Power-Gas Infrastructure Planning under Weather-induced Supply and Demand Uncertainties

TL;DR

This paper develops robust optimization models for power-gas infrastructure planning that incorporate weather-induced uncertainties in demand and renewable supply, considering climate change impacts and risk aversion.

Contribution

It introduces two distributionally robust optimization approaches using moment and Wasserstein ambiguity sets for integrated power-gas planning under climate-related uncertainties.

Findings

DRO models outperform stochastic programming in uncertain scenarios.

The models effectively incorporate climate change effects into infrastructure planning.

Near-optimal solutions are obtained efficiently with the proposed approximation scheme.

Abstract

Implementing economy-wide decarbonization strategies based on decarbonizing the power grid via variable renewable energy (VRE) expansion and electrification of end-uses requires new approaches for energy infrastructure planning that consider, among other factors, weather-induced uncertainty in demand and VRE supply. An energy planning model that fails to account for these uncertainties can hinder the intended transition efforts to a low-carbon grid and increase the risk of supply shortage especially during extreme weather conditions. Here, we consider the generation and transmission expansion problem of joint power-gas infrastructure and operations planning under the uncertainty of both demand and renewable supply. We propose two distributionally robust optimization approaches based on moment (MDRO) and Wasserstein distance (WDRO) ambiguity sets to endogenize these uncertainties and account for the change in the underlying distribution of these parameters that is caused by the climate change, among other factors. Furthermore, our model considers the risk-aversion of the energy planners in the modeling framework via the conditional value-at-risk (CVaR) metric. An equivalent mixed-integer linear programming (MILP) reformulation of both modeling frameworks is presented, and a computationally efficient approximation scheme to obtain near-optimal solutions is proposed. We demonstrate the resulting DRO planning models and solution strategy via a New England case study under different levels of end-use electrification and decarbonization targets. Our experiments systematically explore different modeling aspects and compare the DRO models with stochastic programming (SP) results.