Overview of Model Predictive Control in Buildings Tony Kelman MPC Lab, Berkeley Mechanical Engineering Email: kelman@berkeley.edu TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAAAAAAAAAAAAAAAA

Outline Model predictive control Modeling and MPC in buildings Basic idea and elements Advantages, disadvantages Modeling and MPC in buildings What works, what doesn’t Generating models using historical data Advanced control behavior Experimental projects Success stories Increased scope and capabilities over time Introduce MPC context

Model Predictive Control System model – state evolution vs inputs and disturbances Constraints on inputs or states – requirements, actuator limits Cost function – reference tracking, energy, comfort Forecast trajectories of future disturbance inputs – weather, occupancy, utility rates Optimization algorithm – fast enough to solve in real time Use predictive knowledge for control Basic components System model Constraints on inputs or states Cost function Forecast trajectories of future disturbance inputs Optimization algorithm Advantages: multivariable, model based, nonlinear, constraint satisfaction, incorporates predictions Disadvantages: computational complexity, design effort of accurate modeling Additional info in grey for readability

Model Predictive Control Initialize with current measurements at time t Predict response over horizon of p steps Solve for best input sequence, apply first element u*(t) Repeat at time t+1 with new measurements (feedback)

Optimization Formulation Predicted states xk, inputs uk, disturbances wk At each time step, solve: Constrained finite time optimal control problem Optimization much faster if explicit structure of J, f, g (and derivatives) can be provided

Outline Model predictive control Modeling and MPC in buildings Basic idea and elements Advantages, disadvantages Modeling and MPC in buildings What works, what doesn’t Generating models using historical data Advanced control behavior Experimental projects Success stories Increased scope and capabilities over time

Modeling for Building Energy Systems Common practice is black-box simulation DOE2, EnergyPlus, TRNSYS, etc Useful for design, very difficult to use for control Derivative-free optimization not very efficient or scalable Need model structure for optimization and control Simpler approach: reduced order modeling Physics based model structure Data driven parameter identification Can adjust accuracy vs complexity tradeoff Large scale real time optimization tractable

HVAC Example System HVAC good target for energy savings by better control Common configuration for commercial buildings: VAV with reheat Control inputs: supply fan, cooling coil, heating coils, zone dampers, air handling unit dampers States: zone temperatures

Thermal Zone Model

Simplest Useful Model Abstraction Network of bilinear systems A (simple extension to multiple states per zone, RC network analog) Thermal zone model Static nonlinearities Equipment performance maps (chillers, cooling towers, pumps, fans, coils) Equality and inequality constraints Comfort range Dynamic coupling: thermal zones, supply air & return air Uncertain load predictions Human: occupancy, thermal comfort, … Environment: ambient temperature, solar radiation, …

How to Generate Reduced Models Several options to create model data Direct physics based lumped parameters Model reduction from high fidelity design tools Use historical data for model identification Identification results vs measured data, Bancroft library

Using Data to Quantify Uncertainty SMPC Prediction model Historical load realization Load Ambient temperature

Advanced Control Behavior A. Kelman, Y. Ma, A. Daly, F. Borrelli, Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments, IEEE Control System Magazine, 32(1), page 44-64, February 2012. Time-varying price Penalize peak power MPC is able to incorporate time-varying energy price and reduce peak power consumption

Outline Model predictive control Modeling and MPC in buildings Basic idea and elements Advantages, disadvantages Modeling and MPC in buildings What works, what doesn’t Generating models using historical data Advanced control behavior Experimental projects Success stories Increased scope and capabilities over time

Experimental Projects UC Merced –, Merced, CA 4% Improvement LBNL+UTRC- Storage, Chiller Optimization Horizon 24hrs, Sampling 30min Problem Size: ~300 variables , ~1440 constraints CERL Engineering Research Laboratory, Champaign, IL 15% improvement. UTRC- HVAC distribution – 5 zones Horizon 4hrs, Sampling 20 min, Problem Size: ~1600 variables , ~1400 constraints Naval Station Great Lakes, North Chicago, Illinois UTRC- Conversion + Storage – 250 zones Problem Size: ~~20k variables , ~?? constraints CITRIS Building (UC Berkeley) – Major issues Siemens - Generation + HVAC distribution -135 Zones Problem Size: ~~10k variables , ~?? constraints Brower Center (Slab Radiant), Berkeley, CA Architecture Department Models based on step tests experiments White Oak, Silver Spring, MD Honeywell Microgrid Optimization Simplified models and BLOM tool critical for real-time implementation of large MPC experiments

Distributed Implementation

Distributed Implementation Coordinator Dual variables Supply Fan Cooling coil damper Heating coil VAV damper Zone temperature