Unit Commitment in Electric Energy Systems.
Date:
June 22, 23, 25, and 26. AFTERNOON: 15 to 19h (Mo, Tu, Th) and 15 to 18h (Fr).
Instructor
Miguel F. Anjos (University of Edinburgh)
Miguel F. Anjos holds the Chair of Operational Research at the School of Mathematics, University of Edinburgh, U.K. He previously held faculty positions at Polytechnique Montreal, the University of Waterloo, and the University of Southampton. He is the Founding Academic Director of the Trottier Institute for Energy at Polytechnique Montreal. His accolades include an Inria International Chair, a Canada Research Chair, the NSERC-Hydro-Quebec-Schneider Electric Industrial Research Chair, a Humboldt Research Fellowship, a Schöller Senior Fellowship, INFORMS and IEEE Senior Memberships, and the Queen Elizabeth II Diamond Jubilee Medal. He is a Fellow of EUROPT, the Canadian Academy of Engineering, and the Academy for the Mathematical Sciences. The research interests of Professor Anjos are in mathematical optimization and its industrial applications. He has published four books and more than 100 scientific journal articles, and has led research collaborations with companies such as EDF, ExPretio, Hydro-Quebec, National Grid ESO, Rio Tinto, and Schneider Electric. He was the Editor-in-Chief of Optimization and Engineering, is currently Area Editor for the Journal of Optimization Theory and Applications and for RAIRO-OR, and is a member of several other editorial boards. Professor Anjos currently serves on the Board of the Operational Research Society (UK) and as Chair of the Society's Research Committee. He previously served as INFORMS Vice-President for International Activities, as President of the INFORMS Section on Energy, Natural Resources, and the Environment, as Chair of the Mathematical Optimization Society, as Program Director for the SIAM Activity Group on Optimization, and as Vice-Chair of the INFORMS Optimization Society.
Language
English
Description
The unit commitment (UC) problem addresses a fundamental decision that is taken when operating a power system, namely to set the schedule of power production for each generating unit in the system so that the demand for electricity is met at minimum cost. The schedule must also ensure that each unit operates within its technical limits; these typically include ramping constraints and minimum uptime/downtime constraints. Units that are scheduled to produce electricity during a given time period are said to be committed for that period. Various jurisdictions solve UC on a daily basis. In particular, it is the standard tool to clear spot markets, and particularly the day-ahead markets in the USA. In North American jurisdictions without markets, the system operators use UC to determine the day-ahead commitments and dispatches. This course will cover some of the most relevant mathematical optimization models for UC and aspects of computationally solving them.
Course goals
- Demonstrate knowledge of the relevance of UC to power system operations, through discussion of how modelling techniques in the course relate to practical industrial questions.
- Formulate UC operational models, and implement and solve them using a suitable optimization solver.
- Interpret and analyse the solutions of the models, and deduce their implications in practice.
Course contents
- Period 1: Basics of Unit Commitment and Modern Electric Energy Systems (3 h + 1h lab)
- Period 2: Network-Constrained Unit Commitment (3 h + 1 h lab)
- Period 3: Unit Commitment Under Uncertainty (3 h + 1 h lab)
- Period 4: Additional Topics in Unit Commitment Modelling (3 h)
The course will be based on the tutorial:
M.F. Anjos and A.J. Conejo. Unit Commitment in Electric Energy Systems, Foundations and Trends in Electronic Energy Systems (2017) 1 (4): 220–310. http://dx.doi.org/10.1561/3100000014
Prerequisites
200643 - MMIO - Models and Methods From Operations or equivalent; if not sure, contact the instructor.
Targeted at
Master's students in Statistics and Operations Research
Teaching Methodology and Activities
The course will consist of a combination of theoretical sessions and lab exercises.
Evaluation
Completion of an assigned individual piece of work, on a topic to be agreed with the instructor.
Software requirements
Access to AMPL (Modeling language for mathematical optimization) and to a solver such as CPLEX; Gurobi, HiGHS or similar.
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