Economic-Environmental Optimal Management of Smart Residential Micro-Grid Considering CCHP System

References

• T. Som, and N. Chakraborty, “Studies on economic feasibility of an autonomous power delivery system utilizing alternative hybrid distributed energy resources,” IEEE Trans. Power Syst., vol. 29, no. 1, pp. 172–181, 2014.
   Web of Science ®Google Scholar
• N. Kanwar, N. Gupta, K. R. Niazi, and A. Swarnkar, “Optimal allocation of distributed energy resources using improved Meta-heuristic techniques,” Electr. Pow. Compo. Sys., vol. 44, no. 13, pp. 1466–1477, 2016.
   Web of Science ®Google Scholar
• N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Microgrids,” IEEE Power and Energ. Mag., vol. 5, no. 4, pp. 78–94, 2007.
   Web of Science ®Google Scholar
• D. W. Wu, and R. Z. Wang, “Combined cooling, heating and power: A review,” Prog. Energy Combust. Sci., vol. 32, no. 5-6, pp. 459–495, 2006.
   Web of Science ®Google Scholar
• M. M. Maghanki, B. Ghobadian, G. Najafi, and R. J. Galogah, “Micro combined heat and power (MCHP) technologies and applications,” Renewable Sustainable Energy Rev., vol. 28, pp. 510–524, 2013.
   Web of Science ®Google Scholar
• M. H. Moradi, M. Hajinazari, S. Jamasb, and M. Paripour, “An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming,” Energy, vol. 49, pp. 86–101, 2013.
   Web of Science ®Google Scholar
• C. Chen, S. Duan, T. Cai, B. Liu, and G. Hu, “Smart energy management system for optimal microgrid economic operation,” IET Renewable Power Gener., vol. 5, no. 3, pp. 258–267, 2011.
   Web of Science ®Google Scholar
• Z. Bao, Q. Zhou, Z. Yang, Q. Yang, L. Xu, and T. Wu, “A multi time-scale and multi energy-type coordinated microgrid scheduling solution—part I: Model and methodology,” IEEE Trans. Power Sys., vol. 30, no. 5, pp. 2257–2266, 2015.
   Web of Science ®Google Scholar
• Z. Wu, W. Gu, R. Wang, X. Yuan, and W. Liu, “Economic optimal schedule of CHP microgrid system using chance constrained programming and particle swarm optimization,” In 2011 IEEE Power and Energy Society General Meeting, IEEE, pp. 1–11, 2011.
   Google Scholar
• M. Giuntoli, and D. Poli, “Optimized thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storages,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 942–955, 2013.
   Web of Science ®Google Scholar
• J. Shen, C. Jiang, Y. Liu, and J. Qian, “A microgrid energy management system with demand response for providing grid peak shaving,” Electr. Pow. Compo. Sys., vol. 44, no. 8, pp. 843–852, 2016.
   Web of Science ®Google Scholar
• J. Aghaei, and M.-I. Alizadeh, “Demand response in smart electricity grids equipped with renewable energy sources: A review,” Renewable Sustainable Energy Rev., vol. 18, pp. 64–72, 2013.
   Web of Science ®Google Scholar
• P. Siano, “Demand response and smart grids—A survey,” Renewable Sustainable Energy Rev., vol. 30, pp. 461–478, 2014.
   Web of Science ®Google Scholar
• S. Esmaeili, B. Vahidi, M. Parvizimosaed, and F. Brahman, “Development of a thermal and electrical energy management in residential building micro-grid,” J. Renewable Sustainable Energy, vol. 6, no. 1, pp. 013126, 2014.
   Web of Science ®Google Scholar
• N. Liu, L. He, X. Yu, and L. Ma, “Multi-party energy management for grid-connected microgrids with heat and electricity coupled demand response,” IEEE Transactions on Industrial Informatics, vol. 14, no. 5, pp. 1887–1897, 2018.
   Web of Science ®Google Scholar
• D. Setlhaolo, S. Sichilalu, and J. Zhang, “Residential load management in an energy hub with heat pump water heater,” Appl. Energy, vol. 208, pp. 551–560, 2017.
   Web of Science ®Google Scholar
• S. D. Beigvand, H. Abdi, and M. La Scala, “Hybrid gravitational search algorithm-particle swarm optimization with time varying acceleration coefficients for large scale CHPED problem,” Energy, vol. 126, pp. 841–853, 2017.
   Web of Science ®Google Scholar
• M. Nazari-Heris, S. Abapour, and B. Mohammadi-Ivatloo, “Optimal economic dispatch of FC-CHP based heat and power micro-grids,” Appl. Therm. Eng., vol. 114, pp. 756–769, 2017.
   Web of Science ®Google Scholar
• A. H. Fathima, and K. Palanisamy, “Optimization in microgrids with hybrid energy systems–A review,” Renewable Sustainable Energy Rev., vol. 45, pp. 431–446, 2015.
   Web of Science ®Google Scholar
• C. R. Raquel, and P. C. Naval, Jr. “An effective use of crowding distance in multiobjective particle swarm optimization,” In Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, ACM, pp. 257–264, 2005.
   Google Scholar
• Z.-H. Zhan, J. Zhang, Y. Li, and H. S.-H. Chung, “Adaptive particle swarm optimization,” IEEE Trans. Syst. Man. Cybern. B Cybern., vol. 39, no. 6, pp. 1362–1381, 2009.
   PubMed Web of Science ®Google Scholar
• A. L. F. I. Alireza, “PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems,” Acta Autom. Sin., vol. 37, no. 5, pp. 541–549, 2011.
   Google Scholar
• S. Agrawal, B. K. Panigrahi, and M. K. Tiwari, “Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch,” IEEE Trans. Evol. Comput., vol. 12, no. 5, pp. 529–541, 2008.
   Web of Science ®Google Scholar
• R. Gelman, M. Hummon, J. McLaren, and E. Doris, NREL's Clean Energy Policy Analyses Project: 2009 US State Clean Energy Data Book, October 2010. No. DOE/GO-102010-3139. Golden, CO. National Renewable Energy Laboratory (NREL), 2010.
   Google Scholar
• Historical spot market prices of PJM market. [Online]. Available: http://www.pjm.com/markets-and-operations/energy/day-ahead.aspx.
   Google Scholar
• Historical outdoor temperature, National Weather Service, [Online]. National Weather Service. Available: http://w2.weather.gov/climate.
   Google Scholar
• Historical irradiation data for PV panels, National Weather Service. Available: http://w2.weather.gov/climate/local_data.php?wfo=rnk.
   Google Scholar
• Q. Gu, H. Ren, W. Gao, and J. Ren, “Integrated assessment of combined cooling heating and power systems under different design and management options for residential buildings in shanghai,” Energ. Buildings, vol. 51, pp. 143–152, 2012.
   Web of Science ®Google Scholar
• G. Pulido, and C. Coello, “Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer,” In Genetic and Evolutionary Computation–GECCO 2004. Berlin/Heidelberg: Springer, 2004, pp. 225–237.
   Google Scholar