Renewable electricity capacity planning with uncertainty at multiple scales

Journal Article · 07 September 2023 · Computational Management Science

DOI: https://doi.org/10.1007/s10287-023-00472-0 · OSTI ID:1999274

Ferris, Michael C.; Philpott, Andy

Abstract We formulate and compare optimization models of investment in renewable generation using a suite of social planning models that compute optimal generation capacity investments for a hydro-dominated electricity system where inflow uncertainty results in a risk of energy shortage. The models optimize the expected cost of capacity expansion and operation allowing for investments in hydro, geothermal, solar, wind, and thermal plant, as well as battery storage for smoothing load profiles. A novel feature is the integration of uncertain seasonal hydroelectric energy supply and short-term variability in renewable supply in a two-stage stochastic programming framework. The models are applied to data from the New Zealand electricity system and used to estimate the costs of moving to a 100% renewable electricity system by 2035. We also explore the outcomes obtained when applying different forms of CO ₂ constraint that limit respectively non-renewable capacity, non-renewable generation, and CO ₂ emissions on average, almost surely, or in a chance-constrained setting, and show how our models can be used to investigate the merits of a proposed pumped-hydro scheme in New Zealand’s South Island.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1999274
• Journal Information: Computational Management Science, Journal Name: Computational Management Science Journal Issue: 1 Vol. 20; ISSN 1619-697X
• Publisher: Springer Science + Business MediaCopyright Statement
• Country of Publication: Germany
• Language: English