International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 10, No. 6, 8 Jun. 2018
Cover page and Table of Contents: PDF (size: 879KB)
Intelligent Scheduling of Demand Side Energy Usage in Smart Grid Using a Metaheuristic Approach
Full Text (PDF, 879KB), PP.30-39
Views: 0 Downloads: 0
Author(s)
Nilima R. Das
1,*
Satyananda C. Rai
2
Ajit Nayak
1
Index Terms
Smart Grid, DSM, Metaheuristic optimization
Abstract
As the global demand for electricity is growing continuously, the sources use more fossil fuels to generate electricity which in turn increases the level of carbon dioxide in the atmosphere. Moreover the electrical system becomes unreliable during the peak hours if the demand for electricity is very high. So there is a need to have a grid system which can handle these cases in a smarter way. A Smart Grid is such an electrical grid system which can control and manage electricity demand in a more reliable and economic manner using various energy efficient resources and a variety of operational measures like smart meters, smart appliances and smart communication system. The smart grid uses a technique called energy demand management at consumer side which motivates the consumers to control and reduce their demand for energy during peak hours. This makes the whole system more reliable and efficient. The demand side management (DSM) includes various methods such as increasing awareness among the consumers and giving them some financial incentives which can encourage them to be a part of the DSM program. In this paper a novel Demand Side Management technique has been proposed for a typical smart grid scenario which comprises users with energy storage devices using a metaheuristic approach to have an optimal load scheduling that results in reduced peak hour demands.
Cite This Paper
Nilima R. Das, Satyananda C. Rai, Ajit Nayak, "Intelligent Scheduling of Demand Side Energy Usage in Smart Grid Using a Metaheuristic Approach", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.6, pp.30-39, 2018. DOI:10.5815/ijisa.2018.06.04
Reference
[1]J. Jhi-Young, A. Sang-Ho, Y. Yong-Tae and C. Jong-Woong, “Option Valuation Applied to Implementing Demand Response via Critical Peak Pricing”, Proceedings of IEEE Power Engineering Society General Meeting, pp. 1-7, July 2007.
[2]M.A. Piette, G. Ghatikar, S. Kiliccote, D. Watson, E Koch and D. Hennage, “Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings”, Journal of Computing Science and Information Engeneering, vol. 9, no. 2, pp.1–9, June 2009.
[3]H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober and A. Leon-Garcia, “Autonomous Demand-side Management Based on Game-theoretic Energy Consumption Scheduling for the Future Smart Grid”, IEEE Transactions Smart Grid, vol.1, no.3, pp.320-331, November 2010.
[4]H Mohsenian-Rad and A. Leon-Garcia, “Optimal Residential Load Control with Price Prediction in Real-time Electricity Pricing Environments”, IEEE Trans Smart Grid, vol. 1, no.2, pp. 120–133, August 2010.
[5]S. Caron and G. Kesidis, “Incentive-based Energy Consumption Scheduling Algorithms for the Smart Grid”, in Proc. IEEE International Conference on Smart Grid Communications, pp. 391–396, November 2010.
[6]Z. Zhu, J. Tang, S. Lambotharan, W. H. Chin and Z. Fan, “An Integer Linear Programming Based Optimization for Home Demand-side Management in Smart Grid”, innovative smart grid technologies, IEEE PES, pp.1-5, April 2012.
[7]P. Yang, G. Tang and A. Nehorai, “A Game-theoretic Approach for Optimal Time-of-use Electricity Pricing”, IEEE Transactions on Power Systems, vol. 28, no. 2, pp.884-892, May 2013.
[8]X. Chen, T. Wei and S. Hu, “Uncertainty-aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home”, IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 932 – 941, March 2013.
[9]I. Atzeni, L. G. Ordóñez, G. Scutar i, D. P. Palomar and J. R. Fonollosa, “Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-side of the Smart Grid”, IEEE transactions on signal processing, vol. 61, no. 10, pp. 2454-2472, May 2013.
[10]Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar and J. R. Fonollosa, “Demand Side Management via Distributed Energy Generation and Storage Optimization”, IEEE Transactions on Smart Grid, vol. 4, No. 2, pp. 866-876, June 2013.
[11]H. Chen, Y. Li, R.H.Y. Louie and B. Vucetic, “Autonomous Demand-side Management based on Energy Consumption Scheduling and Instantaneous Load Billing: An Aggregative Game Approach”, IEEE transactions on Smart Grid, vol. 5, no. 4, pp.1744-1754, July 2014.
[12]M.M. Jalali and A. Kazemi, “Demand Side Management in a Smart Grid with Multiple Electricity Suppliers”, Energy 2015, vol. 81, pp. 766-776, March 2015.
[13]C. A. Raj , E. Aravind , B. R. Sundaram and S. K.Vasudevan, “Smart Meter Based on Real Time Pricing”, Smart Grid Technologies, vol. 21, pp. 120-124, August 2015.
[14]F. Ye, Y. Qian and R.Q. Hu, “A Real-time Information Based Demand-side Management System in Smart Grid”, IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, pp. 329-339, 2016.
[15]K. Al-jabery, Z. Xu, W. Yu, D. C. Wunsch, J. Xiong, and Y. Shi, “Demand Side Management of Domestic Electric Water Heaters Using Approximate Dynamic Programming”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE, vol. 36, no.5, pp. 775-788, August 2016.
[16]A. R. Khan, A. Mahmood, A. Safdar, Z. A. Khan and N. A. Khan, “Load Forecasting, Dynamic Pricing and DSM in Smart Grid: A Review”, Renewable and sustainable Energy Reviews, vol. 54, pp. 1311-1322, February 2016.
[17]H. O. Salami and E. Y. Mamman, “A Genetic Algorithm for Allocating Project Supervisors to Students”, I.J. Intelligent Systems and Applications, vol. 8, no. 10, pp. 51-59, October 2016.
[18]M. A. Tawfeek and G. F. Elhady, “Hybrid Algorithm Based on Swarm Intelligence Techniques for Dynamic Scheduling in Cloud Computing”, I.J. Intelligent Systems and Applications, vol. 8, no. 11, pp. 61-69, November 2016.
[19]B. V. Chawda and J.M. Patel, “Investigating Performance of Various Natural Computing Algorithms”, I.J. Intelligent Systems and Applications, vol. 9, no. 1, pp. 46-59, January 2017.
[20]I.J Poolo, “A Smart Grid Demand Side Management Framework Based on Advanced Metering Infrastructure”, American Journal of Electrical and Electronics Engineering, vol. 5, no. 4, pp. 152-158, July 2017.