Joint Planning of Smart EV Charging Stations and DGs in Eco-Friendly Remote Hybrid Microgrids

• Published in: IEEE transactions on smart grid 2019-09, Vol.10 (5), p.5819-5830
• Main Authors: Shaaban, Mostafa F., Mohamed, Sayed, Ismail, Muhammad, Qaraqe, Khalid A., Serpedin, Erchin
• Format: Article
• Language: English
• Subjects: Algorithms; Charging stations; Classification; Community planning; Computer simulation; Distributed generation; Electric power grids; Electric vehicle charging; Electric vehicle charging stations; Electric vehicles; EV charging stations; Genetic algorithms; Greenhouse effect; Greenhouse gases; hybrid microgrids; islanded microgrids; Load modeling; microgrid planning; Microgrids; Mixed integer; Multiple objective analysis; Planning; Reactive power; remote microgrids; Sorting algorithms; State of charge
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2019.2891900

This paper proposes an efficient planning algorithm for allocating smart electric vehicle (EV) charging stations in remote communities. The planning problem jointly allocates and sizes a set of distributed generators (DGs) along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging. The planning algorithm specifies optimal locations and sizes of the EV charging stations and DG units that minimize two conflicting objectives: 1) deployment and operation costs and 2) associated green house gas emissions, while satisfying the microgrid technical constraints. This is achieved by iteratively solving a multi-objective mixed integer non-linear program. An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting genetic algorithm. Given the allocation and sizing decisions, an inner sub-problem ensures smart, reliable, and eco-friendly operation of the microgrid by solving a non-linear scheduling problem. The proposed algorithm results in a Pareto frontier that captures the tradeoff between the conflicting planning objectives. Simulation studies investigate the performance of the proposed planning algorithm in order to obtain a compromise planning solution.