Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy

By Yue Yao, Jin-hua Xu, Delong Sun. 2020

133 citations recorded by the source corpus.

Abstract Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions.

Subfields: Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/bd06b7154c293122d947adf0be970e440daa5e22