**Author details**

The proposed algorithm uses Monte Carlo simulation techniques combined with linear regression for successively approximating and refining the continuation and risk functions. A novel and efficient procedure for updating these functions, combining calculations of inde‐ pendent computing threads and without storing the entire datasets, is proposed. This feature enables exploiting the currently widespread multicore processor architectures and deploying

In order to demonstrate the practicability of the envisioned approach, the proposed algorithm has been applied to find the optimal trading strategy of a power generation portfolio in forward and spot electricity markets. Power trading and risk management is currently a central activity of power companies running in liberalized electricity markets. The probability density functions of the profits a generator would make by participating in either the spot or the forward markets are extremely different. The forms and boundaries of these probability functions have drastic implications for risk when generators get involved in the spot or the forward markets. Generators can hedge price risk of spot markets by contracting forward, but by exposing themselves to delivery risk. Hence, the optimization problem is formulated as the maximization of the expected profit of the trading policy while the downside risk is constrain‐ ed. For doing so, the generator selects and combines a portfolio of annual and quarterly forward contracts as well as involvement in the spot market. A frictional market with non-

A detailed chronological 4-state reliability model of generating units has been adopted for replicating stochastic behavior of random outages. Large stochastic ensembles of spot prices and forward prices time series have been synthetized for this application. In order to retain subadditivity, downside risk is measured by CVaR. The approximation of CVaR by a momentbased risk metric drastically improves computational efficiency while providing accurate and

Applying ADP-based optimization techniques to electricity markets is a novel undertaking and opens a prospectively fertile avenue for research. In future works, further algorithmic enhancement are foreseen. Application of these methods for designing trading strategies that considers a larger set of available financial contracts as well as generation portfolios comprising renewable resources would provide results and findings of high practical significance.

This work was supported in part by the National Scientific and Technical Research Council (CONICET) and the Agency for Promotion of Science & Technology (ANPCyT), Argentina. The financial support of the German Academic Exchange Service (DAAD) is also gratefully acknowledged. The authors also thank the support of the colleagues at the Institute of Power Systems and Power Economics (IAEW), RWTH Aachen, Germany, especially Univ.-Prof. Dr.- Ing. Albert Moser as well as colleagues at Institute of Electrical Energy (IEE), National

the algorithm in large computation clusters.

122 Dynamic Programming and Bayesian Inference, Concepts and Applications

negligible transaction costs is considered.

consistent risk estimations.

**Acknowledgements**

University of San Juan, Argentina.

Miguel Gil-Pugliese1\* and Fernando Olsina2

\*Address all correspondence to: miguel.gil.pugliese@gmail.com

1 Institute of Power Systems and Power Economics (IAEW), RWTH Aachen, Germany

2 Institute of Electrical Energy (IEE), National University of San Juan, Argentina
