Investigating the economics of the power sector under high penetration of variable renewable energies

Yuhji Matsuo, Seiya Endo, Yu Nagatomi, Yoshiaki Shibata, Ryoichi Komiyama and Yasumasa Fujii

Applied Energy, 2020, vol. 267, issue C, No S0306261919316435

Abstract: In recent years, several studies have been published focusing on the economics of the power sector under the high penetration of variable renewable energy (VRE) following the rapid expansion of VRE capacities worldwide. However, detailed analyses of the fluctuations in meteorological conditions have hitherto been scarce, despite the expectation that VRE output will be significantly affected by them. To fill this gap, we investigated the economic likelihood of achieving a zero-emission power system in Japan by 2050, using multi-annual meteorological data from 1990 to 2017. We used a detailed linear programming optimization model, as well as a method that uses cumulative residual loads (CRL), which proved to be useful for estimating required energy storage capacities and for understanding the complex substitution of power generation and storage technologies. The estimated unit cost for a 100% renewable electricity system depends heavily on meteorological conditions, standing at 20.9 JPY/kWh with a standard deviation of 1.2 JPY/kWh, which declines to 18.3 JPY/kWh with a standard deviation of 0.6 JPY/kWh for a system with hydrogen storage. The calculations indicate that the required storage capacity is determined mainly by the duration of “windless and sunless” periods, or “dark doldrums”, and the greatest risk under high VRE penetrations is the possibility of supply disruption during such periods. The results also highlight the considerable value of “firm capacities”, such as thermal and nuclear power generation, under a high share of VRE, although it should be noted that the profitability of these capacities may differ significantly from current situations, with large VRE outputs with negligible marginal costs.

Keywords: Variable renewable energy; Electricity storage; Hydrogen; Decarbonisation; Meteorological data; Linear programming (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (23)

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DOI: 10.1016/j.apenergy.2019.113956

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