|
Optimal planning of a virtual power plant considering uncertainties in electric vehicles, renewable energy resources, and participating demand response
|
Rana Heydari    , Javad Nikoukar * 1, Majid Gandomkar |
|
|
|
Abstract: (2612 Views) |
Due to the progress of renewable energy technologies and the intention of energy policymakers to use these clean and cheap resources, many studies have focused on ways to take advantage of these energies. Limitations, such as low capacity, output power uncertainty, and sustainability problems, make the use of distributed energy resources costly and difficult. Among distributed generation resources, renewable energy resources such as wind energy and solar energy are more environmentally friendly and are used more than other technologies. Despite the many advantages of these resources, their output power depends on such factors as wind speed and solar intensity, which cannot be accurately predicted. For this reason, the infiltration[MSH1] of high levels of these resources into power systems increases system uncertainty and can reduce reliability while system reliability is very important for power system designers and operators, as well as energy consumers. To solve these problems, a new concept, named virtual power plant, is proposed. A virtual power plant is a collection of distributed energy resources that come together to participate in the market. Virtual power plants can efficiently coordinate, aggregate, and manage different distributed energy resources such as distributed generation, energy storage systems, and controllable loads. These plants are flexible agents for a range of distributed energy sources that can be used in wholesale markets to provide services to system operators. The energy management system is the heart of a virtual power plant that coordinates the flow of power from generators, controllable loads, and energy storage devices. This paper proposes a full model for optimal planning of a virtual power plant if uncertainties of distributed generation sources such as wind and solar energy, as well as electrical vehicles, are considered. To prevent the negative effects of the presence of electric vehicles on electricity networks, especially in virtual power plants, it is necessary to charge these vehicles in a controlled manner and with careful planning. In addition, the demand response whose modeling is based on price-based demand response for non-users and encouragement-based for electric vehicles is optimized on two scenarios, and a 32-bus network is studied. The main goal of the research is to maximize the profit of the virtual power plant for the simultaneous use of load response and electric vehicles with the capability of connecting to the grid.
|
|
| Keywords: Virtual power plant, Demand respons, Electric vehicles, Mixed integer linear programming |
|
|
Full-Text [PDF 753 kb]
(1073 Downloads)
|
Type of Study: Research |
Received: 2020/12/3 | Accepted: 2021/06/28 | Published: 2021/09/11
|
|
|
|
|
|
|
| References |
1. [1] Asmus P. Microgrids, virtual power plants and our distributed energy future. Electr J 2010;23:72-82. [ DOI:10.1016/j.tej.2010.11.001] 2. [2] Pedrasaa MAA, Spoonerb TD, MacGillb IF. A novel energy service model and optimal scheduling algorithm for residential distributed energy resources. 3. [3] Wencong, S., Mo-Yuen, C., (2012). Performance evaluation of an EDA-based large-scale plug-in hybrid electric vehicle charging algorithm. IEEE Trans Smart Grid; 3(1): 308e15. [ DOI:10.1109/TSG.2011.2151888] 4. [4] Tushar, W., Saad, W., Poor, HV., Smith, DB., (2012). Economics of electric vehicle charging: a game theoretic approach. IEEE Trans Smart Grid;3(4): 1767e78. [ DOI:10.1109/TSG.2012.2211901] 5. [5] Rahimi, F., & Ipakchi, A. (2010). Demand response as a market resource under the smart grid paradigm. Smart Grid, IEEE Transactions on, 1(1), 82-88. [ DOI:10.1109/TSG.2010.2045906] 6. [6] Chu, C. M., Jong, T. L., & Huang, Y. W. (2005, June). A direct load control of air-conditioning loads with thermal comfort control. In Power Engineering Society General Meeting, 2005. IEEE (pp. 664-669). 7. [7] Herter, K. (2007). Residential implementation of critical-peak pricing of electricity. Energy Policy, 35(4), 2121-2130. [ DOI:10.1016/j.enpol.2006.06.019] 8. [8] Triki, C., & Violi, A. (2009). Dynamic pricing of electricity in retail markets. 4OR, 7(1), 21-36. [ DOI:10.1007/s10288-007-0056-2] 9. [9] Xiong, G., Chen, C., Kishore, S., & Yener, A. (2011, January). Smart (in-home) power scheduling for demand response on the smart grid. In Innovative smart grid technologies (ISGT), 2011 IEEE PES (pp. 1-7). IEEE. 10. [10] Samadi, P., Mohsenian-Rad, A. H., Schober, R., Wong, V. W., & Jatskevich, J. (2010, October). Optimal real-time pricing algorithm based on utility maximization for smart grid. In Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on (pp. 415-420). IEEE. [ DOI:10.1109/SMARTGRID.2010.5622077] 11. [11] Mohsenian-Rad, A. H., Wong, V. W., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. Smart Grid, IEEE Transactions on, 1(3), 320-331. [ DOI:10.1109/TSG.2010.2089069] 12. [12] Adika, C. O., & Wang, L. (2014). Demand-side bidding strategy for residential energy management in a smart grid environment. Smart Grid, IEEE Transactions on, 5(4), 1724-1733. [ DOI:10.1109/TSG.2014.2303096] 13. [13] Wen, F., & David, A. K. (2001). Optimal bidding strategies for competitive generators and large consumers. International Journal of Electrical Power & [ DOI:10.1016/S0142-0615(00)00032-6] 14. [14] Herranz, R., Munoz San Roque, A., Villar, J., & Campos, F. A. (2012). Optimal demand-side bidding strategies in electricity spot markets. Power Systems, IEEE Transactions on, 27(3), 1204-1213. [ DOI:10.1109/TPWRS.2012.2185960] 15. [15] Richter Jr, C. W., & Sheblé, G. B. (1998). Genetic algorithm evolution of utility bidding strategies for the competitive marketplace. Power Systems, IEEE Transactions on, 13(1), 256-261. [ DOI:10.1109/59.651644] 16. [16] Conejo, A. J., Nogales, F. J., & Arroyo, J. M. (2002). Price-taker bidding strategy under price uncertainty. Power Systems, IEEE Transactions on, 17(4), 1081-1088. [ DOI:10.1109/TPWRS.2002.804948] 17. [17] Conejo, A. J., Contreras, J., Arroyo, J. M., & De la Torre, S. (2002). Optimal response of an oligopolistic generating company to a competitive pool-based electric power market. Power Systems, IEEE Transactions on, 17(2), 424-430. [ DOI:10.1109/TPWRS.2002.1007913] 18. [18] Steen D, Tuan LA, Carlson O, Bertling L. (2012). Assessment of electric vehicle charging scenarios based on demographical data. IEEE Trans Smart Grid; 3(3):1457e68. [ DOI:10.1109/TSG.2012.2195687] 19. [19] Linni, J., Honghong, X., Guoqing, X., Xinyu, Z., Dongfang, Z., Shao, ZY., (2013). Regulated charging of plug-in hybrid electric vehicles for minimizing load variance in household smart microgrid. IEEE Trans Ind Electron; 60(8):3218e26. [ DOI:10.1109/TIE.2012.2198037] 20. [20] Yifeng, H., Venkatesh, B., Ling, G., (2012). Optimal scheduling for charging and discharging of electric vehicles. IEEE Trans Smart Grid;3(3): 1095e105. [ DOI:10.1109/TSG.2011.2173507] 21. [21] Sundstrom, O., Binding, C., (2012). Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Trans Smart Grid; 3(1): 26e37. [ DOI:10.1109/TSG.2011.2168431] 22. [22] Wu, D., Aliprantis, DC., Ying, L., (2011). On the choice between uncontrolled and controlled charging by owners of PHEVs. IEEE Trans Power Deliv; 26(4):2882e4. [ DOI:10.1109/TPWRD.2011.2159671] 23. [23] Saber, AY., Venayagamoorthy, GK., (2012). Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles. Syst J IEEE; 6(1):103e9. [ DOI:10.1109/JSYST.2011.2163012] 24. [24] Pipattanasomporn, M., Kuzlu, M., Rahman, S., (2012). An algorithm for intelligent home energy management and demand response Analysis. IEEE Trans Smart Grid; 3(4):2166e73. [ DOI:10.1109/TSG.2012.2201182] 25. [25] Nunna, H., Doolla, S., (2012), Demand response in smart distribution system with multiple microgrids. IEEE Trans Smart Grid; 3(4):1641e9. [ DOI:10.1109/TSG.2012.2208658] 26. [26] Kempton, W., Tomi_c, J., (2005). Vehicle-to-grid power fundamentals: calculating capacity and net revenue. J Power Sources; 144(1):268e79. [ DOI:10.1016/j.jpowsour.2004.12.025] 27. [27] Sousa, T., Morais, H., Soares, J., Vale, Z., (2012). Day-ahead resource scheduling in smart grids considering vehicle-to-grid and network constraints. Appl Energy; 96:183e93. [ DOI:10.1016/j.apenergy.2012.01.053] 28. [28] Battistelli, C., Baringo, L., Conejo, A., (2012). Optimal energy management of small electric energy systems including V2G facilities and renewable energy sources. Electr Power Syst Res; 92:50e9. [ DOI:10.1016/j.epsr.2012.06.002] 29. [29] Pang, C., Dutta, P., Kezunovic, M., (2012). BEVs/PHEVs as dispersed energy storage for V2B uses in the smart grid. IEEE Trans Smart Grid; 3(1):473e82. [ DOI:10.1109/TSG.2011.2172228] 30. [30] Sanchez-Martin, P., Sanchez, G., Morales-Espana, G., (2012). Direct load control decision model for aggregated EV charging points. IEEE Trans Power Syst; 27(3):1577e84. [ DOI:10.1109/TPWRS.2011.2180546] 31. [31] Guille, C., Gross, G., (2009). A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy; 37(11):4379e90. [ DOI:10.1016/j.enpol.2009.05.053] 32. [32] Han, S., Han, S., & Sezaki, K. (2010). Development of an optimal vehicle-to-grid aggregator for frequency regulation. Smart Grid, IEEE Transactions on, 1(1), 65-72. [ DOI:10.1109/TSG.2010.2045163] 33. [33] Santos, A.; McGuckin, N.; Nakamoto, H.; Gray, D.; Liss, S. Summary of Travel Trends: 2009 National Household Travel Survey; Technical Report; U.S. Department of Transportation, Federal Highway Administration: Washington, DC, USA, 2009. 34. [34] Kenworthy, J.R. Transport Energy Use and Greenhouse Gases in Urban Passenger System: A case study of 84 Global Cities. In Proceedings of the Third Conference of the Regional Government Network for Sustainable Development, Notre Dame University, Fremantle, Australia, 17-19 September 2003. 35. [35] X. Ai and J. J. Xu, "Study on the microgrid and distribution network cooperation model based on interactive scheduling,"Power Syst. Protect. Control, vol. 41, no. 1, pp. 143-149, 2013. 36. [36] D. K. Khatod, V. Pant, and J. Sharma, "Evolutionary programming based optimal placement of renewable distributed generators,"IEEE Trans. Power Syst., vol. 28, no. 2, pp. 683-695, May 2013. [ DOI:10.1109/TPWRS.2012.2211044] 37. [37] P. Faria, "Demand Response in future power systems management-A conceptual framework and simulation tool," Master degree thesis, School of Engineering - Polytechnic of Porto, Portugal, 2011.
|
|
Heydari R, Nikoukar J, Gandomkar M. Optimal planning of a virtual power plant considering uncertainties in electric vehicles, renewable energy resources, and participating demand response. ieijqp 2021; 10 (3) :34-47 URL: http://ieijqp.ir/article-1-796-en.html
|
