Searcharxiv⌕ Search

arXiv subjects

Tomás Tapia

Publications and source records attributed to Tomás Tapia.

3 recordsLinked to original sources

Reachability Guarantees for Energy Arbitrage

Batteries performing energy arbitrage may overdischarge ahead of a high-demand or critical operating hour when they re-optimize in real time and depart from the day-ahead schedule. This paper introduces a framework for battery energy arbitrage under uncertain market prices that integrates chance-constrained terminal state-of-charge (SoC) requirements with online threshold policies. We first cast the multi-interval arbitrage problem as a stochastic dynamic program enhanced by a probabilistic end-of-horizon SoC constraint, ensuring with high confidence that the battery terminates within a prescribed energy band. We then apply a $k$-search algorithm to derive explicit charging (buying) and discharging (selling) thresholds with a provable worst-case competitive ratio, and compute the corresponding action probabilities over the decision horizon. To compute exact distributions under operational limits, we develop a probability redistribution pruning method and use it to quantify the likelihood of meeting the terminal SoC band. Leveraging the resulting SoC distribution, we estimate the minimum stopping time required to satisfy the SoC chance constraint. Computational experiments on historical real price data demonstrate that the proposed framework improves SoC estimation and supports chance-constraint satisfaction, while also showing that reachability is highly sensitive to a given initial SoC and operating start time.

eess.SY↗

Robust Capacity Expansion under Wildfire Ignition Risk and High Renewable Penetration

In power systems, the risk of wildfire ignition has increased significantly in recent years. The impact and severity of these events on energy dispatch, as well as their societal ramifications, make wildfire prevention critical for power system planning and operation. A common intervention by system operators is to de-energize transmission lines to mitigate the risk of fire caused by equipment failures. With the growing integration of variable renewable generation, managing and preparing the system to de-energization under wildfire risk has become even more challenging. In this context, mitigation decisions such as installing battery energy storage systems and undergrounding transmission lines can reduce the risk and adverse effects associated with de-energization and renewable generation variability. This paper presents a robust optimization model to determine the optimal location of battery storage and undergrounding of transmission line investment, utilizing representative weeks and uncertainty sets to capture the temporal relationship of uncertain variables. Specifically, this paper addresses: (i) the worst-case realization of ignition risk leading to the de-energization of transmission lines, combined with the worst-case realization of renewable energy availability, and (ii) the optimal investment decisions for energy storage capacity and undergrounding of transmission lines that are exposed to ignition risk. The proposed model is formulated as a mixed-integer linear programming (MILP) problem, employing duality theory and binary decomposition to address nonlinearities, and is solved using a column-and-constraint generation algorithm. The proposed framework is evaluated on a model of the San Diego power system, demonstrating its practical effectiveness in improving the resilience to wildfire risk.

math.OC↗

Learning Reachability of Energy Storage Arbitrage

Power systems face increasing weather-driven variability and, therefore, increasingly rely on flexible but energy-limited storage resources. Energy storage can buffer this variability, but its value depends on intertemporal decisions under uncertain prices. Without accounting for the future reliability value of stored energy, batteries may act myopically, discharging too early or failing to preserve reserves during critical hours. This paper introduces a stopping-time reward that, together with a state-of-charge (SoC) range target penalty, aligns arbitrage incentives with system reliability by rewarding storage that maintains sufficient SoC before critical hours. We formulate the problem as an online optimization with a chance-constrained terminal SoC and embed it in an end-to-end (E2E) learning framework, jointly training the price predictor and control policy. The proposed design enhances reachability of target SoC ranges, improves profit under volatile conditions, and reduces its standard deviation.

eess.SY↗