Document Type : Original Article
Author
University of Wisconsin-Madison, Madison, WI, USA
Abstract
Environment and financial matters provide careful attention to electric vehicles (EV) and economical power resource benefits. There is an answer suggestion for increasing the effect of this benefit that is using the Potential of electric vehicles. The capacity for electric vehicles needs getting ready for Smart Dispensation Systems (SDS). Demand response schemes, as an appropriate gadget using the potential of ratifier in the perfect organization of the framework, grants dynamic closeness in the system of control supporters implementation shift and these undertakings, in the essential situations, can grant the demand requirements decreasing, in a short time period. In the proposed paper, endeavors to give a multipurpose schematization of EV in view of the keen lattice maintainable resources, wear vulnerability brought about by unlimited resources and EVs, due to the demand response undertakings, EV cell accumulating structure, restrict the performing expenses and the amount of force system contamination, by improving methodology. Enhanced advancement calculation is used for dealing with the propelling issue. Working expenses dropped considerably additionally using financial pattern of the request reaction and vehicle charge/release and intelligent plan in the times that the heap is little. Viability of the suggested strategy is connected to 94 standard transport power framework.
Keywords
[2] Bagal, Hamid Asadi, et al. “Risk-assessment of photovoltaic-wind-battery-network based large industrial consumer using information gap decision theory.” Solar Energy 169 (2018): 343-352.
[3] Leng, Hua, et al. “A new wind power prediction method based on riDPelet transforms, hybrid feature selection and closed-loop forecasting.” Advanced Engineering Informatics 36 (2018): 20-30.
[4] Nouri, Alireza, et al. “Optimal performance of fuel cell-CHP-battery based micro-network under real-time energy management: An epsilon constraint method and fuzzy satisfying approach.” Energy (2018).
[5] Hamian, Melika, et al. “A framework to expedite joint energy-reserve payment expense minimization using a custom-designed method based on Mixed Integer Genetic Algorithm.” Engineering Applications of Artificial Intelligence 72 (2018): 203-212.
[6] Mohammadi, Mohsen, et al. “Small-scale building load forecast based on hybrid forecast engine.” Neural Processing Letters 48.1 (2018): 329-351.
[7] Ghadimi, Noradin, et al. “Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting.” Energy (2018).
[8] Ghadimi, Noradin, et al. “Application of a new hybrid forecast engine with feature selection algorithm in a power system.” International Journal of Ambient Energy (2017): 1-10.
[9] Honarmand, M. Zakariazadeh, A. Jadid, S. 2014.Integrated scheduling of reproducible production and electric vehicles parking lot in a smart micronetwork.Energy Conversion and Management, 86: 745-755.
[10] Morais, H. Sousa, T. Soares, J. Faria, P.Vale, Z. 2015. Dispensed energy resources management using plug-in hybrid electric vehicles as a fuel-shifting demand response resource. Energy Conversion and Management, 97: 78-93.
[11] Jian, L. Zheng, Y. Xiao, X. Chan, C. 2015.Optimal scheduling for vehicle-to-network operation with stochastic connection of plug-in electric vehicles to smart network, Applied Energy, 146: 150-161.
[12] Honarmand, M. Zakariazadeh, AJadid,S. 2015.Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-network concept and battery condition, Applied Energy, 95: 1-8.
[13] Abedinia, Oveis, Masoud Bekravi, and Noradin Ghadimi. “Intelligent controller based wide-area control in power system.” International Journal of Ambiguity, Fuzziness and Science-Based Systems 25.01 (2017): 1-30.
[14] Liu, L. Kong, F. Peng, Y. Wang, Q. 2015. A review on electric vehicles interacting with reproducible energy in smart network, Reproducible and Sustainable Energy Reviews,50: 648–661.
[15] Viral, R. Khatod, D.K. 2012. Optimal schematization of dispensed production systems in dispensation system: A review, Reproducible and Sustainable Energy Reviews, 16 (7): 5146-5165.
[16] Siano, P. 2014. Demand response and smart networks—A survey, Reproducible and Sustainable Energy Reviews, 30:461–478.
[17] Mwasilu, F. Justo, J.J. Do, T.D. Jung, J.W. 2014. Electric vehicles and smart network interaction: A review on vehicle to network and reproducible energy sources integration, Reproducible and Sustainable Energy Reviews, 34: 501–516.
[18] Daniel, A. R.Chen, A. A.1991.Stochastic simulation and forecasting of hourly average wind speed sequences in Jamaica.Solar Energy, 46(1):1–11.
[19] Bagen, B. 2005.Reliability and expense/worth evaluation of generating systems utilizing wind and solar energy. PhD Dissertation, University of Saskatchewan, Saskatoon, Canada.
[20] Chedid, R. Akiki, H. Rahman, S. 1995. A decision support technique for the design of hybrid solar-wind power systems. Electrical Power and Energy Systems, 13(1): 76–83.
[21] Zhao, J. Kucuksari, S. Mazhari, E. Son, Y-J.2013. Integrated analysis of high-penetration PV and PHEV with energy repository and demand response. Applied Energy. 112(12):35–51.
[22] Liu, T.; Jiao, L.; Ma,W.; Ma, J.; Shang, R. A new quantum-Behaved particle swarm optimization based on cultural evolution mechanism for multiobjective problems. Appl. Soft Comput. 2016, 46, 267–283.
[23] Laumanns, M.; Thiele, L.; Deb, K. Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evol. Comput. 2002, 10, 263–282.
[24] Shi, Y.; Eberhart, R.Amodified particle swarm optimizer. In Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, AK, USA, 4–9 May 1998; pp. 69–73.
[25] Li, L.M.; Lu, K.D.; Zeng, G.Q.; Wu, L.; Chen, M.-R. A novel real-Coded population-Based extremal optimization algorithm with polynomial mutation: A non-Parametric statistical study on continuous optimization problems. Neurocomputing 2016, 174, 577–587. [CrossRef]
[26] Chen, Y.; Zou, X.; Xie,W. Convergence of multi-Objective evolutionary algorithms to a uniformly dispensed representation of the Pareto front. Inf. Sci. 2011, 181, 3336–3355. [CrossRef]
[27] Abedinia, O., Naderi, M. S., Jalili, A., & Mokhtarpour, A. (2011). A novel hybrid GA-PSO technique for optimal tuning of fuzzy controller to improve multi-machine power system stability. International Review of Electrical Engineering, 6(2).
[28] 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), 40-49.
[29] 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. International Transactions on Electrical Energy Systems, 23(8), 1504-1522.
[30] Abedinia, O., Amjady, N., & Kiani, K. (2012). Optimal complex economic load dispatch solution using particle swarm optimization with time varying acceleration coefficient. International Review of Electrical Engineering, 7(2).
)