Optimizing energy arbitrage with battery storage and the long-term valuation of batteries requires a holistic understanding of battery placement on the energy grid. For this demonstration, we rely on an nodal model that includes all the factors that impact an energy system (including fuel prices, complexity of the transmission grid, different generation sources, including solar, wind, and thermal as well as variable demand), to create a real-world example.
We are interested in modeling a battery, named Fermi, with 90% charge efficiency and a 100% discharge efficiency. By factoring in a maximum power of 15 MW and an overall total capacity of 45 MWh, we can replicate a real-world battery asset.
We start by examining energy pricing over a single week in August. PLEXOS enables us to identify optimal charge and discharge times to maximize returns. In the graph below, energy pricing is shown in blue. Where most models assume the daily frequency and amount of charge and discharge to remain constant, PLEXOS maximizes returns by charging at the lowest price points even when multiple days may elapse between charge cycles.