Efficient Decision Making under Uncertainty in a Power System Investment Problem

Liu, Jialin1; Teytaud, Olivier2

10.1109/SSCI44817.2019.9003075

2019

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

697-704

6-9 Dec. 2019

Xiamen, China

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

The optimization of power systems involves complex uncertainties, such as technological progress, political context, geopolitical constraints. These uncertainties are difficult to modelize as probabilities, due to the lack of data for future technologies and due to partially adversarial geopolitical decision makers. Tools for such difficult decision making problems include Wald and Savage criteria, probabilistic reasoning and Nash equilibria. We investigate the rationale behind the use of a two- player Nash equilibrium approach in such a difficult context, and show that the approach is computationally efficient for large problems. Moreover, it automatically provides a selection of interesting decisions and critical scenarios for decision makers and is computationally cheaper than the Wald or Savage, thanks to the use of the sparsity of Nash equilibrium. It also has a natural interpretation in the sense that Nature does not make decisions taking into account our own decisions. The proposed approach was tested on instances of an artificial power system investment problem and can be applied to other problems, that can be modelled as a two-player matrix game or of which a payoff matrix can be built.
© 2019 IEEE.

Power system investment scenario-based decision making Nash equilibrium two-player matrix game

第一

英语

EI ; CPCI-S

Agence de l'Environnement et de la Maîtrise de l'Energie[]

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467200100

20201108276921

Artificial intelligence ; Computation theory ; Game theory ; Investments

Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Artificial Intelligence:723.4 ; Management:912.2 ; Probability Theory:922.1

EV Compendex

1.Southern University of Science and Technology, Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving, Intelligent Systems of Guangdong Province, Shenzhen; 518055, China
2.CNRS - Univ. Paris-Saclay, Inria TAU, LRI, UMR 8623, 91190, France

Liu, Jialin,Teytaud, Olivier. Efficient Decision Making under Uncertainty in a Power System Investment Problem[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:697-704.