Advertisement

Application of new multi-objective optimization algorithm for EV scheduling in smart grid through the uncertainties

  • WanJun Yin
  • Dinesh Mavaluru
  • Munir Ahmed
  • Mazhar Abbas
  • Aida DarvishanEmail author
Original Research

Abstract

Ecological and economics issues are caused to give careful consideration to electric vehicles (EV) and sustainable power source assets. One of the proposed answers for increment the impact of these assets, is to utilize the electric vehicles potential. The capability of electric vehicles require planning for Smart Distribution Systems (SDS). Request reaction programs, as a suitable device to utilize endorsers’ potential in ideal administration of the system, gives dynamic nearness of supporters in control framework execution change and these projects, in basic conditions, can give the request prerequisites diminishment, in a brief timeframe. In this work, attempts to presents a multi-objective scheduling of EV based on the sustainable assets in smart grid, cover uncertainty caused by inexhaustible assets and EVs, by considering of the request reaction projects and EV battery stockpiling framework, limit the working expenses and the measure of intensity framework contamination, with enhancing procedures. Improved optimization algorithm is utilized for taking care of the advancing issue. Operating costs dropped much further utilizing monetary model of the demand response and vehicle charge/discharge and smart program in the hours when the load is lower. Effectiveness of proposed method is applied on 33 bus standard power system.

Keywords

Multi-objective scheduling EV Renewables source Demand response Optimization 

Notes

Acknowledgements

This work is partially supported by the Education Department of Sichuan Province (GZY18C49), The Key Program of Guangyuan Municipal Science and Technology Project (2018ZCZDYF016).

References

  1. Abedinia O, Naderi MS, Jalili A, Mokhtarpour A (2011) A novel hybrid GA-PSO technique for optimal tuning of fuzzy controller to improve multi-machine power system stability. Int Rev Electr Eng 6(2)Google Scholar
  2. Abedinia O, Amjady N, Kiani K (2012) Optimal complex economic load dispatch solution using particle swarm optimization with time varying acceleration coefficient. Int Rev Electr Eng 7(2)Google Scholar
  3. Abedinia O, Amjady N, Ghasemi A, Hejrati Z (2013) Solution of economic load dispatch problem via hybrid particle swarm optimization with time-varying acceleration coefficients and bacteria foraging algorithm techniques. Int Trans Electr Energy Syst 23(8):1504–1522CrossRefGoogle Scholar
  4. Abedinia O, Ghasemi A, Ojaroudi N (2016) Improved time varying inertia weight PSO for solved economic load dispatch with subsidies and wind power effects. Complexity 21(4):40–49MathSciNetCrossRefGoogle Scholar
  5. Abedinia O, Amjady N, Ghadimi N (2018) Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput Intell 34(1):241–260MathSciNetCrossRefGoogle Scholar
  6. Ahangarnejad AH, Azar A (2014) Designing of an assembly machine for center deviation adaptive bearing pressure. Life Sci J 11(2s):863–873Google Scholar
  7. Ahangarnejad AH, Başlamışlı S (2016) Adap-tyre: DEKF filtering for vehicle state estimation based on tyre parameter adaptation. Int J Veh Des 71(1–4):52–74CrossRefGoogle Scholar
  8. Ahangarnejad AH, Melzi S (2018) Numerical analysis of the influence of an actively controlled spoiler on the handling of a sports car. J Vib Control 24(22):5437–5448MathSciNetCrossRefGoogle Scholar
  9. Azar SA, Ahangarnejad AH (2014) Simulating the diesel engine vibration with fuzzy neural network. Res J Appl Sci Eng Technol 8(9):1045–1051CrossRefGoogle Scholar
  10. Babaeean A, Tashk AB, Bandarabadi M, Rastegar S (2008) Target tracking using wavelet features and RVM classifier. In: Fourth international conference on natural computation, 2008. ICNC’08, vol 4. IEEE, New York, pp 569–572Google Scholar
  11. Boait P, Mahdavi Ardestani B, Snape JR (2013) Accommodating renewable generation through an aggregator-focused method for inducing demand side response from electricity consumers. IET Renew Power Gener 7(6):689–699CrossRefGoogle Scholar
  12. Chedid R, Akiki H, Rahman S (1998) A decision support technique for the design of hybrid solar-wind power systems. IEEE Trans Energy Convers 13(1):76–83CrossRefGoogle Scholar
  13. Chen Y, Zou X, Xie W (2011) Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front. Inf Sci 181(16):3336–3355MathSciNetCrossRefzbMATHGoogle Scholar
  14. Chu ZQ, Sasanipour J, Saeedi M, Baghban A, Mansoori H (2017) Modeling of wax deposition produced in the pipelines using PSO-ANFIS approach. Pet Sci Technol 35(20):1974–1981CrossRefGoogle Scholar
  15. Duan M, Darvishan A, Mohammaditab R, Wakil K, Abedinia O (2018) A novel hybrid prediction model for aggregated loads of buildings by considering the electric vehicles. Sustain Cities Soc 41:205–219CrossRefGoogle Scholar
  16. Fakoor M, Kosari A, Jafarzadeh M (2015) Revision on fuzzy artificial potential field for humanoid robot path planning in unknown environment. Int J Adv Mechatron Syst 6(4):174–183CrossRefGoogle Scholar
  17. Fakoor M, Kosari A, Jafarzadeh M (2016) Humanoid robot path planning with fuzzy Markov decision processes. J Appl Res Technol 14(5):300–310CrossRefGoogle Scholar
  18. Fallahtafti F, Alavikia M, Arshi AR (2013) Bond graph application in sports engineering: evaluating the effects of impact parameters on tennis elbow injury. In: 20th Iranian conference on biomedical engineering (ICBME), 2013. IEEE, New York, pp 255–259Google Scholar
  19. Feili H, Rabiei E, Ahmadian P, Karimi J, Majidi B (2016) Prioritization of renewable energy systems using AHP method with economic analysis perspective in Iran. In: 2nd International conference on modern researches in management, economics and accounting, Kuala Lumpur, MalaysiaGoogle Scholar
  20. Gao W, Yan L, Saeedi MH, Nik HS (2018) Ultimate bound estimation set and chaos synchronization for a financial risk system. Math Comput Simul 154:19–33MathSciNetCrossRefGoogle Scholar
  21. Gao W, Darvishan A, Toghani M, Mohammadi M, Abedinia O, Ghadimi N (2019) Different states of multi-block based forecast engine for price and load prediction. Int J Electr Power Energy Syst 104:423–435CrossRefGoogle Scholar
  22. Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2018) Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161:130–142CrossRefGoogle Scholar
  23. Hajimiragha AH, Cañizares CA, Fowler MW, Moazeni S, Elkamel A (2011) A robust optimization approach for planning the transition to plug-in hybrid electric vehicles. IEEE Trans Power Syst 26(4):2264–2274CrossRefGoogle Scholar
  24. Hamian M, Darvishan A, Hosseinzadeh M, Lariche MJ, Ghadimi N, Nouri A (2018) A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm. Eng Appl Artif Intell 72:203–212CrossRefGoogle Scholar
  25. Khodaei H, Hajiali M, Darvishan A, Sepehr M, Ghadimi N (2018) Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl Therm Eng 137:395–405CrossRefGoogle Scholar
  26. Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multiobjective optimization. Evol Comput 10(3):263–282CrossRefGoogle Scholar
  27. Leeder T, Fallahtafti F, Schieber M, Myers SA, Boron JB, Yentes JM (2018) Optic flow improves step width and length in older adults while performing dual task. Aging Clin Exp Res.  https://doi.org/10.1007/s40520-018-1059-x Google Scholar
  28. Li LM, Lu KD, Zeng GQ, Wu L, Chen MR (2016) A novel real-coded population-based extremal optimization algorithm with polynomial mutation: a non-parametric statistical study on continuous optimization problems. Neurocomputing 174:577–587CrossRefGoogle Scholar
  29. Liu X, Yazdanpanah AR, Mancini GJ, Tan J (2015) Control of a magnetic actuated robotic surgical camera system for single incision laparoscopic surgery. In: IEEE international conference on robotics and biomimetics (ROBIO), 2015. IEEE, New York, pp 1396–1402Google Scholar
  30. Liu T, Jiao L, Ma W, Ma J, Shang R (2016) A new quantum-behaved particle swarm optimization based on cultural evolution mechanism for multiobjective problems. Knowl Based Syst 101:90–99CrossRefGoogle Scholar
  31. Liu Z, Hajiali M, Torabi A, Ahmadi B, Simoes R (2018) Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting. J Ambient Intell Humaniz Comput 9(6):1919–1931CrossRefGoogle Scholar
  32. Moavenian M, Gharib MR, Daneshvar A, Alimardani S (2011) Control of human hand considering uncertainties. In: International conference on advanced mechatronic systems (ICAMechS), 2011. IEEE, New York, pp 17–22Google Scholar
  33. Mohammadi M, Talebpour F, Safaee E, Ghadimi N, Abedinia O (2018) Small-scale building load forecast based on hybrid forecast engine. Neural Process Lett 48(1):329–351CrossRefGoogle Scholar
  34. Mohammadzadeh A, Ghoddoosian A, Noori-Damghani M (2011) Balancing of the flexible rotors with particle swarm optimization method. Int Rev Mech Eng 5(3):490–496Google Scholar
  35. Morsali R, Mohammadi M, Maleksaeedi I, Ghadimi N (2014) A new multiobjective procedure for solving nonconvex environmental/economic power dispatch. Complexity 20(2):47–62MathSciNetCrossRefGoogle Scholar
  36. Nejad HC, Farshad M, Rahatabad FN, Khayat O (2016a) Gradient-based back-propagation dynamical iterative learning scheme for the neuro-fuzzy inference system. Expert Syst 33(1):70–76CrossRefGoogle Scholar
  37. Nejad HC, Farshad M, Khayat O, Rahatabad FN (2016b) Performance verification of a fuzzy wavelet neural network in the first order partial derivative approximation of nonlinear functions. Neural Process Lett 43(1):219–230CrossRefGoogle Scholar
  38. Nouri A, Khodaei H, Darvishan A, Sharifian S, Ghadimi N (2018) Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: an epsilon constraint method and fuzzy satisfying approach. Energy 159:121–133CrossRefGoogle Scholar
  39. Nouri Damghani M, Mohammadzadeh Gonabadi A (2017) Numerical study of energy absorption in aluminum foam sandwich panel structures using drop hammer test. J Sandw Struct Mater 21(1):3–18.  https://doi.org/10.1177/1099636216685315 CrossRefGoogle Scholar
  40. Rabiei E, Feili H, Ahmadian P, Majidi B, Karimi J (2015) The economic analysis between wind energy and biogas energy to determine economic policy in the renewable energy systems in IranGoogle Scholar
  41. Rastegar S, Babaeian A, Bandarabadi M, Toopchi Y (2009) Airplane detection and tracking using wavelet features and SVM classifier. In: 41st Southeastern symposium on system theory, 2009. SSST 2009. IEEE, New York, pp 64–67Google Scholar
  42. Safa A, Abdolmalaki RY (2019) Robust output feedback tracking control for inertially stabilized platforms with matched and unmatched uncertainties. IEEE Trans Control Syst Technol 27(1):118–131Google Scholar
  43. Shao Z, Wu P, Gao Y, Gutman I, Zhang X (2017) On the maximum ABC index of graphs without pendent vertices. Appl Math Comput 315:298–312MathSciNetGoogle Scholar
  44. Shao Z, Wakil K, Usak M, Heidari MA, Wang B, Simoes R (2018a) Kriging empirical mode decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid. Appl Therm Eng 145:58–70CrossRefGoogle Scholar
  45. Shao Z, Wu P, Zhang X, Dimitrov D, Liu JB (2018b) On the maximum ABC index of graphs with prescribed size and without pendent vertices. IEEE Access 6:27604–27616CrossRefGoogle Scholar
  46. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence. IEEE, New York, pp 69–73Google Scholar
  47. Torabi A, Mousavy SAK, Dashti V, Saeedi M, Yousefi N (2018) A new prediction model based on cascade NN for wind power prediction. Comput Econ.  https://doi.org/10.1007/s10614-018-9795-8 Google Scholar
  48. Zakariazadeh A, Jadid S, Siano P (2014) Multi-objective scheduling of electric vehicles in smart distribution system. Energy Convers Manag 79:43–53CrossRefGoogle Scholar
  49. Zhao J, Kucuksari S, Mazhari E, Son YJ (2013) Integrated analysis of high-penetration PV and PHEV with energy storage and demand response. Appl Energy 112:35–51CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • WanJun Yin
    • 1
    • 2
  • Dinesh Mavaluru
    • 3
  • Munir Ahmed
    • 4
  • Mazhar Abbas
    • 4
  • Aida Darvishan
    • 5
    Email author
  1. 1.School of Electronic & Mechanical EngineeringXidian UniversityXi’anPeople’s Republic of China
  2. 2.Sichuan Vocational College of Information TechnologyGuangyuanPeople’s Republic of China
  3. 3.College of Computing and InformaticsSaudi Electronic UniversityRiyadhSaudi Arabia
  4. 4.Department of Management SciencesCOMSATS University IslamabadIslamabadPakistan
  5. 5.Department of Industrial EngineeringUniversity of HoustonHoustonUSA

Personalised recommendations