The growing integration of Battery Energy Storage Systems into electricity markets has highlighted the importance of coordinated participation across energy and balancing services to fully exploit their operational flexibility. However, existing revenue-stacking models often simplify market sequences and neglect the impact of rolling forecasts, leading to unrealistic scheduling and overestimated revenues. This paper addresses this gap by introducing a sequential, data-driven optimization framework for Front-of-the-Meter Battery Energy Storage Systems that mirrors actual market operations. The framework explicitly models market mechanisms and its respective Gate Closure Times across Frequency Containment Reserve, automated Frequency Restoration Reserve, Day-Ahead Auction, and Intraday Continuous markets. Each market stage optimizes expected revenue over all remaining markets using updated price forecasts while maintaining feasibility within both technical and regulatory limits. A key contribution is the opportunity-cost-based bidding strategy, which endogenously derives market-consistent bid prices and quantities from residual capacity optimization. Validation for a representative operating day demonstrates that the framework yields consistent and feasible schedules, effectively adapts to updated forecasts, and minimizes deviations between planned and realized dispatch, thereby enhancing the realism and profitability of BESS operation.
Steffen Kortmann, Hannah Sanders, Andreas Ulbig· 0 citations
Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the policy is tested on the physical grid. This assessment is difficult for two reasons. First, simulations cannot capture every disturbance, modeling error, and device interaction present in the real grid. Second, historical measurements reflect operation under existing control policies, whereas a new policy may drive the grid into different operating conditions. To address these challenges, we propose Distributionally Robust Conformal Safety Screening (DR-CSS), a policy-agnostic framework for pre-deployment, scenario-by-scenario screening of a new control policy using historical data and a nominal simulator. For each new scenario, the simulator predicts a future voltage trajectory for the whole grid; DR-CSS then constructs a conformal safety interval around this prediction using historical simulation-to-reality errors. The interval is further enlarged to account for closed-loop changes induced by the deployment of the new policy and its interactions with the remaining controllers. To the best of our knowledge, DR-CSS is the first framework in power systems to combine historical data from an existing control policy with an imperfect simulator for pre-deployment safety screening of a new policy. Experiments on the IEEE 33-bus and IEEE 141-bus systems evaluate the deployment of learning-based voltage control policies and show that DR-CSS identifies all unsafe test scenarios. To reduce unnecessary warnings on safe scenarios, we adapt the safety intervals to different operating conditions and gradually introduce new policies with recalibration after each stage. These extensions increase the informational value of the safety screening and support safer deployment decisions in active distribution grids.
Sarra Bouchkati, P. Ellinas, Adriana Geisler et al.· 0 citations
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