Multi-year analysis for optimal behind-the-meter battery storage sizing and scheduling: A Norwegian case study

Electricity can be a significant cost for large commercial/industrial consumers, and optimal dispatch of behind-the-meter battery storage systems (BTM-BSS) have the potential to reduce these costs. Using the Oslo airport in Norway as a case study, this study proposes a Mixed-Integer Nonlinear Programming (MINLP) optimization model for monthly BTM-BSS dispatch, to explore the potential electricity cost savings. The model schedules the BTM-BSS dispatch for each month between 2015 and 2022 (on an hourly basis). The results demonstrate the financial viability of BTM-BSS. The paper shows that the potential operational savings obtained are dependent on tariff (both energy costs and power costs) set by the regulator, and power charges reduction is prioritized over energy charges reduction. The study further presents a methodology to calculate the optimal BTM-BSS size for the system, based on capital costs, multi-year electricity tariffs and energy demand. By employing a multiyear analytical approach, the study uncovers that the optimal BTM-BSS sizing distinctly varies from what is suggested by a single-year analysis, highlighting the importance of long-term data consideration. The optimal battery size of 3 MW/12MWh for a 267MWh average daily system demand, reduces the peak load of the system by 9.35 % on average and the total electricity costs by 6.4 %. The break-even period is 8.9 years, yielding a Return on Investment (ROI) of 124 %. A sensitivity analysis examining how BTM-BSS profitability and battery dispatch curves vary with electricity tariffs (power charges, hourly energy price) and battery operational constraints is also presented. Battery degradation is also introduced in a scenario to quantify its impact on the BTM-BSS operations and profitability. Overall, the findings highlight the potential benefits of optimally operating BTM-BSS in real-world settings and the importance of sizing the BTM-BSS systems on multiyear datasets. • A methodology for optimal sizing of BTM-BSS based on multi-year analysis. • Multiyear vs single year analysis present vastly different financial outlooks. • Optimal BTM-BSS sizing maximizes investment returns. • Power charges reduction is prioritized over energy charges reduction. • BTM-BSS is economically viable for large electricity consumers in Norway.