Towards a Health-Based Power Grid Optimization in the Artificial Intelligence Era

TL;DR

This paper introduces a novel power grid optimization approach that prioritizes minimizing health impacts from air pollution while considering energy efficiency and emission constraints, aiming for sustainable and health-conscious energy solutions.

Contribution

It presents the first optimization model focusing on reducing adverse health effects from air pollution in power grid planning, integrating health considerations into emission and efficiency constraints.

Findings

Optimal fuel mix reduces health-related air pollution impacts.

The model balances emission constraints with health outcomes.

Results demonstrate potential health benefits of health-based optimization.

Abstract

The electric power sector is one of the largest contributors to greenhouse gas emissions in the world. In recent years, there has been an unprecedented increase in electricity demand driven by the so-called Artificial Intelligence (AI) revolution. Although AI has and will continue to have a transformative impact, its environmental and health impacts are often overlooked. The standard approach to power grid optimization aims to minimize CO$_2$ emissions. In this paper, we propose a new holistic paradigm. Our proposed optimization directly targets the minimization of adverse health outcomes under energy efficiency and emission constraints. We show the first example of an optimal fuel mix allocation problem aiming to minimize the average number of adverse health effects resulting from exposure to hazardous air pollutants with constraints on the average and marginal emissions. We argue that this new health-based power grid optimization is essential to promote truly sustainable technological advances that align both with global climate goals and public health priorities.