Multi-Criteria Decision-Making and Robust Optimization Methodology for Generator Sizing of a Microgrid

Abstract

Microgrids provide multiple benefits to end-use customers and electric utilities, including enhanced reliability and resilience, reduced operational costs, streamlined renewable generation integration, and improved energy efficiency. However, the microgrid technology's large capital cost remains a major barrier to establishing its economic viability. This paper addresses this challenge by proposing a practical methodology for microgrid generation sizing. The proposed methodology uses the concept of robust optimization and a multi-criteria decision-making process, taking overall cost, emission reduction, and demand response into account as important factors in optimal generation sizing. The objective is to minimize the supply gap throughout the year, which is defined as the unmet load or required load curtailment under various load and solar generation scenarios. Numerical simulations on a real-world microgrid, ComEd's Bronzeville Community Microgrid (BCM) on Chicago's South Side, exhibit the practicality of the proposed method and its applications for electric utilities. The study proposes an optimal size of 4.8 MW considering the commercially available generator sizes for the BCM, which has a total peak load of 7 MW, 0.75 MW of PV and 0.5MW/2MWh of Battery energy storage installed.

Applications of Probabilistic Forecasting in Smart Grids: A Review

“…Optimal location and sizing of renewable distributed generation and the charging stations based on maximization the station's total payoff [119] Loads and renewable generations Robust optimization…”