CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid

by Gilbert Mahlangu, Samuel Musungwini, Sindiso Nleya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 35
Year of Publication: 2018
Authors: Gilbert Mahlangu, Samuel Musungwini, Sindiso Nleya
10.5120/ijca2018916786

Gilbert Mahlangu, Samuel Musungwini, Sindiso Nleya . An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid. International Journal of Computer Applications. 179, 35 ( Apr 2018), 10-17. DOI=10.5120/ijca2018916786

@article{ 10.5120/ijca2018916786,
author = { Gilbert Mahlangu, Samuel Musungwini, Sindiso Nleya },
title = { An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 179 },
number = { 35 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 10-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number35/29225-2018916786/ },
doi = { 10.5120/ijca2018916786 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:57:28.772138+05:30
%A Gilbert Mahlangu
%A Samuel Musungwini
%A Sindiso Nleya
%T An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 35
%P 10-17
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Several new technologies, for example, the Internet of Things (IoTs) continue to surface in the frontage of ubiquitous and pervasive computing. Internet of things is slowly being embraced in different domains to support control and monitoring systems. The advent of microprocessors embedded with communication facilities has enabled the possibility of integrating ICTs within people and their environments. With the power utilities industry grappling with serious issues like load management which has a potential of damaging their equipment, first movers are turning to advanced technologies such as the IoTs to achieve demand-side management. The purpose of this study was to conduct a theoretical and empirical review on the approaches used by Power utilities to manage load in order to determine how IoTs can be used particularly by Zimbabwe’s Power utility to solve the supply-demand problem. Currently, the load management approaches used by the Power utility in Zimbabwe are load shifting and load shedding. A conceptual model was developed using C++ guided by the algorithm for load management. The model development was motivated by the research gap establish from the literature review and the problem faced by the Power utility in Zimbabwe in balancing demand and supply during peak periods. The idea is modelled around continuous monitoring of the feeder or substation, activating the automatic control and carrying out load allocation procedure based on priority settings, connection, and re-connection timings. The proposed model is also based on the dependence of demand and consumer priority perception. The model will ensure that electricity is available for basic and essential use in times of increased demand. It requires that load demand is constantly monitored to trigger automatic control and appliances should connect to the electric power grid using smart plugs.

References
  1. A. Shinn, K. Nakatani, and W. Rodriguez, “Analyzing the Role of the Intenet-of-Things in Business and Technologically-Smart Cities,” vol. 6, no. 4, pp. 149–158, 2017.
  2. J. M. Mudumbe, “The internet of things for a smart South African grid architecture,” no. 2014, pp. 95–107.
  3. R. Miceli, “Energy Management and Smart Grids,” pp. 2262–2290, 2013.
  4. F. Aloul, A. R. Al-ali, R. Al-dalky, and M. Al-mardini, “Smart Grid Security : Threats , Vulnerabilities and Solutions,” Smart Grid Clean Energy Smart, no. 971, pp. 1–6, 2012.
  5. D. Volk, “Electricity Networks : Infrastructure and Operations Too complex for a resource ?,” 2013.
  6. D. Newbery and A. Eberhard, “South African Network Infrastructure Review : Updated 2008 A paper written for National Treasury and the Department of Public Enterprises Government of South Africa,” 2008.
  7. T. Foley et al., Renewables 2015 global status report. 2015.
  8. F. Mattern and C. Floerkemeier, “From the Internet of Computers to the Internet of Things.”
  9. D. Evans, “The Internet of Things How the Next Evolution of the Internet The Internet of Things How the Next Evolution of the Internet Is Changing Everything,” no. April, 2011.
  10. A. Crapo, R. Piasecki, D. Street, S. Francisco, and X. Wang, “The Smart Grid as a Semantically Enabled Internet of Things Grid-Interop Forum 2011,” 2011.
  11. S. Ramakrishnan, “WoT ( Web of Things ) for Energy Management in a Smart Grid-Connected Home,” vol. 10, 2013.
  12. R. N. Calheiros, E. Alexandre, A. B. Do Carmo, C. A. F. De Rose, and R. Buyya, “Towards self-managed adaptive emulation of grid environments,” Proc. - IEEE Symp. Comput. Commun., pp. 818–823, 2009.
  13. G. Misra, V. Kumar, A. Agarwal, and K. Agarwal, “Internet of Things ( IoT ) – A Technological Analysis and Survey on Vision , Concepts , Challenges , Innovation Directions , Technologies , and Applications ( An Upcoming or Future Generation Computer Communication System Technology ),” vol. 4, no. 1, pp. 23–32, 2016.
  14. A. Al-fuqaha, S. Member, M. Guizani, M. Mohammadi, and S. Member, “Internet of Things : A Survey on Enabling,” vol. 17, no. 4, pp. 2347–2376, 2015.
  15. C. Alcaraz, P. Najera, J. Lopez, and R. Roman, “Wireless Sensor Networks and the Internet of Things : Do We Need a Complete Integration ?,” 2010.
  16. M. M. Eissa, S. M. Wasfy, and M. M. Sallam, “Load Management System Using Intelligent Monitoring and Control System for Commercial and Industrial Sectors,” pp. 3–18, 2012.
  17. S. Ashok and R. Banerjee, “Load-management applications for the industrial sector.”
  18. H. Svahnstr, “Demand Side Management in Smart Grids.”
  19. G. Shahgholian and M. E. Salary, “Effect of Load Shedding Strategy on Interconnected Power Systems Stability When a Blackout Occurs,” vol. 4, no. 2, pp. 212–217, 2012.
  20. J. Heeter, R. Vora, S. Mathur, N. Renewable, and P. Madrigal, “Wheeling and Banking Renewable Energy Wheeling and Banking Strategies for Optimal Renewable Energy Deployment : International Experiences,” no. March, 2016.
  21. S. Barker, A. Mishra, D. Irwin, P. Shenoy, and J. Albrecht, “SmartCap : Flattening Peak Electricity Demand in Smart Homes.”
  22. A. Reinhardt et al., “SmartMeter . KOM : A Low-cost Wireless Sensor for Distributed Power Metering,” no. October, 2011.
  23. Rasheed et al., Energy optimization in smart homes using customer preference and dynamic pricing, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Conceptual model Internet of Things (IoTs) load management electric power grid consumer priority demand dependence switchable appliances Algorithm