Affiliation:
1. School of Electrical & Information Engineering, The University of Sydney, Sydney 2006, NSW, Australia
Abstract
Abstract
Microgrids play a critical role in the transition from conventional centralized power systems to the smart distributed networks of the future. To achieve the greatest outputs from microgrids, a comprehensive multi-objective optimization plan is necessary. Among various conflicting planning objectives, emissions and cost are primary concerns in microgrid optimization. In this work, two novel procedures, i.e., non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), were developed to minimize emissions and cost in combined heat- and power-based (CHP) industrial microgrids (IMGs) simultaneously, by applying the most practical constraints and considering the variable loads. Two different scenarios, the presence and absence of photovoltaics (PV) and PV storage systems, were analyzed. The results concluded that when considering PVs and PV storage systems, the NSGA-II algorithm provides the most optimized solution in minimizing economic and environmental objectives.
Subject
Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment
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