EV Fleet Energy Management Strategy For Smart Microgrids Considering Multiple Objectives: Techno-Economic Perspective

Abstract

Rapid advancements in battery technologies led to dramatic growth in adoption of electric vehicles (EVs) all over the world. On the other hand, ever-increasing renewable energy sources (RES) in microgrids (MGs) posing numerous challenges ahead. In this context, EVs can be used as virtual storage units to confront the intermittency aspect of RES in MG scenarios. This work proposes an EV fleet control strategy to implement a three-layer energy management system: Optimal storage distribution (OSD), optimal power exchange (OPE) and smart EV ranking (SER). The key objectives are minimizing grid dependency, energy cost, EV battery degradation and to maximize EV storage usage. Water filling algorithm is used to obtain OSD and multi-objective optimization problem is formulated and solved by e-constraint method to obtain OPE. SER is implemented using a fuzzy logic controller where a number of decision variables are involved. EV battery degradation has been considered through SER by including a key decision variable, EV usage probability (EUP). EUP has been obtained using a probabilistic approach that accounts all possible state transitions of each EV in the given time interval. An on-grid MG scenario with EV fleets and RES is considered to implement the proposed EMS.