Abstract
Integration of Demand Side Management (DSM) strategies within Smart Grid (SG) helps the utilities to mange and control the power consumer load to meet the power demand. Schemes adapted by DSM are used for reducing the load on utilities at peak time, which is achieved by managing the user appliances according to the changes in load on utility and individual smart home. This work is focused on hourly scheduling of the appliances being used in a smart home targeting the daily electricity cost minimization. A new heuristic scheme is introduced for hourly appliances scheduling on user side in this paper. The proposed scheme works at the electricity management controller level, installed in a smart home, within a SG infrastructure. The proposed scheme results are compared with other heuristic schemes as well. From extensive simulations it is depicted that proposed scheme performs best and outperforms other schemes in term of electricity cost minimization.
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References
U.S. Energy Information Administration, Annual Energy Outlook 2018, pp. 121, 122, Residential and commercial energy consumption grows gradually from 2017 to 2050. https://www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf. Accessed Oct 2018
Luo, F., Ranzi, G., Wan, C., Xu, Z., Dong, Z.Y.: A multi-stage home energy management system with residential photovoltaic penetration. IEEE Trans. Ind. Inform. 15(1), 116–126 (2018)
Gungor, V.C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., Hancke, G.P.: Smart grid and smart homes: key players and pilot projects. IEEE Ind. Electron. Mag. 6(4), 18–34 (2012)
Huang, H., Cai, Y., Xu, H., Yu, H.: A multi-agent minority-game- based demand-response management of smart buildings towards peak load reduction. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 36(4), 573–585 (2017)
Ruelens, F., Claessens, B.J., Vandael, S., De Schutter, B., Babuška, R., Belmans, R.: Residential demand response of thermostatically controlled loads using batch reinforcement learning. IEEE Trans. Smart Grid 8(5), 2149–2159 (2017)
Pourmousavi, S.A., Patrick, S.N., Nehrir, M.H.: Real-time demand response through aggregate electric water heaters for load shifting and balancing wind generation. IEEE Trans. Smart Grid 5(2), 769–778 (2014)
Sakurama, K., Miura, M.: Communication-based decentralized demand response for smart microgrids. IEEE Trans. Ind. Electron. 64(6), 5192–5202 (2017)
Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., Mishra, S.: Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Trans. Ind. Informat. 12(3), 1005–1016 (2016)
Mirowski, P., Chen, S., Kam Ho, T., Yu, C.N.: Demand forecasting in smart grids. Bell Labs Tech. J. 18(4), 135–158 (2014)
Wu, Z., Zhao, T., He, L., Shen, X.: Smart grid meter analytics for revenue protection. In: Proceedings of the IEEE International Conference on Power System Technology, pp. 782–787 (2014)
Jindal, A., Kumar, N., Singh, M.: A data analytical approach using support vector machine for demand response management in smart grid. In: 2016 IEEE Power and Energy Society General Meeting (PESGM) 17–21 July, pp. 1–5 (2016)
Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.-M.: Optimized clusters for disaggregated electricity load forecasting. REVSTAT 8, 105–124 (2010)
Alzate, C., Sinn, M.: Improved electricity load forecasting via kernel spectral clustering of smart meters. In IEEE 13th International Conference on Data Mining (ICDM), pp. 943–948 (2013)
Wijaya, T.K., Vasirani, M., Humeau, S., Aberer, K.: Cluster-based aggregate forecasting for residential electricity demand using smart meter data. In: IEEE International Conference on Big Data (Big Data)
Jindal, A., Singh, M., Kumar, N.: Consumption-aware data analytical demand response scheme for peak load reduction in smart grid. IEEE Trans. Ind. Electron. 65(11), 8993–9004 (2018)
Moon, J., Kim, K.-H., Kim, Y., Hwang, E.: A short-term electric load forecasting scheme using 2-stage predictive analytics. In: 2018 IEEE International Conference on Big Data and Smart Computing (2018)
Keles, D., Scelle, J., Paraschiv, F., Fichtner, W.: Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. Energy 162, 218–230 (2016)
Erol-Kantarci, M., Mouftah, H.T.: Energy-efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues. IEEE Commun. Surv. Tutor. 17(1), 179–197 (2015)
Huang, D., Zareipour, H., Rosehart, W.D., Amjady, N.: Data mining for electricity price classification and the application to demand-side management. IEEE Trans. Smart Grid 3(2), 808–817 (2012)
Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inform. Technol. Biomed. 14(2), 274–283 (2010)
Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT) (2012)
Maa, K., Hua, S., Yanga, J., Xua, X., Guanb, X.: Appliances scheduling via cooperative multi-swarm PSO underday-ahead prices and photovoltaic generation. Appl. Soft Comput. 62, 504–513 (2018)
Bazydło, G., Wermiński, S.: Demand side management through home area network systems. Electr. Power Eng. Syst. 97, 174–185 (2018)
Hong, S.H., Yu, M., Huang, X.: A real-time demand response algorithm for heterogeneous devices in buildings and homes. Energy 80, 123–132 (2015)
Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3, 1244–1252 (2012)
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Abbasi, R.A. et al. (2020). Minimizing Daily Electricity Cost Using Bird Chase Scheme with Electricity Management Controller in a Smart Home. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_7
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