A NLP approach to optimally size an energy storage system for proper utilization of renewable energy sources

Abstract

The fast depleting fossil fuel resources and increasing global pollution due to harmful emissions has increased the need of the use of alternate clean energy sources, the renewable energy sources. These energy sources are abundant in nature and cause minimal or no harm to the environment. However, their intermittent nature causes concern while integrating the renewable energy sources to the grid. The varying nature of these sources results in imbalance in the demand and supply. Through the use of energy storage systems, the imbalance due to the intermittency can be mitigated and stability, reliability of the system can be restored. The energy storage systems have varying capacity and utilizing the optimal size for service requirements is very important. It helps to enhance the economic benefits; the total cost of the system is reduced and also helps in accommodating higher penetration of the renewable energy sources. Different energy storage systems have different operational parameters and capital cost. It is therefore important to select the appropriate energy storage system considering the cost and other parameters such as response speed, round-trip efficiency, depth of discharge, etc. In this paper, the optimal sizing problem of the storage system is developed as an optimization problem using nonlinear programming approach in GAMS considering various operational constraints to get the minimum total cost of the system. The model is then tested with the data of a renewable energy farm and the corresponding results are studied. The results show the optimal capacity of the energy storage system and other parameters showing its operation with respect to the energy generation and demand of the farm. In the study, two different storage systems namely battery energy storage system and pumped hydro storage system have been considered. The optimal sizing model is tested with each one of them and a comparative study is done considering their different operational parameters and cost. The results show the respective capacities and cost for the different energy storage systems.