Unit commitment using nearest neighbor as a short-term proxy

High-accuracy; Large power systems; Nearest neighbors; Orders of magnitude; Runtimes; Short term; Traditional approaches; Unit-commitment; Hierarchical systems

Abstract :

[en] We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems. Experimental results on updated versions of IEEE-RTS79 and IEEE-RTS96 show high accuracy measured on operational cost, achieved in runtimes that are lower in several orders of magnitude than the traditional approach. © 2018 Power Systems Computation Conference.

Disciplines :

Computer science
Electrical & electronics engineering

Author, co-author :

Dalal, G.; Israel Institute of Technology, Department of Electrical Engineering Technion, Israel

Gilboa, E.; Israel Institute of Technology, Department of Electrical Engineering Technion, Israel

Mannor, S.; Israel Institute of Technology, Department of Electrical Engineering Technion, Israel

Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation

Language :

English

Title :

Unit commitment using nearest neighbor as a short-term proxy

Publication date :

June 2018

Event name :

20th Power Systems Computation Conference, PSCC 2018

Event date :

11 June 2018 through 15 June 2018

Audience :

International

Main work title :

20th Power Systems Computation Conference, PSCC 2018

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Peer reviewed :

Peer reviewed

European Projects :

FP7 - 608540 - GARPUR - Generally Accepted Reliability Principle with Uncertainty modelling and through probabilistic Risk assessment

Funders :

CE - Commission Européenne

Commentary :

139082 9781910963104

Available on ORBi :

since 11 March 2019

Statistics

Number of views

60 (1 by ULiège)

Number of downloads

83 (0 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

GARPUR Consortium, "D2. 2, guidelines for implementing the new reliability assessment and optimization methodology, " Available: http://www. garpur-project. eu/deliverables, Oct. 2016.
P. B. et al., "Grid impacts of wind power: a summary of recent studies in the united states, " Wind Energy, 2004.
M. R. Milligan, "Modelling utility-scale wind power plants. Part 2: Capacity credit, " Wind Energy, vol. 3, no. 4, pp. 167-206, 2000.
G. Dalal, E. Gilboa, and S. Mannor, "Distributed scenario-based optimization for asset management in a hierarchical decision making environment, " in Power Systems Computation Conference, 2016.
T. Cover and P. Hart, "Nearest neighbor pattern classification, " IEEE transactions on information theory, vol. 13, no. 1, pp. 21-27, 1967.
B. Cornélusse, C. Wera, and L. Wehenkel, "Automatic learning for the classification of primary frequency control behaviour, " in Power Tech, 2007 IEEE Lausanne. IEEE, 2007, pp. 273-278.
G. Dalal, E. Gilboa, and S. Mannor, "Hierarchical decision making in electricity grid management, " in Proceedings of The 33rd International Conference on Machine Learning, 2016, pp. 2197-2206.
B. Cornelusse, G. Vignal, B. Defourny, and L. Wehenkel, "Supervised learning of intra-daily recourse strategies for generation management under uncertainties, " in PowerTech, 2009 IEEE Bucharest. IEEE, 2009.
E. Rachelson, A. B. Abbes, and S. Diemer, "Combining mixed integer programming and supervised learning for fast re-planning, " in 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.
"IBM ILOG CPLEX Optimizer, " http://www-01. ibm. com/software/integration/optimization/cplex-optimizer/, 2010.
S. Pineda, J. M. Morales, and T. K. Boomsma, "Impact of forecast errors on expansion planning of power systems with a renewables target, " European Journal of Operational Research, 2016.
N. Bhatia et al., "Survey of nearest neighbor techniques, " arXiv preprint arXiv: 1007. 0085, 2010.
A. Bellet, A. Habrard, and M. Sebban, "A survey on metric learning for feature vectors and structured data, " arXiv preprint arXiv: 1306. 6709, 2013.
"The language of technical computing, " http://www. matlab. com.
J. Lfberg, "Yalmip: A toolbox for modeling and optimization in MATLAB, " in Proceedings of the CACSD Conference, Taipei, Taiwan, 2004. [Online]. Available: http://users. isy. liu. se/johanl/yalmip
H. Pandzic, T. Qiu, and D. S. Kirschen, "Comparison of state-of-the-art transmission constrained unit commitment formulations, " in Power and Energy Society General Meeting (PES), 2013 IEEE, 2013.
E. E. D. University of Washington, "Renewable energy analysis lab, " http://www. ee. washington. edu/research/real/library. html.
Y. Dvorkin, H. Pandzic, M. Ortega-Vazquez, D. S. Kirschen et al., "A hybrid stochastic/interval approach to transmission-constrained unit commitment, " Power Systems, IEEE Transactions on, 2015.
R. Loisel, A. Mercier, C. Gatzen, N. Elms, and H. Petric, "Valuation framework for large scale electricity storage in a case with wind curtailment, " Energy Policy, vol. 38, no. 11, pp. 7323-7337, 2010.
A. Ouammi, H. Dagdougui, R. Sacile, and A. Mimet, "Monthly and seasonal assessment of wind energy characteristics at four monitored locations in liguria region (italy), " Renewable and Sustainable Energy Reviews, vol. 14, no. 7, pp. 1959-1968, 2010.
G. Dalal, E. Gilboa, S. Mannor, and L. Wehenkel, "Chance-constrained outage scheduling using a machine learning proxy, " arXiv preprint arXiv: 1801. 00500, 2018.
H. Samet, The design and analysis of spatial data structures. Addison-Wesley Reading, MA, 1990, vol. 199.
R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, "Matpower: Steady-state operations, planning, and analysis tools for power systems research and education, " Power Systems, IEEE Transactions on, vol. 26, no. 1, pp. 12-19, 2011.