Smart grid demand response management using internet of things for load shedding and smart-direct load control

This paper proposes the use of a novel algorithm for smart direct load control and load shedding to minimize the power outage in sudden grid load changes, as well as reduce the Peak-to-Average Ratio (PAR). The algorithm uses forecasting, shedding, and smart direct load control. The algorithm also uses the Internet of Things and stream analytics to provide real-time load control, and generates a daily schedule for customers' equipped with IEDs, based on their demands, comfort, and the forecasted load model. The demand response techniques are utilized for real time load control and optimization. To test the algorithm, a simulation system was developed that takes into account one hundred customers owning randomly selected appliances. The results indicated that load shedding using ARIMA time series prediction model and applying smart direct load control (S-DLC) and Internet of Things can remarkably reduce customers' power outage.

[1]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[2]  Georgios B. Giannakis,et al.  Residential Load Control: Distributed Scheduling and Convergence With Lost AMI Messages , 2012, IEEE Transactions on Smart Grid.

[3]  S. Maithel.  Energy Efficiency and Renewable Energy , 2008 .

[4]  S. A. Soliman,et al.  Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model , 2004 .

[5]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[6]  Luan Wen-peng.  Advanced Metering Infrastructure , 2009 .

[7]  Kun-Yuan Huang,et al.  Integrating direct load control with interruptible load management to provide instantaneous reserves for ancillary services , 2004, IEEE Transactions on Power Systems.

[8]  G. Donner,et al.  An intelligent load shedding (ILS) system application in a large industrial facility , 2005, Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005..

[9]  Amir Safdarian,et al.  A Distributed Algorithm for Managing Residential Demand Response in Smart Grids , 2014, IEEE Transactions on Industrial Informatics.

[10]  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.

[11]  G. Strbac,et al.  Decentralized Participation of Flexible Demand in Electricity Markets—Part I: Market Mechanism , 2013, IEEE Transactions on Power Systems.

[12]  Anna Scaglione,et al.  Real-Time Power Balancing Via Decentralized Coordinated Home Energy Scheduling , 2013, IEEE Transactions on Smart Grid.

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

[14]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[15]  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.

[16]  Prashant J. Shenoy,et al.  Predicting solar generation from weather forecasts using machine learning , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[17]  Daniel Pérez Palomar,et al.  Demand-Side Management via Distributed Energy Generation and Storage Optimization , 2013, IEEE Transactions on Smart Grid.

[18]  Phani Chavali,et al.  Parallel Load Schedule Optimization With Renewable Distributed Generators in Smart Grids , 2013, IEEE Transactions on Smart Grid.

[19]  Ujjwal Kumar,et al.  Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India , 2010 .

[20]  Kankar Bhattacharya,et al.  Competitive framework for procurement of interruptible load services , 2003 .

[21]  Yann-Chang Huang,et al.  Integrating direct load control with interruptible load management to provide instantaneous reserves for ancillary services , 2004 .

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