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Multistage Stochastic Portfolio Optimisation in Deregulated Electricity Markets Using Linear Decision Rules

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  • Paula Rocha
  • Daniel Kuhn

Abstract

The deregulation of electricity markets increases the financial risk faced by retailers who procure electric energy on the spot market to meet their customers’ electricity demand. To hedge against this exposure, retailers often hold a portfolio of electricity derivative contracts. In this paper, we propose a multistage stochastic mean-variance optimisation model for the management of such a portfolio. To reduce computational complexity, we perform two approximations: stage-aggregation and linear decision rules (LDR). The LDR approach consists of restricting the set of decision rules to those affine in the history of the random parameters. When applied to mean-variance optimisation models, it leads to convex quadratic programs. Since their size grows typically only polynomially with the number of periods, they can be efficiently solved. Our numerical experiments illustrate the value of adaptivity inherent in the LDR method and its potential for enabling scalability to problems with many periods.

Suggested Citation

  • Paula Rocha & Daniel Kuhn, 2010. "Multistage Stochastic Portfolio Optimisation in Deregulated Electricity Markets Using Linear Decision Rules," Working Papers 040, COMISEF.
    • Handle: RePEc:com:wpaper:040
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    Cited by:

    1. Frans J. C. T. Ruiter & Aharon Ben-Tal & Ruud C. M. Brekelmans & Dick Hertog, 2017. "Robust optimization of uncertain multistage inventory systems with inexact data in decision rules," Computational Management Science, Springer, vol. 14(1), pages 45-66, January.

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    Keywords

    OR in energy; electricity portfolio management; stochastic programming; risk management; linear decision rules;
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