Probabilistic Optimization Techniques in Smart Power System

Muhammad Riaz, Sadiq Ahmad, Irshad Hussain, Muhammad Naeem and Lucian Mihet-Popa
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Muhammad Riaz: Department of Electrical and Computer Engineering, Wah Campus, COMSATS University, Wah 47040, Pakistan
Sadiq Ahmad: Department of Electrical and Computer Engineering, Wah Campus, COMSATS University, Wah 47040, Pakistan
Irshad Hussain: Faculty of Electrical and Computer Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan
Muhammad Naeem: Department of Electrical and Computer Engineering, Wah Campus, COMSATS University, Wah 47040, Pakistan
Lucian Mihet-Popa: Faculty of Information Technology, Engineering and Economics, Oestfold University College, 1757 Halden, Norway

Energies, 2022, vol. 15, issue 3, 1-39

Abstract: Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

Keywords: probabilistic optimization; stochastic optimization; robust optimization; distributional robust optimization; chance constrained optimization; energy management; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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