Open AccessA MOPSO-Based Optimal Demand Response Management System for the Integration of Wind-PV-FC-Battery Smart Grid
Author(s) -
Adel A. A. El-Gammal
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8367.118419
Subject(s) - demand response , particle swarm optimization , renewable energy , smart grid , battery (electricity) , wind power , computer science , grid , photovoltaic system , environmental economics , reliability engineering , automotive engineering , mathematical optimization , electricity , simulation , power (physics) , engineering , electrical engineering , economics , physics , geometry , mathematics , quantum mechanics , machine learning
This paper proposes the Multi-Objective Particle Swarm Optimization to optimize the performance of hybrid WindPV-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.