Stochastic programming of energy system operations considering terminal energy storage levels

Energy storage units offer vital balancing power for energy systems with an increasing amount of variable renewable energy (VRE) sources. The operation of such systems can be optimized by stochastic programming, which anticipates the uncertainty related to the variable renewable energy sources. However, these optimization problems can only be formulated for optimization horizons with a finite length, due to the rapidly increasing problem size and uncertainty in VRE production. Realistic valuation of the stored energy at the end of a horizon is important for long-term operation of the system. In this work, we investigate two different valuation methods, which are based on forecasted electricity prices, for storage-only and producer-oriented energy systems that participate in the day-ahead market. On a case study on the German day-ahead market, the methods yield competitive profits with reduced cycling of the energy storage unit and deviations with respect to the day-ahead trading.