Citation
Mehdi Maasoumy. "Controlling Energy-Efficient Buildings in the Context of Smart Grid: A Cyber-Physical System Approach". PhD thesis, University of California, Berkeley, December, 2013.

Abstract
The building sector is responsible for about 40% of energy consumption, 40% of greenhouse gas emissions, and 70% of electricity use in the US. Over 50% of the energy consumed in buildings is directly related to space heating, cooling and ventilation. Optimal control of heating, ventilation and air conditioning (HVAC) systems is crucial for reducing energy consumption in buildings. We present a physics-based mathematical model of thermal behavior of buildings, along with a novel Parameter Adaptive Building (PAB) model framework to update the model parameters, as new measurements arrive, to reduce the model uncertainties. We then present a Model Predictive Control (MPC), and a Robust Model Predictive Control (RMPC) algorithm and a methodology for selecting a controller type, i.e. RMPC or MPC, versus Rule Based Control (RBC) as a function of model uncertainty. We then address the Cyber-Physical" aspect of a building HVAC system in the design flow. We present a co-design framework that analyzes the interaction between the control algorithm and the embedded platform through a set of interface variables, and demonstrate how the design space is explored to optimize the energy cost and monetary cost, while satisfying the constraints for occupant comfort level. The last part of this dissertation is centered on the role of smart buildings in the context of the smart grid. Commercial buildings have inherent flexibility in how their HVAC systems consume electricity. We first propose a means to define and quantify the flexibility of a commercial building. We then present a contractual framework that could be used by the building operator and the utility company to declare flexibility on one side and reward structure on the other side. We also present a control mechanism for the building to decide its flexibility for the next contractual period to maximize the reward. We also present a Model Predictive Control (MPC) scheme to direct the ancillary service power flow from buildings to improve upon the classical Automatic Generation Control (AGC) practice. We show how constraints such as slow and fast ramping rates for various ancillary service providers, and short-term load forecast information can be integrated into the proposed MPC framework. Finally, results from at-scale experiments are presented to demonstrate the feasibility of the proposed algorithm.

Electronic downloads

• MehdiPhDthesis.pdf · application/pdf · 10878 kbytes

• HTML

  Mehdi Maasoumy. <a
  href="http://www.terraswarm.org/pubs/259.html"
  ><i>Controlling Energy-Efficient Buildings in the
  Context of Smart Grid: A Cyber-Physical System
  Approach</i></a>, PhD thesis,  University of
  California, Berkeley, December, 2013.

• Plain text

  Mehdi Maasoumy. "Controlling Energy-Efficient Buildings
  in the Context of Smart Grid: A Cyber-Physical System
  Approach". PhD thesis,  University of California,
  Berkeley, December, 2013.

• BibTeX

  @phdthesis{Maasoumy13_ControllingEnergyEfficientBuildingsInContextOfSmart,
      author = {Mehdi Maasoumy},
      title = {Controlling Energy-Efficient Buildings in the
                Context of Smart Grid: A Cyber-Physical System
                Approach},
      school = {University of California, Berkeley},
      month = {December},
      year = {2013},
      abstract = {The building sector is responsible for about 40%
                of energy consumption, 40% of greenhouse gas
                emissions, and 70% of electricity use in the US.
                Over 50% of the energy consumed in buildings is
                directly related to space heating, cooling and
                ventilation. Optimal control of heating,
                ventilation and air conditioning (HVAC) systems is
                crucial for reducing energy consumption in
                buildings. We present a physics-based mathematical
                model of thermal behavior of buildings, along with
                a novel Parameter Adaptive Building (PAB) model
                framework to update the model parameters, as new
                measurements arrive, to reduce the model
                uncertainties. We then present a Model Predictive
                Control (MPC), and a Robust Model Predictive
                Control (RMPC) algorithm and a methodology for
                selecting a controller type, i.e. RMPC or MPC,
                versus Rule Based Control (RBC) as a function of
                model uncertainty. We then address the
                Cyber-Physical" aspect of a building HVAC system
                in the design flow. We present a co-design
                framework that analyzes the interaction between
                the control algorithm and the embedded platform
                through a set of interface variables, and
                demonstrate how the design space is explored to
                optimize the energy cost and monetary cost, while
                satisfying the constraints for occupant comfort
                level. The last part of this dissertation is
                centered on the role of smart buildings in the
                context of the smart grid. Commercial buildings
                have inherent flexibility in how their HVAC
                systems consume electricity. We first propose a
                means to define and quantify the flexibility of a
                commercial building. We then present a contractual
                framework that could be used by the building
                operator and the utility company to declare
                flexibility on one side and reward structure on
                the other side. We also present a control
                mechanism for the building to decide its
                flexibility for the next contractual period to
                maximize the reward. We also present a Model
                Predictive Control (MPC) scheme to direct the
                ancillary service power flow from buildings to
                improve upon the classical Automatic Generation
                Control (AGC) practice. We show how constraints
                such as slow and fast ramping rates for various
                ancillary service providers, and short-term load
                forecast information can be integrated into the
                proposed MPC framework. Finally, results from
                at-scale experiments are presented to demonstrate
                the feasibility of the proposed algorithm. },
      URL = {http://terraswarm.org/pubs/259.html}
  }
  

Posted by Mehdi Maasoumy on 10 Feb 2014.