ENERGY MANAGEMENT SYSTEM FOR SMART GRID CONSUMERS WITH ADVANCED USAGE INFORMATION

This paper presents an online energy management system based on a virtual energy provisioning (VEP) concept. VEP is a novel demand side management inspired by the lean supply chain technology. It aims to control the process of energy generation, delivery, and consumption under the mechanism called “order-then-production” principle. This means electricity should be ordered by end consumers in advance before actually produced and delivered. The utility company, after aggregating consumers’ advance demand information, can decide an optimal generation and distribution scheme to meet the actual need. In this paper, the VEP principle and its impacts on smart grid systems will be discussed.

[1]  W. Charytoniuk,et al.  Neural network based demand forecasting in a deregulated environment , 1999, 1999 IEEE Industrial and Commercial Power Systems Technical Conference (Cat. No.99CH36371).

[2]  L. Tesfatsion,et al.  Modeling of Suppliers' Learning Behaviors in an Electricity Market Environment , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[3]  S. Ashok,et al.  Peak Load Management in Electrolytic Process Industries , 2008, IEEE Transactions on Power Systems.

[4]  James A. Momoh,et al.  Smart grid design for efficient and flexible power networks operation and control , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[5]  James Heidell,et al.  Is There a Case for Broadband Utility Communications Networks? Valuing and Pricing Incremental Communications Capacity on Electric Utility Smart Grid Networks , 2010 .

[6]  A. Feliachi,et al.  Effects of dynamic pricing on residential electricity bill , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[7]  Wei-Jen Lee,et al.  An AMI System for the Deregulated Electricity Markets , 2009 .

[8]  D.H.O. McQueen,et al.  Monte Carlo simulation of residential electricity demand for forecasting maximum demand on distribution networks , 2004, IEEE Transactions on Power Systems.

[9]  Josep M. Guerrero,et al.  Distributed energy resources in grid interactive AC microgrids , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.

[10]  E. S. Gopi,et al.  Probability And Random Process , 2007 .

[11]  M. Ostertag,et al.  High data rate, medium voltage powerline communications for hybrid DA/DSM , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).

[12]  Jin Tongdan Online Virtual Electricity Provisioning System and Its Preliminary Design , 2011 .

[13]  V. Vittal,et al.  A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption , 2008, IEEE Transactions on Power Systems.

[14]  J. Eto,et al.  Understanding the cost of power interruptions to U.S. electricity consumers , 2004 .

[15]  F. Alvarado,et al.  Designing incentive compatible contracts for effective demand management , 2000 .

[16]  P. McSharry,et al.  Probabilistic forecasts of the magnitude and timing of peak electricity demand , 2005, IEEE Transactions on Power Systems.

[17]  D. L. Pepyne,et al.  Gaming and Price Spikes in Electric Power Markets , 2001, IEEE Power Engineering Review.

[18]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[19]  L.H. Tsoukalas,et al.  From smart grids to an energy internet: Assumptions, architectures and requirements , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[20]  Tongdan Jin,et al.  Ordering Electricity via Internet and its Potentials for Smart Grid Systems , 2010, IEEE Transactions on Smart Grid.

[21]  Paras Mandal,et al.  Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[22]  B. Kouvaritakis,et al.  Gaming strategy for electric power with random demand , 2005, IEEE Transactions on Power Systems.

[23]  A. Kusko,et al.  Stored energy - Short-term and long-term energy storage methods , 2007, IEEE Industry Applications Magazine.