A computationally efficient algorithm for computing convex hull prices

Knueven, Bernard; Ostrowski, James; Castillo, Anya; Watson, Jean-Paul

doi:10.1016/j.cie.2021.107806

A computationally efficient algorithm for computing convex hull prices

Journal Article · 13 November 2022 · Computers and Industrial Engineering

DOI:https://doi.org/10.1016/j.cie.2021.107806· OSTI ID:1832863

Knueven, Bernard ^[1]; Ostrowski, James ^[2]; Castillo, Anya ^[3]; Watson, Jean-Paul ^[4]

National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Univ. of Tennessee, Knoxville, TN (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource's capabilities, they create challenges for market clearing processes. For example, system operators may be required to execute side payments to participants whose costs are not covered through energy sales as determined via traditional locational marginal pricing schemes. Convex hull pricing minimizes this and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. Computing convex hull prices involves solving either a large-scale linear program or the Lagrangian dual of the corresponding non-convex scheduling problem. Further, the former approach requires explicit descriptions of market participants' convex hulls. While linear programs for computing convex hull prices are large, their structure is naturally decomposable by generators. Here, in this work, we propose and empirically analyze a Benders decomposition approach to computing convex hull prices that leverages recent advances in convex hull formulations for thermal generating units. We demonstrate across a large set of test instances that our decomposition approach only requires modest computational effort, obtaining solutions at least an order of magnitude faster than the equivalent large-scale linear programming approach. Overall, we provide a computationally feasible method for computing convex hull prices for industrial scale market clearing problems, enabling the possibility of practical adoption of this advanced pricing mechanism.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity (OE)

Grant/Contract Number:: AC36-08GO28308; AC52-07NA27344; SC0018175; NA0003525

OSTI ID:: 1832863

Alternate ID(s):: OSTI ID: 2339871
OSTI ID: 1832963
OSTI ID: 1873255

Report Number(s):: NREL/JA--2C00-77070; MainId:26016; UUID:07870850-8b80-4227-bc8e-a4abb0939e6f; MainAdminID:63304

Journal Information:: Computers and Industrial Engineering, Journal Name: Computers and Industrial Engineering Vol. 163; ISSN 0360-8352

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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