Optimal operation of multi-source electric vehicle connected microgrid using metaheuristic algorithm

N. S. Jayalakshmi, Vinay Kumar Jadoun, D. N. Gaonkar, Ashish Shrivastava, Neeraj Kanwar, K. K. Nandini

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In this paper, a multi-source microgrid (MG) has been considered which inducts power from solar photovoltaic (PV), wind turbine, pumped hydro storage system (PHSS) and diesel generator (DG). A problem formulation is proposed on a multi-source MG considering an electric vehicle (EV) as source and load demand. A modified operation strategy is proposed to achieve the lowest possible fuel usage of DG and to optimize the operation of multi-sources used in the MG. When the sum of PV, wind power production and EV discharge is less than the load requirement, the required deficit power should be delivered by DG and PHS. This work considers PV and wind as the primary energy supplying sources, while DG, EV and PHS as the additional energy suppliers with EV and PHS as energy storage systems. By properly coordinating EVs, they can become a major contributor to the successful execution of the MG concept. In this work, a modified charging/discharging algorithm is presented to check the effect of EVs to supply a portion of peak loads with PHS to reduce the fuel consumption of DG in three diverse modes of operation. A modified whale optimization algorithm (WOA) and teaching learning-based optimization (TLBO) are applied to effectively solve this proposed complex problem using the MATLAB platform. The optimum solutions obtained after different independent trials by both the techniques are compared with the latest published techniques. It can be observed that modified WOA performs better than TLBO and other recently published methods on the base case and proposed multi-source MG case in three diverse modes of operation. The outcomes of the simulation confirm the effectiveness of modified WOA in reducing fuel consumption.

Original languageEnglish
Article number105067
JournalJournal of Energy Storage
Publication statusPublished - 25-08-2022

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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