Research on the Demand of Dynamic Housing and the Application of Distributed Generation Management in Micro-Smart Grid

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Smart grid is a very important trend of power system. With the sharing of smart grid and micro-grid from the traditional power grid, the energy cost and the environmental degradation of power grid have reduced. This paper introduces a dynamic demand response and distributed generation management approach according to micro-smart grid of a residential area. This method has dynamic update mechanism, demand response automation operation and human intervention. Distributed management coordinates with demand response. For example, random load and wind power generation are used to reduce the cost of energy consumption of a residential area. This paper achieves the study of the demand of dynamic housing and the application of distributed generation management in micro-smart grid by the form of distributed generation management modeling. It introduces the specific distribution form of the distributed micro-smart grid taking the building of new rural micro smart grid for example. It also introduces the circuit schematic diagram of micro smart grid.

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3217-3221

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] M.H. Albadi, E.F. El-Saadany. Demand response in electricity markets: An overview. in Proc. IEEE Power and Engineering Society General Meeting, 2010(2): 1205-1210.

DOI: 10.1109/pes.2007.385728

Google Scholar

[2] J. W. Chuah, A. Raghunathan, N. K. Jha. An evaluation of energy-saving technologies for residential purposes. in Proc. IEEE Power and Engineering Society General Meeting, 2010(1): 2230-2237.

DOI: 10.1109/pes.2010.5589388

Google Scholar

[3] A.L. Dimeas, N.D. Hatziargyriou. Operation of a multiagent system for microgrid control. IEEE Transactions on Power System, 2010(8): 255-262.

DOI: 10.1109/tpwrs.2005.852060

Google Scholar

[4] J. Chahwan, C. Abbey, G. Joos . VRB modelling for the study of output terminal voltages, internal losses and performance. in Proc. IEEE Canada Electrical Power Conference, 2009(8): 305-312.

DOI: 10.1109/epc.2007.4520363

Google Scholar

[5] N. Hatziargyriou. MICROGRIDS-Large Scale Integration of micro-generation to low voltage. in Proc. Int. Conf. on the Integration of Renewable Energy Sources & Distributed Energy Resources, 2011(2): 305-312.

Google Scholar

[6] L. A. Souza Ribeiro, O. R. Saavedra, S. L. de Lima, J. G. de Matos. Isolated micro-Grids with renewable hybrid generation: The case of lençóis island. IEEE Transactions on Sustainable Energy, 2010(5): 743-750.

DOI: 10.1109/tste.2010.2073723

Google Scholar

[7] P.B. Luh, L.D. Michel, P. Friedl, C. Guan, Y. Wang. Load forecasting and demand response. in Proc. IEEE Power and Engineering Society General Meeting, 2011(1): 1332-1340.

Google Scholar

[8] A.D. Hawkes, M.A. Leach. Cost-effective operating strategy for residential micro-combined heat and power[J]. Energy, 2010(11): 560-568.

DOI: 10.1016/j.energy.2006.06.001

Google Scholar

[9] Federico Zenith, Sigurd Skogestad. Control of fuel cell power output[J]. J. of Process Control, 2010(9): 160-167.

Google Scholar

[10] J. Kennedy, R. Eberhar. Particle swarm optimization. in Proc. IEEE Int. Conf. on Neural Networks, 2009(1): 1255-1262.

Google Scholar

[11] R. Eberhart, J. Kennedy. A new optimizer using particle swarm theory. in Proc. Int. Symp. on Micro Machine & Human Science, 2009(4): 1032-1040.

DOI: 10.1109/mhs.1995.494215

Google Scholar

[12] K. E. Parsopoulos, M. N. Vrahatis. Particle Swarm Optimization and Intelligence: Advances and Applications. New York: Information Science Reference, 2010(3): 2205-2211.

Google Scholar

[13] Y. del Valle, G,K. Venayagamoorthy, S. Mohagheghi, J. -C. Hernandez, and R.G. Harley . Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 2009(2): 1101-1107.

DOI: 10.1109/tevc.2007.896686

Google Scholar

[14] Y. Yare, G.K. Venayagamoorthy, U.O. Aliyu. Optimal generator maintenance scheduling using a modified discrete PSO[J]. IET Generation, Transmission & Distribution, 2009(11): 234-239.

DOI: 10.1049/iet-gtd:20080030

Google Scholar

[15] Weihong Xu, Guoliang Wu. Research on the convergence of energy-efficient elevators and smart grid [J]. Shanghai Energy Conservation, 2010(01): 23-34.

Google Scholar

[16] Dingjian Chen. Low-voltage integrated smart grid with electricity monitoring and management system [J]. Low-voltage electrical, 2010(03): 34-35.

Google Scholar