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Smart residential energy management system for demand response in buildings with energy storage devices 被引量:1

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摘要 In the present scenario,the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation.Demand side management(DSM)is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives.Consumers are expected to respond(demand response(DR))in various ways to attain these benefits.Nowadays,residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals.In this paper,the use of a smart residential energy management system(SREMS)is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances.Further,the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery(charging/floating/discharging)and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit(CCL).The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.
出处 《Frontiers in Energy》 SCIE CSCD 2019年第4期715-730,共16页 能源前沿(英文版)
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  • 1Wen L. The application of temporal pattern clustering algorithms in DSM. In: 2006 6th International Conference on Intelligent Systems Design and Applications. Jinan, China, 2006, 569 573. 被引量:1
  • 2Pedrasa A A, Spooner T D, MacGill I F. Scheduling of demand side resources using binary particle swarm optimization. IEEE Transac- tions on Power Systems, 2009, 24(3): 1173 1181. 被引量:1
  • 3C~rdenas J J, Garcia A, Romeral J L, Andrade F. A genetic algorithm approach to optimization of power peaks in an automated warehouse. In: Proceedings of the 35th IEEE Industrial Electronics Society Congress. IEEE Press, 2009, 32923302. 被引量:1
  • 4Bakker V, Bosman M G C, Molderink A, Hurink J L, Smit G J M. Demand side load management using a three step optimization methodology. In: 1st IEEE International Conference on Smart Grid Communications. Gaithersburg, USA, 2010, 431-436. 被引量:1
  • 5Samadi P, Mohsenian-Rad A H, Schober R, Wong V W S, Jatskevich J. Optimal real-time pricing algorithm based on utility maximization for smart grid. In: IEEE International Conference onSmart Grid Communications, Gaithersburg, USA, 2010, 415~420. 被引量:1
  • 6Logenthiran T, Srinivasan D, Shun T Z. Demand side management in smart grid using heuristic optimization. IEEE Transactions on Smart Grid, 2012, 3(3): 1244-1252. 被引量:1
  • 7Et-Tolba El H, Maaroufiand M, Ouassaid M. Demand side management algorithms and modeling in smart grids. In: 2013 International Renewable and Sustainable Energy Conference. Ouarzazate, Morocco, 2013, 531-536. 被引量:1
  • 8Fadlullah Z, Quan D M, Kato N, Stojmenovie I. GTES: an optimized game-theoretic demand side management scheme for smart grid. IEEE Systems Journal, 2014, 8(2): 588-597. 被引量:1
  • 9Mohsenian-Rad A H, Wong V W S, Jatskevich J, Schober R, Leon- Garcia A. Autonomous demand-side management based on game- theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 2010, 1(3): 320-331. 被引量:1
  • 10Mohsenian-Rad A H, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 2010, 1(2): 12~ 133. 被引量:1

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