renewable energy, independent energy resources, solar, wind, energy storage, Bayesian analysis, prediction
With the rise of renewable energy comes significant challenges and benefits. The current studies on the incorporation of renewable-energy policies and energy-storage technologies attempt to address the optimization of hybrid energy systems (HESs). However, there is a gap between the currents needs of HES in small towns for energy independence and the understanding of integrated optimization approaches for employing the technology. The purpose of this research is to determine the technical, systematic and financial requirements needed to allow a city or community to become independent of the utilization of traditional energy and develop a reliable program for a clean and environment-friendly energy supply. This paper presents the sensitivity analysis and Bayesian prediction (SABP) method for the optimized design of a hybrid photo-voltaic wind energy system. This method uses the actual data to analyze and compare the main, optimized and desired scenarios of HES designs. The results show that optimized design can minimize the cost of the energy generated while reliably matching local electricity demand. The SABP system helps to eliminate the dependence on traditional energy resources, reduce transition costs by purchasing electricity, and decrease the financial burden of a small city.
This research received the Jiangsu University “Green and Blue Project” Excellent Teaching Team Project (Project No. JSQLGC2017-TD60) and the Innovation Research Team Project of Taizhou Polytechnic College (Project No. TZYTD-16-5).
Jiang, Fengchang; Xie, Haiyan; and Ellen, Oliver, "Hybrid Energy System with Optimized Storage for Improvement of Sustainability in a Small Town" (2018). Faculty Publications – Technology. 3.