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Optimal electricity generation portfolios

The impact of price spread modelling

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Abstract

It is common practice to base investment decisions on price projections which are gained from simulations using price processes. The choice of the underlying process is crucial for the simulation outcome. For power plants the core question is the existence of stable long-term cointegration relations. Therefore we investigate the impacts of different ways to model price movements in a portfolio selection model for the German electricity market. Three different approaches of modelling fuel prices are compared: initially, all prices are modelled as correlated random walks. Thereafter the coal price is modelled as random walk. The gas price follows the coal price through a mean-reversion process. Lastly, all prices are modelled as mean reversion processes with correlated residuals. The prices of electricity base and peak futures are simulated using historical correlations with gas and coal prices. Yearly base and peak prices are transformed into an estimated price duration curve followed by the steps power plant dispatch, operational margin and net present value calculation and finally the portfolio selection. The analysis shows that the chosen price process assumptions have significant impacts on the resulting portfolio structure and the weights of individual technologies.

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Authors and Affiliations

  1. Chair for Management Science and Energy Economics, University of Duisburg-Essen, 45117, Essen, Germany

    Daniel Ziegler, Katrin Schmitz & Christoph Weber

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  1. Daniel Ziegler
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  2. Katrin Schmitz
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  3. Christoph Weber
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Correspondence to Daniel Ziegler.

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Ziegler, D., Schmitz, K. & Weber, C. Optimal electricity generation portfolios. Comput Manag Sci 9, 381–399 (2012). https://doi.org/10.1007/s10287-012-0150-6

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