Sensitivity of energy storage system optimization program to the source of renewable energy in the presence of demand side management: A behind-the-meter case study

Abstract

The trend of installing renewable energy in behind-the-meter (BTM) has increased to support energy transition. Energy-saving methodologies, such as energy storage systems (ESSs) and demand side management (DSM), are available to augment renewable energies and maximize their benefits. This research examines the impact of different renewable energy sources on energy-saving strategies, system installations, and long-term planning decisions. This study presents an optimization framework to determine the Energy Storage Systems (ESS) capacity and Demand Side Management (DSM) strategies. The effect of solar and wind renewable energies on the optimization program results was compared to aid the selection of suitable renewable energy and energy-saving methodologies for an energy load profile. Renewable energy and its penetration were studied based on its impact on energy purchase, system installation size, savings, renewable curtailment, and levelized cost of storage (LCOS). ESS and DSM options are compared, and their synergies are explored to minimize energy purchases and control peak power. Furthermore, the ongoing changes in influential factors, such as electricity prices, battery costs, and roundtrip efficiency, underscore their importance in system installation planning and to maintain a realistic perspective on these parameters. Therefore, an analysis of these influential factors is conducted, serving as a sensitivity analysis for the proposed model. This shows that system estimates can triple, highlighting the necessity of realistically considering these factors to avert potential failure or loss. This study is performed on the case of the Natural Energy Laboratory of Hawaii Authority (NELHA), the world's largest multizone seawater utility. The proposed optimization framework selects suitable renewable energy and energy-saving methodologies for the considered case and is validated with the state-of-the-art tool. The proposed optimization framework in this study will support water utilities interested in installing renewable energy and energy storage systems.