Energy Storage and Forecasting Error Impact Analysis In Photovoltaic Equipped Residential Nano-Grids

Battery energy storage systems (BESS) enable many applications for photovoltaic (PV) equipped nano-grids. Stored excessive energy is utilized for energy arbitrage, demand response during blackouts, and peak shaving. This technology helped utility service providers deal with the duck-curve-effect and intermittency of renewable energy systems. In this paper, we investigate the benefits of using energy storage systems in PV nano-grids for residential sectors and evaluate the relationship between battery presence and system performance. We also analyze the effects of battery degradation on energy independence and self-sufficiency and investigate the relationship between simple payback time and state of health (SoH) levels of nano-grid components. Additionally, we propose a simple rule-based energy supervision strategy and evaluate its performance under various forecasting error levels, highlighting the importance of high-performance forecasting methods for proper energy management systems. Our contributions will help select and optimize PV-battery solutions and energy management algorithms to achieve the highest benefit and minimize investment risks.