Strategic Implicit Balancing With Energy Storage Systems via Stochastic Model Predictive Control
Ruben Smets, Kenneth Bruninx, Jérémie Bottieau, Jean‐François Toubeau, Erik Delarue
Abstract
Battery Energy Storage Systems (BESS) may exploit the increasing price volatility in imbalance settlement mechanisms via inter-temporal arbitrage. However, participating in these markets requires a careful trade-off between expected profits, accounting for the impact of BESS actions on prevailing imbalance prices, the financial risks and the incurred battery degradation costs. This paper introduces a novel forecast-informed Model Predictive Control (MPC) methodology in which a strategic and potentially risk-averse BESS performs implicit balancing by taking out-of-balance positions in near-real time. Thereby it anticipates expected imbalance prices in a European-style balancing market, and takes into account state of charge-dependent battery degradation costs. To this end, an attention-based recurrent neural network forecasting technique is leveraged to predict the System Imbalance. The proposed methodology is tested on a real-life case study of the Belgian balancing market. Expected profits of a 2 MW/2 MWh BESS (21,784 €/MW/month) are shown to exceed those of different benchmarks available in the literature, including the profit associated with participating in the day-ahead energy market with perfect price foresight (7,082 €/MW/month). From a system perspective, these implicit balancing actions performed by the BESS owner reduce the system imbalance in 75% of all cases, thus improving the cost-efficiency of power systems.