Genetic Based Reinforcement Learning Load Control for Smart Grids

Abstract:

As the improvement of smart grids, the customer participation has reinvigorated interest in demand-side features such as load control for domestic users. A genetic based reinforcement learning (RL) load controller is proposed. The genetic is used to adjust the parameters of the controller. The RL algorithm, which is independent of the mathematic model, shows the particular superiority in load control. By means of learning procedures, the proposed controller can learn to take the best actions to regulate the energy usage for equipments with the features of high comfortable for energy usage and low electric charge meanwhile. Simulation results show that the proposed load controller can promote the performance energy usage in smart grids.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 2423-2426
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.2423
• Online since: December 2013
• Authors: Xin Li*, Dan Yu, Chuan Zhi Zang
• Keywords: Load Control, Reinforcement Learning, Smart Grid (SG)

References

[1] D. O'Neil, M. Levorato, A. Goldsmith, U. Mitra, Residential Demand Response Using Reinforcement Learning, IEEE International Conference on Samrt Grid Communications, (2010), pp.409-414.

DOI: 10.1109/smartgrid.2010.5622078

[2] S. D. Maqbool, M. Baba, E. A. Al-Amma. Effects of Demand Elasticity and Price Variation on Load Profile, IEEE PES Conference on Innovative Smart Grid Technologies - Middle East, (2011), pp.1-5.

DOI: 10.1109/isgt-mideast.2011.6220793

[3] M. LeMay, R. Nelli, G. Gross, A. Gunter, An Integrated Architecture for Demand Response Communications and Control, International Conference on System Sciences, (2008), p.174.

DOI: 10.1109/hicss.2008.60

[4] C. L. Su, and D. Kirschen, Direct Participation of Demand-Side in a Pool-based electricity market, Power System Technology, Vol. 31, No. 20 (2007), pp.7-14.

[5] Q. Dam, S. Mohagheghi, J. Stoupis, Intelligent Demand Response Scheme for Customer Side Load Management, IEEE Energy 2030 Conference, (2008), pp.1-7.

DOI: 10.1109/energy.2008.4781013

[6] A. F. Atiya, A. G. Parlos and L. Ingber, A reinforcement learning method based on adaptive simulated annealing, in Proc. of the 46th International Midwest Symposium on Circuits and Systems, (2003), pp.121-124.

DOI: 10.1109/mwscas.2003.1562233

[7] M. Rajesh, Kandadai and J. M. Tien, A knowledge-based generating hierarchical fuzzy-neural controller, IEEE Transactions on Neural Networks, Vol. 8, No. 6 (1997), pp.1531-1540.

DOI: 10.1109/72.641474