Chance-constrained economic dispatch with renewable energy and storage
AffiliationUniv Arizona, Dept Syst & Ind Engn
Sample average approximation
Partial sample average approximation
Renewable energy integration
MetadataShow full item record
CitationCheng, J., Chen, R.LY., Najm, H.N. et al. Comput Optim Appl (2018) 70: 479. https://doi.org/10.1007/s10589-018-0006-2
Rights© Springer Science+Business Media, LLC, part of Springer Nature 2018
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AbstractIncreasing 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.
Note12 month embargo; published online: 19 April 2018
VersionFinal accepted manuscript
SponsorsLaboratory Directed Research and Development (LDRD) program of the Sandia National Laboratories; U.S. Department of Energys National Nuclear Security Administration [DE-NA0003525]; Bisgrove Scholars program (Science Foundation Arizona)