Hydroassets Portfolio Management for Intraday Electricity Trading from a Discrete Time Stochastic Optimization Perspective

TL;DR

This paper introduces a computationally efficient stochastic optimization method for hydroasset portfolio management in intraday electricity trading, incorporating risk aversion and utility functions, and avoiding the curse of dimensionality.

Contribution

It develops a novel interior point algorithm using a bushy recombining tree for multi-period stochastic optimization in hydroasset management, integrating risk preferences and utility functions.

Findings

Efficient solution for hydroasset portfolio optimization in intraday markets.

Inclusion of risk aversion and utility functions in the optimization model.

Fast computational results using a bushy recombining tree approach.

Abstract

Hydro storage system optimization is becoming one of the most challenging tasks in Energy Finance. While currently the state-of-the-art of the commercial software in the industry implements mainly linear models, we would like to introduce risk aversion and a generic utility function. At the same time, we aim to develop and implement a computational efficient algorithm, which is not affected by the curse of dimensionality and does not utilize subjective heuristics to prevent it. For the short term power market we propose a simultaneous solution for both dispatch and bidding problems. Following the Blomvall and Lindberg (2002) interior point model, we set up a stochastic multiperiod optimization procedure by means of a "bushy" recombining tree that provides fast computational results. Inequality constraints are packed into the objective function by the logarithmic barrier approach and the utility function is approximated by its second order Taylor polynomial. The optimal solution for the original problem is obtained as a diagonal sequence where the first diagonal dimension is the parameter controlling the logarithmic penalty and the second is the parameter for the Newton step in the construction of the approximated solution. Optimal intraday electricity trading and water values for hydro assets as shadow prices are computed. The algorithm is implemented in Mathematica.