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Carol Miller

Publications and source records attributed to Carol Miller.

3 recordsLinked to original sources

A Resilience Evaluation Framework for Electric Distribution Systems: Historical Weather Conditioning, Sensitivity Analysis, and a Flooding-Aware Extension

Evaluating resilience in electric distribution systems under severe weather requires models that can connect network topology, hazard simulation, fragility modeling, restoration assumptions, repair strategy, and downstream consequences. This paper extends our prior graph-based resilience evaluation framework for power distribution systems in three ways: it adds analysis conditioned on historical events with real outage and weather data, introduces sensitivity studies for key modeling assumptions, and includes a coupled power-flooding extension for sewage-backup assessment. Historical wind events drive Monte Carlo simulations conditioned on real weather, and the observed outage trajectories are treated as realized historical samples for comparison. Wind-event resilience metrics stabilize at approximately 256 episodes, and outage peak, duration, and outage intensity change systematically with fragility parameters, network topology, restoration assumptions, and repair strategies. In a separate 1000-episode joint power-flooding simulation, episodes with at least one flooded customer occur in 1.9% of episodes overall, and both flood occurrence and flood intensity increase with outage intensity, showing a selective power-to-flood consequence pathway. Overall, the framework provides a practical basis for resilience assessment, comparative scenario analysis, and coupled power-flooding studies in a limited public-data setting, while also suggesting that more detailed utility data could further improve simulation realism.

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Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.

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Improving Short-Term Electricity Price Forecasting Using Day-Ahead LMP with ARIMA Models

Short-term electricity price forecasting has become important for demand side management and power generation scheduling. Especially as the electricity market becomes more competitive, a more accurate price prediction than the day-ahead locational marginal price (DALMP) published by the independent system operator (ISO) will benefit participants in the market by increasing profit or improving load demand scheduling. Hence, the main idea of this paper is to use autoregressive integrated moving average (ARIMA) models to obtain a better LMP prediction than the DALMP by utilizing the published DALMP, historical real-time LMP (RTLMP) and other useful information. First, a set of seasonal ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed and compared with autoregressive moving average (ARMA) models that use the differences between DALMP and RTLMP on their forecasting capability. A generalized autoregressive conditional heteroskedasticity (GARCH) model is implemented to further improve the forecasting by accounting for the price volatility. The models are trained and evaluated using real market data in the Midcontinent Independent System Operator (MISO) region. The evaluation results indicate that the ARMAX-GARCH model, where an exogenous time series indicates weekend days, improves the short-term electricity price prediction accuracy and outperforms the other proposed ARIMA models

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