Microgrid Optimal Sizing and Scheduling for Feasibility Studies

Community microgrids play a significant role in the decarbonization of the power grid while providing support for resilience, backup, and stability, due to their high penetration of renewables and Distributed Energy Resources. However, the feasibility analysis of community microgrids is a complex process often requiring multiple building scenarios, complicated technology models, weather forecasting, and a high level of technical expertise. This paper aims to abstract the complexity of the optimal planning process by presenting a comprehensive mathematical formulation and optimization technique used for sizing and scheduling of power generation, CHP, and storage technologies. The problem involves a dual formulation utilizing a metaheuristic Genetic Algorithm for sizing of the technology components under specified constraints while minimizing total investment cost, and a Mixed Integer Linear Programming problem for optimal scheduling of generation and storage technologies, ensuring compliance with system-related constraints. This model has been implemented as part of an online microgrid and district energy planning tool developed in a collaboration between the University of Houston, Fugro and, the Houston Advanced Research Center (HARC) through a grant funded by the US Department of Energy (DOE).