A Systematic Approach to the Optimal Planning of Energy Mix for Electric Vehicle Policy

• Main Authors: Aritotle T. Ubando, Ivan Henderson V. Gue, Monorom Rith, Jeremias Gonzaga, Neil Stephen A. Lopez, Jose Bienvenido Manuel M. Biona
• Format: Article
• Language: English
• Published: AIDIC Servizi S.r.l. 2019-10-01
• Series: Chemical Engineering Transactions
• Online Access: https://www.cetjournal.it/index.php/cet/article/view/10643

Electric vehicle offers a cleaner and sustainable alternative to transportation as it eliminates direct carbon dioxide emission through the conventional internal combustion engine. With the increase in the global population and economic development, the demand for transportation and the adoption of electric vehicles is unprecedented. However, the adoption of electric vehicle on a national-scale requires long-term planning of infrastructure development, and energy generation and distribution. The study focuses on the development of a systematic mathematical programming approach in the optimal planning of the energy mix of the additional power generation capacity arising from the adoption of the electric vehicle in a developing country. The study considers the 2030 horizon which includes, the cost of power generation and distribution per energy mix, and the forecasted commissioning and decommissioning of energy plants. The study proposes a fuzzy mixed-integer non-linear programming model in the optimal planning of the energy mix for the adoption of EV while minimizing carbon footprint, minimizing the total capital cost, and minimizing the electricity cost. A case study in the adoption of electric vehicle in the Philippines will be utilized to demonstrate the capability of the model. In addition, a comparison of the electricity cost of the business as usual (BAU) scenario and this study has been evaluated. The results show that the various renewable energy technologies for power generation are selected initially from 2019 to 2022 and 2029 to 2030, while the fossil-fuel based power plants were utilized from 2023 to 2028. The results revealed the electricity cost from the study is relatively lower than the BAU scenario. The results of the model are intended to aid and guide policymakers in the potential adoption of electric vehicles, especially in the energy planning sector.