On the financial consequences of simplified battery sizing models without considering operational details

Existing battery sizing methods tend to oversimplify battery operation within their sizing frameworks by ignoring several practical aspects of operation. Such assumptions may lead to suboptimal battery capacity, resulting in significant financial losses in battery projects. In this study, we compared the most common existing battery sizing methods in the literature with a battery sizing model that incorporates more realistic battery operation, specifically using receding horizon operation, also known as model predictive control. This approach continuously updates battery decisions based on new data and forecasts, ensuring realistic operation over the sizing period. In our comprehensive simulation studies, we quantified the financial losses caused by the suboptimal capacities obtained by these models for a realistic case study related to community battery storage (CBS). We developed a case study by constructing a mathematical framework for CBS and local end users. Our analysis indicates that conventional sizing strategies can cause financial losses as much as 22% in a simulation study with 84-day out-of-sample data including 120 end users in real wholesale market scenarios in New South Wales, Australia. • Quantifies financial losses up to 22% caused by simplified battery sizing methods. • Introduces a receding horizon operation (RHO) model for improved battery sizing accuracy. • Uses real tariffs, wholesale market prices and user data to assess sizing outcomes. • Benchmarks existing RHO models and perfect foresight sizing methods. • Highlights trade-offs in battery cost, peak reduction, and energy use for local energy communities.