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Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

Author

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  • João Soares
  • Tiago Pinto
  • Fernando Lezama
  • Hugo Morais

Abstract

This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

Suggested Citation

  • João Soares & Tiago Pinto & Fernando Lezama & Hugo Morais, 2018. "Survey on Complex Optimization and Simulation for the New Power Systems Paradigm," Complexity, Hindawi, vol. 2018, pages 1-32, August.
    • Handle: RePEc:hin:complx:2340628
    DOI: 10.1155/2018/2340628
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    References listed on IDEAS

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    Cited by:

    1. Dharmesh Dabhi & Kartik Pandya & Joao Soares & Fernando Lezama & Zita Vale, 2022. "Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets," Energies, MDPI, vol. 15(13), pages 1-20, July.
    2. Zahra Foroozandeh & Sérgio Ramos & João Soares & Fernando Lezama & Zita Vale & António Gomes & Rodrigo L. Joench, 2020. "A Mixed Binary Linear Programming Model for Optimal Energy Management of Smart Buildings," Energies, MDPI, vol. 13(7), pages 1-16, April.
    3. Aviad Navon & Gefen Ben Yosef & Ram Machlev & Shmuel Shapira & Nilanjan Roy Chowdhury & Juri Belikov & Ariel Orda & Yoash Levron, 2020. "Applications of Game Theory to Design and Operation of Modern Power Systems: A Comprehensive Review," Energies, MDPI, vol. 13(15), pages 1-35, August.
    4. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    5. Joao Soares & Bruno Canizes & Zita Vale, 2021. "Rethinking the Distribution Power Network Planning and Operation for a Sustainable Smart Grid and Smooth Interaction with Electrified Transportation," Energies, MDPI, vol. 14(23), pages 1-4, November.
    6. Yinuo Huang & Licheng Wang & Kai Wang, 2019. "Investigation of Var Compensation Schemes in Unbalanced Distribution Systems," Complexity, Hindawi, vol. 2019, pages 1-13, October.
    7. Jiayu Cheng & Dongliang Duan & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2021. "Adaptive Control for Energy Exchange with Probabilistic Interval Predictors in Isolated Microgrids," Energies, MDPI, vol. 14(2), pages 1-23, January.
    8. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    9. Koos van der Linden & Natalia Romero & Mathijs M. de Weerdt, 2021. "Benchmarking Flexible Electric Loads Scheduling Algorithms," Energies, MDPI, vol. 14(5), pages 1-16, February.

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