Simulating a V2G Concept for Optimized EV Fleet Charging in India

Abstract

<div class="section abstract"><div class="htmlview paragraph">Road transport is bound to play a major role in the imminent transition to green energy. India has pledged to reach net-zero greenhouse gas emissions by 2070 at the COP26 [<span class="xref">1</span>] and is committed to have 30% electric vehicle (EV) sales by 2030 [<span class="xref">2</span>]. The Indian government is promoting fleet electrification through initiatives like FAME–II. India’s EV market is expected to grow at an annual rate of 90% between 2022 and 2030 [<span class="xref">3</span>].</div><div class="htmlview paragraph">With this projection combined with climate targets, comes an anticipated exponential rise in renewable energy contribution to the national power grid, accompanied by a huge transport-related demand for electricity. NITI Aayog – India’s public policy think tank – and the Ministry of Power are already looking into the expansion of EV charging infrastructure in India as part of smart grid implementation.</div><div class="htmlview paragraph">The deployment of Vehicle-to-Grid (V2G) technology as an extension of the smart charging initiative is essential for a smooth transition to renewable energy. The possibility of bi-directional energy flow between the EV battery and the power grid can be used to stabilize the grid demand curve during peak hours. This will also encourage usage of local energy sources like rooftop solar and further incentivize participation in the frequency response services market as a source of revenue for end-users and charging point operators (CPOs).</div><div class="htmlview paragraph">In this context, this study aims to quantify the benefits of load shifting strategies and dynamic tariffs with respect to V2G charging. An EV fleet at one charging point has been investigated. This was done by making certain data assumptions (outside the current legislative purview) like start and target SoC, arrival and departure times of the EVs etc. Weather data (e.g., solar irradiation etc.) and market data, wherever available from open-source platforms have been aggregated and used. Cost minima are achieved by a combination of peak shaving, rooftop solar self-consumption, and participation in energy markets. Relative to grid-compliant charging, the simulation results show a significant (~28%) cost reduction using the V2G smart charging algorithm.</div></div>