Stochastic scheduling of generating units with weekly energy storage: A hybrid decomposition approach

Constante-Flores, Gonzalo E.; Conejo, Antonio J.; Lima, Ricardo M.

doi:10.1016/j.ijepes.2022.108613

Title: Stochastic scheduling of generating units with weekly energy storage: A hybrid decomposition approach

Journal Article · Tue Oct 18 00:00:00 EDT 2022 · International Journal of Electrical Power and Energy Systems

DOI:https://doi.org/10.1016/j.ijepes.2022.108613· OSTI ID:1988712

^[1];

^[2]

The Ohio State University, Columbus, OH (United States)
King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)

We propose a solution method for the large-scale stochastic unit commitment (SUC) problem with weekly-dispatched energy storage and significant weather-dependent stochastic generating capacity. Weekly storage facilities that mostly charge during weekends and discharge during weekdays require a weekly scheduling of generating units, which result in a large-scale optimization problem. This SUC problem is formulated as a two-stage stochastic model and we use the conditional value-at-risk as a risk measure. Using a Benders framework, the proposed solution method decomposes the problem into a mixed-integer linear master problem and linear and continuous subproblems. The master problem corresponds to the first-stage decisions throughout the week and includes all the commitment (binary) variables and their corresponding constraints. The subproblems correspond to the actual dispatch of the generating units on a weekly basis. Based on the success of column-and-constraint generation algorithms to solve robust optimization problems, we improve the low communication between the master problem and the subproblems in the standard Benders decomposition by adding primal variables and constraints from the subproblems to the master problem, which provides a better approximation of the recourse function. Furthermore, our computational experiments demonstrate the effectiveness of the proposed decomposition method using an instance of the South Carolina synthetic system with 90 generating units under 40 scenarios.

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Research Organization:: Duke Univ., Durham, NC (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0001283; 2171-1618

OSTI ID:: 1988712

Alternate ID(s):: OSTI ID: 1894536

Journal Information:: International Journal of Electrical Power and Energy Systems, Vol. 145; ISSN 0142-0615

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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25 ENERGY STORAGE
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