A new methodology of peak energy demand reduction using coordinated real-time scheduling of EVs

Extensive usage of electric vehicles (EVs) is very effective, through coordinated scheduling, to provide the solution for scarcity of energy, greenhouse emissions, environmental pollution, and in addition to achieve economic stability and energy security of any nation. India’s pledge of reduction in greenhouse gases emission by 45% till 2030 would catalyze the huge increment in the number of EVs. However, the increased load from the growing number of EVs will lead to the technical challenges in the existing distribution network such as deterioration of voltage profile and increased peak load due to unplanned EV charging stations. Since, the charging load of EVs is very uncertain in terms of amount of charging power, charging location, and charging time due to its movable nature. To address these potential issues, the smart EVs and smart power grid equipped with EV aggregators would help to assess the accurate charging demand at specific times in a day-cycle. This work examines the fundamental difficulties that arise because of EVs charging load to the network at various penetration levels. The simulation and analysis of the EV models by using random values of arrival and departure times, the SoC are used to calculate the charging load of EVs at random time of day-cycle by using Monte-Carlo approach. The EVs are scheduled to take measures to reduce peak and manage the flat load profiles in a coordinated manner based on the charging margin and charging urgency indices. Daily load data of India and IIT Kanpur campus are used for simulation and validation of proposed methodology. The study in this paper shows the result of coordinated scheduling. The load curve peak is shaved up to 7.0%.