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Optimal scheduling of distributed energy resources in smart grids: A complementarity approach

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  • Aghajani, Saemeh
  • Kalantar, Mohsen

Abstract

Utilizing electric vehicles in transportation systems has emerged as a promising approach for reducing amount of fossil fuels. In order to provide a procedure to derive the optimal charging/discharging strategy of these vehicles, this paper proposes a bilevel approach in which distribution system operator and parking lot operator are its two main levels. In the upper-level, the objective is minimizing distribution system operator cost considering all available resources in the system including renewable generation and parking lot and in the lower-level the parking minimizes its own cost. In addition, the renewable generation uncertainty is covered through spinning reserve provided by both parking lot and distributed generations. The proposed method is able to strategically determine the amount of energy and reserve interacted between two operators through integrated operation of all the resources in the network. The bilevel problem is reduced to a single level optimization problem by using of a mathematical problem with equilibrium constraints. The results show that the use of parking lot in reserve scheduling changes the scheduling of other available resources in the system. Moreover, by implementing the proposed model, the global optimal performance for distribution system operator with various interactions can be achieved by the equilibrium point.

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  • Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Optimal scheduling of distributed energy resources in smart grids: A complementarity approach," Energy, Elsevier, vol. 141(C), pages 2135-2144.
    • Handle: RePEc:eee:energy:v:141:y:2017:i:c:p:2135-2144
    DOI: 10.1016/j.energy.2017.11.139
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    References listed on IDEAS

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    1. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    2. Kavousi-Fard, Abdollah & Niknam, Taher, 2014. "Multi-objective stochastic Distribution Feeder Reconfiguration from the reliability point of view," Energy, Elsevier, vol. 64(C), pages 342-354.
    3. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    4. Sarabi, Siyamak & Davigny, Arnaud & Courtecuisse, Vincent & Riffonneau, Yann & Robyns, Benoît, 2016. "Potential of vehicle-to-grid ancillary services considering the uncertainties in plug-in electric vehicle availability and service/localization limitations in distribution grids," Applied Energy, Elsevier, vol. 171(C), pages 523-540.
    5. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
    6. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    7. Zhang, Qi & Ishihara, Keiichi N. & Mclellan, Benjamin C. & Tezuka, Tetsuo, 2012. "Scenario analysis on future electricity supply and demand in Japan," Energy, Elsevier, vol. 38(1), pages 376-385.
    8. Huang, Shoujun & Yang, Jun & Li, Shanjun, 2017. "Black-Scholes option pricing strategy and risk-averse coordination for designing vehicle-to-grid reserve contracts," Energy, Elsevier, vol. 137(C), pages 325-335.
    9. Rahbari, Omid & Vafaeipour, Majid & Omar, Noshin & Rosen, Marc A. & Hegazy, Omar & Timmermans, Jean-Marc & Heibati, Seyedmohammadreza & Bossche, Peter Van Den, 2017. "An optimal versatile control approach for plug-in electric vehicles to integrate renewable energy sources and smart grids," Energy, Elsevier, vol. 134(C), pages 1053-1067.
    10. San Román, Tomás Gómez & Momber, Ilan & Abbad, Michel Rivier & Sánchez Miralles, Álvaro, 2011. "Regulatory framework and business models for charging plug-in electric vehicles: Infrastructure, agents, and commercial relationships," Energy Policy, Elsevier, vol. 39(10), pages 6360-6375, October.
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    Cited by:

    1. Luo, Lizi & Wu, Zhi & Gu, Wei & Huang, He & Gao, Song & Han, Jun, 2020. "Coordinated allocation of distributed generation resources and electric vehicle charging stations in distribution systems with vehicle-to-grid interaction," Energy, Elsevier, vol. 192(C).
    2. Shahbazitabar, Maryam & Abdi, Hamdi, 2018. "A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation," Energy, Elsevier, vol. 161(C), pages 308-324.

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