Risk-based cooperative scheduling of demand response and electric vehicle aggregators

Document Type : Article


Department of Electrical and Computer Engineering, University of Tabriz, 29 Bahman Blvd., Tabriz, Iran


This paper proposes a new cooperative scheduling framework for demand response aggregators (DRAs) and electric vehicle aggregators (EVAs) in a day-ahead market. The proposed model implements the information-gap decision theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees obtaining the predetermined profit by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenario-based and a bi-level IGDT based methods, respectively. The DR aggregator provides DR from two demand side management programs including time-of-use (TOU) and reward-based DR. Then, the obtained DR is offered into day-ahead markets. Furthermore, the EVA not only meet the EV owners’ demand economically, but also participates in the day-ahead mark while willing to set DR contracts with the DR aggregator. The objective function is to maximize the total profit of DR and EV aggregators perusing two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated on a case study with realistic data of electricity markets.


1.Gadham, K.R. and Ghose, T. Importance of social  welfare point for the analysis of demand response",  in IEEE First International Conference on Control,  Measurement and Instrumentation (CMI), pp. 182-185  (2016). 
2. Aalami, H.A. and Khatibzadeh, A. Regulation of  market clearing price based on nonlinear models of  demand bidding and emergency demand response  programs", International Transactions on Electrical  Energy Systems, 26(11), pp. 2463-2478 (2016). 
3. Huang, S., Wu, L., In_eld, D., and Zhang, T. Using  electric vehicle eet as responsive demand for power  system frequency support", In 2013 IEEE Vehicle  Power and Propulsion Conference (VPPC), pp. 1-5  (2013). 
4. Zhao, F., Liu, F., Liu, Z., and Hao, H. The correlated  impacts of fuel consumption improvements and vehicle  electri_cation on vehicle greenhouse gas emissions in  China", Journal of Cleaner Production, 207, pp. 702-  716 (2019).  5. Shamshirband, M., Salehi, J., and Gazijahani, F.S.  Decentralized trading of plug-in electric vehicle aggregation  agents for optimal energy management of smart  renewable penetrated microgrids with the aim of CO2  emission reduction", Journal of Cleaner Production,  200, pp. 622-640 (2018).  6. Schneider, K., Gerkensmeyer, C., Kintner-Meyer, M.,  and Fletcher, R. Impact assessment of plug-in hybrid  vehicles on paci_c northwest distribution systems",  in Power and Energy Society General Meeting-  Conversion and Delivery of Electrical Energy in the  21st Century, pp. 1-6 (2008).  7. Kintner-Meyer, M., Schneider, K., and Pratt, R.,  Impacts Assessment of Plug-in Hybrid Vehicles on  Electric Utilities and Regional US Power Grids, Part  1: Technical Analysis, Paci_c Northwest National  Laboratory, 1 (2007).  8. Zhili, D., Boqiang, L., and Chunxu, G. Development  path of electric vehicles in China under environmental  and energy security constraints", Resources, Conservation  and Recycling, 143, pp. 17-26 (2019).  9. Kempton, W. and Dhanju, A. Electric vehicles with  V2G", Windtech International, 2(2), p. 18 (2006).  10. Bessa, R.J. and Matos, M. Global against divided  optimization for the participation of an EV aggregator  in the day-ahead electricity market. Part I: Theory",  Electric Power Systems Research, 95, pp. 309-318  (2013).  11. Mahmoudi, N., Saha, T.K., and Eghbal, M. A new  trading framework for demand response aggregators",  In 2014 IEEE PES General Meeting-Conference &  Exposition, pp. 1-5 (2014).  12. Zhao, J., Wan, C., Xu, Z., and Wang, J. Risk-based  day-ahead scheduling of electric vehicle aggregator  using information gap decision theory", IEEE Transactions  on Smart Grid, 8(4), pp. 1609-1618 (2015).  3580 P. Aliasghari et al./Scientia Iranica, Transactions D: Computer Science & ... 26 (2019) 3571{3581  13. Gonz_alez, R.M., et al. Optimizing electricity consumption  of buildings in a microgrid through demand  response", In 2017 IEEE Manchester PowerTech, pp.  1-6 (2017). DOI:10.1109/PTC.2017.7980983  14. Wai, C.H., Beaudin, M., Zareipour, H., Schellenberg,  A., and Lu, N. Cooling devices in demand response:  A comparison of control methods", IEEE Transactions  on Smart Grid, 6(1), pp. 249-260 (2014).  15. Nguyen, D.T., Nguyen, H.T., and Le, L.B. Dynamic  pricing design for demand response integration in  power distribution networks", IEEE Transactions on  Power Systems, 31(5), pp. 3457-3472 (2016).  16. Kim, D.-M. and Kim, J.-O. Design of emergency  demand response program using analytic hierarchy  process", IEEE Transactions on Smart Grid, 3(2), pp.  635-644 (2012).  17. Liu, G. and Tomsovic, K. A full demand response  model in co-optimized energy and reserve market",  Electric Power Systems Research, 111, pp. 62-70  (2014).  18. Parvania, M. and Fotuhi-Firuzabad, M. Demand response  scheduling by stochastic SCUC", IEEE Transactions  on Smart Grid, 1(1), pp. 89-98 (2010).  19. Wei, M. A top-down market model for demand  response procurement via aggregators", In  2015 IEEE Eindhoven PowerTech, pp. 1-5 (2015).  DOI:10.1109/PTC.2015.7232657  20. Fang, X., Hu, Q., Li, F., Wang, B., and Li, Y.  Coupon-based demand response considering wind  power uncertainty: A strategic bidding model for  load serving entities", IEEE Transactions on Power  Systems, 31(2), pp. 1025-1037 (2015).  21. Wei, W., Liu, F., and Mei, S. Energy pricing and dispatch  for smart grid retailers under demand response  and market price uncertainty", IEEE Transactions on  Smart Grid, 6(3), pp. 1364-1374 (2014).  22. Jiang, Z. and Qian, A. Agent-based simulation for  symmetric electricity market considering price-based  demand response", Journal of Modern Power Systems  and Clean Energy, 5(5), pp. 810-819 (2017).  23. Mahmoudi, N., Heydarian-Forushani, E., Sha_e-khah,  M., Saha, T.K., Golshan, M., and Siano, P. A  bottom-up approach for demand response aggregators'  participation in electricity markets", Electric Power  Systems Research, 143, pp. 121-129 (2017).  24. Vahid-Ghavidel, M., Mahmoudi, N., and Mohammadi-  Ivatloo, B. Self-scheduling of demand response aggregators  in short-term markets based on information gap  decision theory", IEEE Transactions on Smart Grid,  10(2), pp. 2115-2126 (2019).  25. Majidi, M., Mohammadi-Ivatloo, B., and Soroudi, A.  Application of information gap decision theory in  practical energy problems: A comprehensive review",  Applied Energy, 249, pp. 157-165 (2019).  26. Ahmadian, A., Sedghi, M., Mohammadi-ivatloo, B.,  Elkamel, A., Golkar, M.A., and Fowler, M. Costbene  _t analysis of V2G implementation in distribution  networks considering PEVs battery degradation",  IEEE Transactions on Sustainable Energy, 9(2), pp.  961-970 (2017).  27. Lin, H., Liu, Y., Sun, Q., Xiong, R., Li, H., and  Wennersten, R. The impact of electric vehicle penetration  and charging patterns on the management of  energy hub-A multi-agent system simulation", Applied  Energy, 230, pp. 189-206 (2018).  28. Ghahramani, M., Nojavan, S., Zare, K., and  Mohammadi-ivatloo, B. Short-term scheduling of future  distribution network in high penetration of electric  vehicles in deregulated energy market", Operation  of Distributed Energy Resources in Smart Distribution  Networks, Elsevier, pp. 139-159 (2018).  29. Kim, Y., Kong, S., and Joo, S.-K. Stochastic charging  coordination method for electric vehicle (EV) aggregator  considering uncertainty in EV departures" Journal  of Electrical Engineering and Technology, 11(5), pp.  1049-1056 (2016).  30. Karfopoulos, E., Marmaras, C.E., and Hatziargyriou,  N. Charging control model for electric vehicle supplier  aggregator", in Innovative Smart Grid Technologies  (ISGT Europe) International Conference and Exhibition,  pp. 1-7 (2012).  31. Momber, I., Siddiqui, A., San Rom_an, T.G., and  Soder, L. Risk averse scheduling by a PEV aggregator  under uncertainty", IEEE Transactions on Power  Systems, 30(2), pp. 882-891 (2015).  32. Alipour, M., Mohammadi-Ivatloo, B., Moradi-  Dalvand, M., and Zare, K. Stochastic scheduling of  aggregators of plug-in electric vehicles for participation  in energy and ancillary service markets", Energy, 118,  pp. 1168-1179 (2017).  33. Carpinelli, G., Caramia, P., Mottola, F., and Proto,  D. Exponential weighted method and a compromise  programming method for multi-objective operation of  plug-in vehicle aggregators in microgrids", International  Journal of Electrical Power & Energy Systems,  56, pp. 374-384 (2014).  34. _Skugor, B. and Deur, J. Dynamic programming-based  optimisation of charging an electric vehicle eet system  represented by an aggregate battery model", Energy,  92, pp. 456-465 (2015).  35. Jannati, J. and Nazarpour, D. Optimal performance  of electric vehicles parking lot considering environmental  issue", Journal of Cleaner Production, 206, pp.  1073-1088 (2019).  36. Xu, J. and Wong, V.W. An approximate dynamic  programming approach for coordinated charging  control at vehicle-to-grid aggregator", In Smart  Grid Communications (SmartGridComm) International  Conference, pp. 279-284 (2011).  37. Zhao, J., Wan, C., Xu, Z., and Wang, J. Risk-based  day-ahead scheduling of electric vehicle aggregator  P. Aliasghari et al./Scientia Iranica, Transactions D: Computer Science & ... 26 (2019) 3571{3581 3581  using information gap decision theory", IEEE Trans.  Smart Grid, 99, pp. 1-10 (2015).  38. Ben-Haim, Y., Information-Gap Decision Theory: Decisions  Under Severe Uncertainty, Academic Press  London (2001).  39. Sha_ee, S., Zareipour, H., Knight, A.M., Amjady,  N., and Mohammadi-Ivatloo, B. Risk-constrained  bidding and o_ering strategy for a merchant compressed  air energy storage plant", IEEE Transactions  on Power Systems, 32(2), pp. 946-957 (2016).  40. Aliasghari, P., Mohammadi-Ivatloo, B., Alipour, M.,  Abapour, M., and Zare, K. Optimal scheduling of  plug-in electric vehicles and renewable micro-grid in  energy and reserve markets considering demand response  program", Journal of Cleaner Production, 186,  pp. 293-303 (2018).