An Optimal Scheduling of Plug-in Hybrid Electric Vehicle Operation Using Discrete Jellyfish Search Optimizer

Abstract

This paper presents an optimal plug-in hybrid electric vehicle (PHEV) operation scheduling framework using a discrete jellyfish search optimizer (DJSO) to tackle intermittent issue of consumption and generation from renewable resources. The PHEV scheduling framework aims to improve the efficiency of distribution system operation by minimizing transmission power losses. This can be achieved by suggesting the best location for charging and discharging of PHEVs and number of their participation at each location. A modified 33-bus benchmark system is used as a test system to showcase the performance of the proposed framework in two worst case situations: high generation at low demand and high demand at no generation. The performance of DJSO is evaluated by comparing it with other well-known heuristic optimization techniques. In addition, various decision functions are considered to find the most suitable decision function in solving the PHEV scheduling problem. The results show that JSO outperforms GSA, GWO and WDO in terms of accuracy for an optimal PHEV scheduling solution. A simple rounding decision function is found to be the best function to transform JSO into a discrete optimization in the form of DJSO. The improvement of power system efficiency can be achieved up to 12.7% during high generation at low demand, and 14.8% during high demand at no generation when the PHEV scheduling scheme is in place. As a conclusion, a new paradigm of PHEVs as a medium for energy transportation will help to reduce burden on the existing infrastructure.