Towards sustainable energy systems: Multi-objective microgrid sizing for environmental and economic optimization

This paper proposes a new method for the multi-objective sizing of microgrids, which aims to minimize both the investment and operation costs, as well as the carbon footprint of their components and energy usage. The method employs Mixed Integer Linear Programming (MILP) and Pareto optimization to assess the balance between economic and environmental goals, constructed using the ϵ-constraint method. Additionally, the overall operation of a grid-connected microgrid is optimized considering unintentional islanding contingencies through a stochastic scenario-based mathematical programming model. Tests were conducted using data from CampusGrid, a real microgrid located at the University of Campinas (UNICAMP) in Brazil. The model determines the optimal size and type of Distributed Energy Resources (DERs), such as local Thermal Generation (TG), Photovoltaic (PV) systems, Battery Energy Storage Systems (BESSs), and load/generation curtailment requirements in islanded mode. For carbon-intensity comparison, a case study was conducted using attributes and parameters from the city of Beijing in China. The results provide valuable insights into the optimal sizing and configuration of microgrids, with an emphasis on cost-efficient and environmentally sounding energy solutions.