OPTIMAL BATTERY SIZING OF MICROGRIDS USING MACHINE LEARNING

Abstract

Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated by using an existing technique algorithm and proposed technique algorithms. The performance of the six best-performing existing feature selection algorithms is analyzed. The results show that the existing feature selection algorithms improve performance of the proposed methodology. Ranker search shows the best result with a SROCC of 0.9447, LCC of 0.9756, KCC of 0.8488 and RMSE of 0.0525. The performance of the proposed feature selection algorithms is analyzed. The experimental results show that the proposed feature selection algorithms improve the performance of the proposed methodology. LCSRKC indicates the best result with a SROCC of 0.9578, LCC of 0.9817, KCC of 0.8731 and RMSE of 0.0475.