Consumer Preference Electricity Usage Plan for Demand Side Management in the Smart Grid

High peak demand is often a challenge to the grid and could result into measures such as procurement of additional plants to meet the peak demand, higher tariffs for consumers, undesirable load shedding or even black-outs. However, these issues can be mitigated by introducing Demand Side Management (DSM) techniques for effective energy management of consumers’ peak demand. In this paper, an enhanced Device Operation Knowledge - Electricity Usage Plan (DOK-EUP) is proposed, which applies time independencies of selected smart home appliances for peak demand reduction based on their operation principles and for consumer's benefit. The proposed DOK-EUP technique was tested with the surveyed demand profile of a Time-of-Use (TOU) consumer and results showed lower morning and evening peak demands, lower peak-to-peak difference, shift in peak period to traditional off-peak periods, financial savings for the consumers and utility provider, and a cleaner environment.

[1]  Thillainathan Logenthiran,et al.  Demand Side Management in Smart Grid Using Heuristic Optimization , 2012, IEEE Transactions on Smart Grid.

[2]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[3]  Catherine Rosenberg,et al.  An analysis of peak demand reductions due to elasticity of domestic appliances , 2012, 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy).

[4]  K. Ouahada,et al.  Effective energy consumption scheduling in smart homes , 2015, AFRICON 2015.

[5]  Hendrik C. Ferreira,et al.  Wireless Sensor Networks and Advanced Metering Infrastructure Deployment in Smart Grid , 2013, AFRICOMM.

[6]  O. M. Longe,et al.  Time programmable smart devices for peak demand reduction of smart homes in a microgrid , 2014, 2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST).

[7]  George J. Pappas,et al.  Green scheduling of control systems for peak demand reduction , 2011, IEEE Conference on Decision and Control and European Control Conference.

[8]  Saifur Rahman,et al.  Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities , 2014, IEEE Transactions on Smart Grid.

[9]  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 .

[10]  Mihaela van der Schaar,et al.  Demand Side Management in Smart Grids Using a Repeated Game Framework , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Walid G. Morsi,et al.  A novel demand side management program using water heaters and particle swarm optimization , 2010, 2010 IEEE Electrical Power & Energy Conference.

[12]  M. A. Zehir,et al.  Demand Side Management potential of refrigerators with different energy classes , 2012, 2012 47th International Universities Power Engineering Conference (UPEC).

[13]  Zhu Han,et al.  Demand side management to reduce Peak-to-Average Ratio using game theory in smart grid , 2012, 2012 Proceedings IEEE INFOCOM Workshops.

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

[15]  Kenneth A. Loparo,et al.  Smart homes in Poland: Challenges and opportunities , 2014, ISGT 2014.

[16]  Youngwook Kim,et al.  Home appliance load disaggregation using cepstrum-smoothing-based method , 2015, IEEE Transactions on Consumer Electronics.