Power portfolio optimization with traded contract products

Power sector restructuring has prompted the application of modern portfolio theory among market participants. Much research has been devoted to power portfolio optimization problems. However, the portfolio composition adopted in literature is rather hypothetical than realistic. From an engineering perspective, it is necessary to use real traded contract products to construct the portfolio. In this paper, clarification is made on commonly traded power contracts in the market, followed by a discussion of their pricing schemes. It is emphasized that actively traded electricity futures/forwards and options actually belong to commodity swaps and swaptions respectively. A power portfolio is then constructed for a generation company with these basic power contracts and the spot transaction as well. An optimization model is formulated to solve the asset allocation with Conditional Value at Risk (CVaR) as the risk measure. The viability of the model is tested through a numerical study. ©2010 IEEE.published_or_final_versionThe 2010 IEEE Power and Energy Society General Meeting, Minneapolis, MN., 25-29 July 2010. In Conference Proceedings, 2010, p. 1-

Worst-case CVaR based portfolio optimization models with applications to scenario planning

This article studies three robust portfolio optimization models under partially known distributions. The proposed models are composed of min-max optimization problems under the worst-case conditional value-at-risk consideration. By using the duality theory, the models are reduced to simple mathematical programming problems where the underlying random variables have a mixture distribution or a box discrete distribution. They become linear programming problems when the loss function is linear. The solutions between the original problems and the reduced ones are proved to be identical. Furthermore, for the mixture distribution, it is shown that the three profit-risk optimization models have the same efficient frontier. The reformulated linear program shows the usability of the method. As an illustration, the robust models are applied to allocations of generation assets in power markets. Numerical simulations confirm the theoretical analysis. © 2009 Taylor & Francis.link_to_subscribed_fulltex