Modeling, Simulation and Control of Smart and Connected Communities

This study attempts to establish the need for a framework to assess the impact of connected buildings in a smart community. The contribution is a software framework designed to optimize buildings and grids at a district level. The following research products are developed: (1) An innovative method to model a cluster of buildings—with people’s behavior embedded in the cluster’s dynamics— and their controls so that they can be integrated with grid operation and services; (2) a novel optimization framework to solve complex, centralized control problems for large-scale systems, leveraging convex programming approaches; and (3) a methodology to assess the impacts of connected buildings in terms of (a) the grid’s operational stability and safety and (b) buildings’ optimized energy consumption. To test the proposed framework, a large-scale simulation of a subtransmission network with three power generating stations and serving over 300 artificial buildings is conducted.

[1]  Nikolaos Gatsis,et al.  A stochastic approximation approach to load shedding inpower networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Georgios B. Giannakis,et al.  Decomposition Algorithms for Market Clearing With Large-Scale Demand Response , 2013, IEEE Transactions on Smart Grid.

[3]  Anna Laura Pisello,et al.  A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy , 2012 .

[4]  Nikolaos Gatsis,et al.  Decentralized Stochastic Optimal Power Flow in Radial Networks With Distributed Generation , 2016, IEEE Transactions on Smart Grid.

[5]  Mehdi Maasoumy,et al.  Comparison of control strategies for energy efficient building HVAC systems , 2014, ANSS 2014.

[6]  Mariacristina Roscia,et al.  Definition methodology for the smart cities model , 2012 .

[7]  Jitesh H. Panchal,et al.  Decentralized Control Framework and Stability Analysis for Networked Control Systems , 2015 .

[8]  David H. Blum,et al.  DYNAMIC SIMULATION OF REGULATION DEMAND RESPONSE BY VAV HVAC SYSTEMS , 2014 .

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

[10]  Peng Zhao,et al.  Dynamic frequency regulation resources of commercial buildings through combined building system resources using a supervisory control methodology , 2015 .

[11]  Charles E. Fosha,et al.  Optimum Megawatt-Frequency Control of Multiarea Electric Energy Systems , 1970 .

[12]  Zheng O'Neill,et al.  Development and calibration of an online energy model for campus buildings , 2014 .

[13]  Physics-Based Models , 2009, Encyclopedia of Biometrics.

[14]  Georgios B. Giannakis,et al.  Disaggregated bundle methods for distributed market clearing in power networks , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[15]  Christoph F. Reinhart,et al.  Lightswitch-2002: a model for manual and automated control of electric lighting and blinds , 2004 .

[16]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[17]  Joshua A. Taylor.  Convex Optimization of Power Systems , 2015 .

[18]  Vijay Modi,et al.  Spatial distribution of urban building energy consumption by end use , 2012 .

[19]  Charles E. Fosha,et al.  A New Approach Via OptimalControlTheory , 1970 .

[20]  Thierry S. Nouidui,et al.  Cyber–Physical Modeling of Distributed Resources for Distribution System Operations , 2015, Proceedings of the IEEE.

[21]  Francesco Borrelli,et al.  Model Predictive Control of thermal energy storage in building cooling systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[22]  Jitesh H. Panchal,et al.  Unknown input observer design and analysis for networked control systems , 2015, Int. J. Control.

[23]  Shem Heiple,et al.  Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles , 2008 .