Photovoltaic-charging pile partition coordination voltage management method and device

Abstract

The application discloses a photovoltaic-charging pile partition coordination voltage management method and device, real-time collection of node data, distributed photovoltaic output data and charging pile data of a power distribution network and normalization processing, construction of a voltage-reactive power sensitivity matrix and a voltage-active power sensitivity matrix based on a Jacobian matrix method; dynamic partitioning of the power distribution network based on electrical distance and sensitivity similarity, clustering algorithm, ensuring that each partition has independent voltage regulation capability; then, based on voltage sensitivity, constructing a voltage control model within the partition, calculating the power regulation initial value of the photovoltaic and charging pile; finally, based on the alternating direction multiplier method, constructing a partition coordination optimization model for solving, obtaining a global optimal regulation scheme. The technical problem that the existing photovoltaic-charging pile voltage management method does not consider the regulation characteristic difference and partition regulation of the photovoltaic and charging pile, and the response speed is slow, and it is difficult to adapt to the dynamic change operation demand of the power distribution network is solved.

Description

Technical Field

[0001] This invention relates to the field of power grid power quality management technology, and in particular to a method and apparatus for regional coordinated voltage management of photovoltaic-charging piles. Background Technology

[0002] The large-scale integration of distributed photovoltaic (PV) power and electric vehicles into the distribution network has brought challenges to the safe operation of the network. Among these challenges, high / low voltage exceedance issues are particularly prominent, becoming a major bottleneck restricting the safe and efficient operation of the distribution network. Distributed PV power generation is affected by meteorological conditions such as radiation intensity, exhibiting characteristics of randomness, intermittency, and fluctuation. The active and reactive power outputs of PV inverters may fluctuate significantly, leading to bidirectional power flow in the distribution network and causing voltage exceedances. Simultaneously, the randomness of electric vehicle charging behavior further amplifies voltage fluctuations.

[0003] Existing photovoltaic (PV)-charging pile voltage management methods employ centralized optimization, which suffers from slow response speed and high computational complexity, making it difficult to adapt to the dynamic operational needs of the distribution network. Some collaborative control methods simply combine the regulation capabilities of PV and charging piles without fully considering the differences in their regulation characteristics or zonal regulation, resulting in poor voltage management effectiveness. Therefore, providing a zonal collaborative voltage management method for PV-charging piles, which fully leverages the synergistic regulation advantages of distributed PV and charging piles to quickly and effectively resolve voltage exceedance issues and adapt to the dynamic operational needs of the distribution network, is a technical problem urgently needing to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides a photovoltaic-charging pile zoned coordinated voltage management method and device to solve the technical problems of existing photovoltaic-charging pile voltage management methods that do not consider the differences in regulation characteristics between photovoltaic and charging piles and zoned regulation, and have slow response speeds, making it difficult to adapt to the dynamic changes in the operation requirements of the power distribution network.

[0005] In view of this, the first aspect of the present invention provides a photovoltaic-charging pile zoned coordinated voltage management method, comprising:

[0006] Real-time collection of node data, distributed photovoltaic power output data, and charging pile data of the power distribution network, and normalization processing of the node data, distributed photovoltaic data, and charging pile data of the power distribution network;

[0007] Based on the normalized distribution network node data, distributed photovoltaic data, and charging pile data, the Jacobian matrix method is used to calculate the voltage-reactive power sensitivity matrix and the voltage-active power sensitivity matrix.

[0008] Based on the electrical distance and sensitivity similarity of nodes in the distribution network, and with the goal of minimizing the comprehensive error of the electrical distance and sensitivity similarity of nodes in each partition, a clustering algorithm is used to dynamically partition the nodes of the distribution network. Each partition contains at least one distributed photovoltaic power station and one charging pile.

[0009] A voltage control model within a partition is constructed based on voltage sensitivity, and the voltage state of the partition is determined based on the voltage control model.

[0010] Based on the voltage status of each zone, reactive power priority regulation and photovoltaic active power reduction are carried out in sequence according to the control priority to obtain the initial value of power regulation for each resource.

[0011] Based on the initial values ​​of power regulation of each resource, a segmented collaborative optimization model is constructed using the alternating direction multiplier method. The collaborative optimization model is constructed with the goal of minimizing the voltage deviation of the partition nodes.

[0012] The globally optimal control scheme is obtained by iteratively solving the collaborative optimization model, and voltage management of the distribution network is carried out based on the globally optimal control scheme.

[0013] Optionally, the node data of the distribution network includes the node's voltage amplitude, active power, and reactive power, and the charging pile data includes the charging / discharging power and operating status data of the charging pile.

[0014] Optionally, the voltage-reactive sensitivity matrix is ​​obtained by inverting the reactive power sensitivity submatrix to voltage amplitude in the Jacobian matrix, and the voltage-active sensitivity matrix is ​​obtained by inverting the active power sensitivity submatrix to voltage amplitude in the Jacobian matrix.

[0015] Optionally, based on the electrical distance and sensitivity similarity of nodes in the distribution network, and with the objective of minimizing the comprehensive error of the electrical distance and sensitivity similarity of nodes within each partition, a clustering algorithm is used to dynamically partition the distribution network nodes, including:

[0016] Calculate the electrical distance between nodes in the distribution network based on impedance parameters;

[0017] The sensitivity similarity of each node is calculated based on the voltage-reactive power sensitivity matrix;

[0018] With the goal of minimizing the combined error of electrical distance and sensitivity similarity of nodes within each partition, the K-means clustering algorithm is used to dynamically partition the distribution network.

[0019] Optionally, a voltage control model within the partition is constructed based on voltage sensitivity, and the partition voltage state is determined according to the voltage control model, including:

[0020] Define the allowable range of node voltages;

[0021] If a node exceeds its voltage limit within a given zone, the system determines whether such a node exists. If so, it enters the voltage limit control state; otherwise, it does not.

[0022] Optionally, based on the voltage status of each zone, reactive power priority regulation and photovoltaic active power reduction are performed sequentially according to the control priority to obtain the initial values ​​for power regulation of each resource, including:

[0023] If the partition is in a voltage over-limit state control state, the control priority of each control resource is determined by the average absolute value of voltage-reactive power sensitivity;

[0024] Calculate the reactive power regulation of photovoltaic and charging piles according to the voltage over-limit type, and regulate the reactive power of photovoltaic and charging piles according to the control priority.

[0025] If voltage exceeds the limit even after reactive power regulation of photovoltaic and charging piles, the active power reduction of photovoltaic is calculated based on the voltage-active power sensitivity matrix.

[0026] Summarize the initial power adjustment values ​​for the control resources in each zone.

[0027] Optionally, the reactive power regulation of photovoltaic power and charging piles is calculated separately according to the voltage over-limit type, including:

[0028] If the voltage over-limit type is over-upper limit, then the reactive power regulation of the photovoltaic system is:

[0029] ;

[0030] If the voltage over-limit type is below the lower limit, then the reactive power regulation of the photovoltaic system is:

[0031] ;

[0032] in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. Let j be the set of distributed photovoltaic systems within the partition, and j be the index of the controllable resource. The photovoltaic reactive power regulation coefficient when the upper limit is exceeded. Let i be the set of nodes whose voltage exceeds the upper limit, and i be the node index. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power regulation of photovoltaic power when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the set of nodes whose voltage exceeds the lower limit.

[0033] If the voltage over-limit type is "over-upper limit", then the reactive power regulation amount of the charging pile is:

[0034] ;

[0035] If the voltage over-limit type is "below the lower limit", then the reactive power regulation of the charging pile is:

[0036] ;

[0037] in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. This refers to a collection of charging stations within a given zone. The reactive power adjustment coefficient of the charging pile when it exceeds the upper limit. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power adjustment amount of the charging pile when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the maximum reactive power regulation of the charging pile.

[0038] Optionally, the formula for calculating the photovoltaic active power reduction based on the voltage-active power sensitivity matrix is:

[0039] ;

[0040] in, To reduce the active power output of photovoltaic power generation, The photovoltaic active power regulation coefficient when the upper limit is exceeded. For unresolved voltage over-limit differences, To determine the voltage active power sensitivity of node i under the active power regulation unit value of resource j. This represents the set of nodes that have not yet resolved the issue of voltage exceeding the upper limit.

[0041] Optionally, the collaborative optimization model includes an objective function and constraints;

[0042] The objective function is:

[0043] ;

[0044] Where F is the objective function, and n is the total number of nodes in the distribution network. Let be the voltage magnitude at node i. is the rated reference voltage of node i.

[0045] A second aspect of the present invention provides a photovoltaic-charging pile zoned coordinated voltage management device, comprising:

[0046] The data acquisition module is used to collect node data, distributed photovoltaic power output data and charging pile data of the power distribution network in real time, and to normalize the node data, distributed photovoltaic data and charging pile data of the power distribution network.

[0047] The sensitivity calculation module is used to calculate the voltage-reactive power sensitivity matrix and voltage-active power sensitivity matrix based on the normalized distribution network node data, distributed photovoltaic data and charging pile data, using the Jacobian matrix method.

[0048] The partitioning module is used to dynamically partition the distribution network nodes based on the electrical distance and sensitivity similarity of the nodes in the distribution network. With the goal of minimizing the comprehensive error of the electrical distance and sensitivity similarity of the nodes in each partition, a clustering algorithm is used to partition the distribution network nodes. Each partition contains at least one distributed photovoltaic power station and one charging pile.

[0049] The partition status judgment module is used to construct a voltage control model within a partition based on voltage sensitivity, and to judge the voltage status of the partition based on the voltage control model.

[0050] The adjustment module is used to perform reactive power priority adjustment and photovoltaic active power reduction according to the zone voltage status and the control priority, and obtain the initial value of each resource power adjustment;

[0051] The collaborative optimization model construction module is used to construct a segmented collaborative optimization model based on the initial values ​​of power adjustment of each resource and the alternating direction multiplier method, with the goal of minimizing the voltage deviation of the partition nodes.

[0052] The governance module is used to obtain the globally optimal control scheme by iteratively solving the collaborative optimization model, and to perform voltage control on the distribution network based on the globally optimal control scheme.

[0053] As can be seen from the above technical solutions, the photovoltaic-charging pile zoned coordinated voltage management method provided by the present invention has the following advantages:

[0054] This invention provides a photovoltaic-charging pile zoned collaborative voltage management method. It collects real-time node data, distributed photovoltaic power output data, and charging pile data from the distribution network. The method normalizes these data and constructs voltage-reactive power sensitivity matrices and voltage-active power sensitivity matrices based on the Jacobian matrix method. Based on electrical distance and sensitivity similarity, a clustering algorithm is used to dynamically zone the distribution network, ensuring each zone has independent voltage regulation capabilities. A voltage control model within each zone is then constructed based on voltage sensitivity to calculate initial power regulation values ​​for both photovoltaic and charging piles. Finally, a zoned collaborative optimization model is constructed using the alternating direction multiplier method to obtain the globally optimal regulation scheme. By combining dynamic zoning with differentiated regulation, the method fully explores the collaborative regulation potential of photovoltaic and charging piles, achieving precise and efficient control of the distribution network voltage. This solves the technical problems of existing photovoltaic-charging pile voltage management methods that do not consider the differences in regulation characteristics and zoned regulation between photovoltaic and charging piles, and have slow response speeds, making them difficult to adapt to the dynamic operating requirements of the distribution network. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic flowchart of a photovoltaic-charging pile zoned collaborative voltage management method provided in an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of a photovoltaic-charging pile zoned collaborative voltage management device provided in an embodiment of the present invention. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] For easier understanding, please refer to Figure 1 This invention provides an embodiment of a photovoltaic-charging pile zoned coordinated voltage management method, comprising:

[0060] Step 101: Collect node data, distributed photovoltaic power output data, and charging pile data of the distribution network in real time, and normalize the node data, distributed photovoltaic data, and charging pile data of the distribution network.

[0061] It should be noted that real-time data collection includes node data of the distribution network, distributed photovoltaic power output data, and charging pile data. Node data of the distribution network includes node voltage amplitude, active power, and reactive power, while charging pile data includes charging / discharging power and operating status data. Outliers in the collected data are removed using the 3σ criterion, and the data is then normalized.

[0062] Step 102: Based on the normalized distribution network node data, distributed photovoltaic data, and charging pile data, calculate the voltage-reactive power sensitivity matrix and voltage-active power sensitivity matrix using the Jacobian matrix method.

[0063] It should be noted that, based on the normalized node data, distributed photovoltaic data, and charging pile data of the distribution network, the Jacobian matrix method is used to calculate the voltage-reactive power sensitivity of each node in the distribution network, and a voltage-reactive power sensitivity matrix is ​​constructed. For a distribution network with n nodes, the polar coordinate form of the distribution network power flow equation is:

[0064] ;

[0065] in, The active power injected into node i. The reactive power injected into node i Let be the voltage magnitude at node i. Let J be the voltage magnitude at node j. Let be the voltage phase angle difference between node i and node j. Let i be the electrical conductance between node i and node j. Let be the susceptance between node i and node j.

[0066] Taking the total differential of the power flow equations and rearranging them, we obtain the Jacobian matrix J, which is expressed as:

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] in, Inject changes in active power into the nodes. Inject changes in active power into the nodes. This represents the change in the phase angle of the node voltage. This represents the relative change in the node voltage amplitude.

[0073] The Jacobian matrix J can be divided into four matrices, namely:

[0074] ;

[0075] in, This is the sensitivity submatrix of active power to voltage amplitude. This is the reactive power sensitivity submatrix to voltage amplitude. This is the sensitivity submatrix of active power to voltage phase angle. This is the reactive power sensitivity submatrix to voltage phase angle.

[0076] In distribution network voltage control, voltage amplitude is mainly affected by reactive power, while the effect of phase angle on voltage amplitude is negligible. Therefore, the voltage-reactive power sensitivity matrix S is derived from the reactive power sensitivity submatrix on voltage amplitude. The inverse matrix is ​​obtained by solving for it, and the specific formula is as follows:

[0077] ;

[0078] elements of the matrix The formula for calculating the change in voltage at node i when the reactive power regulation unit value at node j is:

[0079] ;

[0080] in, The reactive power injected into node j. The larger the absolute value, the more significant the impact of reactive power at node j on the voltage at node i, providing a quantitative basis for subsequent partitioning and control strategy formulation.

[0081] Based on the subsequent active power reduction and control requirements within the zone, a voltage-active power sensitivity is constructed. The sensitivity submatrix of active power to voltage amplitude The inverse matrix is ​​obtained by solving for the inverse matrix; the specific formula is as follows:

[0082] ;

[0083] matrix elements in The change in voltage at node i represents the change in active power at node j when the active power changes by a unit value. Its calculation formula is:

[0084] ;

[0085] The larger the absolute value, the greater the impact of active power regulation at node j on the voltage at node i, providing a quantitative basis for formulating photovoltaic active power reduction schemes within the zone.

[0086] Step 103: Based on the electrical distance and sensitivity similarity of nodes in the distribution network, and with the goal of minimizing the comprehensive error of the electrical distance and sensitivity similarity of nodes in each partition, a clustering algorithm is used to dynamically partition the nodes of the distribution network. Each partition contains at least one distributed photovoltaic power station and one charging pile.

[0087] It should be noted that, based on electrical distance and sensitivity similarity, the distribution network is dynamically partitioned, dividing voltage control resources such as distributed photovoltaic systems and charging piles into several independent control zones. The electrical distance between nodes in the distribution network is calculated based on inter-node impedance parameters, using the following formula:

[0088] ;

[0089] in, Let i be the electrical distance between node i and node j. Let be the resistance of the line between node i and node j. Let be the reactance of the line between node i and node j.

[0090] Based on the voltage-reactive power sensitivity matrix, the sensitivity similarity coefficient of each node is calculated. The similarity coefficient is calculated using cosine similarity and is used to measure the similarity of the voltage response characteristics of different nodes to reactive power regulation.

[0091] ;

[0092] in, Let be the sensitivity similarity coefficient between node i and node j, with a value range of [-1, 1]. The closer the coefficient is to 1, the more similar the voltage response characteristics of the two nodes are. , Let be the voltage-reactive power sensitivity vectors at nodes i and j, respectively. The dot product of two sensitivity vectors, i.e. . , These are the magnitudes of the two sensitivity vectors, i.e. , .

[0093] Dynamic partitioning of the distribution network is performed based on clustering algorithms. Combining electrical distance and sensitivity similarity coefficients, an improved K-means clustering algorithm is used to partition the distribution network nodes. The clustering termination condition is set when the change rate of node affiliation between two consecutive clustering results is less than a preset threshold to ensure the stability of the partitioning results. At the same time, each partition contains at least one distributed photovoltaic power station and one charging pile to ensure that the partition has independent voltage regulation capabilities and avoid the situation of insufficient regulation resources.

[0094] The objective function of the improved K-means clustering is to minimize the combined error of electrical distance and sensitivity similarity of nodes within each partition:

[0095] ;

[0096] Where k is the number of clusters (i.e. the number of partitions). For the c-th cluster, Let i be the electrical distance between node i and the c-th cluster center. Let be the sensitivity similarity coefficient between node i and the c-th cluster center, and α be the weighting coefficient used to balance the influence of electrical distance and sensitivity similarity.

[0097] Iterative partitioning process: Initialize k cluster centers, randomly select k nodes as initial cluster centers; calculate the comprehensive error from each node to each cluster center according to the objective function, and assign the node to the cluster with the smallest comprehensive error; update each cluster center, calculate the mean electrical distance and the mean sensitivity vector of all nodes in each cluster, and use them as new cluster centers; calculate the rate of change of node affiliation between two adjacent clustering results: ,in, This indicates the number of nodes whose affiliation has changed. Indicates the total number of nodes. If... ( If the threshold is set to 0, clustering terminates and the partitioning result is output; otherwise, iteration continues.

[0098] Step 104: Construct a voltage control model within the partition based on voltage sensitivity, and determine the voltage state of the partition based on the voltage control model.

[0099] It should be noted that the set of all nodes in the distribution network is defined as follows: The set of nodes in the c-th partition is The voltage of the i-th node is The allowable voltage deviation range is Based on this, two types of states are distinguished: voltage over-limit and voltage normal state.

[0100] Voltage over-limit states include voltage over-limit and voltage over-limit. The set of voltage over-limit nodes within a partition is defined as follows: Further subdivided into two types of over-limits: voltage over-limit node set: The main causes of overvoltage are excessive active power output and excess reactive power from photovoltaic systems, requiring voltage regulation to suppress voltage rise; the set of nodes where voltage exceeds the lower limit is as follows: The causes are mostly excessive load, insufficient photovoltaic output, and reactive power shortage, requiring voltage regulation by increasing the voltage; when When there is at least one voltage-over-limit node in the partition, it is determined to be a voltage-over-limit state and voltage regulation is required.

[0101] Normal voltage condition: when When all node voltages within a partition are within the allowable range, the voltage is considered to be in a normal state, requiring no active adjustment; only real-time monitoring of the operating status and sensitivity changes is needed.

[0102] Step 105: Based on the voltage status of each zone, perform reactive power priority regulation and photovoltaic active power reduction in sequence according to the regulation priority to obtain the initial values ​​of power regulation for each resource.

[0103] It should be noted that the set of controllable resources within a partition is defined as follows: ,in, For distributed photovoltaic collections within the zone, This refers to the set of charging piles within a given zone; for any resource adjustment... Calculate the mean absolute value of voltage-reactive power sensitivity at the node where the limit is exceeded, based on the type of limit exceedance: For nodes where the voltage exceeds the upper limit, the mean absolute value of sensitivity is: For nodes with voltages below the lower limit, the average absolute value of the sensitivity is: , Let i be the voltage reactive power sensitivity of node i under the reactive power regulation unit value of resource j.

[0104] The larger the mean absolute value of sensitivity, the more significant the impact of reactive power regulation of the control resource on the voltage of the corresponding type of over-limit node, and the higher the control priority. Among them, the priorities of photovoltaic power stations and charging piles are independently ranked, and the equipment with the larger mean absolute value of sensitivity in their respective sets is controlled first.

[0105] Calculate the voltage over-limit difference for both types of over-limit scenarios, and then calculate the corresponding adjustment amount. The voltage over-limit difference for node i that exceeds the upper limit is... The voltage difference at node i below the lower limit is... .

[0106] When the node voltage exceeds the upper limit, capacitive reactive power needs to be output. The photovoltaic reactive power regulation is as follows:

[0107] ;

[0108] When the node voltage falls below the lower limit, inductive reactive power needs to be output. The photovoltaic reactive power regulation is as follows:

[0109] ;

[0110] in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. This refers to a collection of charging stations within a given zone. The reactive power adjustment coefficient of the charging pile when it exceeds the upper limit. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power adjustment amount of the charging pile when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the maximum reactive power regulation of the charging pile.

[0111] If the voltage over-limit type is "over-upper limit", then the reactive power regulation amount of the charging pile is:

[0112] ;

[0113] If the voltage over-limit type is "below the lower limit", then the reactive power regulation of the charging pile is:

[0114] ;

[0115] in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. Let j be the set of distributed photovoltaic systems within the partition, and j be the index of the controllable resource. The photovoltaic reactive power regulation coefficient when the upper limit is exceeded. Let i be the set of nodes whose voltage exceeds the upper limit, and i be the node index. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power regulation of photovoltaic power when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the set of nodes whose voltage exceeds the lower limit.

[0116] Based on the reactive power regulation of photovoltaic and charging piles, reactive power regulation is carried out on photovoltaic and charging piles according to the regulation priority.

[0117] If voltage exceedances persist after reactive power regulation of photovoltaic (PV) systems and charging piles, the PV active power reduction is calculated based on the voltage-active power sensitivity matrix. The reduction is then calculated using SP quantization based on the remaining exceedance types and the voltage-active power sensitivity matrix. If the unresolved voltage exceedance difference is... ( If the active power reduction is calculated, then the expression for calculating the active power reduction is:

[0118] ;

[0119] in, To reduce the active power output of photovoltaic power generation, The photovoltaic active power regulation coefficient when the upper limit is exceeded. For unresolved voltage over-limit differences, To determine the voltage active power sensitivity of node i under the active power regulation unit value of resource j. This represents the set of nodes that have not yet resolved the issue of voltage exceeding the upper limit.

[0120] Summarize the initial power regulation values ​​of the control resources in each region (including the initial value of photovoltaic active power reduction). Initial value of photovoltaic reactive power regulation when exceeding the upper limit Initial value of reactive power regulation of charging pile when exceeding the limit This serves as the initial input for global collaborative optimization, improving the efficiency of subsequent partitioned regulation and optimization.

[0121] Step 106: Based on the initial values ​​of power adjustment for each resource, construct a segmented collaborative optimization model using the alternating direction multiplier method, with the goal of minimizing the voltage deviation of the segmented nodes.

[0122] It should be noted that, based on the initial value of power regulation, a segmented collaborative optimization model is constructed using the Alternating Direction Method of Multipliers (ADMM). With the goal of minimizing the voltage deviation of the segmented nodes, the globally optimal control scheme is obtained through iterative solution, and a closed-loop feedback adjustment is formed.

[0123] The collaborative optimization model includes an objective function and constraints;

[0124] The objective function is:

[0125] ;

[0126] Where F is the objective function, and n is the total number of nodes in the distribution network. Let be the voltage magnitude at node i. is the rated reference voltage of node i.

[0127] The constraints of the zoned coordinated voltage optimization model include node voltage constraints, coordination variable constraints, and distribution network power flow constraints.

[0128] Node voltage constraints:

[0129] ;

[0130] in, This represents the minimum node voltage. This represents the maximum value of the node voltage.

[0131] Coordination variable constraints:

[0132] The main coordination variables include boundary node voltage and boundary line power. For boundary node i of adjacent partitions c and d, the constraint of equal voltage must be satisfied, that is:

[0133] ;

[0134] in, Let i be the voltage at node i in partition c. Let be the voltage at node i in partition d.

[0135] The power flow on both sides of the boundary line l must satisfy ,and , Let L be the active power of the boundary line l of partition c. Let L be the active power of the boundary line l of partition d. The maximum active power of the boundary line l.

[0136] Distribution network power flow constraints:

[0137] Power flow constraints in polar coordinate form:

[0138] .

[0139] Step 107: Obtain the global optimal control scheme by iteratively solving the collaborative optimization model, and perform voltage management on the distribution network according to the global optimal control scheme.

[0140] It should be noted that the ADMM algorithm is used to solve the above model; the initial values ​​of photovoltaic and charging pile power regulation in the partition are used as the initial solution for algorithm iteration; then the partition is solved independently to calculate the optimal voltage regulation scheme in the region and obtain the local voltage regulation amount; then the boundary variables are uniformly corrected to ensure that the electrical state of adjacent partitions is consistent and to eliminate regulation conflicts; the global optimal regulation scheme is obtained through iteration.

[0141] After the control command is issued, adjustments are made through closed-loop feedback. This involves real-time monitoring of voltage, power, and network loss data at each zone node, comparing the actual operating data with the optimized target value, and reconstructing the voltage-reactive and voltage-active sensitivity matrices according to the above steps. This process adjusts the zone results and control strategies to form a closed-loop control, ensuring that the voltage limit exceedance problem can be completely resolved.

[0142] By combining dynamic zoning with differentiated regulation, the problems of complex calculations, slow response, and poor zoning adaptability in traditional centralized optimization are effectively solved. The regulation potential of photovoltaic and charging piles is fully explored, photovoltaic curtailment losses are reduced, the level of new energy consumption is improved, conflicts in regulation between zoning areas are resolved, the voltage regulation accuracy and flexible adjustment capability of the distribution network are improved, the long-term safe and stable operation of the distribution network is guaranteed, and the actual operation needs of the distribution network with a high proportion of distributed photovoltaic and charging pile access are adapted to.

[0143] This invention provides a photovoltaic-charging pile zoned collaborative voltage management method. It collects real-time node data, distributed photovoltaic power output data, and charging pile data from the distribution network. The method normalizes these data and constructs voltage-reactive power sensitivity matrices and voltage-active power sensitivity matrices based on the Jacobian matrix method. Based on electrical distance and sensitivity similarity, a clustering algorithm is used to dynamically zone the distribution network, ensuring each zone has independent voltage regulation capabilities. A voltage control model within each zone is then constructed based on voltage sensitivity to calculate initial power regulation values ​​for both photovoltaic and charging piles. Finally, a zoned collaborative optimization model is constructed using the alternating direction multiplier method to obtain the globally optimal regulation scheme. By combining dynamic zoning with differentiated regulation, the method fully explores the collaborative regulation potential of photovoltaic and charging piles, achieving precise and efficient control of the distribution network voltage. This solves the technical problems of existing photovoltaic-charging pile voltage management methods that do not consider the differences in regulation characteristics and zoned regulation between photovoltaic and charging piles, and have slow response speeds, making them difficult to adapt to the dynamic operating requirements of the distribution network.

[0144] For easier understanding, please refer to Figure 2 This invention provides an embodiment of a photovoltaic-charging pile zoned collaborative voltage management device, comprising:

[0145] The data acquisition module is used to collect node data, distributed photovoltaic power output data and charging pile data of the power distribution network in real time, and to normalize the node data, distributed photovoltaic data and charging pile data of the power distribution network.

[0146] The sensitivity calculation module is used to calculate the voltage-reactive power sensitivity matrix and voltage-active power sensitivity matrix based on the normalized distribution network node data, distributed photovoltaic data and charging pile data, using the Jacobian matrix method.

[0147] The partitioning module is used to dynamically partition the distribution network nodes based on the electrical distance and sensitivity similarity of the nodes in the distribution network. With the goal of minimizing the comprehensive error of the electrical distance and sensitivity similarity of the nodes in each partition, a clustering algorithm is used to partition the distribution network nodes. Each partition contains at least one distributed photovoltaic power station and one charging pile.

[0148] The partition status judgment module is used to construct a voltage control model within a partition based on voltage sensitivity, and to judge the voltage status of the partition based on the voltage control model.

[0149] The adjustment module is used to perform reactive power priority adjustment and photovoltaic active power reduction according to the zone voltage status and the control priority, and obtain the initial value of each resource power adjustment;

[0150] The collaborative optimization model construction module is used to construct a segmented collaborative optimization model based on the initial values ​​of power adjustment of each resource and the alternating direction multiplier method, with the goal of minimizing the voltage deviation of the partition nodes.

[0151] The governance module is used to obtain the globally optimal control scheme by iteratively solving the collaborative optimization model, and to perform voltage control on the distribution network based on the globally optimal control scheme.

[0152] In one embodiment, the node data of the distribution network includes the node's voltage amplitude, active power, and reactive power, while the charging pile data includes the charging / discharging power and operating status data of the charging pile.

[0153] In one embodiment, the voltage-reactive sensitivity matrix is ​​obtained by inverting the reactive power sensitivity submatrix to voltage amplitude in the Jacobian matrix, and the voltage-active sensitivity matrix is ​​obtained by inverting the active power sensitivity submatrix to voltage amplitude in the Jacobian matrix.

[0154] In one embodiment, based on the electrical distance and sensitivity similarity of nodes in the distribution network, and with the objective of minimizing the comprehensive error of the electrical distance and sensitivity similarity of nodes within each partition, a clustering algorithm is used to dynamically partition the distribution network nodes, including:

[0155] Calculate the electrical distance between nodes in the distribution network based on impedance parameters;

[0156] The sensitivity similarity of each node is calculated based on the voltage-reactive power sensitivity matrix;

[0157] With the goal of minimizing the combined error of electrical distance and sensitivity similarity of nodes within each partition, the K-means clustering algorithm is used to dynamically partition the distribution network.

[0158] In one embodiment, a voltage control model within a partition is constructed based on voltage sensitivity, and the partition voltage state is determined according to the voltage control model, including:

[0159] Define the allowable range of node voltages;

[0160] If a node exceeds its voltage limit within a given zone, the system determines whether such a node exists. If so, it enters the voltage limit control state; otherwise, it does not.

[0161] In one embodiment, based on the zone voltage state, reactive power priority regulation and photovoltaic active power reduction are performed sequentially according to regulation priority to obtain the initial values ​​of power regulation for each resource, including:

[0162] If the partition is in a voltage over-limit state control state, the control priority of each control resource is determined by the average absolute value of voltage-reactive power sensitivity;

[0163] Calculate the reactive power regulation of photovoltaic and charging piles according to the voltage over-limit type, and regulate the reactive power of photovoltaic and charging piles according to the control priority.

[0164] If voltage exceeds the limit even after reactive power regulation of photovoltaic and charging piles, the active power reduction of photovoltaic is calculated based on the voltage-active power sensitivity matrix.

[0165] Summarize the initial power adjustment values ​​for the control resources in each zone.

[0166] In one embodiment, the reactive power regulation of photovoltaic power and charging piles is calculated separately according to the voltage over-limit type, including:

[0167] If the voltage over-limit type is over-upper limit, then the reactive power regulation of the photovoltaic system is:

[0168] ;

[0169] If the voltage over-limit type is below the lower limit, then the reactive power regulation of the photovoltaic system is:

[0170] ;

[0171] in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. Let j be the set of distributed photovoltaic systems within the partition, and j be the index of the controllable resource. The photovoltaic reactive power regulation coefficient when the upper limit is exceeded. Let i be the set of nodes whose voltage exceeds the upper limit, and i be the node index. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power regulation of photovoltaic power when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the set of nodes whose voltage exceeds the lower limit.

[0172] If the voltage over-limit type is "over-upper limit", then the reactive power regulation amount of the charging pile is:

[0173] ;

[0174] If the voltage over-limit type is "below the lower limit", then the reactive power regulation of the charging pile is:

[0175] ;

[0176] in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. This refers to a collection of charging stations within a given zone. The reactive power adjustment coefficient of the charging pile when it exceeds the upper limit. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power adjustment amount of the charging pile when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the maximum reactive power regulation of the charging pile.

[0177] In one embodiment, the formula for calculating the photovoltaic active power reduction based on the voltage-active power sensitivity matrix is:

[0178] ;

[0179] in, To reduce the active power output of photovoltaic power generation, The photovoltaic active power regulation coefficient when the upper limit is exceeded. For unresolved voltage over-limit differences, To determine the voltage active power sensitivity of node i under the active power regulation unit value of resource j. This represents the set of nodes that have not yet resolved the issue of voltage exceeding the upper limit.

[0180] In one embodiment, the collaborative optimization model includes an objective function and constraints;

[0181] The objective function is:

[0182] ;

[0183] Where F is the objective function, and n is the total number of nodes in the distribution network. Let be the voltage magnitude at node i. is the rated reference voltage of node i.

[0184] The photovoltaic-charging pile zoned collaborative voltage management device provided in this embodiment of the invention is used to execute the photovoltaic-charging pile zoned collaborative voltage management method provided in this invention. Its principle and the technical effects achieved are the same as those of the photovoltaic-charging pile zoned collaborative voltage management method provided in this invention, and will not be repeated here.

[0185] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0186] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coordinated voltage management of photovoltaic-charging pile zones, characterized in that, include: Real-time data collection of node data, distributed photovoltaic power output data, and charging pile data of the power distribution network, and normalization processing of the node data, distributed photovoltaic data, and charging pile data of the power distribution network; Based on the normalized distribution network node data, distributed photovoltaic data, and charging pile data, the Jacobian matrix method is used to calculate the voltage-reactive power sensitivity matrix and the voltage-active power sensitivity matrix. Based on the electrical distance and sensitivity similarity of nodes in the distribution network, and with the goal of minimizing the comprehensive error of the electrical distance and sensitivity similarity of nodes in each partition, a clustering algorithm is used to dynamically partition the nodes of the distribution network. Each partition contains at least one distributed photovoltaic power station and one charging pile. A voltage control model within a partition is constructed based on voltage sensitivity, and the voltage state of the partition is determined based on the voltage control model. Based on the voltage status of each zone, reactive power priority regulation and photovoltaic active power reduction are carried out in sequence according to the control priority to obtain the initial value of power regulation for each resource. Based on the initial values ​​of power regulation of each resource, a segmented collaborative optimization model is constructed using the alternating direction multiplier method. The collaborative optimization model is constructed with the goal of minimizing the voltage deviation of the partition nodes. The globally optimal control scheme is obtained by iteratively solving the collaborative optimization model, and voltage management of the distribution network is carried out based on the globally optimal control scheme.

2. The photovoltaic-charging pile zoned coordinated voltage management method according to claim 1, characterized in that, The node data of the distribution network includes the node's voltage amplitude, active power, and reactive power, while the charging pile data includes the charging / discharging power and operating status data of the charging pile.

3. The photovoltaic-charging pile zoned coordinated voltage management method according to claim 1, characterized in that, The voltage-reactive sensitivity matrix is ​​obtained by inverting the reactive power sensitivity submatrix to voltage amplitude in the Jacobian matrix, and the voltage-active power sensitivity matrix is ​​obtained by inverting the active power sensitivity submatrix to voltage amplitude in the Jacobian matrix.

4. The photovoltaic-charging pile zoned coordinated voltage management method according to any one of claims 1-3, characterized in that, Based on the electrical distance and sensitivity similarity of nodes in the distribution network, and with the objective of minimizing the comprehensive error of the electrical distance and sensitivity similarity of nodes within each partition, a clustering algorithm is used to dynamically partition the distribution network nodes, including: Calculate the electrical distance between nodes in the distribution network based on impedance parameters; The sensitivity similarity of each node is calculated based on the voltage-reactive power sensitivity matrix; With the goal of minimizing the combined error of electrical distance and sensitivity similarity of nodes within each partition, the K-means clustering algorithm is used to dynamically partition the distribution network.

5. The photovoltaic-charging pile zoned coordinated voltage management method according to claim 1, characterized in that, A voltage control model is constructed within the partition based on voltage sensitivity. The voltage state of the partition is determined based on the voltage control model, including: Define the allowable range of node voltages; If a node exceeds its voltage limit within a given zone, the system determines whether such a node exists. If so, it enters the voltage limit control state; otherwise, it does not.

6. The photovoltaic-charging pile zoned coordinated voltage management method according to claim 5, characterized in that, Based on the voltage status of each zone, reactive power priority regulation and photovoltaic active power reduction are performed sequentially according to the control priority to obtain the initial values ​​of power regulation for each resource, including: If the partition is in a voltage over-limit state control state, the control priority of each control resource is determined by the average absolute value of voltage-reactive power sensitivity; Calculate the reactive power regulation of photovoltaic and charging piles according to the voltage over-limit type, and regulate the reactive power of photovoltaic and charging piles according to the control priority. If voltage exceeds the limit even after reactive power regulation of photovoltaic and charging piles, the active power reduction of photovoltaic is calculated based on the voltage-active power sensitivity matrix. Summarize the initial power adjustment values ​​for the control resources in each zone.

7. The photovoltaic-charging pile zoned coordinated voltage management method according to claim 6, characterized in that, Calculate the reactive power regulation of photovoltaic systems and charging piles according to the voltage over-limit type, including: If the voltage over-limit type is over-upper limit, then the reactive power regulation of the photovoltaic system is: ； If the voltage over-limit type is below the lower limit, then the reactive power regulation of the photovoltaic system is: ； in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. Let j be the set of distributed photovoltaic systems within the partition, and j be the index of the controllable resource. The photovoltaic reactive power regulation coefficient when the upper limit is exceeded. Let i be the set of nodes whose voltage exceeds the upper limit, and i be the node index. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power regulation of photovoltaic power when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the set of nodes whose voltage exceeds the lower limit. If the voltage over-limit type is "over-upper limit", then the reactive power regulation amount of the charging pile is: ； If the voltage over-limit type is "below the lower limit", then the reactive power regulation of the charging pile is: ； in, The amount of photovoltaic reactive power regulation when the upper limit is exceeded. This refers to a collection of charging stations within a given zone. The reactive power adjustment coefficient of the charging pile when it exceeds the upper limit. This represents the voltage limit difference for the i-th node that exceeds the upper limit. To determine the voltage reactive power sensitivity of node i under the unit value of reactive power regulation of resource j, The reactive power adjustment amount of the charging pile when the lower limit is exceeded. The photovoltaic reactive power regulation coefficient when the lower limit is exceeded. This is the maximum reactive power regulation of the charging pile.

8. The photovoltaic-charging pile zoned coordinated voltage management method according to claim 6, characterized in that, The formula for calculating the photovoltaic active power reduction based on the voltage-active power sensitivity matrix is ​​as follows: ； in, To reduce the active power output of photovoltaic power generation, The photovoltaic active power regulation coefficient when the upper limit is exceeded. For unresolved voltage over-limit differences, To determine the voltage active power sensitivity of node i under the active power regulation unit value of resource j. This represents the set of nodes that have not yet resolved the issue of voltage exceeding the upper limit.

9. The photovoltaic-charging pile zoned coordinated voltage management method according to claim 1, characterized in that, The collaborative optimization model includes an objective function and constraints; The objective function is: ； Where F is the objective function, and n is the total number of nodes in the distribution network. Let be the voltage amplitude at node i. is the rated reference voltage of node i.

10. A photovoltaic-charging pile zoned collaborative voltage management device, characterized in that, include: The data acquisition module is used to collect node data, distributed photovoltaic power output data and charging pile data of the power distribution network in real time, and to normalize the node data, distributed photovoltaic data and charging pile data of the power distribution network. The sensitivity calculation module is used to calculate the voltage-reactive power sensitivity matrix and voltage-active power sensitivity matrix based on the normalized distribution network node data, distributed photovoltaic data and charging pile data, using the Jacobian matrix method. The partitioning module is used to dynamically partition the distribution network nodes based on the electrical distance and sensitivity similarity of the nodes in the distribution network. With the goal of minimizing the comprehensive error of the electrical distance and sensitivity similarity of the nodes in each partition, a clustering algorithm is used to partition the distribution network nodes. Each partition contains at least one distributed photovoltaic power station and one charging pile. The partition status judgment module is used to construct a voltage control model within a partition based on voltage sensitivity, and to judge the voltage status of the partition based on the voltage control model. The adjustment module is used to perform reactive power priority adjustment and photovoltaic active power reduction according to the zone voltage status and the control priority, and obtain the initial value of each resource power adjustment; The collaborative optimization model construction module is used to construct a segmented collaborative optimization model based on the initial values ​​of power adjustment of each resource and the alternating direction multiplier method, with the goal of minimizing the voltage deviation of the partition nodes. The governance module is used to obtain the globally optimal control scheme by iteratively solving the collaborative optimization model, and to perform voltage control on the distribution network based on the globally optimal control scheme.

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