Simple moving average based capacity optimization for VRLA battery in PV power smoothing application using MCTLBO

Rapid depletion of fossil fuel reserves and alarming increase of environmental pollution shift the researchers' attention towards renewable energy sources technologies like solar photovoltaic (PV). But solar radiation being affected by natural factors, result in uncertain power generation which leads to lower power system reliability. This paper proposes a smoothing strategy of generated PV power using gelled electrolyte valve regulated lead acid (VRLA) type battery energy storage system (BESS). The BESS stores the excess energy and releases it to meet the load demand in case of power surplus and deficit, respectively. The IEEE-RBTS is considered as the basic system for the study. But using large BESS incur humungous cost. Hence Multi-course teaching learning based multi-objective optimization technique (MCTLBO) is utilized to find out the optimal size of the PV panel, the BESS and the smoothening duration. Here, the objectives are to obtain minimum financial loss due to power outage as well as minimum BESS life cycle cost. MCTLBO is proposed here to improve the performance of the traditional teaching learning based optimization technique and it shows promising results. Factors affecting the power output of the PV panels are also considered here. The simulation is performed considering real time solar irradiance and temperature data of a city located on the eastern coast of India and the results obtained are both technically and economically viable in Indian context.