arXiv subjects

Alberto J. Lamadrid

Publications and source records attributed to Alberto J. Lamadrid.

5 recordsLinked to original sources

arXiv2026

Environmental and Economic Implications of Artificial Intelligence Data Centers in the United States

In this study, we use electricity demand growth, cooling requirements, and backup system operation to evaluate the environmental and economic implications of artificial intelligence data centers in the United States. Our results indicate that impacts are not determined solely by facility design, but by the broader electricity, water, and land-use systems in which these facilities operate. Emissions are primarily driven by electricity consumption and therefore depend on marginal generation mixes, transmission constraints, and the spatial and temporal distribution of demand. Analysis further shows that local effects include pressures on water resources, increased noise exposure, and land-use changes, with outcomes varying across regions and infrastructure conditions. The assessment of technological and operational measures shows that improvements in energy efficiency, cooling configurations, and operational strategies can reduce these impacts, although their effectiveness depends on system-level conditions. Evaluation of regulatory and market structures suggests that existing frameworks may not fully account for location- and time-specific externalities. These findings support the need for integrated policy approaches that align data center deployment and operation with electricity system characteristics, water availability, and land-use planning to improve overall environmental and economic performance.

econ.GN↗

arXiv2026

Co-optimizing the Smart Grid and Electric Public Transit Bus System

As climate change provides impetus for investing in smart cities, with electrified public transit systems, we consider electric public transportation buses in an urban area, which play a role in the power system operations in addition to their typical function of serving public transit demand. Our model considers a social planner, such that the transit authority and the operator of the electricity system co-optimize the power system to minimize the total operational cost of the grid, while satisfying additional transportation constraints on buses. We provide deterministic and stochastic formulations to co-optimize the system. Each stochastic formulation provides a different set of recourse actions to manage the variable renewable energy uncertainty: ramping up/down the conventional generators, or charging/discharging of the transit fleet. We demonstrate the capabilities of the model and the benefit obtained via a coordinated strategy. We compare the efficacies of these recourse actions to provide additional managerial insights. We analyze the effect of different pricing strategies on the co-optimization. We also conduct congestion analysis in the power network, comparing our cooperative approach to a non-cooperative strategy when we assume electrified fleet sizes grow with greater battery capacities. Given the recent momentum towards building smarter cities and electrifying transit systems, our results provide policy directions towards a sustainable future. We test our models using modified MATPOWER case files and verify our results with different sized power networks. This study is motivated by a project with a large transit authority in California.

math.OC↗

arXiv2019

A Novel Smoothed Loss and Penalty Function for Noncrossing Composite Quantile Estimation via Deep Neural Networks

Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid when integrating renewable energy sources such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. Traditionally quantile regression is applied for such forecasting and recently quantile regression neural networks have become popular for weather and renewable energy forecasting. However, one major shortcoming of composite quantile estimation in neural networks is the quantile crossover problem. This paper analyzes the effectiveness of a novel smoothed loss and penalty function for neural network architectures to prevent the quantile crossover problem. Its efficacy is examined on the wind power forecasting problem. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 10\%, to 90\% prediction intervals which are evaluated using a quantile score and reliability measures. Benchmark models such as the persistence and climatology distributions, multiple quantile regression, and support vector quantile regression are used for comparison where results demonstrate the proposed approach leads to improved performance while preventing the problem of overlapping quantile estimates.

eess.SP↗

arXiv2017

Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy sources such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of a novel approach for nonparametric probabilistic forecasting of wind power that combines a smooth approximation of the pinball loss function with a neural network architecture and a weighting initialization scheme to prevent the quantile cross over problem. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 10%, to 90% prediction intervals which are evaluated using a quantile score and reliability measures. Benchmark models such as the persistence and climatology distributions, multiple quantile regression, and support vector quantile regression are used for comparison where results demonstrate the proposed approach leads to improved performance while preventing the problem of overlapping quantile estimates.

stat.ML↗

arXiv2017

Pricing in non-convex markets with quadratic deliverability costs

The problem of obtaining market-clearing prices for markets with non-convexities has been widely studied in the literature. This is particularly the case in electricity markets, where worldwide deregulation leads to markets in which non-convexities arise from the decisions of market operators regarding which generators are committed to provide electricity power. Here, we extend seminal results in this area to address the problem of obtaining market-clearing prices for markets in which beyond non-convexities, it is relevant to account for convex quadratic market costs. In a general market, such costs arise from quadratic commodity costs or transactions costs. In an electricity market, such quadratic costs arise when ramping costs need to be considered due to the presence of renewable energy sources, which continue to increase their participation in electricity markets. To illustrate our results, we compute and analyze the clearing prices of a classical market problem with the addition of ramping costs.

math.OC↗