Title: Scheduling energy storage unit with GWO for smart home integrated with renewable energy
Authors: Srivathsan Lakshminarayanan; Musbah Abdulgader; Devinder Kaur
Addresses: Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH 34607, USA ' Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH 34607, USA ' Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH 34607, USA
Abstract: The paper proposes a novel swarm-based grey wolf optimiser (GWO) algorithm to optimally schedule the energy storage unit for a smart home integrated with the renewable energy resources such as wind and solar. The proposed method does not impose any restrictions regarding when to use appliances and ensures that all the household demands are met any time of the day. It reduces the cost of energy consumption for the user and at the same time balances the load on the grid by drawing less energy from the grid when the demand is high. Whenever the renewable energy resources are generating power, they are used for meeting the demand and to charge the ESU. The excess power generated is sold back to the utility at the same hourly price. The GWO was tested using data obtained from the United States Department of Energy for Chicago and outperformed PSO.
Keywords: smart grid; grey wolf optimiser; GWO; swarm intelligence; green energy; natural computing; energy storage.
International Journal of Artificial Intelligence and Soft Computing, 2020 Vol.7 No.2, pp.146 - 163
Accepted: 14 Nov 2019
Published online: 08 Mar 2021 *