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Stochastic dynamic programming approach to managing power system uncertainty with distributed storage

Luckny Zéphyr () and C. Lindsay Anderson ()
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Luckny Zéphyr: Cornell University
C. Lindsay Anderson: Cornell University

Computational Management Science, 2018, vol. 15, issue 1, 87-110

Abstract: Abstract Wind integration in power grids is challenging because of the uncertain nature of wind speed. Forecasting errors may have costly consequences. Indeed, power might be purchased at highest prices to meet the load, and in case of surplus, power may be wasted. Energy storage may provide some recourse against the uncertainty of wind generation. Because of their sequential nature, in theory, power scheduling problems may be solved via stochastic dynamic programming. However, this scheme is limited to small networks by the so-called curse of dimensionality. This paper analyzes the management of a network composed of conventional power units and wind turbines through approximate dynamic programming, more precisely stochastic dual dynamic programming. A general power network model with ramping constraints on the conventional generators is considered. The approximate method is tested on several networks of different sizes. The numerical experiments also include comparisons with classical dynamic programming on a small network. The results show that the combination of approximation techniques enables to solve the problem in reasonable time.

Keywords: Power grid management; Energy storage; Stochastic dynamic programming; Stochastic dual dynamic programming; Approximate dynamic programming; Generalized linear programming (search for similar items in EconPapers)
Date: 2018
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