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Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles

Author

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  • Mohammad Rasoul Narimani

    (Department of electrical and computer engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA)

  • Maigha

    (Department of electrical and computer engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA)

  • Jhi-Young Joo

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Mariesa Crow

    (Department of electrical and computer engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA)

Abstract

In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated.

Suggested Citation

  • Mohammad Rasoul Narimani & Maigha & Jhi-Young Joo & Mariesa Crow, 2017. "Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles," Energies, MDPI, vol. 10(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:624-:d:97500
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    References listed on IDEAS

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    1. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
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    3. Maigha & Mariesa L. Crow, 2014. "Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging," Energies, MDPI, vol. 7(4), pages 1-23, March.
    4. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
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    Citations

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    Cited by:

    1. Wyrwa, Artur & Suwała, Wojciech & Pluta, Marcin & Raczyński, Maciej & Zyśk, Janusz & Tokarski, Stanisław, 2022. "A new approach for coupling the short- and long-term planning models to design a pathway to carbon neutrality in a coal-based power system," Energy, Elsevier, vol. 239(PE).
    2. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2018. "Solving the dynamic economic dispatch by a memory-based global differential evolution and a repair technique of constraint handling," Energy, Elsevier, vol. 147(C), pages 59-80.
    3. Godiana Hagile Philipo & Josephine Nakato Kakande & Stefan Krauter, 2022. "Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping," Energies, MDPI, vol. 15(14), pages 1-18, July.
    4. Jiangtao Yu & Chang-Hwan Kim & Abdul Wadood & Tahir Khurshiad & Sang-Bong Rhee, 2018. "A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 11(8), pages 1-25, July.
    5. Narimani, Hossein & Razavi, Seyed-Ehsan & Azizivahed, Ali & Naderi, Ehsan & Fathi, Mehdi & Ataei, Mohammad H. & Narimani, Mohammad Rasoul, 2018. "A multi-objective framework for multi-area economic emission dispatch," Energy, Elsevier, vol. 154(C), pages 126-142.
    6. Azizivahed, Ali & Narimani, Hossein & Fathi, Mehdi & Naderi, Ehsan & Safarpour, Hamid Reza & Narimani, Mohammad Rasoul, 2018. "Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems," Energy, Elsevier, vol. 147(C), pages 896-914.
    7. Azizivahed, Ali & Narimani, Hossein & Naderi, Ehsan & Fathi, Mehdi & Narimani, Mohammad Rasoul, 2017. "A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration," Energy, Elsevier, vol. 138(C), pages 355-373.
    8. Di Lorenzo, Gianfranco & Rotondo, Sara & Araneo, Rodolfo & Petrone, Giovanni & Martirano, Luigi, 2021. "Innovative power-sharing model for buildings and energy communities," Renewable Energy, Elsevier, vol. 172(C), pages 1087-1102.
    9. de Souza Dutra, Michael David & da Conceição Júnior, Gerson & de Paula Ferreira, William & Campos Chaves, Matheus Roberto, 2020. "A customized transition towards smart homes: A fast framework for economic analyses," Applied Energy, Elsevier, vol. 262(C).
    10. Li Han & Rongchang Zhang & Xuesong Wang & Yu Dong, 2018. "Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction," Energies, MDPI, vol. 11(8), pages 1-27, August.
    11. Hossein Lotfi, 2022. "A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    12. Basu, M., 2021. "Fuel constrained dynamic economic dispatch with demand side management," Energy, Elsevier, vol. 223(C).
    13. Marcin Pluta & Artur Wyrwa & Wojciech Suwała & Janusz Zyśk & Maciej Raczyński & Stanisław Tokarski, 2020. "A Generalized Unit Commitment and Economic Dispatch Approach for Analysing the Polish Power System under High Renewable Penetration," Energies, MDPI, vol. 13(8), pages 1-18, April.

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