Chance-constrained economic dispatch with renewable energy and storage

Jianqiang Cheng (), Richard Li-Yang Chen (), Habib N. Najm (), Ali Pinar (), Cosmin Safta () and Jean-Paul Watson ()
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Jianqiang Cheng: University of Arizona
Richard Li-Yang Chen: Sandia National Laboratories
Habib N. Najm: Sandia National Laboratories
Ali Pinar: Sandia National Laboratories
Cosmin Safta: Sandia National Laboratories
Jean-Paul Watson: Sandia National Laboratories

Computational Optimization and Applications, 2018, vol. 70, issue 2, No 6, 479-502

Abstract: Abstract Increasing penetration levels of renewables have transformed how power systems are operated. High levels of uncertainty in production make it increasingly difficulty to guarantee operational feasibility; instead, constraints may only be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, we require that wind energy contribute at least a prespecified proportion of the total demand and that the scheduled wind energy is deliverable with high probability. We develop an approximate partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chance-constrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed satisfaction tolerance, and approximately 100 times faster than standard sample average approximation. Finally, the improved efficiency of our PSAA approach enables solution of a larger WECC-240 test system in minutes.

Keywords: Chance constraints; Sample average approximation; Partial sample average approximation; Economic dispatch; Renewable energy integration; Energy storage; 90C15; 90C59; 90C90 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10589-018-0006-2

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