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Electric vehicles (EVs) are an important option to decarbonize the passenger transport sector and, therefore, critical to be adequately represented in energy system models. One of the main challenges is to model the volatility associated with charging EVs. We provide an overview of existing modeling approaches for this. We especially compare methods for simulating charging profiles and discuss their advantages and disadvantages, depending on the application. On that basis, we pick one simulation approach and generate time series for a case study of Germany in 2030. We assess the results and compare them with a large empirical dataset on EV charging in the UK. We derive recommendations for the future modeling of EVs.
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.
Economic implications of forecasting electricity generation from variable renewable energy sources
(2020)
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.