Heuristic approaches to size microgrids: a methodology to compile multiple design options

Abstract

With the aim of maximizing profits of specific business applications, economics, and sometimes reliability and environmental constraints, have been widely guiding developers when designing microgrids. However, mathematical indicators alone, yet relevant, may not be able to fully capture the sociopolitical and geographical circumstances under which developers operate, especially in rural areas of developing countries. In this paper, we propose a methodology for obtaining microgrid designs that not only achieves the traditional economic-efficient optimal solution but also suggests multiple design options that increase the eligibility for developers, which can select an option given their particular circumstances. Based on a consolidated heuristic method, Particle Swarm Optimization, our algorithm identifies several design options of the microgrid's components, by using an iterative approach that stores the simulations occurring in each iteration. The results from an illustrative numerical case study highlight that significantly different designs can lead to similar values of the objective function, i.e. investment and operational costs. Our proposed methodology is of particular interest for developers, who have the opportunity to choose among a set of different technological solutions, but similar in economic terms.With the aim of maximizing profits of specific business applications, economics, and sometimes reliability and environmental constraints, have been widely guiding developers when designing microgrids. However, mathematical indicators alone, yet relevant, may not be able to fully capture the sociopolitical and geographical circumstances under which developers operate, especially in rural areas of developing countries. In this paper, we propose a methodology for obtaining microgrid designs that not only achieves the traditional economic-efficient optimal solution but also suggests multiple design options that increase the eligibility for developers, which can select an option given their particular circumstances. Based on a consolidated heuristic method, Particle Swarm Optimization, our algorithm identifies several design options of the microgrid's components, by using an iterative approach that stores the simulations occurring in each iteration. The results from an illustrative numerical case study highlight that significantly different designs can lead to similar values of the objective function, i.e. investment and operational costs. Our proposed methodology is of particular interest for developers, who have the opportunity to choose among a set of different technological solutions, but similar in economic terms.