A Novel Matching Formulation for Startup Costs in Unit Commitment

Journal Article · Tue Sep 18 04:00:00 EDT 2018 · Optimization Online Repository

OSTI ID:1492383

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

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Discrete Math & Optimization
2. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Industrial and Systems Engineering
3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Data Science & Cyber Analytics

We present a novel formulation for startup cost computation in the unit commitment problem (UC). Both the proposed formulation and existing formulations in the literature are placed in a formal, theoretical dominance hierarchy based on their respective linear programming relaxations. The proposed formulation is tested empirically against existing formulations on large-scale unit commitment instances drawn from real-world data. While requiring more variables than the current state-of-the-art formulation, our proposed formulation requires fewer constraints, and is empirically demonstrated to be as tight as a perfect formulation for startup costs. This tightening reduces the computational burden in comparison to existing formulations, especially for UC instances with large variability in net-load due to renewables production.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Univ. of Tennessee, Knoxville, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF) (United States)
• DOE Contract Number: NA0003525; SC0018175
• OSTI ID: 1492383
• Report Number(s): SAND--2018-11765J; 669284
• Journal Information: Optimization Online Repository, Journal Name: Optimization Online Repository Vol. 2018; ISSN 9999-0042
• Publisher: Mathematical Optimization Society
• Country of Publication: United States
• Language: English

Related Subjects

96 KNOWLEDGE MANAGEMENT AND PRESERVATION
Unit Commitment
mixed-integer programming
strong lower bounds