Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 30, Issue 4, Pages 1913-1924Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2014.2355204
Keywords
Electricity market operations; mixed integer programming; pricing; risk constraints; robust optimization; stochastic programming; uncertainty; unit commitment
Categories
Funding
- National Science Foundation [CMMI-1355939, CMMI-1234057]
- U.S. Department of Energy Office of Electricity Delivery and Energy Reliability
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1234057, 1355939] Funding Source: National Science Foundation
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Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC's birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.
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