Application of Model Predictive Control for active load management in a distributed power system with high wind penetration

Summary form only given. This paper introduces an experimental platform (SYSLAB) for the research on advanced control and power system communication in distributed power systems and one of its components-an intelligent office building (PowerFlexHouse), which is used to investigate the technical potential for active load management. It also presents in detail how to implement a thermal Model Predictive Controller (MPC) for the heaters' power consumption prediction in the PowerFlexHouse. It demonstrates that this MPC strategy can realize load shifting, and using good predictions in MPC-based control, a better matching of demand and supply can be achieved. With this demand side control study, it is expected that MPC strategy for active load management can dramatically raise energy efficiency and improve grid reliability, when there is a high penetration of intermittent energy resources in the power system.

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

[2]  Gerard J. M. Smit,et al.  Management and Control of Domestic Smart Grid Technology , 2010, IEEE Transactions on Smart Grid.

[3]  Henrik W. Bindner,et al.  Active load management in an intelligent building using model predictive control strategy , 2011, 2011 IEEE Trondheim PowerTech.

[4]  Anders Thavlov,et al.  Model Predictive Control Strategy for a Load Management Research Facility in the Distributed Power System with High Wind Penetration -Towards a Danish Power System with 50% Wind Penetration , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[5]  Hanne Sæle,et al.  Demand Response From Household Customers: Experiences From a Pilot Study in Norway , 2011, IEEE Transactions on Smart Grid.

[6]  M. G. Pitman,et al.  Transport and Energy , 1976 .

[7]  Bart De Schutter,et al.  Adaptive prediction model accuracy in the control of residential energy resources , 2008, 2008 IEEE International Conference on Control Applications.

[8]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[9]  Qing-Shan Jia,et al.  Energy-Efficient Buildings Facilitated by Microgrid , 2010, IEEE Transactions on Smart Grid.

[10]  M Morari,et al.  Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions , 2010, Proceedings of the 2010 American Control Conference.

[11]  Ronald Soeterboek,et al.  Predictive Control: A Unified Approach , 1992 .

[12]  Riccardo Scattolini,et al.  Architectures for distributed and hierarchical Model Predictive Control - A review , 2009 .

[13]  Manfred Morari,et al.  Reducing peak electricity demand in building climate control using real-time pricing and model predictive control , 2010, 49th IEEE Conference on Decision and Control (CDC).

[14]  John Bagterp Jørgensen,et al.  The potential of Economic MPC for power management , 2010, 49th IEEE Conference on Decision and Control (CDC).

[15]  Philip Haves,et al.  Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.

[16]  Jose Medina,et al.  Demand Response and Distribution Grid Operations: Opportunities and Challenges , 2010, IEEE Transactions on Smart Grid.

[17]  Birgitte Bak-Jensen,et al.  The Relationship Between Electricity Price and Wind Power Generation in Danish Electricity Markets , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[18]  Anders Thavlov.  Dynamic Optimization of Power Consumption , 2008 .

[19]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.