Conducting a parametric study on microgrid energy and economic performance with system optimization using Taguchi-ANOVA approach

Microgrids can effectively decrease carbon emissions and help achieve sustainable development of society. However, higher renewable energy penetration may go against reliable energy supply due to the inherent intermission and randomness of renewable energy sources. To address this issue, battery storage, backup generators, and power supply from the electrical grid can be considered. High-capacity renewable generators and battery storage can enhance renewable energy penetration but are costly. Meanwhile, although power supply from the electrical grid can ensure the reliability, it may impose huge stress on the electrical grid. Peak load shifting to decrease the fluctuation of the power supply from electrical grid is, therefore, a necessary solution to relieve the stress from the grid. In this study, a comprehensive parametric study of the microgrid performance was conducted regarding renewable energy penetration, economics, and peak load shifting. Five key factors of PV panel area, wind turbine capacity, gas turbine capacity, battery storage, and the portfolios of the chillers were considered each with three levels. Taguchi-ANOVA approach was adopted to conduct the parametric study and obtain the overall optimal condition with relatively low computational cost. The simulation results showed that the PV panel area had the highest significance in renewable energy penetration. In contrast, the gas turbine capacity as the backup power generator was found to have the highest significance in economics and peak load shifting. Thereafter, the microgrid optimal design was determined considering equal weight for all three output indicators as a trade-off among higher renewable energy penetration, peak load shifting, and economic benefit. The optimal values for the mentioned five factors were PV area of 375 m², wind turbine of 90 kW, backup power generator capacity of 78 kW, and the chiller portfolio of three 100 kW chillers.