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Incorporating short-term topological variations in optimal energy management of MGs considering ancillary services by electric vehicles

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  • Anand, M.P.
  • Golshannavaz, Sajjad
  • Ongsakul, Weerakorn
  • Rajapakse, Athula

Abstract

This study proposes a well-defined energy management system (EMS) intended for smart microgrids (MGs) operational management. The proposed EMS aims at optimal allocation of active elements including electric vehicles (EVs), distributed generations (DGs), and responsive loads (RLs). To avoid the adverse effects of EVs' burden, a price-sensitive priority-based smart charging approach is outlined. As an innovative point, the potential of EVs in providing ancillary services such as reactive power provision is addressed in improving the MG's techno-economical performance. Moreover, the infield automatically controlled switches (ACSs) are accommodated to yield in optimal hourly reconfigurations. This practice would definitely impinge on the scheduling patterns of active elements and enhance the performance of the MG. Furthermore, the effects of reconfigurations and EVs' reactive power processes are explored on the capacity release of DGs, EV reliance, and imported power from the upper grid. The released/unallocated capacities are offered as reserve resources for undertaking the renewables' intermittency and load forecasting uncertainties. The proposed approach is formulated as a mixed-integer nonlinear programming problem and tackled based on time-varying inertia weight factor particle swam optimization. A medium-voltage MG is put under numerical analysis to assess the performance of the proposed EMS. Results are discussed in depth.

Suggested Citation

  • Anand, M.P. & Golshannavaz, Sajjad & Ongsakul, Weerakorn & Rajapakse, Athula, 2016. "Incorporating short-term topological variations in optimal energy management of MGs considering ancillary services by electric vehicles," Energy, Elsevier, vol. 112(C), pages 241-253.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:241-253
    DOI: 10.1016/j.energy.2016.06.078
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    References listed on IDEAS

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    Cited by:

    1. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    2. Amiri, Saeed Salimi & Jadid, Shahram & Saboori, Hedayat, 2018. "Multi-objective optimum charging management of electric vehicles through battery swapping stations," Energy, Elsevier, vol. 165(PB), pages 549-562.
    3. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid & Josep M. Guerrero, 2018. "A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems," Energies, MDPI, vol. 11(7), pages 1-19, July.
    4. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    5. Jian Chen & Fangyi Li & Ranran Yang & Dawei Ma, 2020. "Impacts of Increasing Private Charging Piles on Electric Vehicles’ Charging Profiles: A Case Study in Hefei City, China," Energies, MDPI, vol. 13(17), pages 1-17, August.
    6. Pinto, Rui & Bessa, Ricardo J. & Matos, Manuel A., 2017. "Multi-period flexibility forecast for low voltage prosumers," Energy, Elsevier, vol. 141(C), pages 2251-2263.

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