A ‘spatial‐temporal’ bi‐layer optimal control strategy of large‐scale electric vehicles (EVs) in a multi‐area Virtual Power Plant (VPP) oriented to dual carbon target

Abstract

Virtual power plant (VPP) is a comprehensive demand response (DR) technology to aggregate various power supply and consumption resources to help stabilise power grid fluctuations and reduce CO2 emissions. Fast-charging electric vehicles (EVs) with higher charging power and shorter charging time are the most potential high-quality DR resources of VPP in the future. In this paper, a new ‘spatial-temporal’ bi-layer optimal control strategy is proposed to solve the daily real-time DR dispatching problem of large-scale EVs including fast-charging ones in a multi-area VPP oriented to China's dual carbon target. This proposed bi-layer strategy has two main contributions in both spatial and temporal domains. One is that, compared with the limited geographical area in traditional VPP, the upper layer of the proposed strategy considers the DR problem of EVs in a VPP with multiple geographical areas and establishes a spatial optimisation model of EVs DR considering both energy and carbon transaction costs of the overall VPP. The other is that rather than traditional slow-charging EVs DR control with 1-h temporal interval, the lower layer of the proposed strategy adds fast-charging EVs in the real-time orderly charging and discharging DR control by a shorter temporal interval of 15 min and designs two different charging and discharging price stimulation scenarios. The simulation results in the last section of this paper show that the proposed ‘spatial-temporal’ bi-layer optimal control strategy can effectively guide the charging and discharging behaviours of EVs in both spatial and temporal domains of a multi-area VPP, achieving lower energy and carbon transaction cost and smaller load fluctuations.