A forecast-driven stochastic optimization method for proactive activation of manual reserves

Abstract

Reducing operating balancing costs is paramount for an affordable transition towards renewable-dominated power systems. In European balancing markets, operating balancing costs are driven by the activation of automatic and manual frequency restoration reserves, respectively aFRR and mFRR. An inadequate combination of both products for resolving grid imbalances may result in economic inefficiencies where, e.g., saturated aFRR can lead to balancing price spikes. To avoid such situation, we propose a proactive activation policy of manual reserves, aiming at an optimal trade-off between aFRR and mFRR products via a stochastic optimization method. The tool is fed with 1-min time trajectories of system imbalances covering the next quarter hour. The one minute temporal granularity allows modeling the ramping phenomena of mFRR products, while keeping track of the faster activation of aFRR products. The proposed balancing energy activation methodology is tested on Belgian market data, which currently adopts a reactive balancing strategy. Ex-post comparisons of the proposed balancing strategy with a reactive one show that our methodology allows a decrease of the balancing activation costs. This is expected as early activation of mFRR, when appropriately provided, allows avoiding the activation of extremely high aFRR bids.