Cost reduction and peak shaving through domestic load shifting and DERs

With the development of home area network, residents have the opportunity to schedule their power usage at the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, an energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented that considers both energy consumption and generation simultaneously. The proposed model arranges the household electrical and thermal appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. In this model, the home gateway receives the electricity price information as well as the resident desired options in order to efficiently schedule the appliances and shave the peak as well. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, PV panel and a battery over a 24-h period.

[1]  Mohammad Hassan Amirioun,et al.  A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex , 2014 .

[2]  Han-Lin Li,et al.  Approximately global optimization for assortment problems using piecewise linearization techniques , 2002, Eur. J. Oper. Res..

[3]  Ziyad M. Salameh,et al.  Optimum photovoltaic array size for a hybrid wind/PV system , 1994 .

[4]  淳 伊賀,et al.  太陽光発電システムの「月別温度係数」の特徴とその活用の具体化 , 2006 .

[5]  Shahram Jadid,et al.  Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS , 2015 .

[6]  Andrés Feijóo,et al.  Wind power distributions: A review of their applications , 2010 .

[7]  Ziyad M. Salameh,et al.  Photovoltaic module-site matching based on the capacity factors , 1995 .

[8]  Ryohei Yokoyama,et al.  A mixed-integer linear programming approach for cogeneration-based residential energy supply networks with power and heat interchanges , 2014 .

[9]  Sarvapali D. Ramchurn,et al.  Agent-based homeostatic control for green energy in the smart grid , 2011, TIST.

[10]  Gang Xiong,et al.  Smart (in-home) power scheduling for demand response on the smart grid , 2011, ISGT 2011.

[11]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[12]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[13]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[14]  Lazaros G. Papageorgiou,et al.  Efficient energy consumption and operation management in a smart building with microgrid , 2013 .

[15]  I. E. Lane,et al.  A load model to support demand management decisions on domestic storage water heater control strategy , 1996 .

[16]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[17]  Lotfi Krichen,et al.  A dynamic power management strategy of a grid connected hybrid generation system using wind, photovoltaic and Flywheel Energy Storage System in residential applications , 2014 .

[18]  Qing-Shan Jia,et al.  Energy-Efficient Buildings Facilitated by Microgrid , 2010, IEEE Transactions on Smart Grid.

[19]  F. Y. Ettoumi,et al.  Statistical analysis of solar measurements in Algeria using beta distributions , 2002 .

[20]  Seung Ho Hong,et al.  User-expected price-based demand response algorithm for a home-to-grid system , 2014 .

[21]  P. Kriett,et al.  Optimal control of a residential microgrid , 2012 .

[22]  Christos S. Ioakimidis,et al.  Design, architecture and implementation of a residential energy box management tool in a SmartGrid , 2014 .

[23]  Peter B. Luh,et al.  An integrated control of shading blinds, natural ventilation, and HVAC systems for energy saving and human comfort , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[24]  Shahram Jadid,et al.  Optimal joint scheduling of electrical and thermal appliances in a smart home environment , 2015 .

[25]  Wu Jie,et al.  A multi-agent solution to energy management in hybrid renewable energy generation system , 2011 .

[26]  Sean P. Meyn,et al.  Building thermal model reduction via aggregation of states , 2010, Proceedings of the 2010 American Control Conference.