Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050

Francois Rozon (), Craig McGregor and Michael Owen
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Francois Rozon: Faculty of Engineering, Stellenbosch University, Stellenbosch 7602, South Africa
Craig McGregor: Faculty of Engineering, Stellenbosch University, Stellenbosch 7602, South Africa
Michael Owen: Faculty of Engineering, Stellenbosch University, Stellenbosch 7602, South Africa

Energies, 2023, vol. 16, issue 19, 1-20

Abstract: Published forecasts underestimate renewable energy capacity growth and potential cost reductions, creating uncertainty around investment decisions and slowing progress. Scenario-based projections diverge widely, driven by variations in modelling techniques and underlying assumptions, with policy-based models typically being overly conservative. With historical generation capacity and cost data readily available, this research demonstrates that data-driven approaches can be leveraged to improve long-term capacity and cost forecasts of solar, wind, and battery storage technologies. Unlike exponential growth models prevailing over shorter time scales, logistic curves requiring asymptotic limits, or machine learning algorithms dependent on extensive datasets, this analysis demonstrates that temporal quadratic regressions are a better starting point to represent capacity growth trends over two to three decades. When coupled with published learning rates, trend-based capacity forecasts provided tighter and lower capital and levelized cost of energy outlooks than most reviewed scenarios, with photovoltaics global average levelized cost of energy reducing from 0.057/kWh to below USD 0.03/kWh by 2030 and below USD 0.02/kWh by 2040. Greater transparency on manufacturing ecosystems is proposed so that more advanced analytical techniques can be utilized. This analysis indicates that without direct interventions to accelerate the growth in wind power generation, global renewable energy technology deployment will fall short of the generation capacities required to meet climate change objectives.

Keywords: solar and wind energy; growth dynamic; forecast accuracy; generation capacity; learning rate; Capex and LCOE (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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