Research on Distributed Energy Storage Aggregation Considering Dynamic and Static Parameters

Under the background of high proportion of new energy connected to the distribution network, distributed energy storage participation in demand response has become an effective measure to improve the active support capability of new energy power generation and the level of safe and stable operation...

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Main Authors: Wu, Xinlong, Zhou, Yang, Wang, Hongxing, Ge, Yuan, Yu, Nuo, Wang, Shinong
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Demand response
distributed energy storage aggregation
dynamic parameters
Heuristic algorithms
K-means algorithm
Power grids
Power measurement
Power system dynamics
Power system stability
static parameters
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creator Wu, Xinlong
Zhou, Yang
Wang, Hongxing
Ge, Yuan
Yu, Nuo
Wang, Shinong
description Under the background of high proportion of new energy connected to the distribution network, distributed energy storage participation in demand response has become an effective measure to improve the active support capability of new energy power generation and the level of safe and stable operation of the system. However, the direct participation of distributed energy storage with small capacity and large quantity in demand response will cause control difficulties and other problems. For this reason, the parameters of distributed energy storage system level and its own level are selected, and a distributed energy storage aggregation method based on K-means algorithm considering dynamic and static parameters is proposed. First, three static parameters of system stability, system reliability and responsiveness at the level of distributed energy storage connection system are considered for aggregation. Subsequently, on the basis of static parameter aggregation, the three dynamic parameters of energy storage output power, energy storage convergence time and energy storage dispatchable capacity at the level of distributed energy storage itself are considered for secondary aggregation. Finally, the effectiveness of the aggregation method is verified by evaluating the effect of dynamic parameter aggregation, which realizes the efficient aggregation of massive distributed energy storage.
doi_str_mv 10.1109/CAC59555.2023.10452009
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subjects Clustering algorithms
Demand response
distributed energy storage aggregation
dynamic parameters
Heuristic algorithms
K-means algorithm
Power grids
Power measurement
Power system dynamics
Power system stability
static parameters
title Research on Distributed Energy Storage Aggregation Considering Dynamic and Static Parameters
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