Strategic Implicit Balancing With Energy Storage Systems via Stochastic Model Predictive Control

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.