Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

Author:

Li Dongdong1,Zhang Kai1ORCID,Yao Yin1ORCID,Lin Shunfu1

Affiliation:

1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Abstract

The charging profiles of plug-in electric vehicles (PEVs) have large volatility. It has brought great challenges for aggregator to accurately complete load identification and aggregated configuration. Therefore, an analysis and configuration method of responsive capacity based on clustering and the Markov model are proposed in this paper. Firstly, the adaptive two-scale clustering algorithm based on spectral clustering (ATCSC) is applied to the clustering of charging piles. The offset compensation of the extreme points is used to form the distance measurement in the clustering process. Then, the responsive aggregated power can be obtained after the change control of suitable charging piles. Finally, the variation characteristics of the actual charging profiles based on the Markov model are introduced to the reliability evaluation in the load curtailment service. Simulation results reveal the following. (1) The robustness of the proposed method is better especially for the PEV charging profiles with strong volatility. (2) The validity of the aggregated configuration is verified. Additionally, the sum of power deviation is 0.0707 kW when the change interval of control strategy is 15 min. (3) The maximum shortage of configuration is −98.0875 kW as the entropy of the volatility is 37.027.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Modeling and Simulation

Reference33 articles.

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1. An Autonomous Response Method to Cluster Classification of Large Charging Piles;2023 5th International Conference on Power and Energy Technology (ICPET);2023-07-27

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