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Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms


  • Ghasemi, Ahmad
  • Mortazavi, Seyed Saeidollah
  • Mashhour, Elaheh


This paper presents a new optimization framework to optimize the bidding strategy of a smart distribution company (SDC) in a day-ahead (DA) energy market. This SDC contains wind farms as stochastic DG units as well as plug-in electric vehicles (PEVs) as responsive loads. The intermittent nature of wind power may result in significant imbalance penalty costs for the SDC participated in the DA energy market. The proposed optimization framework uses the potential of plug-in electric vehicles (PEVs) and battery energy storage (BES) to manage possible imbalances of wind farms. In order to modify the charging pattern of PEVs, hourly electricity prices are calculated in the optimization framework and sent to PEV owners via smart communication system. PEV owners change their charging pattern in response to these hourly prices with the aim of reducing their electricity bills. In addition to responsive loads, BES and wind farms, the SDC also contains dispatchable distributed generators (DGs), distribution network and non-responsive loads. The two-point estimate method (TPEM) is used to model the uncertainties associated with wind farms power generation. Moreover, Benders decomposition technique (BDT) is implemented to simplify the optimization procedure. Finally, the effectiveness of the proposed framework is evaluated on several case studies.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:124-136
    DOI: 10.1016/j.renene.2015.06.018

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    References listed on IDEAS

    1. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    2. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
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    4. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
    5. 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.
    6. Mohammadi, Sirus & Mozafari, Babak & Solimani, Soodabeh & Niknam, Taher, 2013. "An Adaptive Modified Firefly Optimisation Algorithm based on Hong's Point Estimate Method to optimal operation management in a microgrid with consideration of uncertainties," Energy, Elsevier, vol. 51(C), pages 339-348.
    7. Moghaddam, Iman Gerami & Nick, Mostafa & Fallahi, Farhad & Sanei, Mohsen & Mortazavi, Saeid, 2013. "Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market," Renewable Energy, Elsevier, vol. 55(C), pages 252-259.
    8. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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    Cited by:

    1. Soares, João & Ghazvini, Mohammad Ali Fotouhi & Borges, Nuno & Vale, Zita, 2017. "Dynamic electricity pricing for electric vehicles using stochastic programming," Energy, Elsevier, vol. 122(C), pages 111-127.
    2. repec:eee:renene:v:123:y:2018:i:c:p:460-474 is not listed on IDEAS
    3. repec:eee:energy:v:142:y:2018:i:c:p:1-13 is not listed on IDEAS
    4. repec:eee:energy:v:139:y:2017:i:c:p:315-328 is not listed on IDEAS
    5. 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.
    6. repec:gam:jeners:v:11:y:2018:i:10:p:2547-:d:171758 is not listed on IDEAS
    7. repec:gam:jeners:v:11:y:2018:i:12:p:3296-:d:185547 is not listed on IDEAS
    8. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units," Renewable Energy, Elsevier, vol. 95(C), pages 74-84.
    9. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    10. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    11. Haque, A.N.M.M. & Ibn Saif, A.U.N. & Nguyen, P.H. & Torbaghan, S.S., 2016. "Exploration of dispatch model integrating wind generators and electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1441-1451.
    12. repec:gam:jeners:v:11:y:2018:i:5:p:1140-:d:144457 is not listed on IDEAS
    13. repec:eee:appene:v:204:y:2017:i:c:p:143-162 is not listed on IDEAS
    14. repec:eee:ejores:v:264:y:2018:i:2:p:582-606 is not listed on IDEAS
    15. repec:gam:jeners:v:10:y:2017:i:12:p:2162-:d:123420 is not listed on IDEAS
    16. Sharifi, R. & Fathi, S.H. & Vahidinasab, V., 2017. "A review on Demand-side tools in electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 565-572.


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