We propose a new approach to optimize operations of hydro storage systems with multiple connected reservoirs which participate in wholesale electricity markets. Our formulation integrates short-term intraday with long-term interday decisions. The intraday problem considers bidding decisions as well as storage operation during the day and is formulated as a stochastic program. The interday problem is modeled as a Markov decision process of managing storage operation over time, for which we propose integrating stochastic dual dynamic programming with approximate dynamic programming. We show that the approximate solution converges towards an upper bound of the optimal solution. To demonstrate the efficiency of the solution approach, we fit an econometric model to actual price and inflow data and apply the approach to a case study of an existing hydro storage system. Our results indicate that the approach is tractable for a real-world application and that the gap between theoretical upper and a simulated lower bound decreases sufficiently fast.

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.

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