TY - CHAP A1 - Madlener, Reinhard A1 - Ruhnau, Oliver ED - Sioshansi, Fereidoon T1 - Variable renewables and demand flexibility: Day-ahead versus intraday valuation T2 - Variable Generation, Flexible Demand N2 - This chapter discusses trading in the day-ahead and intraday electricity markets, first in general and then from the perspectives of renewable power generators and demand response, respectively. This includes an overview of the relevant literature, a theoretical framework, and illustrative quantitative examples. The uncertainty of renewable electricity supply drives price volatility, especially in the intraday market, and leads to balancing cost for renewables, which can be optimized through forecasting and trading. The increased intraday price volatility can be exploited and moderated by demand response, which thereby helps to balance the uncertainty of renewables. KW - Centre for Sustainability Y1 - 2020 SN - 9780128238103 U6 - https://doi.org/10.1016/B978-0-12-823810-3.00005-4 SP - 309 EP - 327 PB - Academic Press 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 -