The Optimal Share of Variable Renewables: How the Variability of Wind and Solar Power affects their Welfare-optimal Deployment
By Lion Hirth. 2012
181 citations recorded by the source corpus.
Abstract This paper estimates the welfare-optimal market share of wind and solar power, explicitly taking into account their output variability. We present a theoretical valuation framework that consistently accounts for the impact of fluctuations over time, forecast errors, and the location of generators in the power grid on the marginal value of electricity from renewables. Then the optimal share of wind and solar power in Northwestern Europe’s generation mix is estimated from a calibrated numerical model. We find the optimal long-term wind share to be 20%, three times more than today; however, we also find significant parameter uncertainty. Variability significantly impacts results: if winds were constant, the optimal share would be 60%. In addition, the effect of technological change, price shocks, and policies on the optimal share is assessed. We present and explain several surprising findings, including a negative impact of CO2 prices on optimal wind deployment.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/ab829972f9e48433dd97a82474016cdd834e9381
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In Economics and Econometrics
In Electrical and Electronic Engineering
In Renewable Energy, Sustainability and the Environment
Citations in the corpus
- Market value of solar power: Is photovoltaics cost-competitive? (2015, unknown): “EMMA has been applied previously in [29], [37], and [58], where more model details can be found.”
- The Market Value of Variable Renewables The Effect of Solar and Wind Power Variability on their Relative Price (2013, unknown): “One can turn the question around and estimate the cost-optimal (or market equilibrium) amount of wind power, which we do in a related paper (Hirth 2012b).”
- European electricity sector decarbonization under different levels of foresight (2019, background): “[25]; Schmid and Knopf [26]; Hirth [27]; Plebmann and Blechinger [28]; M€ ost and Fichtner [29].”
- Buffering Volatility: A Study on the Limits of Germany's Energy Revolution (2017, unknown): “Hirth (2015) argues that the need to hold back-up power in reserve may limit the optimal wind share to only 20%.”
- The benefits of flexibility: The value of wind energy with hydropower (2016, background): “The intersection of market value and levelized electricity costs defines the costoptimal deployment level [15].”