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Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties


  • Shayegan-Rad, Ali
  • Badri, Ali
  • Zangeneh, Ali


This paper presents a day-ahead scheduling framework for virtual power plant (VPP) in a joint energy and regulation reserve (RR) markets. The proposed VPP clusters a mix of generation units in term of synchronous distributed generation (SDG) and wind power plant (WPP) as well as storage facilities such as electrical vehicles (EVs) and small pumped storage plant (PSP). It is assumed that VPP provides required RR through its SDG and small PSP based on the delivery request probability of day-ahead market. In order to aggregate EVs, the VPP establishes bilateral incentive contracts with vehicle owners. Moreover, impact of carbon dioxide (CO2) emission of SDG is included by means of penalty cost function. Different uncertain parameters with regard to wind generation, EV owner behaviors, energy and RR market prices and regulation up and down probabilities are considered using a point estimate method (PEM). The case studies are applied to demonstrate the effectiveness of the scheduling model.

Suggested Citation

  • Shayegan-Rad, Ali & Badri, Ali & Zangeneh, Ali, 2017. "Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties," Energy, Elsevier, vol. 121(C), pages 114-125.
  • Handle: RePEc:eee:energy:v:121:y:2017:i:c:p:114-125
    DOI: 10.1016/

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    References listed on IDEAS

    1. Ghadikolaei, Hadi Moghimi & Tajik, Elham & Aghaei, Jamshid & Charwand, Mansour, 2012. "Integrated day-ahead and hour-ahead operation model of discos in retail electricity markets considering DGs and CO2 emission penalty cost," Applied Energy, Elsevier, vol. 95(C), pages 174-185.
    2. Raineri, R. & Rios, S. & Schiele, D., 2006. "Technical and economic aspects of ancillary services markets in the electric power industry: an international comparison," Energy Policy, Elsevier, vol. 34(13), pages 1540-1555, September.
    3. Tajeddini, Mohammad Amin & Rahimi-Kian, Ashkan & Soroudi, Alireza, 2014. "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energy, Elsevier, vol. 73(C), pages 958-967.
    4. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    5. Ghasemi, Ahmad & Mortazavi, Seyed Saeidollah & Mashhour, Elaheh, 2016. "Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms," Renewable Energy, Elsevier, vol. 85(C), pages 124-136.
    6. Arslan, Okan & Karasan, Oya Ekin, 2013. "Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks," Energy, Elsevier, vol. 60(C), pages 116-124.
    7. Vahidinasab, V. & Jadid, S., 2009. "Multiobjective environmental/techno-economic approach for strategic bidding in energy markets," Applied Energy, Elsevier, vol. 86(4), pages 496-504, April.
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    1. repec:gam:jeners:v:11:y:2018:i:5:p:1242-:d:146116 is not listed on IDEAS
    2. repec:eee:energy:v:155:y:2018:i:c:p:678-689 is not listed on IDEAS
    3. repec:eee:energy:v:148:y:2018:i:c:p:1-15 is not listed on IDEAS


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