TY - RPRT A1 - Ruhnau, Oliver T1 - Market-based renewables: How flexible hydrogen electrolyzers stabilize wind and solar market values N2 - Wind and solar energy are often expected to fall victim to their own success: the higher their share in electricity production, the more their revenue on electricity markets (their “market value”) declines. While in conventional power systems, the market value may converge to zero, this study demonstrates that “green” hydrogen production, through adding electricity demand in low-price hours, can effectively and permanently halt the decline. With an analytical derivation, a Monte Carlo simulation, and a numerical electricity market model, I find that – due to flexible hydrogen production – market values in 2050 likely converge above €19 ± 9 per MWh for solar energy and above €27 ± 8 per MWh for wind energy. This is in the range of the projected levelized costs of renewables and has profound implications. Market-based renewables may hence be within reach. KW - Renewable energy KW - Hydrogen electrolysis KW - Electricity market KW - Electricity economics KW - Integrated energy system Y1 - 2020 UR - https://www.econstor.eu/handle/10419/227075 ER - TY - JOUR A1 - Ruhnau, Oliver A1 - Hennig, Patrick A1 - Madlener, Reinhard T1 - Economic implications of forecasting electricity generation from variable renewable energy sources JF - Renewable Energy N2 - Short-term forecasting of electricity generation from variable renewable energy sources is not an end in itself but should provide some net benefit to its user. In the case of electricity trading, which is in the focus of this paper, the benefit can be quantified in terms of an improved economic outcome. Although some effort has been made to evaluate and to improve the profitability of electricity forecasts, the understanding of the underlying effects has remained incomplete so far. In this paper, we develop a more comprehensive theoretical framework of the connection between the statistical and the economic properties of day-ahead electricity forecasts. We find that, apart from the accuracy and the bias, which have already been extensively researched, the correlation between the forecast errors and the market price spread determines the economic implications - a phenomenon which we refer to as ‘correlation effect’. Our analysis is completed by a case study on solar electricity forecasting in Germany which illustrates the relevance and the limits of both our theoretical framework and the correlation effect. KW - Forecasting evaluation KW - Renewable energy KW - Electricity markets KW - Balancing costs KW - Artificial neural network Y1 - 2020 U6 - https://doi.org/10.1016/j.renene.2020.06.110 SN - 0960-1481 IS - 161 SP - 1318 EP - 1327 ER -