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Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response

Author

Listed:
  • Mostafa Darvishi

    (Department of Electrical Engineering, Ilam Branch, Islamic Azad University, Ilam, Iran)

  • Mehrdad Tahmasebi

    (Department of Electrical Engineering, Ilam Branch, Islamic Azad University, Ilam, Iran)

  • Ehsan Shokouhmand

    (Department of Electrical and Computer Engineering, Jundi-Shapur University of Technology, Dezful, Iran)

  • Jagadeesh Pasupuleti

    (Institute of Sustainable Energy, University Tenaga Nasional, Jalan Ikram—UNITEN, Kajang 43000, Selangor, Malaysia)

  • Pitshou Bokoro

    (Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2006, South Africa)

  • Jwan Satei Raafat

    (Department of Electrical Engineering, Sulaimani University, Sulaimaniyah 46001, Iraq)

Abstract

One of the significant environmental issues is global warming, and governments have changed their procedures to reduce carbon emissions. Sustainability is commonly described as having three dimensions: environmental, economic, and social. There are numerous environmental impacts associated with energy systems and the significance of energy for living standards and economic development. Therefore, the movement towards intelligent energy systems and virtual power plants (VPPs) is being pursued more rapidly due to economic and environmental issues. The VPP is one of the technologies used to increase the entire system’s efficiency. Moreover, because of environmental pollution, increased greenhouse gas production, and global warming, countries’ policies have changed towards reducing the use of fossil fuels and increasing the penetration of renewable energy sources (RESs) in distribution networks. However, RESs, such as wind turbines (WT) and photovoltaic (PV) panels, exhibit uncertain behavior. This issue, coupled with their high penetration, poses challenges for network operators in terms of managing the grid. Therefore, the sustainable virtual power plant (SVPP) is a suitable solution to overcome these problems and reduce the emissions in power systems. This study examines the cost of optimal operating of the SVPP and the amount of produced pollution in four different scenarios in the presence of a demand response program (DRP), energy storage system (ESS), etc., and the results are compared. The results indicate that the simultaneous implementation of DRPs and utilization of ESS can lead to a decrease in costs and pollution associated with SVPPs by 1.10% and 29.80%, respectively. Moreover, the operator can resolve the shortage and excess power generation that occurs during some hours.

Suggested Citation

  • Mostafa Darvishi & Mehrdad Tahmasebi & Ehsan Shokouhmand & Jagadeesh Pasupuleti & Pitshou Bokoro & Jwan Satei Raafat, 2023. "Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11012-:d:1193575
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    References listed on IDEAS

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    1. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    2. Mehrdad Tahmasebi & Jagadeesh Pasupuleti & Fatemeh Mohamadian & Mohammad Shakeri & Josep M. Guerrero & M. Reyasudin Basir Khan & Muhammad Shahzad Nazir & Amir Safari & Najmeh Bazmohammadi, 2021. "Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
    3. Kong, Xiangyu & Xiao, Jie & Liu, Dehong & Wu, Jianzhong & Wang, Chengshan & Shen, Yu, 2020. "Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties," Applied Energy, Elsevier, vol. 279(C).
    4. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Ju, Liwei & Yin, Zhe & Zhou, Qingqing & Li, Qiaochu & Wang, Peng & Tian, Wenxu & Li, Peng & Tan, Zhongfu, 2022. "Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas," Applied Energy, Elsevier, vol. 310(C).
    6. Askarzadeh, Alireza, 2017. "Distribution generation by photovoltaic and diesel generator systems: Energy management and size optimization by a new approach for a stand-alone application," Energy, Elsevier, vol. 122(C), pages 542-551.
    7. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Khodabakhshian, Amin & Parastegari, Moein, 2016. "Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties," Energy, Elsevier, vol. 117(P1), pages 176-189.
    8. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    9. Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    10. Kang, Wenfa & Chen, Minyou & Lai, Wei & Luo, Yanyu, 2021. "Distributed real-time power management for virtual energy storage systems using dynamic price," Energy, Elsevier, vol. 216(C).
    11. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    12. Ghahramani, Mehrdad & Nazari-Heris, Morteza & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2019. "Energy and reserve management of a smart distribution system by incorporating responsive-loads /battery/wind turbines considering uncertain parameters," Energy, Elsevier, vol. 183(C), pages 205-219.
    13. Zahid Ullah & Arshad & Hany Hassanin, 2022. "Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources," Energies, MDPI, vol. 15(14), pages 1-16, July.
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