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Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment

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  • Al-Bahrani, Loau Tawfak
  • Horan, Ben
  • Seyedmahmoudian, Mehdi
  • Stojcevski, Alex

Abstract

A multi-objective problem, namely, dynamic economic emission dispatch is simultaneously solved under several practical equality and inequality operating power constraints, while applying the load demand management (LDM) on 30,000 electric vehicles (EVs) during crest shaving and valley filling (CSVF) regions. A novel algorithm named orthogonal particle swarm optimization (OPSO) is proposed for such a problem. Ten thermal-generating units (TGUs) from power-generating systems (PGSs) in two Case Studies, with and without LDM on the load demand of 30,000 EVs, are tested. The comprehensive analysis results reveal that the quantity of emissions released by the 10 TGUs was remarkable affected and reduced around 4005 kg/day and the operating fuel cost was saved around $409/day when applying the LDM on the load demand of 30,000 EVs in the CSVF regions. This study provides important outcomes about the future operation of PGSs when applying the LDM strategy on a large-scale penetration of EVs in smart cities into a sustainable environment. The study outcomes contribute in making the PGS flexible, economical, environmental, stable, and reliable.

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  • Al-Bahrani, Loau Tawfak & Horan, Ben & Seyedmahmoudian, Mehdi & Stojcevski, Alex, 2020. "Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment," Energy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220300530
    DOI: 10.1016/j.energy.2020.116946
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    Cited by:

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    2. Chen, Jiahui & Wang, Fang & He, Xiaoyi & Liang, Xinyu & Huang, Junling & Zhang, Shaojun & Wu, Ye, 2022. "Emission mitigation potential from coordinated charging schemes for future private electric vehicles," Applied Energy, Elsevier, vol. 308(C).
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    4. SeyedGarmroudi, SeyedDavoud & Kayakutlu, Gulgun & Kayalica, M. Ozgur & Çolak, Üner, 2024. "Improved Pelican optimization algorithm for solving load dispatch problems," Energy, Elsevier, vol. 289(C).
    5. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    6. Zhang, Qiang & Zou, Dexuan & Duan, Na, 2023. "An improved differential evolution using self-adaptable cosine similarity for economic emission dispatch," Energy, Elsevier, vol. 283(C).
    7. Neelamsetti Kirn Kumar & Rahul Sanmugam Gopi & Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman & Indragandhi Vairavasundaram & Siripireddy Venkateswarulu, 2022. "Fuzzy Logic-Based Load Frequency Control in an Island Hybrid Power System Model Using Artificial Bee Colony Optimization," Energies, MDPI, vol. 15(6), pages 1-20, March.
    8. Georgios Semertzidis & Dimitrios Stamatakis & Vasilios Tsalavoutis & Athanasios I. Tolis, 2022. "Optimized electric vehicle charging integrated in the unit commitment problem," Operational Research, Springer, vol. 22(5), pages 5137-5204, November.

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