Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs

Abstract

Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO2 emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

“…For functioning day-ahead power forecasting that utilizes numerical temperature prediction, a head-to-head opposition of the physical, data-driven, and mixed approaches is carried out in Ledmaoui et al 27 . For optimizing the scale of an off-grid energy system, a data-driven approach is suggested in Mayer 28 that blends hybrid meta-heuristics and machine learning to anticipate weather patterns over the system's lifetime. A deep learning-driven allocating and battery storage operation and water electrolyzer is carried out in Abdullah et al 29 .…”

“…These methods are offered to take into account four elements: projected data derived from machine learning algorithms; uncertainty; fuzzy multi-objective architecture; and battery energy storage. The following are some of the methods that have been used in the literature: (1) RES optimization in distribution networks without battery storage 9-14 , (2) RES optimization in distribution networks with battery storage 11,[15][16][17][18][19][20][21][22] , (3) fuzzy multi-objective RES optimization 11,16,[19][20][21] , and (4) RES optimization incorporating data forecasting [25][26][27][28][29] . As is evident, no research has been done on the hybrid PV/WT/BES microgrid systems optimization taking into account all four aspects according to the authors' knowledge so far.…”

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

“…Designing and implementing a new metaheuristic optimization algorithm takes time, but there are several pressing basic needs for them that motivate academics to develop a new algorithm [26]. Some new research papers have focused on integrating these algorithms with features of artificial intelligence (AI) to improve their performance [27][28][29]. Many academic papers, including [30,31], have described their fundamental properties and advantages as follows:…”