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

Document Type : Article

Authors

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

Abstract

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.

Keywords

References

References:
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., Infield, D., and Zhang, T. "Using electric vehicle fleet 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 electrification 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 pacific 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, Pacific 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).
13. Gonzalez, 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., Shafie-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 fit 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 Roman, 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  fleet 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 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. Shafiee, 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).
Scientia Iranica
Volume 26, Special Issue on machine learning, data analytics, and advanced optimization techniques...
Transactions on Computer Science & Engineering and Electrical Engineering (D)
November and December 2019
Pages 3571-3581

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