Improved Demand Response with Particle Swarm Optimization

Authors

  • Suparna Dey Department of Electrical Engineering, Tripura University, Agartala ,Tripura, India
  • Monalisa Dasgupta Department of Electrical Engineering, Tripura University, Agartala ,Tripura, India
  • Soumesh Chatterjee Department of Electrical Engineering, National Institute of Technology, Tripura, India
  • Gagari Deb Department of Electrical Engineering, Tripura University, Agartala ,Tripura, India

Keywords:

Demand Response, Dynamic pricing, Elastic Loads, Load Scheduling, Particle Swarm Optimization(PSO)

Abstract

Among all the power management strategies in smart grid, demand response is quiet popular because of its significance impact on saving in peak demand and reduced energy consumption. Electricity price revealed by utility companyis mainly accountable for the demand response to lead the consumer in electricity scheduling.The mutual benefits for both customer and supply side are ensured with the use of price conferring mechanism through demand response.This paper proposes anapproach to minimize the elastic load through price weight given by the utility company. Three types of consumers are designed as residential, industrial and commercial. The ‘per day electricity use’ for these consumers has been reducedby scheduling elastic loads using Particle Swarm Optimization(PSO) technique. The outcome of simulation results show that the recommended algorithm reduces the cost while balancing the peak demand.

References

[1] G. Gaur, N. Mehta, R. Khanna, and S. Kaur, “Demand Side Management in a Smart Grid Environment”, IEEE International Conference on Smart Grid and Smart Cities, India, pp. 227-231, 2017.

[2] M. Awais, N. Javaid, N. Shaheen, Z. Iqbal, G. Rehman, K. Muhammad, and I. Ahmad, “An Efficient Genetic Algorithm Based Demand Side Management Scheme for Smart Grid”, IEEE International Conference on Network-Based Information Systems, pp. 351-356, 2015.

[3] C-R Chen, M-J Lan, C-C Huang, Y-Y Hong, and S. H. Low, “Demand Response Optimization for Smart Home Scheduling Using Genetic Algorithm” , IEEE International Conference on Systems, Man, and Cybernetics,pp. 1461-1465,2013.

[4] A. J. Conejo, J. M. Morales, and L. Baringo, “Real-Time Demand Response Model”, IEEE Transaction on Smart Grid, Vol.1, No.3, pp. 236-242, 2010.

[5] C. Eksin, H. Delic, and A. Ribeiro, “Demand Response Management in Smart Grids With Heterogeneous Consumer Preferences”, IEEE Transaction On Smart Grid, Vol. 6, No. 6, pp. 3082-3094, 2015.

[6] B. Sivaneasan, K. Nandha Kumar, K. T. Tan, and P. L. So, “Preemptive Demand Response Management for Buildings”, IEEE Transactions on Sustainable Energy, Vol. 6, No. 2, pp. 246-256, 2014.

[7] N. Kinhekar, N. Prasad Padhy, and H. Gupta, “Particle Swarm Optimization Based Demand Response for Residential Consumers”, pp. 1-5, IEEE Power & Energy Society General Meeting, 2015.

[8] R. Poli, "An analysis of publications on particle swarm optimization applications", Technical Report CSM-469, 2007.

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Published

2025-11-26

How to Cite

[1]
S. Dey, M. Dasgupta, S. Chatterjee, and G. Deb, “Improved Demand Response with Particle Swarm Optimization”, Int. J. Comp. Sci. Eng., vol. 7, no. 18, pp. 235–239, Nov. 2025.

Issue

Vol. 7 No. 18 (2019): IJCSE Special Issue May Edition

Section

Research Article

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