Abstract
This paper proposes a multi-objective optimization technique for scheduling the charging of electric vehicles (EVs) in electrical distribution systems (DSs). A multi-objective advanced grey wolf optimization algorithm (MOAGWO) is developed to find the Pareto optimal solutions that minimize the DS's operational costs, energy losses costs, voltage violations, and the energy not supplied to EV users using several scenarios. A 449-node system with 63% penetration of EVs is used to demonstrate the efficiency of the proposed method. The quality of the non-dominated optimal solutions found by MOAGWO are validated via a comparison analysis with other well-known methods such as the multi-objective grey wolf optimizer (MOGWO) and the multi-objective particle swarm optimization (MOPSO) algorithm, based on domination rate, spacing index, hypervolume index, and computational cost measurements. The Pareto solutions indicate that the smart charging coordination found by MOAGWO makes the techno-economic operation of the DS possible while satisfying energy-based goals of the EV users.
Original language | English |
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Title of host publication | 2022 57th International Universities Power Engineering Conference |
Subtitle of host publication | Big Data and Smart Grids, UPEC 2022 - Proceedings |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
ISBN (Electronic) | 978-1-6654-5505-3 |
ISBN (Print) | 978-1-6654-5506-0 |
DOIs | |
Publication status | Published - 18 Oct 2022 |
Event | 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Istanbul, Turkey Duration: 30 Aug 2022 → 2 Sept 2022 Conference number: 57 |
Conference
Conference | 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 |
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Abbreviated title | UPEC 2022 |
Country/Territory | Turkey |
City | Istanbul |
Period | 30/08/22 → 2/09/22 |
Keywords
- Electric vehicles
- Meta-heuristic
- Multi-objective optimization
- Pareto front
- Smart grids
- 22/4 OA procedure