arXiv subjects

Napoleon Costilla-Enriquez

Publications and source records attributed to Napoleon Costilla-Enriquez.

3 recordsLinked to original sources

arXiv2026

Adaptive Calibration of Day-Ahead Flexibility Reserve Requirements Under Distribution Shift

The increasing integration of variable renewable generation introduces weather-driven variability and uncertainty into power-system operations. System operators manage the resulting deviations between day-ahead forecasts and real-time net load by scheduling upward and downward flexibility reserves. These requirements have traditionally been set using simple deterministic rules. Newer data-driven methods tailor reserves to forecasts and operating conditions. However, their reliability may deteriorate when conditions depart from the historical data. We address this limitation with a sequential framework that updates day-ahead reserve requirements as operating outcomes become available. Within this framework, Adaptive Conformal Inference targets the frequency of insufficient reserve, whereas Rolling Risk Control targets expected uncovered deviation, the mean residual beyond the scheduled reserve. We prove finite-horizon bounds on realized miscoverage and capped uncovered-deviation loss. We test the framework on real ERCOT data in a chronological deployment simulation that respects operational timing: outcomes from one day first affect the reserve schedule two days later. Training and validation use 2022--2023 data; the uninterrupted test covers 2024--2025. Coverage calibration achieves approximately 90% coverage, with upward and downward miscoverage near 5%. Risk calibration brings expected uncovered deviations close to their budgets while using 5.3% less reserve than the uncalibrated requirements. The framework thus produces adaptive reserve requirements that provably control the selected reliability metrics over finite horizons without assuming stationarity or exchangeability.

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arXiv2026

Source-Agnostic Sizing of Flexibility Reserves

Growing shares of variable renewable energy sources (VRES) increase forecast uncertainty and variability, requiring flexibility reserves that adapt to changing operating conditions and reflect the likelihood and severity of forecast deviations. Conventional fixed-rule or Gaussian approaches misrepresent asymmetric, heavy-tailed errors, leading to inefficient procurement or optimistic risk estimates. This paper presents a source-agnostic framework that constructs conditional error distributions for load, wind, and solar from historical deviations or probabilistic forecasts, combines them into a conditional net-load error distribution, and derives upward and downward reserves using coverage- and risk-based criteria. Nonparametric density estimation captures empirical error behavior without a parametric assumption, while a Conditional Value-at-Risk (CVaR)-based metric quantifies expected uncovered deviations. Experiments on a New York Independent System Operator (NYISO)-based synthetic dataset show that the framework meets target coverage with lower reserve volumes than static benchmarks and avoids the tail-risk distortion of Gaussian-based methods. Because reserves are built directly from the resource uncertainty distributions, the framework is transparent and interpretable, and it integrates seamlessly into existing production-cost models as deterministic reserve constraints, avoiding scenario-based stochastic optimization. It is implemented in the Electric Power Research Institute's (EPRI) DynADOR tool for operational reserve scheduling.

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arXiv2021

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

The rapid integration of variable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process (e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the CAISO system. Results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.

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