Promoting peak shaving while minimizing electricity consumption payment for residential consumers by using storage devices

Promoting peak shaving while minimizing electricity consumption payment for residential consumers by using storage devices

Nowadays, smart meters, sensors, and advanced electricity tariff mechanisms such as time-of-use (ToU), critical peak pricing tariff, and real time tariff enable electricity consumption optimization for residential consumers. The main scope of such mechanisms is to promote peak shaving, which leads to minimization of technical losses and avoidance (or delay) of grid onerous investments. This paper proposes a method to determine the optimum capacity of a storage device (SD) that signi cantly contributes to peak shaving of electricity consumption for residential consumers. Detailed modelling of diverse electric appliances' behavior and consumers' necessities is addressed in order to determine the optimum capacity of the SD. The effects of a small scale photovoltaic panel (PV) owned by residential consumers are also analyzed.

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  • [1] Liu T, Chen S, Liu Y, Xu Z, Che Y, Duan Y. SHE: smart home energy management system for appliance identi cation and personalized scheduling. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing; 13{17 September 2014; Seattle, WA, USA. New York, NY, USA: ACM. pp. 247-250.
  • [2] Paunescu C, Toma L, Bulac C, Eremia M. Energy management system in the smart home. In: The International Conference on Energy and Environment; 7{8 November 2013; Bucharest, Romania. Bucharest, Romania: UPB. pp. 1-8.
  • [3] Zhi C, Lei W, Yong F. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE T Smart Grid 2012; 3: 1949-3053.
  • [4] Zhi C, Lei W. Residential appliance DR energy management with electric privacy protection by online stochastic optimization. IEEE T Smart Grid 2013; 4: 1949-3053.
  • [5] Yang X, Guo S, Yang HT. The establishment of energy consumption optimization model based on genetic algorithm. In: IEEE Int Conf on Automation and Logistics; 1{3 September 2008; Chindao, China. New York, NY, USA: IEEE. pp. 1426-1431.
  • [6] Omari M, Abdelkarim H, Salem B. Optimization of energy consumption based on genetic algorithms optimization and fuzzy classi cation. In: 2nd World Symposium on Web Applications and Networking; 21{23 March 2015; Sousse, Tunisia. New York, NY, USA: IEEE. pp. 1-4.
  • [7] Mohsenian-Rad AH, Garcia AL. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE T Smart Grid 2010; 1: 1949-3053.
  • [8] Zakeria G, Craigieb D, Philpotta A, Toddc M. Optimization of demand response through peak shaving. Oper Res Lett 2014; 42: 0167-6377.
  • [9] Al-Saedi FAT. Peak shaving energy management system for smart house. International Journal of Computer Science Engineering and Technology 2013; 3: 2231-0711.
  • [10] Castillo-Cagigala M, Caama~no-Martnb E, Matallanasa E, Masa-Boteb D, Gutierreza A, Monasterio-Huelina F, Jimenez-Leubea J. PV self-consumption optimization with storage and Active DSM for the residential sector; Sol Energy 2011; 85: 0038-092X.
  • [11] Berges M, Rowe A. Appliance classi cation and energy management using multi-modal sensing. In: BuildSys-3rd ACM Workshop on Embedded Sensing System for Energy-Efficiency in Buildings; 1{4 November 2011; Seattle, WA, USA. New York, NY, USA: ACM. pp. 1-2.
  • [12] B^ara A, Oprea SV. Informatics solutions for data processing of electricity consumption optimization in smart grids. In: International Conference Risk in Contemporary Economy; 19{20 May 2016; Galati, Romania. Galati, Romania: UGAL. pp. 118-123.
  • [13] Willis HL. Power Distribution Planning Reference Book - Revised and Expanded. 2nd ed. New York, NY, USA: CRC Press, 2004.
  • [14] Nissen G. Cost Reduction Opportunities in Local Distribution Grids with Demand Response. MSc, Uppsala University, Uppsala, Sweden, 2010.
  • [15] Oprea SV. Informatics solutions for electricity consumption optimization. In: IEEE 16th International Symposium on Computational Intelligence and Informatics; 19{21 November 2015; Budapest, Hungary. New York, NY, USA: IEEE. pp. 193-198.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: 6
  • Yayıncı: TÜBİTAK
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