; P VSCi The VSC outputs active power. k i and k j They are respectively with nodes i and j The sum of the weights of the connected edges, where m represents the number of nodes in PQ and n represents the total number of nodes excluding the balancing nodes. Take 1 or 0, δ ij =1 indicates a node i and j Directly connected, δ ij =0 indicates a node i andj Not connected Represents a node i and j The weight of the connecting edge (edge ​​weight) is determined by the reactive-voltage or active-voltage sensitivity matrix;

[0016] in, ;

[0017] In the formula, The active voltage sensitivity factor represents node j itself.

[0018] The calculation expression for the reactive power cluster aggregation degree includes:

[0019]

[0020] In the formula, D QUij and D QUji Q represents the reactive voltage sensitivity factor of node i to node j and node j to node i, respectively; PV,i The reactive power capacity of the PV inverter at node i is adjustable; Q VSC,i The VSC adjustable reactive power capacity of node i;

[0021] in, ;

[0022] In the formula, This represents the reactive voltage sensitivity factor of node j itself.

[0023] Optionally, the energy storage device performs power allocation based on the current energy storage capacity and the current state of charge, including:

[0024] The set C of voltage-over-limit nodes in the active power cluster is identified through power flow calculation. k ={C1, C2, ..., C n The adjustment power corresponding to the difference between the voltage recovery amount and the current voltage of the voltage-over-limit node is taken as the target adjustment power P required by each cluster. Ck ;

[0025] The current state of charge of the energy storage device is in cluster C. k When implementing power distribution within the system, the following constraints must be met:

[0026] SOC Ck,i,min ≤SOC Ck,i ±ΔSOC Ck,i ≤SOC Ck,i,max

[0027] In the formula, SOC Ck,i State of charge of energy storage system at point i within the cluster ， ΔSOC Ck,iThe power allocated to the energy storage system at point i within the cluster, SOC Ck,i,max The State of Charge (SOC) is the upper limit of the energy storage system at point i within the cluster. Ck,i,min This represents the state-of-charge limit of the energy storage system at point i within the cluster;

[0028] If ΣΔSOC Ck,i ≤C k If the capacity is such that the net power requirement P of the active power cluster is calculated, then... net =ΣP Ck Power is allocated proportionally to the available energy storage units in the energy storage device. If the local energy storage power of the available energy storage unit is insufficient, power support is requested from adjacent energy storage units.

[0029] If ΣΔSOC Ck,i >C k The excess capacity will be borne by the energy storage devices of adjacent coupled active power clusters.

[0030] Optionally, the energy storage device includes an energy storage optimization voltage regulation mathematical model, and the objective function of the energy storage optimization voltage regulation mathematical model includes at least one of the following:

[0031] (1) Objective function f1 for minimizing voltage deviation:

[0032]

[0033] (2) Objective function f2 for minimizing network loss

[0034]

[0035] In the formula, N AC N DC These are the sets of AC and DC nodes, respectively; U i,t Let L be the voltage amplitude at node i at time t; AC L DC These are collections of AC and DC branches, respectively. Indicates the rated voltage. R represents the square of the current in branch l at time t; l Branch resistance; VSC loss;

[0036] (3) The economically optimal objective function for daily operation of energy storage, f3

[0037]

[0038] In the formula, D TOU (t) represents the time-of-use electricity price at time t; P c,s (t) and P d,s (t) represents the charging and discharging power of the s-th energy storage unit at time t; Ns This represents the number of distributed energy storage devices.

[0039] Optionally, the constraints of the energy storage optimization voltage regulation mathematical model include at least one of the following:

[0040] (1) Power constraints of distributed energy storage connected to cluster k:

[0041]

[0042] In the formula, P ESS,i,k N represents the distributed energy storage power connected to node i within the k-th cluster; k Let k be the number of nodes in the k-th cluster.

[0043] (2) Communication constraints, including

[0044] Node voltage amplitude constraints:

[0045]

[0046] Current constraint:

[0047]

[0048] Power constraints:

[0049]

[0050]

[0051] Transformer turns ratio constraint:

[0052]

[0053] Current constraints:

[0054]

[0055] In the formula, U i I i P i Q i Let represent the voltage amplitude, current amplitude, active power, and reactive power of the i-th node, respectively. This indicates the transformer turns ratio; the subscripts min and max represent the upper and lower limits of the corresponding parameters. i (t), Q i (t) represents the active and reactive power of the load at node i at time t; U i (t), U j (t) represents the voltage amplitudes at nodes i and j at time t, respectively; δ ij (t) represents the phase angle difference between nodes i and j at time t; G ij Bij Let P be the real and imaginary parts of the element in the i-th row and j-th column of the node admittance matrix, respectively; DPV,i (t), Q DPV,i (t) represents the active and reactive power of the distributed photovoltaic system at node i at time t; P ESS,i (t) represents the active power of energy storage at node i at time t; N represents the total number of nodes;

[0056] (3) DC constraint:

[0057]

[0058] In the formula, P s Q s U dc and I dc The active power, reactive power, DC voltage, and DC current injected from the AC bus to the DC side are δ, where δ is the phase angle, and the subscripts min and max indicate the upper and lower limits of the corresponding parameters.

[0059] (4) Cluster node voltage and energy storage active power constraints, including:

[0060] Cluster node voltage constraints:

[0061]

[0062] Energy storage active power constraints:

[0063]

[0064] In the formula, ΔU i,t The voltage amplitude difference between node i and the rated voltage at time t is represented; U0 represents the per-unit voltage reference value at the beginning of the line; P n (t) and Q n (t) represent the load consumption of active power and reactive power, respectively; P DPV,n (t) and Q DPV,n (t) represent the active power injection and reactive power injection of distributed photovoltaic power, respectively; P VSC,n (t) represents the converter regulating output power; R m and X m Both represent the impedance characteristics of the m-node segment; P ESS,n For node n, the bidirectional power flow is stored, where it is positive during discharge, S min and S max These represent the minimum and maximum energy storage capacities, respectively; T is the time series modeling period, and Δt represents the time resolution.

[0065] Optionally, S20 includes:

[0066] When an abnormal voltage is detected at the target node, the reactive power cluster is determined, and the target photovoltaic inverter closest to the over-limit node is called to execute the reactive power balance control strategy. If the reactive power regulation capability of the target photovoltaic inverter is insufficient, the voltage source converter is called to perform constant DC voltage control and constant reactive power control.

[0067] In the active power cluster, the voltage source converter is controlled to switch to constant DC voltage and constant active power control to adjust the cross-regional active power, and power distribution is performed based on the current energy storage power and current state of charge of the energy storage device.

[0068] When the fluctuation of distributed photovoltaic power is detected to be greater than the preset fluctuation threshold, the reactive power cluster is activated to perform reactive power regulation; when the reactive power regulation capability of the reactive power cluster is insufficient, the active power cluster is activated to perform active power regulation.

[0069] Optionally, the power flow solution for the AC component of the AC / DC hybrid distribution network is established using the following mathematical model:

[0070]

[0071] In the formula, Δθ and ΔU represent the phase angle change and voltage change at the node, respectively; D Pθ D Qθ These represent the changes in the node phase angle when a unit amount of active and reactive power is injected into the node, respectively; D PU D QU ΔP and ΔQ represent the changes in node voltage amplitude when a unit amount of active power and reactive power are injected into the node, respectively.

[0072] Wherein, the ∆U, ∆P, and ∆Q of node i satisfy the following expression:

[0073]

[0074] In the formula, ;

[0075] The impact of distributed photovoltaic power generation on the voltage of node i after being connected to the AC distribution network satisfies the following expression:

[0076]

[0077] In the formula, U i1 The voltage at node i after distributed photovoltaic power is connected to the AC distribution network; D PUij D QUij These are the active voltage sensitivity factor and reactive voltage sensitivity factor of node i to node j, respectively; U i0 Let ΔP be the initial voltage at node i.j ΔQ represents the change in injected active power at the node. j This represents the change in the injected reactive power at the node.

[0078] In addition, to achieve the above objectives, this application also provides a computer system, the computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage as described in any of the preceding claims.

[0079] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage as described in any of the preceding claims.

[0080] This application has at least the following beneficial effects:

[0081] 1. The active and reactive power clustering is comprehensively and accurately divided, taking into account power balance and node coupling, as well as the improved modularity index, so as to provide a comprehensive and accurate division of the distribution network.

[0082] 2. For reactive power clusters, the power control capability of photovoltaic inverters can be fully utilized;

[0083] 3. For active power clusters, coordinate VSC with energy storage, and construct a mathematical model for optimized voltage regulation of energy storage, taking into account energy storage power and state of charge (SOC).

[0084] 4. Rationally allocate the power of photovoltaic inverters, VSCs and energy storage devices to achieve coordinated and optimized voltage regulation of active and reactive power;

[0085] 5. This application optimizes the charging and discharging timing strategy of the distributed energy storage system to formulate a power operation scheme that optimizes daily operation economy.

[0086] 6. This application effectively alleviates the voltage over-limit problem caused by large-scale DPV access to AC / DC hybrid distribution networks, improves photovoltaic absorption capacity, effectively controls node voltage, reduces distribution network losses, and achieves good energy storage operation economy. Attached Figure Description

[0087] Figure 1 This is a flowchart illustrating the voltage coordination control method for large-scale distributed photovoltaic AC / DC hybrid distribution networks based on energy storage, as described in this application.

[0088] Figure 2 This is a block diagram of active-reactive coordinated voltage control based on cluster partitioning, as described in the embodiments of this application.

[0089] Figure 3 This is an improved IEEE 33-node distribution network topology diagram related to the embodiments of this application;

[0090] Figure 4 This is a graph showing the system load and total PV output involved in the embodiments of this application;

[0091] Figure 5 This is a schematic diagram illustrating voltage fluctuations after large-scale DPV access in an embodiment of this application.

[0092] Figure 6 This is a schematic diagram showing the node voltage changes in each scheme involved in the embodiments of this application;

[0093] Figure 7 This is a diagram showing the results of active and reactive power cluster partitioning in the improved IEEE 33-node distribution network according to an embodiment of this application. Figure 7 (a) shows the spatiotemporal distribution of voltage at each node in Scheme 1. Figure 7 (b) shows the spatiotemporal distribution of voltage at each node in Scheme 2;

[0094] Figure 8 This is a typical daily power output curve of each cluster energy storage system involved in the embodiments of this application;

[0095] Figure 9 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0096] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0097] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0098] First Embodiment

[0099] Reference Figure 1 This embodiment provides a voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage. The method is applied to the AC / DC hybrid distribution network and includes the following steps:

[0100] S10, the AC / DC hybrid distribution network after regional decoupling is divided into reactive power clusters and active power clusters by the aggregation degree, wherein the aggregation degree includes the aggregation degree of active power clusters and the aggregation degree of reactive power clusters.

[0101] In this embodiment, after decoupling the AC / DC hybrid distribution network by region, the degree of aggregation is used as the criterion to divide the AC / DC hybrid distribution network after regional decoupling into active power clusters and reactive power clusters.

[0102] In some alternative implementations, the regional decoupling can be achieved by: first, physically isolating the tie lines and simultaneously establishing virtual interconnection nodes within the two subsystems, thereby functionally dividing the AC / DC network based on the differentiated configuration of the voltage source converter (VSC) topology; then, creating mirror nodes of other subsystem nodes within the subsystem and forming equivalent connection channels with the local nodes, so that the constraint equations of each subsystem introduce cross-regional boundary conditions, ensuring that the virtual bus voltage and tie line power at the coupling interface meet the consistency requirements.

[0103] After decoupling, the decoupled AC / DC hybrid distribution network is divided into reactive power clusters and active power clusters using aggregation degree. Aggregation degree includes active power cluster aggregation degree and reactive power cluster aggregation degree. Active power cluster aggregation degree is used to assign nodes in the AC / DC hybrid distribution network to active power clusters, while reactive power cluster aggregation degree is used to assign nodes in the AC / DC hybrid distribution network to reactive power clusters.

[0104] Further and optionally, the division step in S10 includes:

[0105] S11, Each node in the AC / DC hybrid distribution network is regarded as an initial cluster, and the initial aggregation degree of the initial cluster is calculated based on the electrical characteristic quantities of the node;

[0106] In this step, based on the distribution network topology and node electrical parameters, the network is abstracted into a weighted graph model. Each node is initially considered an independent cluster, and a modularity function containing electrical characteristics such as node admittance matrix and reactive power sensitivity is established to calculate the initial aggregation degree. This index is used to characterize the tightness of connections within a cluster and the looseness of connections between clusters.

[0107] Further and optionally, in step S11, the aggregation degree can be evaluated using a modularity evaluation index. Specifically, the calculation expression for the active cluster aggregation degree includes:

[0108]

[0109] In the formula, D is the sum of the mean of the active power sensitivity matrix and the active power support capacity of node j.PUij and D PUji P represents the active voltage sensitivity factor of node i to node j and node j to node i, respectively; ESSi For adjustable active power capacity of energy storage; P VSCi The VSC outputs active power. k i and k j They are respectively with nodes i and j The sum of the weights of the connected edges, where m represents the number of nodes in PQ and n represents the total number of nodes excluding the balancing nodes. Take 1 or 0, δ ij =1 indicates a node i and j Directly connected, δ ij =0 indicates a node i and j Not connected Represents a node i and j The weight of the connecting edge (edge ​​weight) is determined by the reactive-voltage or active-voltage sensitivity matrix;

[0110] in, ;

[0111] In the formula, The active voltage sensitivity factor represents node j itself.

[0112] The calculation expression for the reactive power cluster aggregation degree includes:

[0113]

[0114] In the formula, D QUij and D QUji Q represents the reactive voltage sensitivity factor of node i to node j and node j to node i, respectively; PV,i The reactive power capacity of the PV inverter at node i is adjustable; Q VSC,i The VSC adjustable reactive power capacity of node i;

[0115] in, ;

[0116] In the formula, This represents the reactive voltage sensitivity factor of node j itself.

[0117] The photovoltaic power generation system achieves reactive power compensation through inverter control, while the VSC provides reactive power support through reactive power control.

[0118] S12, randomly select a target node i, calculate the aggregation degree difference between the target node i and the adjacent node j, if the aggregation degree difference is positive, then merge the target node i and the adjacent node j, traverse each node in the AC / DC hybrid distribution network to form a new cluster;

[0119] In this step, target node i is randomly selected, and its neighboring node j with the highest electrical coupling is traversed to assess the feasibility of merging. The change in modularity after merging is calculated. :

[0120]

[0121] In the formula, Indicates the system modularity index. This represents the initial network modularity index.

[0122] when The cluster merging operation is performed to form a new cluster. The new cluster is regarded as a supernode to reconstruct the network topology and update the equivalent admittance parameters and electrical distance weights between nodes.

[0123] In addition, some alternative implementations employ a breadth-first search strategy to traverse all network nodes, complete the first round of hierarchical clustering to obtain an intermediate partitioning scheme, and establish a two-layer iterative mechanism: at the local level, node migration optimization is performed to calculate the modularity gain of a single node joining a neighboring cluster; at the global level, partition topology compression is implemented to form an equivalent simplified network for cross-cluster optimization.

[0124] S13, detect whether there is a single-node cluster in the new cluster. If so, iteratively calculate the modularity improvement rate of the single-node cluster joining the neighboring cluster. When the modularity improvement rate of three consecutive iterations is less than the improvement rate threshold or the number of iterations reaches the preset value, output the reactive cluster and active cluster divided in the current iteration as the final cluster division scheme.

[0125] In this step, the system checks for isolated nodes (i.e., single-node clusters) in the partitioning results and initiates a strengthened merging process for such special nodes. A dual termination criterion is set: the algorithm terminates and outputs the final partitioning scheme when the modularity improvement rate is less than a threshold ε for three consecutive iterations or when the maximum number of iterations reaches a preset value.

[0126] In addition, in some alternative implementations, post-execution optimization verification is performed, and power flow calculations are used to verify the strong correlation between voltage and reactive power in each reactive sub-region, ensuring that the partitioning results meet the engineering constraints of local balance.

[0127] S20, the reactive power cluster is controlled by a photovoltaic inverter and / or a voltage source converter; and / or the active power cluster is controlled by a voltage source converter and an energy storage device, wherein the energy storage device performs power distribution based on the current energy storage power and the current state of charge.

[0128] After implementing step S10, refer to Figure 2 The block diagram of active-reactive coordinated voltage control based on cluster partitioning is shown. For objects partitioned into reactive power clusters, photovoltaic inverters and / or voltage source converters are used for control according to different situations.

[0129] In some scenarios, control can be applied to either reactive or active clusters, or both can be controlled together.

[0130] The following are some optional scenarios:

[0131] (i) For situations where photovoltaic inverters and / or voltage source converters are used for control in reactive power clusters:

[0132] When an abnormal voltage is detected at the target node, the reactive power cluster is determined to be located in the reactive power cluster. The target photovoltaic inverter closest to the over-limit node is called to execute the reactive power balance control strategy. At this time, only the photovoltaic inverter is used for control.

[0133] If the reactive power regulation capability of the target photovoltaic inverter is insufficient, the voltage source converter is called to perform constant DC voltage control and constant reactive power control. At this time, the photovoltaic inverter and the voltage source converter are used for control to maintain the reactive power balance of the system.

[0134] (ii) Only for situations where voltage source converters and energy storage devices are used for control in active power clusters.

[0135] In the active power cluster, the voltage source converter is controlled to switch to constant DC voltage and constant active power control to adjust the cross-regional active power, and power distribution is performed based on the current energy storage power and current state of charge of the energy storage device to balance the active power supply and demand within the cluster.

[0136] (iii) Situations requiring coordinated control of active power clusters and reactive power clusters

[0137] When the detected fluctuation in distributed photovoltaic power exceeds the preset fluctuation threshold, the reactive power cluster is activated for reactive power regulation; when the reactive power regulation capacity of the reactive power cluster is insufficient, the active power cluster is activated for active power regulation.

[0138] In this situation, when the output of distributed photovoltaic power fluctuates significantly, it may lead to an imbalance between active and reactive power. At this time, a coordinated optimization strategy of "reactive power priority and active power supplementation" is implemented. First, the photovoltaic inverters and VSCs in the reactive power cluster are started to regulate reactive power. If the reactive power regulation capacity is insufficient, the VSCs and energy storage in the active power cluster are started to regulate active power, maintain power balance, and avoid voltage control failure due to insufficient regulation by a single means.

[0139] In addition, in step S20, for active power clusters, VSC and energy storage coordinate with each other to construct an energy storage optimization voltage regulation mathematical model, taking into account energy storage power and state of charge. The energy storage device allocates power according to the rules in the energy storage optimization voltage regulation mathematical model based on the current energy storage power and the current state of charge.

[0140] In the technical solution provided in this embodiment, the distribution network is divided into reactive power clusters and active power clusters by aggregation degree, so as to realize voltage regulation with coordinated optimization of active and reactive power. The reactive power clusters are divided to control the power of photovoltaic inverters and / or voltage source converters according to different situations, while the active power clusters are allocated power considering the energy storage power and state of charge of energy storage devices. This coordinates the voltage source converters and energy storage devices, thereby alleviating the voltage limit problem caused by large-scale DPV access to AC / DC hybrid distribution networks.

[0141] Second Embodiment

[0142] Based on the first embodiment, this embodiment provides a rule satisfied by an energy storage optimization voltage regulation mathematical model and its construction method. First, when performing power allocation, the energy storage optimization voltage regulation mathematical model includes the following steps:

[0143] Step S100: Identify the set C of voltage-over-limit nodes in the active power cluster through power flow calculation. k ={C1, C2, ..., C n The adjustment power corresponding to the difference between the voltage recovery amount and the current voltage of the voltage-over-limit node is taken as the target adjustment power P required by each cluster. Ck ;

[0144] The current state of charge of the energy storage device is in cluster C. k When implementing power distribution within the system, the following constraints must be met:

[0145] SOC Ck,i,min ≤SOC Ck,i ±ΔSOC Ck,i ≤SOC Ck,i,max

[0146] In the formula, SOC Ck,i State of charge of energy storage system at point i within the cluster ， ΔSOCCk,i The power allocated to the energy storage system at point i within the cluster, SOC Ck,i,max The State of Charge (SOC) is the upper limit of the energy storage system at point i within the cluster. Ck,i,min This represents the state-of-charge limit of the energy storage system at point i within the cluster;

[0147] Step S200, if ΣΔSOC Ck,i ≤C k If the capacity is such that the net power requirement P of the active power cluster is calculated, then... net =ΣP Ck Power is allocated proportionally to the available energy storage units in the energy storage device. If the local energy storage power of the available energy storage unit is insufficient, power support is requested from adjacent energy storage units.

[0148] Step S300, if ΣΔSOC Ck,i >C k The excess capacity will be borne by the energy storage devices of adjacent coupled active power clusters.

[0149] In addition, as some optional implementation methods, after implementing step S300, power flow verification is performed on each feasible power combination, the voltage qualified action scheme is screened, the net benefit is calculated based on the energy storage charging and discharging benefit model, and the power combination with the largest benefit is selected as the final control command.

[0150] Further and optionally, the objective function of the energy storage optimization voltage regulation mathematical model includes at least one of the following:

[0151] (1) Objective function f1 for minimizing voltage deviation:

[0152]

[0153] (2) Objective function f2 for minimizing network loss

[0154]

[0155] In the formula, N AC N DC These are the sets of AC and DC nodes, respectively; U i,t Let L be the voltage amplitude at node i at time t; AC L DC These are collections of AC and DC branches, respectively. Indicates the rated voltage. R represents the square of the current in branch l at time t; l Branch resistance; VSC loss;

[0156] (3) The economically optimal objective function for daily operation of energy storage, f3

[0157]

[0158] In the formula, D TOU (t) represents the time-of-use electricity price at time t; P c,s (t) and P d,s (t) represents the charging and discharging power of the s-th energy storage unit at time t; N s This represents the number of distributed energy storage devices.

[0159] Furthermore, and optionally, the constraints of the energy storage optimization voltage regulation mathematical model include at least one of the following:

[0160] (1) Power constraints of distributed energy storage connected to cluster k:

[0161]

[0162] In the formula, P ESS,i,k N represents the distributed energy storage power connected to node i within the k-th cluster; k Let k be the number of nodes in the k-th cluster.

[0163] (2) Communication constraints, including

[0164] Node voltage amplitude constraints:

[0165]

[0166] Current constraint:

[0167]

[0168] Power constraints:

[0169]

[0170]

[0171] Transformer turns ratio constraint:

[0172]

[0173] Current constraints:

[0174]

[0175] In the formula, U i I i P i Q i Let represent the voltage amplitude, current amplitude, active power, and reactive power of the i-th node, respectively. This indicates the transformer turns ratio; the subscripts min and max represent the upper and lower limits of the corresponding parameters. i (t), Qi (t) represents the active and reactive power of the load at node i at time t; U i (t), U j (t) represents the voltage amplitudes at nodes i and j at time t, respectively; δ ij (t) represents the phase angle difference between nodes i and j at time t; G ij B ij Let P be the real and imaginary parts of the element in the i-th row and j-th column of the node admittance matrix, respectively; DPV,i (t), Q DPV,i (t) represents the active and reactive power of the distributed photovoltaic system at node i at time t; P ESS,i (t) represents the active power of energy storage at node i at time t; N represents the total number of nodes;

[0176] (3) DC constraint:

[0177]

[0178] In the formula, P s Q s U dc and I dc The active power, reactive power, DC voltage, and DC current injected from the AC bus to the DC side are δ, where δ is the phase angle, and the subscripts min and max indicate the upper and lower limits of the corresponding parameters.

[0179] (4) Cluster node voltage and energy storage active power constraints, including:

[0180] Cluster node voltage constraints:

[0181]

[0182] Energy storage active power constraints:

[0183]

[0184] In the formula, ΔU i,t The voltage amplitude difference between node i and the rated voltage at time t is represented; U0 represents the per-unit voltage reference value at the beginning of the line; P n (t) and Q n (t) represent the load consumption of active power and reactive power, respectively; P DPV,n (t) and Q DPV,n (t) represent the active power injection and reactive power injection of distributed photovoltaic power, respectively; P VSC,n (t) represents the converter regulating output power; R m and X m Both represent the impedance characteristics of the m-node segment; P ESS,n For node n, the bidirectional power flow is stored, where it is positive during discharge, S minand S max These represent the minimum and maximum energy storage capacities, respectively; T is the time series modeling period, and Δt represents the time resolution.

[0185] Verification of Examples

[0186] As a verification embodiment, this embodiment performs simulation verification based on a voltage collaborative control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage provided in any of the above embodiments.

[0187] like Figure 3 As shown, the effectiveness of the proposed voltage control strategy for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on cluster partitioning and energy storage collaborative participation is verified using an improved IEEE 33-node distribution network. The system contains 33 nodes, which are divided into AC and DC1 sub-regions via VSC1 access between nodes 30 and 31, and into AC and DC2 sub-regions via VSC2 access between nodes 14 and 15, forming an AC / DC hybrid distribution network. The AC sub-region node set is {1,2,3,...,14,19,...,30}, and the DC sub-region node sets are {31,32,33} and {15,16,17,18}. The system base capacity is 10 MVA, the AC system base voltage is 12.66 kV, and the DC system base voltage is 10 kV. The AC-side PV access nodes are 10, 11, 22, 25, 28, and 30, with access capacities of 0.1, 0.1, 0.3, 0.3, 0.2, and 0.3 MVA, respectively, and the set minimum power factor is 0.95. The DC-side PV access nodes are 16, 18, and 33, with access capacities of 0.6, 0.8, and 0.8 MVA, respectively. Energy storage batteries are installed at nodes 13, 17, 21, 24, 27, and 32, with installed capacities of 40kW, 280kW, 60kW, 60kW, 100kW, and 160kW, respectively, and the energy storage battery state of charge is [0.2 0.8]; the set normal voltage level is [0.95 1.05].

[0188] The changes in total system load and total active power output of DPV are as follows: Figure 4 As shown in the figure, Load represents the total system load, and PV represents the total active power output of the distributed energy storage system. The time-of-use pricing for distributed energy storage is shown in Table 1 below:

[0189] Table 1. Time-of-use pricing for distributed energy storage

[0190]

[0191] According to the distributed photovoltaic (PV) output variation curve (rising phase from 06:00 to 13:00, peaking at 13:00 and then gradually declining), the distributed PV output characteristics show a significant positive correlation with system voltage fluctuations. Simulation results show that during the period from 12:00 to 15:00, the extreme value of PV output triggers reverse power flow in the distribution network, leading to voltage over-limit phenomena at multiple nodes. For example... Figure 5 As shown, the voltage peak of 1.059 pu was reached at 13:00.

[0192] The results of the division between reactive power clusters and active power clusters are shown in Table 2 below:

[0193] Table 2. Cluster Partition Results

[0194]

[0195] It can be seen that if the two DC clusters are classified into active power clusters, then they can be divided into 5 reactive power clusters and 6 active power clusters. Figure 6 This is a schematic diagram showing the voltage changes at each node in each scheme.

[0196] Energy storage capacity is planned at 20% of the total installed capacity of new energy sources. To verify the comprehensive effectiveness of the strategy proposed in this application, two comparative schemes are set up for simulation verification under the same energy storage configuration conditions:

[0197] Option 1: Traditional distributed energy storage voltage regulation strategy. This option does not implement cluster partitioning. It uses a voltage sensitivity matrix to identify neighboring energy storage devices at voltage-over-limit nodes, allocates power based on the rated power of the energy storage and real-time SOC status, and restores voltage through local regulation. Simultaneously, it calculates the daily operating electricity revenue of the energy storage system.

[0198] Option 2: The voltage regulation method proposed in this application. Based on the active-reactive coordinated voltage control strategy based on cluster partitioning described above, the optimal operating power of energy storage is determined with the goals of minimizing voltage deviation and network losses in the AC / DC hybrid distribution network and achieving the best daily operating economy for energy storage.

[0199] In Scheme 1, which does not implement cluster partitioning, a distributed energy storage voltage regulation strategy based on the voltage sensitivity matrix is ​​used for simulation verification. The results are as follows: Figure 7As shown in (a), the distribution network exhibits significant voltage exceedance issues during operation. In areas with high distributed photovoltaic (PV) penetration (such as nodes 18 and 33), the large PV capacity leads to particularly severe voltage exceedances in downstream lines. This reflects a control blind spot in traditional local regulation strategies when dealing with scenarios involving multiple PV installations. During the period from 12:00 to 15:00, distributed PV output reaches its peak, resulting in significant reverse power flow in the distribution network and causing a rise in line voltage. This period exhibits the longest duration and largest magnitude of voltage exceedances, indicating that traditional strategies lack sufficient dynamic response capability to handle sudden changes in PV output.

[0200] After dynamically adjusting the distribution network voltage using the control strategy proposed in this application, the system voltage operating characteristics are as follows: Figure 7 As shown in (b), the voltage waveform diagram shows that the system voltage is always effectively controlled within the preset allowable range throughout the entire scheduling cycle.

[0201] To visually demonstrate the timing characteristics of distributed energy storage devices, Figure 8 The power output curves of various energy storage clusters on a typical day are presented. Negative values ​​in the figure indicate that the energy storage is in a charging state, while positive values ​​represent the discharging state. During peak daytime photovoltaic (PV) generation (09:00-17:00), when distributed PV output exceeds local load demand, the system uses a cluster partitioning mechanism to allocate excess power to various energy storage clusters for charging, thereby suppressing the upward trend of node voltage. At night and in the early morning (00:00-09:00 and 18:00-24:00), due to a sharp decrease or cessation of PV output, the system enters peak load phases. At this time, the energy storage system provides power support to the load through orderly discharge. Simultaneously, to maintain the healthy operation of the energy storage system, the control strategy also adjusts the depth of discharge in real time based on the State of Charge (SOC), reducing power dependence on the main grid while meeting load demand, thus achieving sustainable operation of the energy storage system.

[0202] See Table 3 below for a comparison of optimization results:

[0203] Table 3. Optimization results under different schemes

[0204]

[0205] As can be seen, compared with Scheme 1, Scheme 2, which incorporates the control strategy of this application, exhibits superior voltage characteristics: node voltage fluctuations are significantly mitigated, and voltage stability is significantly enhanced. Through the energy storage charging and discharging strategy, redundant power transmission and losses are effectively reduced, and the overall network loss of the system is significantly decreased. Further economic evaluation of the energy storage system is conducted, and the daily operating electricity revenue is quantitatively analyzed. Based on the energy storage operation and combined with time-of-use pricing (TOC), the charging period is within the normal price range, while the discharging period is within the peak price range. Based on this approach, the daily revenue of energy storage operation is calculated, as shown in Table 3. The results show that, under the premise of meeting the voltage regulation target, the energy storage device can achieve positive economic benefits. This characteristic not only verifies the feasibility of energy storage configuration but also improves the overall efficiency of the energy storage system.

[0206] The reasons why Option 2 is superior to Option 1 are analyzed as follows:

[0207] 1) The traditional voltage regulation strategy (Scheme 1) adopts a coarse power allocation method, which leads to significant cross-node interference when distributed energy storage operates. Scheme 2 introduces a cluster collaborative control architecture, which achieves precise targeted control of energy storage operations through dynamic reactive power support and active power balance in cluster management.

[0208] 2) Scheme 2 first constructs an active / reactive power clustering system for the AC / DC distribution network. When voltage exceedance occurs, the photovoltaic inverters and VSC control within the reactive power cluster achieve dynamic reactive power compensation; in the active power cluster, the VSC and energy storage coordinate, with the VSC prioritizing active power transfer, and the energy storage system balancing power fluctuations locally based on real-time SOC status. For nodes exceeding limits that belong to both clusters, a collaborative optimization strategy of "reactive power priority, active power supplementation" is implemented, significantly improving control response efficiency.

[0209] As one implementation scheme, Figure 9 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0210] like Figure 9As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0211] Those skilled in the art will understand that Figure 9 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0212] like Figure 9 As shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs. The operating system is a program that manages and controls the hardware and software resources of the computer system, as well as the operation of the computer programs and other software or programs.

[0213] exist Figure 9 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the computer program stored in the memory 1005.

[0214] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a computer program stored in the memory and executable on the processor, wherein:

[0215] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0216] S10, the AC / DC hybrid distribution network after regional decoupling is divided into reactive power clusters and active power clusters by the aggregation degree, wherein the aggregation degree includes the aggregation degree of active power clusters and the aggregation degree of reactive power clusters.

[0217] S20, the reactive power cluster is controlled by a photovoltaic inverter and / or a voltage source converter; and / or the active power cluster is controlled by a voltage source converter and an energy storage device, wherein the energy storage device performs power distribution based on the current energy storage power and the current state of charge.

[0218] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0219] S11, Each node in the AC / DC hybrid distribution network is regarded as an initial cluster, and the initial aggregation degree of the initial cluster is calculated based on the electrical characteristic quantities of the node;

[0220] S12, randomly select a target node i, calculate the aggregation degree difference between the target node i and the adjacent node j, if the aggregation degree difference is positive, then merge the target node i and the adjacent node j, traverse each node in the AC / DC hybrid distribution network to form a new cluster;

[0221] S13, detect whether there is a single-node cluster in the new cluster. If so, iteratively calculate the modularity improvement rate of the single-node cluster joining the neighboring cluster. When the modularity improvement rate of three consecutive iterations is less than the improvement rate threshold or the number of iterations reaches the preset value, output the reactive cluster and active cluster divided in the current iteration as the final cluster division scheme.

[0222] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0223] The calculation expression for the active cluster aggregation degree includes:

[0224]

[0225] In the formula, D is the sum of the mean of the active power sensitivity matrix and the active power support capacity of node j. PUij and D PUji P represents the active voltage sensitivity factor of node i to node j and node j to node i, respectively; ESSi For adjustable active power capacity of energy storage; P VSCi The VSC outputs active power. k i and k j They are respectively with nodes i and j The sum of the weights of the connected edges, where m represents the number of nodes in PQ and n represents the total number of nodes excluding the balancing nodes. Take 1 or 0, δ ij =1 indicates a nodei and j Directly connected, δ ij =0 indicates a node i and j Not connected Represents a node i and j The weight of the connecting edge (edge ​​weight) is determined by the reactive-voltage or active-voltage sensitivity matrix;

[0226] in, ;

[0227] In the formula, The active voltage sensitivity factor represents node j itself.

[0228] The calculation expression for the reactive power cluster aggregation degree includes:

[0229]

[0230] In the formula, D QUij and D QUji Q represents the reactive voltage sensitivity factor of node i to node j and node j to node i, respectively; PV,i The reactive power capacity of the PV inverter at node i is adjustable; Q VSC,i The VSC adjustable reactive power capacity of node i;

[0231] in, ;

[0232] In the formula, This represents the reactive voltage sensitivity factor of node j itself.

[0233] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0234] The energy storage device allocates power based on the current energy storage capacity and the current state of charge, including:

[0235] The set C of voltage-over-limit nodes in the active power cluster is identified through power flow calculation. k ={C1, C2, ..., C n The adjustment power corresponding to the difference between the voltage recovery amount and the current voltage of the voltage-over-limit node is taken as the target adjustment power P required by each cluster. Ck ;

[0236] The current state of charge of the energy storage device is in cluster C. k When implementing power distribution within the system, the following constraints must be met:

[0237] SOC Ck,i,min ≤SOCCk,i ±ΔSOC Ck,i ≤SOC Ck,i,max

[0238] In the formula, SOC Ck,i State of charge of energy storage system at point i within the cluster ， ΔSOC Ck,i The power allocated to the energy storage system at point i within the cluster, SOC Ck,i,max The State of Charge (SOC) is the upper limit of the energy storage system at point i within the cluster. Ck,i,min This represents the state-of-charge limit of the energy storage system at point i within the cluster;

[0239] If ΣΔSOC Ck,i ≤C k If the capacity is such that the net power requirement P of the active power cluster is calculated, then... net =ΣP Ck Power is allocated proportionally to the available energy storage units in the energy storage device. If the local energy storage power of the available energy storage unit is insufficient, power support is requested from adjacent energy storage units.

[0240] If ΣΔSOC Ck,i >C k The excess capacity will be borne by the energy storage devices of adjacent coupled active power clusters.

[0241] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0242] The energy storage device includes an energy storage optimization voltage regulation mathematical model, and the objective function of the energy storage optimization voltage regulation mathematical model includes at least one of the following:

[0243] (1) Objective function f1 for minimizing voltage deviation:

[0244]

[0245] (2) Objective function f2 for minimizing network loss

[0246]

[0247] In the formula, N AC N DC These are the sets of AC and DC nodes, respectively; U i,t Let L be the voltage amplitude at node i at time t; AC L DC These are collections of AC and DC branches, respectively. Indicates the rated voltage. R represents the square of the current in branch l at time t; l Branch resistance; VSC loss;

[0248] (3) The economically optimal objective function for daily operation of energy storage, f3

[0249]

[0250] In the formula, D TOU (t) represents the time-of-use electricity price at time t; P c,s (t) and P d,s (t) represents the charging and discharging power of the s-th energy storage unit at time t; N s This represents the number of distributed energy storage devices.

[0251] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0252] The constraints of the energy storage optimized voltage regulation mathematical model include at least one of the following:

[0253] (1) Power constraints of distributed energy storage connected to cluster k:

[0254]

[0255] In the formula, P ESS,i,k N represents the distributed energy storage power connected to node i within the k-th cluster; k Let k be the number of nodes in the k-th cluster.

[0256] (2) Communication constraints, including

[0257] Node voltage amplitude constraints:

[0258]

[0259] Current constraint:

[0260]

[0261] Power constraints:

[0262]

[0263]

[0264] Transformer turns ratio constraint:

[0265]

[0266] Current constraints:

[0267]

[0268] In the formula, U i I i Pi Q i Let represent the voltage amplitude, current amplitude, active power, and reactive power of the i-th node, respectively. This indicates the transformer turns ratio; the subscripts min and max represent the upper and lower limits of the corresponding parameters. i (t), Q i (t) represents the active and reactive power of the load at node i at time t; U i (t), U j (t) represents the voltage amplitudes at nodes i and j at time t, respectively; δ ij (t) represents the phase angle difference between nodes i and j at time t; G ij B ij Let P be the real and imaginary parts of the element in the i-th row and j-th column of the node admittance matrix, respectively; DPV,i (t), Q DPV,i (t) represents the active and reactive power of the distributed photovoltaic system at node i at time t; P ESS,i (t) represents the active power of energy storage at node i at time t; N represents the total number of nodes;

[0269] (3) DC constraint:

[0270]

[0271] In the formula, P s Q s U dc and I dc The active power, reactive power, DC voltage, and DC current injected from the AC bus to the DC side are δ, where δ is the phase angle, and the subscripts min and max indicate the upper and lower limits of the corresponding parameters.

[0272] (4) Cluster node voltage and energy storage active power constraints, including:

[0273] Cluster node voltage constraints:

[0274]

[0275] Energy storage active power constraints:

[0276]

[0277] In the formula, ΔU i,t The voltage amplitude difference between node i and the rated voltage at time t is represented; U0 represents the per-unit voltage reference value at the beginning of the line; P n (t) and Q n (t) represent the load consumption of active power and reactive power, respectively; P DPV,n (t) and Q DPV,n(t) represent the active power injection and reactive power injection of distributed photovoltaic power, respectively; P VSC,n (t) represents the converter regulating output power; R m and X m Both represent the impedance characteristics of the m-node segment; P ESS,n For node n, the bidirectional power flow is stored, where it is positive during discharge, S min and S max These represent the minimum and maximum energy storage capacities, respectively; T is the time series modeling period, and Δt represents the time resolution.

[0278] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0279] When an abnormal voltage is detected at the target node, the reactive power cluster is determined, and the target photovoltaic inverter closest to the over-limit node is called to execute the reactive power balance control strategy. If the reactive power regulation capability of the target photovoltaic inverter is insufficient, the voltage source converter is called to perform constant DC voltage control and constant reactive power control.

[0280] In the active power cluster, the voltage source converter is controlled to switch to constant DC voltage and constant active power control to adjust the cross-regional active power, and power distribution is performed based on the current energy storage power and current state of charge of the energy storage device.

[0281] When the fluctuation of distributed photovoltaic power is detected to be greater than the preset fluctuation threshold, the reactive power cluster is activated to perform reactive power regulation; when the reactive power regulation capability of the reactive power cluster is insufficient, the active power cluster is activated to perform active power regulation.

[0282] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0283] The power flow solution for the AC component of the AC / DC hybrid distribution network is established using the following mathematical model:

[0284]

[0285] In the formula, Δθ and ΔU represent the phase angle change and voltage change at the node, respectively; D Pθ D Qθ These represent the changes in the node phase angle when a unit amount of active and reactive power is injected into the node, respectively; D PU D QU ΔP and ΔQ represent the changes in node voltage amplitude when a unit amount of active power and reactive power are injected into the node, respectively.

[0286] Wherein, the ∆U, ∆P, and ∆Q of node i satisfy the following expression:

[0287]

[0288] In the formula, ;

[0289] The impact of distributed photovoltaic power generation on the voltage of node i after being connected to the AC distribution network satisfies the following expression:

[0290]

[0291] In the formula, U i1 The voltage at node i after distributed photovoltaic power is connected to the AC distribution network; D PUij D QUij These are the active voltage sensitivity factor and reactive voltage sensitivity factor of node i to node j, respectively; U i0 Let ΔP be the initial voltage at node i. j ΔQ represents the change in injected active power at the node. j This represents the change in the injected reactive power at the node.

[0292] Furthermore, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the embodiments of the above methods.

[0293] Therefore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of the voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage as described in the above embodiments.

[0294] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0295] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0296] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0297] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0298] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0299] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0300] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0301] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0302] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage, characterized in that, Applied to AC / DC hybrid distribution networks, the method includes the following steps: S10, the AC / DC hybrid distribution network after regional decoupling is divided into reactive power clusters and active power clusters by the aggregation degree, wherein the aggregation degree includes the aggregation degree of active power clusters and the aggregation degree of reactive power clusters. S20, the reactive power cluster is controlled by a photovoltaic inverter and / or a voltage source converter; and / or the active power cluster is controlled by a voltage source converter and an energy storage device, wherein the energy storage device performs power distribution based on the current energy storage power and the current state of charge; The calculation expression for the active cluster aggregation degree includes: ； In the formula, D is the sum of the mean of the active power sensitivity matrix and the active power support capacity of node j. PUij and D PUji P represents the active voltage sensitivity factor of node i to node j and node j to node i, respectively; ESSi For adjustable active power capacity of energy storage; P VSCi The VSC outputs active power. k i and k j They are respectively with nodes i and j The sum of the weights of the connected edges, where m represents the number of nodes in PQ and n represents the total number of nodes excluding the balancing nodes. Take 1 or 0, δ ij =1 indicates a node i and j Directly connected, δ ij =0 indicates a node i and j Not connected Represents a node i and j The weight of the connection edge is determined by the reactive-voltage or active-voltage sensitivity matrix; in, ; In the formula, The active voltage sensitivity factor represents node j itself. The calculation expression for the reactive power cluster aggregation degree includes: ； In the formula, D QUij and D QUji Q represents the reactive voltage sensitivity factor of node i to node j and node j to node i, respectively; PV,i The reactive power capacity of the PV inverter at node i is adjustable; Q VSC,i The VSC adjustable reactive power capacity of node i; in, ; In the formula, This represents the reactive voltage sensitivity factor of node j itself.

2. The method as described in claim 1, characterized in that, S10 includes: S11, Each node in the AC / DC hybrid distribution network is regarded as an initial cluster, and the initial aggregation degree of the initial cluster is calculated based on the electrical characteristic quantities of the node; S12, randomly select a target node i, calculate the aggregation degree difference between the target node i and the adjacent node j, if the aggregation degree difference is positive, then merge the target node i and the adjacent node j, traverse each node in the AC / DC hybrid distribution network to form a new cluster; S13, detect whether there is a single-node cluster in the new cluster. If so, iteratively calculate the modularity improvement rate of the single-node cluster joining the neighboring cluster. When the modularity improvement rate of three consecutive iterations is less than the improvement rate threshold or the number of iterations reaches the preset value, output the reactive cluster and active cluster divided in the current iteration as the final cluster division scheme.

3. The method as described in claim 1, characterized in that, The energy storage device allocates power based on the current energy storage capacity and the current state of charge, including: The set C of voltage-over-limit nodes in the active power cluster is identified through power flow calculation. k ={C1, C2, ..., C n The adjustment power corresponding to the difference between the voltage recovery amount and the current voltage of the voltage-over-limit node is taken as the target adjustment power P required by each cluster. Ck ; The current state of charge of the energy storage device is in cluster C. k When implementing power distribution within the system, the following constraints must be met: SOC Ck,i,min ≤SOC Ck,i ±ΔSOC Ck,i ≤SOC Ck,i,max； In the formula, SOC Ck,i State of charge of energy storage system at point i within the cluster ， ΔSOC Ck,i The power allocated to the energy storage system at point i within the cluster, SOC Ck,i,max The State of Charge (SOC) is the upper limit of the energy storage system at point i within the cluster. Ck,i,min This represents the state-of-charge limit of the energy storage system at point i within the cluster; If ΣΔSOC Ck,i ≤C k If the capacity is such that the net power requirement P of the active power cluster is calculated, then... net =ΣP Ck Power is allocated proportionally to the available energy storage units in the energy storage device. If the local energy storage power of the available energy storage unit is insufficient, power support is requested from adjacent energy storage units. If ΣΔSOC Ck,i >C k The excess capacity will be borne by the energy storage devices of adjacent coupled active power clusters.

4. The method as described in claim 1 or 3, characterized in that, The energy storage device includes an energy storage optimization voltage regulation mathematical model, and the objective function of the energy storage optimization voltage regulation mathematical model includes at least one of the following: (1) Objective function f1 for minimizing voltage deviation: ； (2) Objective function f2 for minimizing network loss ； In the formula, N AC N DC These are the sets of AC and DC nodes, respectively; U i,t Let L be the voltage amplitude at node i at time t; AC L DC These are collections of AC and DC branches, respectively. Indicates the rated voltage. R represents the square of the current in branch l at time t; l Branch resistance; VSC loss; (3) The economically optimal objective function for daily operation of energy storage, f3 ； In the formula, D TOU (t) represents the time-of-use electricity price at time t; P c,s (t) and P d,s (t) represents the charging and discharging power of the s-th energy storage unit at time t; N s This represents the number of distributed energy storage devices.

5. The method as described in claim 4, characterized in that, The constraints of the energy storage optimized voltage regulation mathematical model include at least one of the following: (1) Power constraints of distributed energy storage connected to cluster k: ； In the formula, P ESS,i,k N represents the distributed energy storage power connected to node i within the k-th cluster; k Let k be the number of nodes in the k-th cluster. (2) Communication constraints, including Node voltage amplitude constraints: ； Current constraint: ； Power constraints: ； ； Transformer turns ratio constraint: ； Current constraints: ； In the formula, U i I i P i Q i Let represent the voltage amplitude, current amplitude, active power, and reactive power of the i-th node, respectively. This indicates the transformer turns ratio; the subscripts min and max represent the upper and lower limits of the corresponding parameters. i (t), Q i (t) represents the active and reactive power of the load at node i at time t; U i (t), U j (t) represents the voltage amplitudes at nodes i and j at time t, respectively; δ ij (t) represents the phase angle difference between nodes i and j at time t; G ij B ij Let P be the real and imaginary parts of the element in the i-th row and j-th column of the node admittance matrix, respectively; DPV,i (t), Q DPV,i (t) represents the active and reactive power of the distributed photovoltaic system at node i at time t; P ESS,i (t) represents the active power of energy storage at node i at time t; N represents the total number of nodes; (3) DC constraint: ； In the formula, P s Q s U dc and I dc The active power, reactive power, DC voltage, and DC current injected from the AC bus to the DC side are δ, where δ is the phase angle, and the subscripts min and max indicate the upper and lower limits of the corresponding parameters. (4) Cluster node voltage and energy storage active power constraints, including: Cluster node voltage constraints: ； Energy storage active power constraints: ； In the formula, ΔU i,t Characterizes the difference between node i and the rated voltage amplitude at time t; U0 represents the per-unit voltage reference value at the beginning of the line; P n (t) and Q n (t) represent the load consumption of active power and reactive power, respectively; P DPV,n (t) and Q DPV,n (t) represent the active power injection and reactive power injection of distributed photovoltaic power, respectively; P VSC,n (t) represents the converter regulating output power; R m and X m Both represent the impedance characteristics of the m-node segment; P ESS,n For node n, the bidirectional power flow is stored, where it is positive during discharge, S min and S max These represent the minimum and maximum energy storage capacities, respectively; T is the time series modeling period, and Δt represents the time resolution.

6. The method as described in claim 1, characterized in that, S20 includes: When an abnormal voltage is detected at the target node, the reactive power cluster is determined, and the target photovoltaic inverter closest to the over-limit node is called to execute the reactive power balance control strategy. If the reactive power regulation capability of the target photovoltaic inverter is insufficient, the voltage source converter is called to perform constant DC voltage control and constant reactive power control. In the active power cluster, the voltage source converter is controlled to switch to constant DC voltage and constant active power control to adjust the cross-regional active power, and power distribution is performed based on the current energy storage power and current state of charge of the energy storage device. When the fluctuation of distributed photovoltaic power is detected to be greater than the preset fluctuation threshold, the reactive power cluster is activated to perform reactive power regulation; when the reactive power regulation capability of the reactive power cluster is insufficient, the active power cluster is activated to perform active power regulation.

7. The method as described in claim 1, characterized in that, The power flow solution for the AC component of the AC / DC hybrid distribution network is established using the following mathematical model: ； In the formula, Δθ and ΔU represent the phase angle change and voltage change at the node, respectively; D Pθ D Qθ These represent the changes in the node phase angle when a unit amount of active and reactive power is injected into the node, respectively; D PU D QU ΔP and ΔQ represent the changes in node voltage amplitude when a unit amount of active power and reactive power are injected into the node, respectively. Wherein, the ∆U, ∆P, and ∆Q of node i satisfy the following expression: ； In the formula, ; The impact of distributed photovoltaic power generation on the voltage of node i after being connected to the AC distribution network satisfies the following expression: ； In the formula, U i1 The voltage at node i after distributed photovoltaic power is connected to the AC distribution network; D PUij D QUij These are the active voltage sensitivity factor and reactive voltage sensitivity factor of node i to node j, respectively; U i0 Let ΔP be the initial voltage at node i. j ΔQ represents the change in injected active power at the node. j This represents the change in the injected reactive power at the node.

8. A computer system, characterized in that, The computer system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the voltage coordinated control method for a large-scale distributed photovoltaic AC / DC hybrid distribution network based on energy storage as described in any one of claims 1 to 7.

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