Efficient and Autonomous Energy Management Techniques for the Future Smart Homes

**Abstract:** ** **

Smart grid with latest technologies provides solid foundation for the implementation of energy management systems at home premises. This paper proposes an autonomous energy management-based cost reduction solution for peak load times using a home energy management system (HEMS). Within a home environment, both the real time and the schedulable appliances are connected with smart meter through HEMS. We formulate an optimization problem under various practical constraints, which is shown to be a mixed integer programming problem that can be solved through a step-wise approach. A novel scheme based on Dijkstra algorithm is proposed, which results in the similar performance to that of the proposed optimal scheme while exhibiting much lower complexity. To further save the computational efforts, a low complexity scheme is also proposed, which produces considerably better results than the non-optimized scheme with the same complexity yet. Simulation results are presented at show the performance and complexity comparison of different proposed solutions and the existing methods.

**EXISTING SYSTEM: **

The existing literature emphasizes on scheduling of load from peak hours to far away off peak hours which may result in disturbing the comfort level at consumer end [18]. The authors in [19] proposed a variable price model such that users have different prices in different scheduling hours. To cope with consumer satisfaction problem some of the priority loads should be kept in the peak or near peak hours. However, there is always a compromise on the comfort levels and the cost of electricity consumption. This motivate us to consider the energy optimization to minimize the cost by scheduling the loads within limited scheduling hours. In this work, we present a more computational efficient energy scheduling with shift-able as well as real time loads in a home environment to encounter the peak demand by minimizing the overall cost. Reducing the complexity of scheduling algorithms helps in reducing power consumption by HEMS, controlling more number of appliances, and availability of more resources for other operations of HEMS [15].

**PROPOSED SYSTEM: **

The contributions of this paper can be summarized as • An optimization problem is formulated to minimize the total cost under maximum available energy constraint and the number of appliances constraints. • To obtain an efficient solution with lower complexity, a novel decomposition framework is adopted where the formulated optimization is split into two independent sub-problems and an optimal solution is presented. • Besides the optimal schemes, we propose a new yet more efficient solution based on the idea of Dijkstra Algorithm which results in a close-optimal performance. • Further, a sub-optimal algorithm is proposed which trades the computational complexity for lower performance. At the end, the solutions are compared with the existing works in terms of complexity and performance. The results show that our proposed algorithms achieve much better performance in terms of total cost than the trivial solutions

**CONCLUSION: **

This paper presented a framework for scheduling the home appliances in limited scheduling hours with lower computational complexity such that the overall cost is minimized without disturbing the operation of non-scheduleable devices. Subject to per time slot energy constraint and the non interruption to real time appliances, we formulated an optimization problem. For tractability of solution, the mixed integer programming problem is decomposed into two subproblems and efficient solutions were proposed to find the optimal as well as low-complex schemes. The simulation results show that the proposed optimal solution yields significant improved performance as compared to the existing methods and the solution without optimization. On the other hand, the low complexity Dijkstra based solution also provides much better performance and is always very near to the optimal scheme. Lastly, we design a suboptimal scheme that provides considerable gain but having complexity as low as the non-optimized solution.

**REFERENCES:**

[1] A. Ipakchi and F. Albuyeh, “Grid of the future,” IEEE Power Energy Mag., vol. 7, no. 2, pp. 52–62, Mar./Apr. 2009.

[2] D. S. Kirschen, “Demand-side view of electricity markets,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 520–527, May 2003.

[3] M. Inoue, T. Higuma, Y. Ito, N. Kushiro, and H. Kubota, “Network architecture for home energy management system,” IEEE Trans. Consum. Electron., vol. 49, no. 3, pp. 606–613, Aug. 2003.

[4] S. Tiptipakorn and W.-J. Lee, “A residential consumer-centered load control strategy in real-time electricity pricing environment,” in Proc. IEEE 39th North Amer. Power Symp., Las Cruces, NM, USA, 2007, pp. 505–510.

[5] Z. Zhao, W. C. Lee, Y. Shin, and K.-B. Song, “An optimal power scheduling method for demand response in home energy management system,” IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 1391–1400, Sep. 2013.

[6] A.-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, Sep. 2010.

[7] M. Shinwari, A. Youssef, and W. Hamouda, “A water-filling based scheduling algorithm for the smart grid,” IEEE Trans. Smart Grid, vol. 3, no. 2, pp. 710–719, Jun. 2012.

[8] A.-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 Trans. Smart Grid, vol. 1, no. 3, pp. 320–331, Dec. 2010.

[9] M. Kai, G. Hu, and C. J. Spanos, “Energy consumption scheduling in smart grid: A non-cooperative game approach,” in Proc. IEEE 9th Asian Control Conf. (ASCC), Istanbul, Turkey, 2013, pp. 1–6.

[10] A. Imamura et al., “Distributed demand scheduling method to reduce energy cost in smart grid,” in Proc. IEEE Human. Technol. Conf. (R10-HTC), Sendai, Japan, 2013, pp. 148–153.