Chance-constrained economic dispatch with renewable energy and storage

Cheng, Jianqiang; Chen, Richard Li-Yang; Najm, Habib N.; Pinar, Ali; Safta, Cosmin; Watson, Jean-Paul

doi:10.1007/s10589-018-0006-2

Chance-constrained economic dispatch with renewable energy and storage

Journal Article · 19 April 2018 · Computational Optimization and Applications

DOI:https://doi.org/10.1007/s10589-018-0006-2· OSTI ID:1427212

^[1]; Chen, Richard Li-Yang ^[2]; Najm, Habib N. ^[2]; Pinar, Ali ^[2]; Safta, Cosmin ^[2]; Watson, Jean-Paul ^[3]

Univ. of Arizona, Tucson, AZ (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Increased penetration of renewables, along with uncertainties associated with them, have transformed how power systems are operated. High levels of uncertainty means that it is not longer possible to guarantee operational feasibility with certainty, instead constraints are required to 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, it is required that wind energy contributes at least a prespecified ratio of the total demand and that the scheduled wind energy is dispatchable with high probability. We develop an approximated partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chanceconstrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed tolerance, and about 100 times faster than sample average approximation. Improved efficiency of our PSAA approach enables solution of WECC-240 system in minutes.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1427212

Report Number(s):: SAND--2015-8031J; 603913

Journal Information:: Computational Optimization and Applications, Journal Name: Computational Optimization and Applications Journal Issue: 2 Vol. 70; ISSN 0926-6003

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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