SUMMARYIn 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.
The world is moving towards smart energy grid with green and clean infrastructure which will enable efficient bidirectional energy supply with reduced carbon footprint. Due to increasing energy demands and the pressing issues of efficient energy use, there is a real need to increase the penetration of gas technologies in the power grid. The government of Canada and stakeholders are looking for ways to increase the reliability and sustainability of the power grid; and gas-power technologies may provide a solution. This paper explores the integration of gas and renewable energy generation technologies within various electricity generation scenarios with the goal of developing designs for a resilient micro energy grid (MEG). The distinct scenarios are then evaluated using an advanced algorithm to provide optimum scenario depending on various key performance indicators (KPIs). KPIs to be examined include: economic, power quality, reliability, and environmental friendliness. This work is done using three different systems; geographic information system (GIS) for recording transmission /distribution lines and generation data, a database to store the information, and a MATLAB-based algorithm for evaluating scenarios. These systems are synthesized and represented into a graphical user interface (GUI), where the user defines the zone, area and cell for desired output and system parameters to generate distinct scenarios to identify the optimum generation.
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