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Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning

Author

Listed:
  • Oveis Abedinia

    (Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Nur-Sultan 010000, Kazakhstan)

  • Mehdi Bagheri

    (Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Nur-Sultan 010000, Kazakhstan)

Abstract

In this article, a novel dynamic economic load dispatch with emission based on a multi-objective model (MODEED) considering demand side management (DSM) is presented. Moreover, the investigation and evaluation of impacts of DSM for the next day are considered. In other words, the aim of economical load dispatch is the suitable and optimized planning for all power units considering different linear and non-linear constrains for power system and generators. In this model, different constrains such as losses of transformation network, impacts of valve-point, ramp-up and ramp-down, the balance of production and demand, the prohibited areas, and the limitations of production are considered as an optimization problem. The proposed model is solved by a novel modified multi-objective artificial bee colony algorithm (MOABC). In order to analyze the effects of DSM on the supply side, the proposed MODEED is evaluated on different scenarios with or without DSM. Indeed, the proposed MOABC algorithm tries to find an optimal solution for the existence function by assistance of crowding distance and Pareto theory. Crowding distance is a suitable criterion to estimate Pareto solutions. The proposed model is carried out on a six-unit test system, and the obtained numerical analyses are compared with the obtained results of other optimization methods. The obtained results of simulations that have been provided in the last section demonstrate the higher efficiency of the proposed optimization algorithm based on Pareto criterion. The main benefits of this algorithm are its fast convergence and searching based on circle movement. In addition, it is obvious from the obtained results that the proposed MODEED with DSM can present benefits for all consumers and generation companies.

Suggested Citation

  • Oveis Abedinia & Mehdi Bagheri, 2021. "Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning," Energies, MDPI, vol. 14(10), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2961-:d:558535
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    References listed on IDEAS

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    1. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    2. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun & Wu, Jiahui & Fan, Xiaochao & Xu, Qidan, 2020. "Dynamic environmental economic dispatch of hybrid renewable energy systems based on tradable green certificates," Energy, Elsevier, vol. 193(C).
    3. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    4. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    5. Qiao, Baihao & Liu, Jing, 2020. "Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm," Renewable Energy, Elsevier, vol. 154(C), pages 316-336.
    6. Alham, M.H. & Elshahed, M. & Ibrahim, Doaa Khalil & Abo El Zahab, Essam El Din, 2016. "A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management," Renewable Energy, Elsevier, vol. 96(PA), pages 800-811.
    7. Craparo, E.M. & Sprague, J.G., 2019. "Integrated supply- and demand-side energy management for expeditionary environmental control," Applied Energy, Elsevier, vol. 233, pages 352-366.
    8. Kuiken, Dirk & Más, Heyd F., 2019. "Integrating demand side management into EU electricity distribution system operation: A Dutch example," Energy Policy, Elsevier, vol. 129(C), pages 153-160.
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    Cited by:

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    2. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).
    3. Ma, Jinpeng & Wu, Shengbin & Raad, Erfan Ahli, 2023. "Renewable source uncertainties effects in multi-carrier microgrids based on an intelligent algorithm," Energy, Elsevier, vol. 265(C).
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