A novel hybrid centralised decentralised framework for electric vehicles coordination

• Published in: IET smart grid Vol. 7; no. 1; pp. 89 - 100
• Main Authors: Verma, Praveen, Dasgupta, Pallab, Chakraborty, Chandan
• Format: Journal Article
• Language: English
• Published: Wiley 01-02-2024
• Subjects: electric vehicle charging; electric vehicles; electric vehicles coordination; energy demand; EVs
• ISSN: 2515-2947; 2515-2947
• DOI: 10.1049/stg2.12144

Hybrid Centralised‐Decentralised Electric Vehicle (EV) coordination policy in the urgent charging scenario is presented. First, a robust and complex optimisation problem considering several key features affecting EV coordination is formulated. Then, a solution strategy for the formulated problem is proposed by decomposing the formulated problem into an EV coordination and simple optimisation problem. The decentralised rule‐based EV coordination strategy works on the principle of direct load flattening and utilises practical EV aggregator‐customer interaction, customer behaviour, and temporospatial shifting of the EVs to flatten the load duration curve at the charging station. Then, the centralised optimisation problem is solved to minimise the operation cost, decrease the power loss, and decrease congestion in the grid. A comparison between uncoordinated and coordinated charging in the case study conducted on the IEEE 24 bus system shows that the proposed approach reduces the average EV load by 1283.26 kW/min, average power loss by 2.465 kW/min, and operation cost by 61.99 $/min during the peak hours.
The overview of the proposed coordination scheme is shown in Figure 1. The key idea in the proposed coordination scheme is to utilise EV customer‐EV aggregator interaction to guide the charging of EVs. The EV customers submit their charging requirements to the EV aggregator at the charging station, after which the EV aggregator estimates the charging power and charging time requirement to serve the EV customer. The central grid operator receives the charging information from all the aggregators in the grid and chooses the most economical dispatch to meet the power requirements of all the EV aggregators in the system.