© The Institution of Engineering and Technology
Distribution network operators and planners usually model the load demand of plug-in electric vehicles (PEVs) to evaluate their effects on operation and planning procedures. Increasing the PEVs’ load modelling accuracy leads to more precise and reliable operation and planning approaches. This study presents a methodology for possibilistic–probabilistic-based PEVs’ load modelling in order to be employed in optimal wind distributed generation (WDG) planning. The proposed methodology considers not only the PEVs temporal uncertainty, but also the uncertain spatial effect of PEVs on WDGs as renewable-based distributed resources. The WDG planning is considered as an optimisation problem which is solved under technical and economic constraints. A hybrid modified particle swarm optimisation/genetic algorithm is proposed for optimisation that is more robust than the conventional algorithms. The effectiveness of the proposed load modelling of PEVs and the proposed algorithm is evaluated in several scenarios.
References
-
-
1)
-
2. Leou, R.C., Teng, J.H., Su, C.L.: ‘Modelling and verifying the load behaviour of electric vehicle charging stations based on field measurements’, IET Gener. Transm. Distrib., 2015, 9, (11), pp. 1112–1119 (doi: 10.1049/iet-gtd.2014.0446).
-
2)
-
21. Ganguly, S., Sahoo, N.C., Das, D.: ‘Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation’, Fuzzy Sets Syst., 2013, 213, pp. 47–73 (doi: 10.1016/j.fss.2012.07.005).
-
3)
-
24. Sedghi, M., Ahmadian, A., Aliakbar-Golkar, M.: ‘Optimal storage planning in active distribution network considering uncertainty of wind power distributed generation’, IEEE Trans. Power Syst., 2016, 31, pp. 304–316 (doi: 10.1109/TPWRS.2015.2404533).
-
4)
-
13. Pashajavid, E., Golkar, M.A.: ‘Charging of plug-in electric vehicles: stochastic modelling of load demand within domestic grids’. Proc. IEEE 20th Iranian Conf. on Electrical Engineering, Tehran, Iran, 15–17 May 2012, pp. 535–539.
-
5)
-
15. Williams, T., Crawford, C.: ‘Probabilistic load flow modeling comparing maximum entropy and Gram–Charlier probability density function reconstructions’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 272–280 (doi: 10.1109/TPWRS.2012.2205714).
-
6)
-
11. Momber, I., Wogrin, S., Gomez San Roman, T.: ‘Retail pricing: a bilevel program for PEV aggregator decisions using indirect load control’, IEEE Trans. Power Syst., 2016, 31, (1), pp. 464–473 (doi: 10.1109/TPWRS.2014.2379637).
-
7)
-
16. Gabash, A., Alkal, M.E., Li, P.: ‘Impact of allowed reverse active power flow on planning PVs and BSSs in distribution networks considering demand and EVs growth’. Proc. IEEE Power & Energy Student Summit (PESS) 2013, IEEE Student Branch Bielefeld, 2013, pp. 11–16.
-
8)
-
14. Zhang, L., Brown, T., Samuelsen, S.: ‘Evaluation of charging infrastructure requirements and operating costs for plug-in electric vehicles’, J. Power Sources, 2013, 240, pp. 515–524 (doi: 10.1016/j.jpowsour.2013.04.048).
-
9)
-
15. Denholm, P., Kuss, M., Margolis, R.M.: ‘Co-benefits of large scale plug-in hybrid electric vehicle and solar PV deployment’, J. Power Sources, 2013, 236, pp. 350–356 (doi: 10.1016/j.jpowsour.2012.10.007).
-
10)
-
3. Haidar, A., Muttaqi, K.M., Haque, M.H.: ‘Multistage time-variant electric vehicle load modelling for capturing accurate electric vehicle behaviour and electric vehicle impact on electricity distribution grids’, IET Gener. Transm. Distrib., 2015, 9, (16), pp. 2705–2716 (doi: 10.1049/iet-gtd.2014.1019).
-
11)
-
25. Carrano, E.G., Guimaraes, F.G., Takahashi, R.H.C., et al: ‘Electric distribution network expansion under load evolution uncertainty using an immune system inspired algorithm: simulation data’. , Universidade Federal de Minas Gerais, 2007.
-
12)
-
5. Yao, W., Chung, C.Y., Wen, F., et al: ‘Scenario-based comprehensive expansion planning for distribution systems considering integration of plug-in electric vehicles’, IEEE Trans. Power Syst., 2016, 31, (1), pp. 317–328 (doi: 10.1109/TPWRS.2015.2403311).
-
13)
-
9. Clement-Nyns, K., Haesen, E., Driesen, J.: ‘The impact of charging plug-in hybrid electric vehicles on a residential distribution grid’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 371–380 (doi: 10.1109/TPWRS.2009.2036481).
-
14)
-
23. Gholami, A., Ansari, J., Jamei, M., Kazemi, A.: ‘Environmental/economic dispatch incorporating renewable energy sources and plug-in vehicles’, IET Gener. Transm. Distrib., 2014, 8, (12), pp. 2183–2198 (doi: 10.1049/iet-gtd.2014.0235).
-
15)
-
2. Shafiee, S., Fotuhi-Firuzabad, M., Rastegar, M.: ‘Investigating the impacts of plug-in hybrid electric vehicles on power distribution systems’, IEEE Trans. Smart Grid, 2013, 4, (3), pp. 1351–1360 (doi: 10.1109/TSG.2013.2251483).
-
16)
-
5. Verbic, G., Canizares, C.A.: ‘Probabilistic optimal power flow in electricity markets based on a two-point estimate method’, IEEE Trans. Power Syst., 2006, 21, (4), pp. 1883–1893 (doi: 10.1109/TPWRS.2006.881146).
-
17)
-
23. Zou, K., Agalgaonkar, A.P., Muttaqi, K.M., et al: ‘Distribution system planning with incorporating DG reactive capability and system uncertainties’, IEEE Trans. Sustain. Energy, 2012, 3, (1), pp. 112–123 (doi: 10.1109/TSTE.2011.2166281).
-
18)
-
6. Khalkhali, K., Abapour, S., Moghaddas-Tafreshi, S.M., et al: ‘Application of data envelopment analysis theorem in plug-in hybrid electric vehicle charging station planning’, IET Gener. Transm. Distrib., 2015, 9, pp. 666–676 (doi: 10.1049/iet-gtd.2014.0554).
-
19)
-
9. Pashajavid, E., Golkar, M.A.: ‘Non-Gaussian multivariate modeling of plug-in electric vehicles load demand’, Int. J. Electr. Power Energy Syst., 2014, 61, pp. 197–207 (doi: 10.1016/j.ijepes.2014.03.021).
-
20)
-
11. Alavi, S.A., Ahmadian, A., Aliakbar-Golkar, M.: ‘Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method’, Energy Convers. Manage., 2015, 95, pp. 314–325 (doi: 10.1016/j.enconman.2015.02.042).
-
21)
-
17. Kollu1, R., Rayapudi, S.R., Narasimham, S.V.L., et al: ‘Mixture probability distribution functions to model wind speed distributions’, Int. J. Energy Environ. Eng., 2012, 3, (1), pp. 1–10 (doi: 10.1186/2251-6832-3-1).
-
22)
-
20. Su, C.L., Lu, C.N.: ‘Two-point estimate method for quantifying transfer capability uncertainty’, IEEE Trans. Power Syst., 2005, 20, pp. 573–579 (doi: 10.1109/TPWRS.2005.846233).
-
23)
-
27. Kusiak, A., Zheng, H.: ‘Optimisation of wind turbine energy and power factor with an evolutionary computation algorithm’, Energy, 2010, 35, pp. 1324–1332 (doi: 10.1016/j.energy.2009.11.015).
-
24)
-
18. Kahraman, C., Onar, S.C.: ‘Intelligent techniques in engineering management: theory and applications’ (Springer, Switzerland, 2015).
-
25)
-
20. Sedghi, M., Ahmadian, A., Pashajavid, E., et al: ‘Storage scheduling for optimal energy management in active distribution network considering load, wind, and plug-in electric vehicles uncertainties’, J. Renew. Sustain. Energy, 2015, 7, (3), pp. 634–648 (doi: 10.1063/1.4922004).
-
26)
-
10. Tehrani, N.H., Wang, P.: ‘Probabilistic estimation of plug-in electric vehicles charging load profile’, Electr. Power Syst. Res., 2015, 124, pp. 133–143 (doi: 10.1016/j.epsr.2015.03.010).
-
27)
-
7. Bahrami, S., Parniani, M.: ‘Game theoretic based charging strategy for plug-in hybrid electric vehicles’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 2368–2375 (doi: 10.1109/TSG.2014.2317523).
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