A Metamodeling Framework for Quantifying Health Damages of Power Grid Expansion Plans

Mark D. Rodgers, David W. Coit, Frank A. Felder and Annmarie G. Carlton
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Mark D. Rodgers: Department of Supply Chain Management, Rutgers Business School, Newark, NJ 07102, USA
David W. Coit: Department of Industrial & Systems Engineering, Rutgers University, Piscataway, NJ 07102, USA
Frank A. Felder: Center for Energy, Economic & Environmental Policy, Rutgers University, New Brunswick, NJ 07102, USA
Annmarie G. Carlton: Department of Chemistry, University of California-Irvine, Irvine, CA 92697, USA

IJERPH, 2019, vol. 16, issue 10, 1-21

Abstract: In this paper, we present an analytical framework to establish a closed-form relationship between electricity generation expansion planning decisions and the resulting negative health externalities. Typical electricity generation expansion planning models determine the optimal technology–capacity–investment strategy that minimizes total investment costs as well as fixed and variable operation and maintenance costs. However, the relationship between these long-term planning decisions and the associated health externalities is highly stochastic and nonlinear, and it is computationally expensive to evaluate. Thus, we developed a closed-form metamodel by executing computer-based experiments of a generation expansion planning model, and we analyzed the resulting model outputs in a United States Environmental Protection Agency (EPA) screening tool that approximates the associated human health externalities. Procedural guidance to verify the accuracy and to select key metamodel parameters to enhance its prediction capability is presented. Specifically, the metamodel presented in this paper can predict the resulting health damages of long-term power grid expansion decisions, thus, enabling researchers and policy makers to quickly assess the health implications of power grid expansion decisions with a high degree of certainty.

Keywords: generation expansion planning; health damages; metamodeling; emissions; simulation; operations research (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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