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Probabilistic Optimization Techniques in Smart Power System

Author

Listed:
  • Muhammad Riaz

    (Department of Electrical and Computer Engineering, Wah Campus, COMSATS University, Wah 47040, Pakistan
    Faculty of Electrical and Computer Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, 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)

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.

Suggested Citation

  • Muhammad Riaz & Sadiq Ahmad & Irshad Hussain & Muhammad Naeem & Lucian Mihet-Popa, 2022. "Probabilistic Optimization Techniques in Smart Power System," Energies, MDPI, vol. 15(3), pages 1-39, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:825-:d:731808
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    Citations

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    Cited by:

    1. Ibrar Ullah & Irshad Hussain & Khalid Rehman & Piotr Wróblewski & Wojciech Lewicki & Balasubramanian Prabhu Kavin, 2022. "Exploiting the Moth–Flame Optimization Algorithm for Optimal Load Management of the University Campus: A Viable Approach in the Academia Sector," Energies, MDPI, vol. 15(10), pages 1-27, May.
    2. Araoye, Timothy Oluwaseun & Ashigwuike, Evans Chinemezu & Mbunwe, Muncho Josephine & Bakinson, Oladipupo Idris & Ozue, ThankGod Izuchukwu, 2024. "Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm," Renewable Energy, Elsevier, vol. 229(C).
    3. Bingqing Xia & Hao Wu & Wenbin Yang & Lu Cao & Yonghua Song, 2022. "Parametric Transient Stability Constrained Optimal Power Flow Solved by Polynomial Approximation Based on the Stochastic Collocation Method," Energies, MDPI, vol. 15(11), pages 1-20, June.
    4. Rahim, Sahar & Wang, Zhen & Ju, Ping, 2022. "Overview and applications of Robust optimization in the avant-garde energy grid infrastructure: A systematic review," Applied Energy, Elsevier, vol. 319(C).
    5. Aswad Adib & Joao Onofre Pereira Pinto & Madhu S. Chinthavali, 2023. "GA-Based Voltage Optimization of Distribution Feeder with High-Penetration of DERs Using Megawatt-Scale Units," Energies, MDPI, vol. 16(13), pages 1-10, June.
    6. Kabulo Loji & Sachin Sharma & Nomhle Loji & Gulshan Sharma & Pitshou N. Bokoro, 2023. "Operational Issues of Contemporary Distribution Systems: A Review on Recent and Emerging Concerns," Energies, MDPI, vol. 16(4), pages 1-21, February.
    7. Fathy, Ahmed, 2023. "Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles," Applied Energy, Elsevier, vol. 334(C).
    8. Tala Talaei Khoei & Naima Kaabouch, 2023. "Machine Learning: Models, Challenges, and Research Directions," Future Internet, MDPI, vol. 15(10), pages 1-29, October.
    9. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.

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