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Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers

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  • Gupta, S.
  • Maulik, A.
  • Das, D.
  • Singh, A.

Abstract

A microgrid comprises renewable and non-renewable power generating sources, controllable loads, energy storage devices and works as a single controllable entity. A gradual shift from conventional internal combustion engine-based vehicles to electric/hybrid electric vehicles has also led to a new load type in the power system. Moreover, demand-side management measures like demand response programs have become popular. This work deals with the optimal coordinated operation of a grid-connected AC microgrid consisting of controllable and uncontrollable power sources, battery storage units, considering plug-in hybrid electric vehicles and demand response programs. Stochastic models of renewable power sources, electric load demand, loads of hybrid electric vehicles (with battery charging characteristic), and grid power price are fed into “Hong’s 2 m point estimate method” embedded optimal operating strategy. The objective is to minimize the cost of operation subject to the satisfaction of technical constraints. A nested stochastic optimization algorithm is implemented to find optimal generation schedule, battery dispatch strategy, and the best incentive value for an incentive-based demand response program. Different charging strategies of hybrid electric vehicles are studied, and their impacts on system operation are investigated. The optimal coordination between a voltage control scheme using a smart transformer with the energy management scheme is also investigated. Simulation studies on a thirty-three bus test system prove the efficacy of the proposed algorithm. The proposed coordinated optimal operating strategy reduces the operating cost by 17.53%∼17.74%. The system loss also reduces by 29.49%∼31.36%.

Suggested Citation

  • Gupta, S. & Maulik, A. & Das, D. & Singh, A., 2022. "Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    • Handle: RePEc:eee:rensus:v:155:y:2022:i:c:s136403212101128x
    DOI: 10.1016/j.rser.2021.111861
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    References listed on IDEAS

    as
    1. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    2. Monfared, Houman Jamshidi & Ghasemi, Ahmad & Loni, Abdolah & Marzband, Mousa, 2019. "A hybrid price-based demand response program for the residential micro-grid," Energy, Elsevier, vol. 185(C), pages 274-285.
    3. Imani, Mahmood Hosseini & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li, 2018. "Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 486-499.
    4. Kayal, Partha & Chanda, C.K., 2015. "Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network," Renewable Energy, Elsevier, vol. 75(C), pages 173-186.
    5. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    6. Hasan, Kazi Nazmul & Preece, Robin & Milanović, Jovica V., 2019. "Existing approaches and trends in uncertainty modelling and probabilistic stability analysis of power systems with renewable generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 168-180.
    7. Khalili, Tohid & Jafari, Amirreza & Abapour, Mehdi & Mohammadi-Ivatloo, Behnam, 2019. "Optimal battery technology selection and incentive-based demand response program utilization for reliability improvement of an insular microgrid," Energy, Elsevier, vol. 169(C), pages 92-104.
    8. Pfeifer, Antun & Dobravec, Viktorija & Pavlinek, Luka & Krajačić, Goran & Duić, Neven, 2018. "Integration of renewable energy and demand response technologies in interconnected energy systems," Energy, Elsevier, vol. 161(C), pages 447-455.
    9. Mohamed, Mohamed A. & Jin, Tao & Su, Wencong, 2020. "Multi-agent energy management of smart islands using primal-dual method of multipliers," Energy, Elsevier, vol. 208(C).
    10. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    11. Gazijahani, Farhad Samadi & Salehi, Javad, 2018. "Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach," Energy, Elsevier, vol. 161(C), pages 999-1015.
    12. Das, Bikash & Mukherjee, V. & Das, Debapriya, 2019. "Optimum DG placement for known power injection from utility/substation by a novel zero bus load flow approach," Energy, Elsevier, vol. 175(C), pages 228-249.
    13. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    14. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    15. Bett, Philip E. & Thornton, Hazel E., 2016. "The climatological relationships between wind and solar energy supply in Britain," Renewable Energy, Elsevier, vol. 87(P1), pages 96-110.
    16. Noor, Sana & Yang, Wentao & Guo, Miao & van Dam, Koen H. & Wang, Xiaonan, 2018. "Energy Demand Side Management within micro-grid networks enhanced by blockchain," Applied Energy, Elsevier, vol. 228(C), pages 1385-1398.
    17. Jithendranath, J. & Das, Debapriya & Guerrero, Josep M., 2021. "Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation," Energy, Elsevier, vol. 222(C).
    18. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.
    19. Kamankesh, Hamidreza & Agelidis, Vassilios G. & Kavousi-Fard, Abdollah, 2016. "Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand," Energy, Elsevier, vol. 100(C), pages 285-297.
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    2. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Two-phase operation for coordinated charging of electric vehicles in a market environment: From electric vehicle aggregators’ perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).

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