Optimal Threshold Policy for In-Home Smart Grid with Renewable Generation Integration

In-home Smart Grid (SG), the integration of Renewable Power Systems (RPSs) with Conventional Power Systems (CPSs), calls for cost-effective management for the electricity usages of end users' household appliances. In this paper, by taking the charging process of RPSs and multiple types of household appliances in to consideration, we have developed analytical models to characterize the electricity cost in the in-home smart grid. Based on these models, we formulate the electricity cost minimization problem as a finite-horizon continuous-time Markov decision process (CTMDP), from which we obtain a threshold policy to minimize the cost. Numerical results show that the threshold policy can manage the electricity usage very effectively.

[1]  W. Marsden I and J , 2012 .

[2]  H. Vincent Poor,et al.  Scheduling Power Consumption With Price Uncertainty , 2011, IEEE Transactions on Smart Grid.

[3]  Leandros Tassiulas,et al.  Optimal Control Policies for Power Demand Scheduling in the Smart Grid , 2012, IEEE Journal on Selected Areas in Communications.

[4]  Miao Pan,et al.  Optimal Power Management of Residential Customers in the Smart Grid , 2012, IEEE Transactions on Parallel and Distributed Systems.

[5]  Phone Lin,et al.  Implementation and performance evaluation for mobility management of a wireless PBX network , 2001, IEEE J. Sel. Areas Commun..

[6]  Anna Scaglione,et al.  From Packet to Power Switching: Digital Direct Load Scheduling , 2012, IEEE Journal on Selected Areas in Communications.

[7]  Miao He,et al.  Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration , 2011, 2011 Proceedings IEEE INFOCOM.

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

[9]  Yuguang Fang,et al.  Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage , 2014, IEEE Transactions on Parallel and Distributed Systems.

[10]  Xianping Guo,et al.  Continuous-Time Markov Decision Processes: Theory and Applications , 2009 .

[11]  Shing-Chow Chan,et al.  Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing , 2012, IEEE Transactions on Smart Grid.

[12]  Eric Allender,et al.  Complexity of finite-horizon Markov decision process problems , 2000, JACM.

[13]  F. Schweppe,et al.  Real Time Pricing to Assist in Load Frequency Control , 1989, IEEE Power Engineering Review.

[14]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[15]  Dusit Niyato,et al.  Optimizations of Power Consumption and Supply in the Smart Grid: Analysis of the Impact of Data Communication Reliability , 2013, IEEE Transactions on Smart Grid.

[16]  Ekram Hossain,et al.  Reliability analysis and redundancy design of smart grid wireless communications system for demand side management , 2012, IEEE Wireless Communications.

[17]  Xianping Guo,et al.  Continuous-Time Markov Decision Processes: Theory and Applications , 2009 .

[18]  Sehyun Park,et al.  Intelligent cloud home energy management system using household appliance priority based scheduling based on prediction of renewable energy capability , 2012, IEEE Transactions on Consumer Electronics.

[19]  Jiming Chen,et al.  Sensing-Performance Tradeoff in Cognitive Radio Enabled Smart Grid , 2013, IEEE Transactions on Smart Grid.

[20]  Miao Pan,et al.  Decentralized Coordination of Energy Utilization for Residential Households in the Smart Grid , 2013, IEEE Transactions on Smart Grid.

[21]  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.