A MOPSO-Based Optimal Demand Response Management System for the Integration of Wind-PV-FC-Battery Smart Grid
Adel Elgammal
Adel Elgammal, Associate Professor, Department of Energy Systems, University of Trinidad and Tobago UTT.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 4402-4410 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8367118419/2019©BEIESP | DOI: 10.35940/ijrte.D8367.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper proposes the Multi-Objective Particle Swarm Optimization to optimize the performance of hybrid Wind-PV-FC-Battery smart grid to minimize operating costs and emissions. The demand response strategy based on the real-time pricing program with the participation of all kinds of consumers such as residential, commercial and industrial consumers is utilized in order to resolve the power generation uncertainty of renewable energy sources. The multi-objective particle swarm optimization based energy management programming model will be leveraged to reduce the operation costs, emission of pollutants, increase the micro grid operator’s demand response benefits and at the same time satisfying the load demand constraints amongst the others. For the purpose of validating the proposed model, the simulation results are considered for different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The simulation results precisely concluded the impact created by the demand side management in reducing the effects of uncertainty that prevails in forecasted power generation through solar cells and wind turbines.
Keywords: Renewable energy sources, Particle swarm optimization, Energy management, Load management, Energy storage.
Scope of the Article: Renewable Energy Technology.