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References

1 
J. Martin, 2009, Distributed vs. centralized electricity generation: are we witnessing a change of paradigm, An introduction to distributed generationGoogle Search
2 
G. Allan, I. Eromenko, M. Gilmartin, I. Kockar, P. J. R. McGregor, S. E. Reviews, 2015, The economics of distributed energy generation: A literature review, Renewable and Sustainable Energy Reviews, Vol. 42, pp. 543-556DOI
3 
Wook Hyun Kwon, Yong-Gi Park, Jae Hyung Roh, Jong-Bae Park, Duehee Lee, 2019, Calculating the Benefit of Distributed Combined Heat Power Generators from Avoiding a Transmission Expansion Cost by Solving a Mixed Integer Linear Programming, The transactions of The Korean Institute of Electrical Engineers, Vol. 68, No. 4, pp. 513-522Google Search
4 
G. Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, W. D’haeseleer, 2005, Distributed generation: definition, benefits and issues, Energy policy, Vol. 33, No. 6, pp. 787-798DOI
5 
M. Bianchi, A. De Pascale, F. Melino, A. Peretto, 2014, Performance prediction of micro-CHP systems using simple virtual operating cycles, Applied thermal engineering, Vol. 71, No. 2, pp. 771-779DOI
6 
C. Pout, R. Hitchin, 2005, Apportioning carbon emissions from CHP systems, Energy conversion and management, Vol. 46, No. 18-19, pp. 2980-2995DOI
7 
C. Milan, M. Stadler, G. Cardoso, S. Mashayekh, 2015, Modeling of non-linear CHP efficiency curves in distributed energy systems, Applied Energy, Vol. 148, pp. 334-347DOI
8 
Samchully, 2019, City gas unit price(Gyeonggi-do)Google Search
9 
J. Kennedy, R. Eberhart, 1995, Particle swarm optimization, in Proceedings of ICNN'95-International Conference on Neural Networks, Vol. 4, pp. 1942-1948: IEEEDOI
10 
R. Hassan, B. Cohanim, O. De Weck, G. Venter, p 2005, A comparison of particle swarm optimization and the genetic algorithm, in 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, pp. 1897DOI
11 
S. Panda, N. P. Padhy, 2008, Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design, Applied soft computing, Vol. 8, No. 4, pp. 1418-1427DOI
12 
Y. Li, et al., 2009, Optimal reactive power dispatch using particle swarms optimization algorithm based Pareto optimal set, in International Symposium on Neural Networks, pp. 152-161: SpringerDOI
13 
K. Dhayalini, S. Sathiyamoorthy, C. A. C. Rajan, 2014, Performance comparison of GA and PSO on Wind and Thermal Generation Dispatch, in Advanced Materials Research, Vol. 2014, pp. 759-763: Trans Tech PublDOI
14 
J. C. Bansal, P. Singh, M. Saraswat, A. Verma, S. S. Jadon, A. Abraham, 2011, Inertia weight strategies in particle swarm optimization, in 2011 Third world congress on nature and biologically inspired computing, pp. 633-640: IEEEDOI
15 
Y. He, W. J. Ma, J. P. Zhang, 2016, The parameters selection of pso algorithm influencing on performance of fault diagnosis, in MATEC Web of conferences, Vol. 63, pp. 02019: EDP SciencesGoogle Search
16 
E. Ozcan, C. K. Mohan, 1999, Particle swarm optimization: surfing the waves, in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Vol. 3, pp. 1939-1944: IEEEDOI
17 
Y. Shi, R. Eberhart, 1998, A modified particle swarm optimizer, in 1998 IEEE international conference on evolutionary computation proceedings, IEEE world congress on computational intelligence (Cat. No. 98TH8360), pp. 69-73: IEEEDOI