Real-Time Opportunistic Scheduling for Residential Demand Response

Demand response is a key feature of the smart grid. The addition of bidirectional communication to today's power grid can provide real-time pricing (RTP) to customers via smart meters. A growing number of appliance companies have started to design and produce smart appliances which embed intelligent control modules to implement residential demand response based on RTP. However, most of the current residential load scheduling schemes are centralized and based on either day-ahead pricing (DAP) or predicted price, which can deviate significantly from the RTP. In this paper, we propose an opportunistic scheduling scheme based on the optimal stopping rule as a real-time distributed scheduling algorithm for smart appliances' automation control. It determines the best time for appliances' operation to balance electricity bill reduction and inconvenience resulting from the operation delay. It is shown that our scheme is a distributed threshold policy when no constraint is considered. When a total power constraint exists, the proposed scheduling algorithm can be implemented in either a centralized or distributed fashion. Our scheme has low complexity and can be easily implemented. Simulation results validate proposed scheduling scheme shifts the operation to off-peak times and consequently leads to significant electricity bill saving with reasonable waiting time.

[1]  D. Kirschen Demand-side view of electricity markets , 2003 .

[2]  T Joseph Lui,et al.  Get Smart , 2010, IEEE Power and Energy Magazine.

[3]  T. Hill Knowing When to Stop: , 2021, The Best Writing on Mathematics 2010.

[4]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[5]  Javier Contreras,et al.  Price maker self-scheduling in a pool-based electricity market: a mixed-integer LP approach , 2002 .

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

[7]  G.R. Yousefi,et al.  Demand Response model considering EDRP and TOU programs , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[8]  G. J. Delport,et al.  Scheduling of cogeneration facilities operating under the real-time pricing agreement , 1998, IEEE International Symposium on Industrial Electronics. Proceedings. ISIE'98 (Cat. No.98TH8357).

[9]  Hiroshi Asano,et al.  Impacts of time-of-use rates on the optimal sizing and operation of cogeneration systems , 1992 .

[10]  Dominik Möst,et al.  Scheduling of Electrical Household Appliances with Price Signals , 2006, OR.

[11]  W. Rosehart,et al.  A survey of load control programs for price and system stability , 2005, IEEE Transactions on Power Systems.

[12]  S. Braithwait,et al.  THE ROLE OF DEMAND RESPONSE IN ELECTRIC POWER MARKET DESIGN , 2002 .

[13]  Albert N. Shiryaev,et al.  Optimal Stopping Rules , 1980, International Encyclopedia of Statistical Science.

[14]  Chi Zhou,et al.  Distributed Opportunistic Scheduling in Power Systems – An Optimal Stopping Approach , 2011 .

[15]  B. Daryanian,et al.  Optimal Demand-Side Response to Electricity Spot Prices for Storage-Type Customers , 1989, IEEE Power Engineering Review.

[16]  J. Contreras,et al.  Price-Maker Self-Scheduling in a Pool-Based Electricity Market: A Mixed-Integer LP Approach , 2002, IEEE Power Engineering Review.