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A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid

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  • Moghaddas Tafreshi, Seyed Masoud
  • Ranjbarzadeh, Hassan
  • Jafari, Mehdi
  • Khayyam, Hamid

Abstract

This paper presents a probabilistic Unit Commitment (UC) model for optimal scheduling of wind power, load forecasts and controllability of vehicles in a microgrid using a stochastic programming framework. The microgrid is made up of microturbines, wind turbine, boiler, Plug-in Electric Vehicles (PEVs), thermal storage and battery storage. The proposed model will help the power grid operators with optimal day- ahead planning even with variable operating conditions in respect of load forecasts, controllability of vehicles and wind generation. A set of valid scenarios is assigned for the uncertainties of wind sources, load and PEVs and objective function in the form of expected value. The objective function is to maximize the expected total profit of the UC schedule for the set of scenarios from the viewpoint of microgrid management. The probabilistic unit commitment optimizes the objective function using Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the stochastic modelling and make a comparison with a simple deterministic one, a typical microgrid is used as a case study. The results can be used to evaluate the effect of integration of PEVs on the economic operation of the microgrid. The results also confirm the necessity to consider the key uncertainties of the microgrid; otherwise the results could overly misrepresent the real world operation of the system.

Suggested Citation

  • Moghaddas Tafreshi, Seyed Masoud & Ranjbarzadeh, Hassan & Jafari, Mehdi & Khayyam, Hamid, 2016. "A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 934-947.
    • Handle: RePEc:eee:rensus:v:66:y:2016:i:c:p:934-947
    DOI: 10.1016/j.rser.2016.08.013
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    References listed on IDEAS

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    2. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    3. Panagiotis Adraktas & Athanasios Dagoumas, 2019. "Integration of Electric Vehicles in the Unit Commitment Problem with Uncertain Renewable Electricity Generation," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 315-333.
    4. 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.
    5. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    6. Hassan Ranjbarzadeh & Seyed Masoud Moghaddas Tafreshi & Mohd Hasan Ali & Abbas Z. Kouzani & Suiyang Khoo, 2022. "A Probabilistic Model for Minimization of Solar Energy Operation Costs as Well as CO 2 Emissions in a Multi-Carrier Microgrid (MCMG)," Energies, MDPI, vol. 15(9), pages 1-24, April.
    7. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.
    8. Mohammadnejad, Mehran & Abdollahi, Amir & Rashidinejad, Masoud, 2020. "Possibilistic-probabilistic self-scheduling of PEVAggregator for participation in spinning reserve market considering uncertain DRPs," Energy, Elsevier, vol. 196(C).

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