A Smart Grid Demand Side Management Framework Based on Advanced Metering Infrastructure

Poolo I.J^1,

¹IJ Energy and Technologies (Pty) Ltd, Republic of South Africa

Cite this paper

Poolo I.J. A Smart Grid Demand Side Management Framework Based on Advanced Metering Infrastructure. American Journal of Electrical and Electronic Engineering. 2017; 5(4):152-158. doi: 10.12691/AJEEE-5-4-5

Abstract

The demand side management has gained considerable prospects and encounters due to the coming in of the Smart Grid. It is also anticipated that the advanced metering infrastructure will enhance the demand side management establishments. This paper discusses the proposed demand side management archetypal that relies on the advanced metering infrastructure for the smart grid. The archetypal has the following components; a collaborative or two-way communication grid, a metering infrastructure and submission software on the user’s end and regulator side correspondingly. We present and discuss the inter-associations for the various minor components that make up the archetypal. In the proposed work, the terminal consumer circulated power resources are taken into account. This archetypal is meant to improve the communication between the power end user and the supply side. It is assumed that it will enhance power distribution and serve as a bench mark to the smart distribution system load management.

Keywords

advanced metering infrastructure, demand side management, distributed energy resource, smart grid

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Dehnavi, E. and Abdi, H., 2016. Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem. Energy, 109, pp.1086-1094.
[2]	Siano, P. and Sarno, D., 2016. Assessing the benefits of residential demand response in a real time distribution energy market. Applied Energy, 161, pp.533-551.
[3]	Boyle S., 1996. DSM progress and lessons in the global context. Energy Policy 24(4):345-59.
[4]	Khondaker, A.N., Hasan, M.A., Rahman, S.M., Malik, K., Shafiullah, M. and Muhyedeen, M.A., 2016. Greenhouse gas emissions from energy sector in the United Arab Emirates–An overview. Renewable and Sustainable Energy Reviews, 59, pp.1317-1325.
[5]	Jayantilal, A and Strbac, G., 1999. Load control services in management of power system security costs IEE Proceedings: Generation Transmission Distribution, 146 (2) 269-275.
[6]	Ayón, X., Gruber, J.K., Hayes, B.P., Usaola, J. and Prodanović, M., 2017. An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands. Applied Energy, 198, pp.1-11.
[7]	Lu, M., Liu, S., Liu, P. and Li, Q., 2016, July. Design and Implementation of Power Information Visualization Platform Based on Smart Meter. In Information Science and Control Engineering (ICISCE), 2016 3rd International Conference on (pp. 297-301).
[8]	Bui, V.H., Kim, H.M. and Song, N.O., 2015. Applying demand response based on TOU and EDRP to optimal microgrid operation. Int. J. Smart Home, 9, pp.41-50.
[9]	RAZAK, A.H.N.B.A., 2017. Malaysian residential housing for the smart grid: identifying optimization attributes for design and energy performance improvements. How to face the scientific communication today. International challenge and digital technology impact on research outputs dissemination, 42, p.109.
[10]	Malik, F.H., Ali, M. and Lehtonen, M., 2014. Intelligent Agent-Based Architecture for Demand Side Management Considering Space Heating and Electric Vehicle Load. Engineering, 6(11), p.670.
[11]	Aljanabi, H., 2012. A Trade-Off between Supply-Side and Demand-Side Management for Urban Water Resources. In World Environmental and Water Resources Congress 2012: Crossing Boundaries (pp. 2700-2706).
[12]	Rankin, R. and Rousseau, P.G., 2008. Demand side management in South Africa at industrial residence water heating systems using in line water heating methodology. Energy conversion and management, 49(1), pp.62-74.
[13]	Sui, H, Ying Sun, Y. and Lee, W-J., 2011. A Demand Side Management Model Based on Advanced Metering Infrastructure, IEEE conference Weihai, Shandong, 6-9 July 2011.
[14]	Wenpeng, L., 2009. Advanced metering infrastructure. Southern Power System Technology, 3(2), pp.6-10.
[15]	Betsy Loeff, 2008. “AMI Anatomy: Core Technologies in Advanced Metering”. Ultrimetrics Newsletter (Automatic Meter Reading Association). http://www.utilimetrics.org/newsletter/index.cfm?fuseaction.
[16]	Bodnar, T., Dering, M.L., Tucker, C. and Hopkinson, K.M., 2016. Using Large-Scale Social Media Networks as a Scalable Sensing System for Modeling Real-Time Energy Utilization Patterns. IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[17]	Lui, T.J.; Stirling, W.; Marcy, H.O., 2010. Get smart. IEEE Power Energy Mag., 8, 66-78.
[18]	Nguyen, T.A.; Aiello, M., 2013. Energy intelligent buildings based on user activity: A survey. Energy Build, 56, 244-257.
[19]	Lanzisera, S.; Dawson-Haggerty, S.; Cheung, H.; Taneja, J.; Culler, D.; Brown, R., 2013. Methods for detailed energy data collection of miscellaneous and electronic loads in a commercial office building. Build. Environ. 2013, 65, 170-177.
[20]	Egerer, J., Weibezahn, J. and Hermann, H., 2016. Two price zones for the German electricity market—Market implications and distributional effects. Energy Economics, 59, pp.365-381.
[21]	Hogan, W.W., 2015. Electricity markets and the clean power plan. The Electricity Journal, 28(9), pp.9-32.
[22]	Ali, M.J., Moungla, H., Younis, M. and Mehaoua, A., 2017. IoT-enabled Channel Selection Approach for WBANs. arXiv preprint arXiv:1703.09508.
[23]	Parvez, I., Sarwat, A.I., Wei, L. and Sundararajan, A., 2016. Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach. Energies, 9(9), p.691.