Optimal capacity allocation in accordance with renewable energy sources: the US electricity market

ABSTRACT

Electricity generation cost and environmental effects of electricity generation continue to be among central themes in energy policy and planning. This study aims to forecast the optimal capacity expansion of the electric power sector in the US energy market for 2022–2050. We propose a fuzzy multi-objective linear programme to minimise cost and environmental effects. We depict how different policies and price evolution may alter the capacity composition by employing sensitivity analyses on contributing factors. The effects of the new composition and implied generation on the cost of electricity and emissions are examined, and yield that direct modelling of capacity factors is meaningful to be considered in energy strategy planning. In the literature, the optimal mix is usually found via a dispatch model yielding hourly or sub-hourly dispatch decisions, which is computationally demanding. As a contribution, we overcome this task with a simpler and effective approach, by modelling of capacity factors directly. Results show that renewable energy is expected to reach more than 1100 GW installed capacity in the US by 2050 by reducing the average cost of electricity generation and $\text{C}{\text{O}}_{\text{2}}$ emissions by more than 70% and 80%, respectively, compared to end-of-2021 estimates.

KEYWORDS:

• Electricity system
• fuzzy sets
• multi-objective programming
• capacity expansion
• variable renewable energy

Disclosure statement

No potential conflict of interest was reported by the author(s).