CN-122000952-A - Self-adaptive distributed energy storage group regulation and group control method and system

CN122000952ACN 122000952 ACN122000952 ACN 122000952ACN-122000952-A

Abstract

The invention provides a self-adaptive distributed energy storage group dispatching group control method and system, which comprise the steps of clustering based on current attenuation degree indexes, SOC adjustment speed indexes, communication delay indexes and electrical distances between the distributed energy storage units and other nodes in a transformer area to obtain a plurality of virtual energy storage groups, calculating performance indexes of the virtual energy storage groups based on performance parameters of the energy storage units in the virtual energy storage groups, inputting the performance indexes of the virtual energy storage groups into a multi-target optimal dispatching model to obtain dispatching instructions of the virtual energy storage groups, decomposing the dispatching instructions of the virtual energy storage groups into the energy storage units and executing the dispatching instructions, and based on multi-dimensional performance index clustering, enabling the internal homogeneity of the virtual energy storage to be high, enabling the instruction execution consistency to be high, improving the regulation precision, remarkably improving the local consumption rate of new energy sources and reducing line losses through optimal dispatching.

Inventors

PAN BO
TIAN YUN
XIE SHIYIN
TANG XING
XU MINGZE

Assignees

嘉兴国电通新能源科技有限公司
北京国电通网络技术有限公司
国网信息通信产业集团有限公司

Dates

Publication Date: 20260508
Application Date: 20251202

Claims (10)

1. The self-adaptive distributed energy storage group regulation and group control method is characterized by comprising the following steps of: clustering based on the current attenuation degree index, the SOC adjustment rate index, the communication delay index and the electrical distances between the distributed energy storage units and other nodes in the transformer area to obtain a plurality of virtual energy storage clusters; calculating performance indexes of the virtual energy storage clusters based on performance parameters of energy storage units in the virtual energy storage clusters respectively; inputting the performance index of each virtual energy storage cluster into a multi-target optimization scheduling model to obtain a scheduling instruction of each virtual energy storage cluster; Decomposing and executing the scheduling instruction of each virtual energy storage cluster to each energy storage unit; the multi-objective optimization scheduling model is built by taking new energy in-situ absorption maximization and line loss minimization as targets.
2. The method of claim 1, wherein the clustering based on the current attenuation degree index, SOC adjustment rate index, communication delay index, and electrical distance from other nodes of each distributed energy storage unit in the platform area to obtain a plurality of virtual energy storage clusters comprises: Weighting and fusing based on the current attenuation degree index, the SOC adjustment rate index and the communication delay index of each distributed energy storage unit in the platform region to obtain the comprehensive characteristic value of each energy storage unit; And clustering the comprehensive characteristic values of the energy storage units to obtain a plurality of virtual energy storage clusters.
3. The adaptive distributed energy storage group control method of claim 2, wherein the integrated eigenvalue is calculated as follows: in the formula, Representing the integrated characteristic value of the ith energy storage cell, An indicator of the degree of decay of the i-th energy storage unit, Representing the SOC regulation rate of the i-th energy storage unit, Indicating the communication delay of the ith energy storage unit, Represents the electrical distance from the ith energy storage unit to the jth energy storage unit, N is the total number of the energy storage units, As a weight of the measure of the degree of attenuation, For the weights of the SOC adjustment rate, As a weight for the communication delay, Is a weight of the electrical distance.
4. The method of claim 1, wherein the performance metrics of the virtual energy storage clusters include one or more of total available capacity, maximum charge/discharge power, equivalent SOC, or communication reliability factor; the total available capacity is calculated as follows: the maximum charge/discharge power is calculated as follows: The calculation formula of the equivalent SOC is as follows: the calculation formula of the communication reliability factor is as follows: in the formula, For the total available capacity of the kth virtual energy storage cluster, For the capacity of the i-th energy storage unit, Is an attenuation degree index of the ith energy storage unit, Indicating that the ith energy storage unit belongs to the kth virtual energy storage cluster, For the maximum charge/discharge power of the kth virtual energy storage cluster, Is the maximum charge/discharge power of the ith energy storage cell, The equivalent SOC of the kth virtual energy storage cluster, For the SOC of the i-th energy storage unit, For the communication reliability factor of the kth virtual energy storage cluster, The average delay of the kth virtual energy storage cluster is denoted by a delay coefficient.
5. The adaptive distributed energy storage group dispatching group control method of claim 1, wherein the process of constructing the multi-objective optimized dispatching model comprises: Establishing an objective function by taking the in-situ absorption maximization and the line loss minimization of new energy as targets; virtual energy storage power constraint, SOC dynamic constraint, power balance constraint and voltage safety constraint are taken as constraint conditions to form a multi-objective optimization scheduling model.
6. The adaptive distributed energy storage group control method of claim 5, wherein the objective function is calculated as follows: in the formula, G represents an objective function, The line loss is indicated by the sign of the line loss, The in-situ consumption rate of new energy is represented, The weight of the line loss is represented.
7. An adaptive distributed energy storage group regulation group control system, comprising: The cluster dividing module is used for clustering based on the current attenuation degree index, the SOC adjustment rate index, the communication delay index and the electrical distances between the distributed energy storage units and other nodes in the transformer area to obtain a plurality of virtual energy storage clusters; the performance index module is used for respectively based on the performance parameters of the energy storage units in each virtual energy storage cluster, and the performance indexes of each virtual energy storage cluster; The instruction calculation module is used for inputting the performance index of each virtual energy storage cluster into the multi-target optimization scheduling model to obtain the scheduling instruction of each virtual energy storage cluster; the instruction decomposition module is used for decomposing the scheduling instructions of each virtual energy storage cluster to each energy storage unit and executing the scheduling instructions; the multi-objective optimization scheduling model is built by taking new energy in-situ absorption maximization and line loss minimization as targets.
8. The adaptive distributed energy storage group control system of claim 7, wherein the cluster division module is specifically configured to: Weighting and fusing based on the current attenuation degree index, the SOC adjustment rate index and the communication delay index of each distributed energy storage unit in the platform region to obtain the comprehensive characteristic value of each energy storage unit; And clustering the comprehensive characteristic values of the energy storage units to obtain a plurality of virtual energy storage clusters.
9. The adaptive distributed energy storage group control system of claim 8, wherein the integrated eigenvalues in the cluster partition module are calculated as follows: in the formula, Representing the integrated characteristic value of the ith energy storage cell, An indicator of the degree of decay of the i-th energy storage unit, Representing the SOC regulation rate of the i-th energy storage unit, Indicating the communication delay of the ith energy storage unit, Represents the electrical distance from the ith energy storage unit to the jth energy storage unit, N is the total number of the energy storage units, As a weight of the measure of the degree of attenuation, For the weights of the SOC adjustment rate, As a weight for the communication delay, Is a weight of the electrical distance.
10. The adaptive distributed energy storage group control system of claim 7, wherein the performance index of the virtual energy storage group in the performance index module comprises one or more of total available capacity, maximum charge/discharge power, equivalent SOC, or communication reliability factor; the total available capacity is calculated as follows: the maximum charge/discharge power is calculated as follows: The calculation formula of the equivalent SOC is as follows: the calculation formula of the communication reliability factor is as follows: in the formula, For the total available capacity of the kth virtual energy storage cluster, For the capacity of the i-th energy storage unit, Is an attenuation degree index of the ith energy storage unit, Indicating that the ith energy storage unit belongs to the kth virtual energy storage cluster, For the maximum charge/discharge power of the kth virtual energy storage cluster, Is the maximum charge/discharge power of the ith energy storage cell, The equivalent SOC of the kth virtual energy storage cluster, For the SOC of the i-th energy storage unit, For the communication reliability factor of the kth virtual energy storage cluster, The average delay of the kth virtual energy storage cluster is denoted by a delay coefficient.

Description

Self-adaptive distributed energy storage group regulation and group control method and system Technical Field The invention relates to the field of electric power operation regulation and control, in particular to a self-adaptive distributed energy storage group regulation and group control method and system. Background With the continuous improvement of the permeability of renewable energy sources such as distributed photovoltaics, wind power and the like in a power distribution network, the problem of source-load space-time mismatch is increasingly prominent, and the phenomena of voltage out-of-limit, power dumping, light discarding, wind discarding and the like frequently occur in a local area. The distributed energy storage is used as a key flexibility adjusting resource, has quick response, bidirectional adjustment and space-time transfer capability, and can effectively stabilize new energy fluctuation, support load peaks and optimize tide distribution by realizing cooperative group adjustment and group control on the distributed energy storage, so that the carrying capacity and operation safety of the power distribution network on high-proportion renewable energy sources are improved. However, the currently mainstream distributed energy storage group control method mostly adopts a static grouping or simple aggregation strategy, and heterogeneity of each energy storage unit in the aspects of battery attenuation degree, SOC dynamic adjustment rate, communication delay, electrical topology position and the like is not fully considered. The control mode of one-cut type is easy to cause inconsistent response in the cluster and large execution deviation of the scheduling instruction, and high-efficiency on-site absorption of new energy and accurate suppression of network loss are difficult to realize. Especially in a changeable running environment, the perception and utilization of the energy storage individual performance difference are lacking, and the overall regulation and control efficiency of the group control system is seriously restricted. Disclosure of Invention In order to solve the problem that the prior art lacks of sensing and utilizing the individual performance difference of energy storage and severely restricts the overall regulation and control efficiency of a group control system in a variable running environment, the invention provides a self-adaptive distributed energy storage group regulation and group control method, which comprises the following steps: clustering based on the current attenuation degree index, the SOC adjustment rate index, the communication delay index and the electrical distances between the distributed energy storage units and other nodes in the transformer area to obtain a plurality of virtual energy storage clusters; calculating performance indexes of the virtual energy storage clusters based on performance parameters of energy storage units in the virtual energy storage clusters respectively; inputting the performance index of each virtual energy storage cluster into a multi-target optimization scheduling model to obtain a scheduling instruction of each virtual energy storage cluster; Decomposing and executing the scheduling instruction of each virtual energy storage cluster to each energy storage unit; the multi-objective optimization scheduling model is built by taking new energy in-situ absorption maximization and line loss minimization as targets. Preferably, the clustering is performed based on a current attenuation degree index, an SOC adjustment rate index, a communication delay index and an electrical distance between the current attenuation degree index, the SOC adjustment rate index and other nodes of each distributed energy storage unit in the platform region to obtain a plurality of virtual energy storage clusters, including: Weighting and fusing based on the current attenuation degree index, the SOC adjustment rate index and the communication delay index of each distributed energy storage unit in the platform region to obtain the comprehensive characteristic value of each energy storage unit; And clustering the comprehensive characteristic values of the energy storage units to obtain a plurality of virtual energy storage clusters. Preferably, the calculation formula of the integrated characteristic value is as follows: in the formula, Representing the integrated characteristic value of the ith energy storage cell,An indicator of the degree of decay of the i-th energy storage unit,Representing the SOC regulation rate of the i-th energy storage unit,Indicating the communication delay of the ith energy storage unit,Represents the electrical distance from the ith energy storage unit to the jth energy storage unit, N is the total number of the energy storage units,As a weight of the measure of the degree of attenuation,For the weights of the SOC adjustment rate,As a weight for the communication delay,Is a weight of the electrical distance. Preferably, the