Optimal day-ahead trading and storage of renewable energies—an approximate dynamic programming approach

Abstract

A renewable power producer who trades on a day-ahead market sells electricity under supply and price uncertainty. Investments in energy storage mitigate the associated financial risks and allow for decoupling the timing of supply and delivery. This paper introduces a model of the optimal bidding strategy for a hybrid system of renewable power generation and energy storage. We formulate the problem as a continuous-state Markov decision process and present a solution based on approximate dynamic programming. We propose an algorithm that combines approximate policy iteration with Least Squares Policy Evaluation (LSPE) which is used to estimate the weights of a polynomial value function approximation. We find that the approximate policies produce significantly better results for the continuous state space than an optimal discrete policy obtained by linear programming. A numerical analysis of the response surface of rewards on model parameters reveals that supply uncertainty, imbalance costs and a negative correlation of market price and supplies are the main drivers for investments in energy storage. Supply and price autocorrelation, on the other hand, have a negative effect on the value of storage.