Microgrid planning based on computational intelligence methods for rural communities: A case study in the José Painecura Mapuche community, Chile

Abstract

Microgrids (MGs) are sustainable solutions for rural zone electrification that use local renewable resources. However, only careful planning at the start of an MG project can ensure its future optimal operation. In this paper, a novel methodology for MG planning by using the uncertainty characterization of renewable resources and demand is presented. Additionally, a model of electricity consumption is proposed and applied in an isolated rural community. In such communities, consumption patterns typically need to be derived as model inputs because consumption measurements are not available for the planning stage. To obtain these inputs, clustering algorithms based on self-organizing maps (SOMs) and fuzzy c-means are used to classify the families of the community given sociodemographic information obtained via surveys. Subsequently, Markov chains (MCs) are employed to generate consumption patterns based on consumption measurements in some dwellings and surveys applied to the community. The nonlinearities and uncertainties associated with renewable resources and consumption are modeled by using prediction interval (PI) models. These PI models provide the required consumption and generation scenarios for deriving the optimal sizing and topological information to address the MG planning problem. The results of the robust planning approach based on scenarios are useful at the feasibility and design phases of an MG project. The proposed methodology is successfully applied to MG planning for a rural Mapuche community, where a conservative criterion was considered to minimize the investment risk. This criterion corresponds to the worst-case scenario in which the demand increases by 19.9% compared to that of the baseline scenario and a lower energy cost is obtained. However, the net present cost and operational costs increase by 14% and 11.75% compared to those of the baseline scenario, respectively.