Optimization Model and Algorithm for Capacity Planning and Operation of Reliable and Carbon-neutral Power Systems with High Penetration of Renewable Generation
In this work, we propose a Generalized Disjunctive Programming (GDP) model that optimizes both long-term capacity planning (such as the number and size of dispatchable/renewable generators, batteries, and transmission lines) and hourly operation (such as on/off schedules of dispatchable generators, power output from each generator, and power flow) to maximize power system reliability while minimizing total cost and CO2 emissions.
Authors
- Benjamin Omell
- Javier Tovar-Facio (ORCID: https://orcid.org/0000-0001-8247-3802)
- Anthony P. Burgard
- John D. Siirola
- Ignacio E. Grossmann (ORCID: https://orcid.org/0000-0002-7210-084X)
- Seolhee Cho (ORCID: https://orcid.org/0000-0002-9495-4926)
- Radhakrishna Gooty
- Christopher McLean
- Kyle Skolfield
- John Shinn
Institutions
- California Energy Commission (US)
- Autonomous University of Chihuahua (MX)
- Lawrence Berkeley National Laboratory (US)
- Sandia National Laboratories (US)
- National Energy Technology Laboratory (US)
- Carnegie Mellon University (US)
Publication Details
- Published
- 2024-10-28
- DOI
- https://doi.org/10.2172/2478221
- Citations
- 1
- Primary Topic
- Advanced Power Generation Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.21
Funders
- U.S. Department of Energy
- Office of Fossil Energy