Performance optimization of integrated gas and power within microgrids using hybrid PSO–PS algorithm. (27th January 2016) - Gabbar, Hossam A.; Labbi, Yacine; Bower, Lowell; Pandya, Devarsh
Summary: In this paper, a hybrid algorithm consisting of particle swarm optimization and pattern search algorithm is proposed to evaluate and optimize the design and operation of microgrids (MGs) in combined gas and power networks. Key performance indicators (KPIs) are modeled and proposed to evaluate and assess MGs. The paper begins by proposing a comprehensive study to define KPIs, which are used to evaluate the following MG parameters: economical efficiency, reliability, environmental conservation, and power quality. Multi‐objective evaluation functions are then developed by building a relationship matrix of MG and KPI components. The results are then displayed as optimized power generation percentages for each technology with values for four KPI categories: cost, quality, reliability and environmental friendliness. Two case studies are examined in this paper; both the province of Ontario and Toronto regional zone under all system parameters with varying percentage of generation via gas technology. Results indicated that the optimal scenario for both Ontario and Toronto was achieved at hybrid PSO–patern search percentage generation via gas technology with improved cost KPI and other KPIs remaining approximately constant. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : In this paper, we present a hybrid particle swarm optimization‐pattern search algorithm to evaluate and optimize the design and operation of microgrids in combined gas and power networks. The principle goal in this study is to improve the main key performance indicator categories, which are: cost, quality, reliability, and environmental friendliness. Two scenarios were analyzed within the paper; results indicated that the optimal scenario was achieved at hybrid particle swarm optimization‐pattern search percentage generation via gas technology.