Open Access
Issue
E3S Web Conf.
Volume 184, 2020
2nd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED 2020)
Article Number 01034
Number of page(s) 15
DOI https://doi.org/10.1051/e3sconf/202018401034
Published online 19 August 2020
  1. A. Bhadoria, V.K. Kamboj, M. Sharma, and S.K. Bath, “A Solution to Non-convex/Convex and Dynamic Economic Load Dispatch Problem Using Moth Flame Optimizer,” Ina. Lett., vol. 3, no. 2, pp. 65-86, (2018). [CrossRef] [Google Scholar]
  2. M. Esmaeeli, S. Golshannavaz, and P. Siano, “Determination of optimal reserve contribution of thermal units to afford the wind power uncertainty,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 4, pp. 1565-1576, (2020). [Google Scholar]
  3. C. De Jonghe, E. Delarue, R. Belmans, and W. D’haeseleer, “Determining optimal electricity technology mix with high level of wind power penetration,” Appl. Energy, vol. 88, no. 6, pp. 2231-2238, (2011). [Google Scholar]
  4. M.S. Shahriar, M.J. Rana, M.A. Asif, M.M. Hasan, and M.M. Hawlader, “Optimization of Unit Commitment Problem for wind-thermal generation using Fuzzy optimization technique,” in Proceedings of 2015 3rd International Conference on Advances in Electrical Engineering, ICAEE 2015, pp. 88-92(2015). [Google Scholar]
  5. M. Wang et al., “A preventive control strategy for static voltage stability based 92(2016on an efficient power plant model of electric vehicles,” J. Mod. Power Syst. Clean Energy, vol. 3, no. 1, pp. 103-113, (2015). [CrossRef] [Google Scholar]
  6. Z. Yang et al., “A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles,” Energy, vol. 170, pp. 889-905,(2019). [CrossRef] [Google Scholar]
  7. W.L. Snyder, H.D. Powell, and J.C. Rayburn, “Dynamic Programming Approach to Unit Commitment,” IEEE Power Eng. Rev., vol. PER-7, no. 5, pp. 41-42, (1987). [CrossRef] [Google Scholar]
  8. S. Virmani, E. Adrian, … K. I.-I. T. on, and undefined 1989, “Implementation of a Lagrangian relaxation based unit commitment problem,” ieeexplore.ieee.org (1989) [Google Scholar]
  9. V.K. Kamboj, S.K. Bath, and J.S. Dhillon, “Implementation of hybrid harmony/random search algorithm considering ensemble and pitch violation for unit commitment problem,” Int. J. Electr. Power Energy Syst., vol. 77, pp. 228-249, (2016). [CrossRef] [Google Scholar]
  10. A.Y. Saber and G.K. Venayagamoorthy, “Unit commitment with vehicle-to-grid using particle swarm optimization,” 2009 IEEE Bucharest PowerTech Innov. Ideas Towar. Electr. Grid Futur., pp. 1-8, (2009). [Google Scholar]
  11. S.A. Kazarlis, “A genetic algorithm solution to the unit commitment problem”, IEEE Transactions on Power Systems, pp. 83 -92, (1996). [Google Scholar]
  12. V.N. Dieu and W. Ongsakul, “Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network,” Electr. Power Syst. Res., Vol. 78, no. 3, pp. 291-301, (2008). [CrossRef] [Google Scholar]
  13. M. Singh, I. Kar, and P. Kumar, “Influence of EV on grid power quality and optimizing the charging schedule to mitigate voltage imbalance and reduce power loss,” Proc. EPE-PEMC 2010 - 14th Int. Power Electron. Motion Control Conf., pp. 196-203, (2010). [Google Scholar]
  14. B. Palmintier and M. Webster, “Impact of unit commitment constraints on generation expansion planning with renewables,” IEEE Power Energy Soc. Gen. Meet., pp. 1-7,(2011). [Google Scholar]
  15. S. Kamboj, W. Kempton, and K.S. Decker, “Deploying Power Grid-Integrated Electric Vehicles as a Multi-Agent System,” Proc. 10th Int. Conf. Auton. Agents Multiagent Syst. – Innov. Appl. Track (AAMAS 2011), no. Aamas, pp. 13-20, (2011). [Google Scholar]
  16. G.O. Suvire, M.G. Molina, and P.E. Mercado, “Improving the integration of wind power generation into AC microgrids using flywheel energy storage,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1945-1954, (2012). [Google Scholar]
  17. A. Foley, B. Tyther, P. Calnan, and B. Ó Gallachóir, “Impacts of Electric Vehicle charging under electricity market operations,” Appl. Energy, vol. 101, no. 2013, pp. 93-102, (2013). [Google Scholar]
  18. P.K. Roy, “Solution of unit commitment problem using gravitational search algorithm,” Int. J. Electr. Power Energy Syst., vol. 53, no. 1, pp. 85-94, (2013). [CrossRef] [Google Scholar]
  19. L. Liu, H. Li, Y. Xue, and W. Liu, “Reactive power compensation and optimization strategy for grid-interactive cascaded photovoltaic systems,” IEEE Trans. Power Electron., vol. 30, no. 1, pp. 188-202, (2015). [Google Scholar]
  20. M.E. El-Hawary, “The smart grid - State-of-the-art and future trends,” Electr. Power Components Syst., vol. 42, no. 3-4, pp. 239-250, (2014). [CrossRef] [Google Scholar]
  21. P.B. Luh et al., “Grid Integration of Distributed Wind Generation: Hybrid Markovian and Interval Unit Commitment,” IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 732-741, (2014). [Google Scholar]
  22. V.K. Kamboj, S.K. Bath, and J.S. Dhillon, “A novel hybrid DE–random search approach for unit commitment problem,” Neural Comput. Appl., vol. 28, no. 7, pp. 1559-1581, (2017). [Google Scholar]
  23. C. CHEN and S. DUAN, “Microgrid economic operation considering plug-in hybrid electric vehicles integration,” J. Mod. Power Syst. Clean Energy, vol. 3, no. 2, pp. 221-231,(2015). [CrossRef] [Google Scholar]
  24. H. LIU, P. ZENG, J. GUO, H. WU, and S. GE, “An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic,” J. Mod. Power Syst. Clean Energy, vol. 3, no. 2, pp. 232-239, (2015). [CrossRef] [Google Scholar]
  25. S. Umamaheswaran and S. Rajiv, “Financing large scale wind and solar projects - A review of emerging experiences in the Indian context,” Renew. Sustain. Energy Rev., vol. 48, pp. 166-177, (2015). [CrossRef] [Google Scholar]
  26. N. Zhang, Z. Hu, X. Han, J. Zhang, and Y. Zhou, “A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power,” Int. J. Electr. Power Energy Syst., vol. 65, pp. 201-209, (2015). [CrossRef] [Google Scholar]
  27. K.S. Reddy, L.K. Panwar, and R. Kumar, “Potential benefits of electric vehicle deployment as responsive reserve in unit commitment,” 9th Int. Conf. Ind. Inf. Syst. ICIIS 2014, (2015). [Google Scholar]
  28. L. Yang, J. Jian, Z. Dong, and C. Tang, “Multi-Cuts Outer Approximation Method for Unit Commitment,” IEEE Trans. Power Syst., vol. 32, no. 2, pp. 1587-1588,(2017). [Google Scholar]
  29. W.S. Tan and M. Shaaban, “A Hybrid Stochastic/Deterministic Unit Commitment Based on Projected Disjunctive MILP Reformulation,” IEEE Trans. Power Syst., vol. 31, no. 6, pp. 5200-5201, 2016. [Google Scholar]
  30. R. Á. Fernández, F.B. Cilleruelo, and I.V. Martínez, “A new approach to battery powered electric vehicles: A hydrogen fuel-cell-based range extender system,” Int. J. Hydrogen Energy, vol. 41, no. 8, pp. 4808-4819, (2016). [Google Scholar]
  31. K.S. Reddy, L.K. Panwar, R. Kumar, and B.K. Panigrahi, “Distributed resource scheduling in smart grid with electric vehicle deployment using fireworks algorithm,” J. Mod. Power Syst. Clean Energy, vol. 4, no. 2, pp. 188-199, (2016). [CrossRef] [Google Scholar]
  32. V. Monteiro, J.G. Pinto, and J.L. Afonso, “Operation Modes for the Electric Vehicle in Smart Grids and Smart Homes: Present and Proposed Modes,” IEEE Trans. Veh. Technol., vol. 65, no. 3, pp. 1007-1020, (2016). [Google Scholar]
  33. Srinivas Rao J., Srinivasa Varma, P., Suresh Kumar. T, International Journal of Power Electronics and Drive Systems, vol. 9, no.3, pp. 1202-1213, (2018). [Google Scholar]
  34. E.S. Ali, S.M. Abd Elazim, and A.Y. Abdelaziz, “Ant Lion Optimization Algorithm for renewable Distributed Generations,” Energy, vol. 116, pp. 445-458, (2016). [CrossRef] [Google Scholar]
  35. C. Deckmyn, J. Van de Vyver, T.L. Vandoorn, B. Meersman, J. Desmet, and L. Vandevelde, “Day-ahead unit commitment model for microgrids,” IET Gener. Transm. Distrib., vol. 11, no. 1, pp. 1-9, (2017). [CrossRef] [Google Scholar]
  36. M. Ban, J. Yu, M. Shahidehpour, and Y. Yao, “Integration of power-to-hydrogen in day-ahead security-constrained unit commitment with high wind penetration,” J. Mod. Power Syst. Clean Energy, vol. 5, no. 3, pp. 337-349, (2017). [CrossRef] [Google Scholar]
  37. S. Chandrashekar, Y. Liu, and R. Sioshansi, “Wind-integration benefits of controlled plug-in electric vehicle charging,” J. Mod. Power Syst. Clean Energy, vol. 5, no. 5, pp. 746-756, (2017). [CrossRef] [Google Scholar]
  38. A. Babin, N. Rizoug, T. Mesbahi, D. Boscher, Z. Hamdoun, and C. Larouci, “Total Cost of Ownership Improvement of Commercial Electric Vehicles Using Battery Sizing and Intelligent Charge Method,” IEEE Trans. Ind. Appl., vol. 54, no. 2, pp. 1691-1700, (2018). [Google Scholar]
  39. H. Li, A.T. Eseye, J. Zhang, and D. Zheng, “Optimal energy management for industrial microgrids with high-penetration renewables,” Prot. Control Mod. Power Syst., vol. 2, no. 1, pp. 1-14, (2017). [CrossRef] [Google Scholar]
  40. J. Meus, K. Poncelet, and E. Delarue, “Applicability of a Clustered Unit Commitment Model in Power System Modeling,” IEEE Trans. Power Syst., vol. 33, no. 2, pp. 2195-2204, (2018). [Google Scholar]
  41. J. Liu, C.D. Laird, J.K. Scott, J.P. Watson, and A. Castillo, “Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints,” IEEE Trans. Power Syst., vol. 34, no. 2, pp. 1139-1150, (2019). [Google Scholar]
  42. F.H. Aghdam and M.T. Hagh, “Security Constrained Unit Commitment (SCUC)formulation and its solving with Modified Imperialist Competitive Algorithm (MICA),” J. King Saud Univ. - Eng. Sci., vol. 31, no. 3, pp. 253-261, (2019). [Google Scholar]
  43. A. Yazdandoost, P. Khazaei, R. Kamali, and S. Saadatian, “An Efficient Scheduling for Security Constraint Unit Commitment Problem Via Modified Genetic Algorithm Based on Multicellular Organisms Mechanisms,” World Autom. Congr. Proc., vol. 2, pp. 58-63, (2018). [Google Scholar]
  44. X. Fang, L. Bai, F. Li, and B.M. Hodge, “Hybrid component and configuration model for combined-cycle units in unit commitment problem,” J. Mod. Power Syst. Clean Energy, vol. 6, no. 6, pp. 1332-1337, (2018). [CrossRef] [Google Scholar]
  45. C. Zhu, F. Lu, H. Zhang, and C.C. Mi, “Robust predictive battery thermal management strategy for connected and automated hybrid electric vehicles based on thermoelectric parameter uncertainty,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 6, no. 4, pp. 1796-1805, (2018). [Google Scholar]
  46. S. Maghsudlu and S. Mohammadi, “Optimal scheduled unit commitment considering suitable power of electric vehicle and photovoltaic uncertainty,” J. Renew. Sustain. Energy, vol. 10, no. 4, (2018). [Google Scholar]
  47. X. Chen, M.B. Mcelroy, Q. Wu, Y. Shu, and Y. Xue, “Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 1, (2019). [Google Scholar]
  48. T.K. Renuka, P. Reji, and S. Sreedharan, “An enhanced particle swarm optimization algorithm for improving the renewable energy penetration and small signal stability in power system,” Renewables Wind. Water, Sol., vol. 5, no. 1, (2018). [Google Scholar]
  49. T. Ghose, H.W. Pandey, and K.R. Gadham, “Risk assessment of microgrid aggregators considering demand response and uncertain renewable energy sources,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 6, pp. 1619-1631, (2019). [CrossRef] [Google Scholar]
  50. M. Bayati, M. Abedi, G.B. Gharehpetian, and M. Farahmandrad, “Short-term interaction between electric vehicles and microgrid in decentralized vehicle-to-grid control methods,” Prot. Control Mod. Power Syst., vol. 4, no. 1, (2019). [CrossRef] [Google Scholar]
  51. J.B. Mogo and I. Kamwa, “Improved deterministic reserve allocation method for multi-area unit scheduling and dispatch under wind uncertainty,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 5, pp. 1142-1154, (2019). [CrossRef] [Google Scholar]

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