Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Probabilistic–possibilistic model for a parking lot in the smart distribution network expansion planning

Conventional distribution network departs to the smart grid. The parking lot will have an important role in the smart grid as a distributed generation. Due to the output power of parking lots is uncertain, More accurate modeling of parking lot output power is necessary for the future of distribution network studies such as Distribution Network Expansion Planning (DNEP). In this paper, a systematic method based on the Z-number concept is utilized to represent the uncertainty of Vehicle to Grid's (V2G's) presence. In order to investigate the impact of V2Gs uncertainty on the DNEP, we proposed a Probabilistic–Possibilistic DNEP in the presence of V2Gs referred to as P-PDNEPV2G. If the V2Gs historical data is incomplete, the proposed structure can significantly consider the effects of V2G on the DNEP. In P-PDNEPV2G, parking lots output power is described as a probabilistic–possibilistic variable by Z-number method. The optimization of P-PDNEPV2G is executed by the Non-Dominated Sorting Genetic Algorithm (NSGA-II). A 24-bus test system and the real 20 kV distribution network of Ghale-Ganj city of Kerman province in Iran are used to demonstrate the effectiveness of the proposed methodology. Eventually, several analyses are conducted to investigate the impact of probabilistic–possibilistic V2G model on the DNEP problem.

References

    1. 1)
      • 22. Galus, M.D., Waraich, R.A., Andersson, G.: ‘Predictive, distributed, hierarchical charging control of PHEVs in the distribution system of a large urban area incorporating a multi agent transportation simulation’ (ETH, Eidgenössische Technische Hochschule, IVT, Institut für Verkehrsplanung und Transport Systeme, Zürich, 2011).
    2. 2)
      • 6. Fletcher, R.H., Strunz, K.: ‘Optimal distribution system horizon planning–part I: formulation’, IEEE Trans. Power Syst., 2007, 22, (2), pp. 791799.
    3. 3)
      • 37. U.S. Department of Transportation: ‘Federal highway administration’ (2009 National Household Travel Survey, Washington, D.C., USA, 2011), Available at http://nhts.ornl.gov.
    4. 4)
      • 19. Mozafar, M.R., Moradi, M.H., Hadi, A.M.: ‘A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm’, Sustain. Cities Soc., 2017, 32, pp. 627637.
    5. 5)
      • 12. Ouyang, W., Cheng, H., Zhang, X., et al: ‘Distribution network planning method considering distributed generation for peak cutting’, Energy Convers. Manage., 2010, 51, (12), pp. 23942401.
    6. 6)
      • 8. El-Zonkoly, A.M.: ‘Multistage expansion planning for distribution networks including unit commitment’, IET Gener. Transm. Distrib., 2013, 7, (7), pp. 766778.
    7. 7)
      • 32. Zhang, H., Liu, D.: ‘Fuzzy modeling and fuzzy control’ (Birkhäuser, Boston, MA, USA, 2006).
    8. 8)
      • 10. Wu, J., Ekanayake, J., Jenkins, N.: ‘Smart electricity distribution networks’ (CRC Press, Boca Raton, FL, USA, 2017).
    9. 9)
      • 24. Tabatabaee, S., Mortazavi, S.S., Niknam, T.: ‘Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources’, Energy, 2017, 121, pp. 480490.
    10. 10)
      • 21. Liu, W., Zhang, M., Zeng, B., et al: ‘Analyzing the impacts of electric vehicle charging on distribution system reliability’. IEEE PES Innovative Smart Grid Technologies, Tianjin, China, May 2012, pp. 16.
    11. 11)
      • 18. Hadi, A.M., Moradi, M.H., Amini, M.H.: ‘Simultaneous allocation of electric vehicles’, parking lots and distributed renewable resources in smart power distribution networks’, Sustain. Cities Soc., 2017, 28, pp. 332342.
    12. 12)
      • 16. Munoz-Delgado, G., Contreras, J., Arroyo, J.M.: ‘Joint expansion planning of distributed generation and distribution networks’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 25792590.
    13. 13)
      • 9. Cossi, A.M., da Silva, L.G.W., Lázaro, R.A.R., et al: ‘Primary power distribution systems planning taking into account reliability, operation and expansion costs’, IET Gener. Transm. Distrib., 2012, 6, (3), p. 274.
    14. 14)
      • 2. Jabr, R.A.: ‘Polyhedral formulations and loop elimination constraints for distribution network expansion planning’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 18881897.
    15. 15)
      • 25. Moghaddas Tafreshi, S.M., Ranjbarzadeh, H., Jafari, M., et al: ‘A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid’, Renew. Sustain. Energy Rev., 2016, 66, pp. 934947.
    16. 16)
      • 31. Coppi, R., D'Urso, P., Giordani, P., et al: ‘Least squares estimation of a linear regression model with LR fuzzy response’, Comput. Stat. Data Anal., 2006, 51, (1), pp. 267286.
    17. 17)
      • 3. Boulaxis, N.G., Papadopoulos, M.P.: ‘Optimal feeder routing in distribution system planning using dynamic programming technique and GIS facilities’, IEEE Power Eng. Rev., 2001, 21, (11), pp. 6363.
    18. 18)
      • 5. El-kady, M.: ‘Computer-aided planning of distribution substation and primary feeders’, IEEE Trans. Power Appar. Syst., 1984, PAS-103, (6), pp. 11831189.
    19. 19)
      • 11. Buchholz, B.M., Styczynski, Z.: ‘Smart grids – fundamentals and technologies in electricity networks’ (Springer-Verlag, Berlin, Heidelberg, 2016).
    20. 20)
      • 26. Wencong, S., Wang, J., Zhang, K.: ‘Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty’, Electr. Power Syst. Res., 2014, 106, pp. 2935.
    21. 21)
      • 13. Gözel, T., Hocaoglu, M.H.: ‘An analytical method for the sizing and siting of distributed generators in radial systems’, Electr. Power Syst. Res., 2009, 79, (6), pp. 912918.
    22. 22)
      • 23. Naghdizadegan Jahromi, S., Askarzadeh, A., Abdollahi, A.: ‘Modelling probabilistic transmission expansion planning in the presence of plug-in electric vehicles uncertainty by multi-state Markov model’, IET Gener. Transm. Distrib., 2017, 11, (7), pp. 17161725.
    23. 23)
      • 20. Sandels, C., Franke, U., Ingvar, N., et al: ‘Vehicle to grid — Monte Carlo simulations for optimal aggregator strategies’. IEEE 2010 Int. Conf. on Power System Technology, Hangzhou, China, October 2010, pp. 18.
    24. 24)
      • 1. Bagheri, A., Monsef, H., Lesani, H.: ‘Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions’, Int. J. Electr. Power Energy Syst., 2015, 73, pp. 5670.
    25. 25)
      • 15. Porkar, S., Poure, P., Abbaspour-Tehrani-fard, A., et al: ‘A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity market’, Electr. Power Syst. Res., 2010, 80, (7), pp. 828837.
    26. 26)
      • 29. Kang, B., Daijun, W., Ya, L, et al: ‘A method of converting Z-number to classical fuzzy number’, J. Inf. Comput. Sci., 2012, 9, (3), pp. 703709.
    27. 27)
      • 33. Deb, K., Pratap, A., Agarwal, S., et al: ‘A fast and elitist multiobjective genetic algorithm: NSGA-II’, IEEE Trans. Evol. Comput., 2002, 6, (2), pp. 182197.
    28. 28)
      • 27. Soroudi, A., Amraee, T.: ‘Decision making under uncertainty in energy systems: state of the art’, Renew. Sustain. Energy Rev., 2013, 28, pp. 376384.
    29. 29)
      • 36. Heidari, S., Fotuhi-Firuzabad, M., Kazemi, S.: ‘Power distribution network expansion planning considering distribution automation’, IEEE Trans. Power Syst., 2015, 30, (3), pp. 12611269.
    30. 30)
      • 17. Sedghi, M., Aliakbar-Golkar, M., Haghifam, M.-R.: ‘Distribution network expansion considering distributed generation and storage units using modified PSO algorithm’, Int. J. Electr. Power Energy Syst., 2013, 52, pp. 221230.
    31. 31)
      • 30. Han, S., Sezaki, K.: ‘Estimation of achievable power capacity from plug-in electric vehicles for V2G frequency regulation: case studies for market participation’, IEEE Trans. Smart Grid, 2011, 2, (4), pp. 632641.
    32. 32)
      • 7. Nazar, M.S., Haghifam, M.R.: ‘Multiobjective electric distribution system expansion planning using hybrid energy hub concept’, Electr. Power Syst. Res., 2009, 79, (6), pp. 899911.
    33. 33)
      • 14. Ahmadigorji, M., Amjady, N.: ‘A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm’, Energy, 2016, 102, pp. 199215.
    34. 34)
      • 28. Zadeh, L.A.: ‘A note on Z-numbers’, Inf. Sci., 2011, 181, (14), pp. 29232932.
    35. 35)
      • 34. Lavorato, M., Rider, M.J., Garcia, A. V, et al: ‘A constructive heuristic algorithm for distribution system planning’, IEEE Trans. Power Syst., 2010, 25, (3), pp. 17341742.
    36. 36)
      • 4. Jahanyari, N., Amini, A., Taghizadeghan, N., et al: ‘Smart distribution grid multistage expansion planning under load forecasting uncertainty’, IET Gener. Transm. Distrib., 2016, 10, (5), pp. 11361144.
    37. 37)
      • 35. Deb, K.: ‘Multi-objective optimization using evolutionary algorithms’ (John Wiley & Sons, Hoboken, NJ, USA, 2001).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.0366
Loading

Related content

content/journals/10.1049/iet-gtd.2018.0366
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address