New Fast Demand Control of Building HVAC Facilities for Smart Grid

Real-time, fast demand control is thought to be promising technology to compensate for highly variable renewable energy sources for the smart grid of the future. Recent HVAC(Heating Ventilation and Air-Conditioning) facilities for buildings (hereinafter air-conditioning systems) use sophisticated refrigerant flow control devices and Mitsubishi Heavy Industries, Ltd. (MHI) would like to propose quick-response (approx. 5 minutes), fast demand control (hereinafter new fast demand control) to replace existing on-off and coarse slow demand controls.It is, however, cumbersome due to the great complexity in controlling building HVAC systems. As a solution to this, we considered the use of a numerical formula model for statistical prediction to calculate the amount of electricity need five minutes in the future. Operational testing using a numerical formula model successfully proved the effectiveness of new, fast demand control.

[1]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[2]  Shuaixun Chen,et al.  Sizing of energy storage for microgrids , 2012, 2012 IEEE Power and Energy Society General Meeting.

[3]  Mohammad Kazem Sheikh-El-Eslami,et al.  Investigation of Economic and Environmental-Driven Demand Response Measures Incorporating UC , 2012, IEEE Transactions on Smart Grid.

[4]  Jacob Østergaard,et al.  Controlling price-responsive heat pumps for overload elimination in distribution systems , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[5]  Christoph Grimm,et al.  A partially decentralised forecast-based demand-side-management approach , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

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

[7]  Hiroyuki Mori,et al.  Hybrid intelligent method of relevant vector machine and regression tree for probabilistic load forecasting , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[8]  Zhi Zhou,et al.  Agent-Based Electricity Market Simulation With Demand Response From Commercial Buildings , 2011, IEEE Transactions on Smart Grid.

[9]  Andrea Garulli,et al.  Load forecasting for active distribution networks , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[10]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[11]  Tomotaka Sato,et al.  Remote Monitoring Integrated State Variables for AR Model Prediction of Daily Total Building Air-Conditioning Power Consumption , 2010 .

[12]  Pamela MacDougall,et al.  Mitigation of wind power fluctuations by intelligent response of demand and distributed generation , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[13]  Jacob Østergaard,et al.  Information and Communications Systems for Control-by-Price of Distributed Energy Resources and Flexible Demand , 2011, IEEE Transactions on Smart Grid.

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

[15]  Masood Parvania,et al.  Demand Response Scheduling by Stochastic SCUC , 2010, IEEE Transactions on Smart Grid.

[16]  Farshid Keynia,et al.  Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy , 2010, IEEE Transactions on Smart Grid.

[17]  Tongdan Jin,et al.  Ordering electricity via Internet and Its potentials for smart grid systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[18]  Bruno Francois,et al.  Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications , 2011, IEEE Transactions on Industrial Electronics.

[19]  H. B. Gooi,et al.  Sizing of Energy Storage for Microgrids , 2012, IEEE Transactions on Smart Grid.