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Model-Based Control of Flexible and Responsive Buildings

Model-Based Control of Flexible and Responsive Buildings. With Application to Desiccant Dehumidification Systems Masters Thesis Proposal Brian Coffey. Contents. Introduction and Overview Precedents / Literature Review Detailed Problem Definition Methodology

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Model-Based Control of Flexible and Responsive Buildings

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  1. Model-Based Control of Flexible and Responsive Buildings With Application to Desiccant Dehumidification Systems Masters Thesis Proposal Brian Coffey

  2. Contents • Introduction and Overview • Precedents / Literature Review • Detailed Problem Definition • Methodology • Simple Case Study for Concept Development • Detailed Case Studies • Summary Contents

  3. Flexible and Responsive Outdoor Enviro • Change the building system to suit the constantly changing environment and user desires Building System Occupant’s Desired Enviro Introduction and Overview http://kdg.mit.edu/Projects/p07.html

  4. Responding to What? • Weather • Signals from Occupants • Electricity and Gas Prices • Signals from an Electrical Grid Manager Why? • Maximize Comfort • Minimize Energy Consumption or Energy Costs • Minimize Cost or Dissatisfaction While Avoiding Blackouts Introduction and Overview

  5. Outdoor Enviro Building System Input Signals Control Unit Output to Control System Rule-Based Control Unit …if outdoorTemp > 25C and timeOfDay > 11am then Close Blinds if priceElectricity < 5 c/kWh then Turn Off Microturbine … vs Model-Based Control Unit conditions are {C}, possible building states are {S1, S2, S3 …} use model to test possible states under given conditions choose the building state that uses the least energy Introduction and Overview

  6. Examples of Model-Based • Solar Shading • Technical University of Vienna • HVAC Control • EFPL’s NEUROBAT • Michael Kummert’s work • Integrated System Control • EFPL’s EDIFICIO Precedents / Literature Review

  7. Examples of Model-Based • Solar Shading (TU Vienna) • EDIFICIO (EPFL) Precedents / Literature Review

  8. System Description Integrated Control Unit Output Signals Input Signals Building Control System External Conditions Building Detailed Problem Definition

  9. System Description • n elements, {x1,x2,x3, … xn} • xi has mi possible states, {si1,si2,si3, … sim} • possible system states M = mi • at the beginning of every time interval t, integrated control unit must determine the system state S for that time period, S = {s1?, s2?,s3?, … sn?} n i=1 Detailed Problem Definition

  10. Optimization Problem At a particular time t (where t = integer multiple of t) Given input signals (weather, energy costs, etc.) C = {c1, c2, c3, … cl} Determine S to minimize Z = f(C,S) Detailed Problem Definition

  11. Optimization Approach • f(C,S) ~ approximated by simulation • If (M*simulationTime < t), then • all the possible states can be tested with a simulation, and the state S which results in the lowest Z is chosen • Else • Only some of the the states can be tested, so which ones to test? Detailed Problem Definition

  12. Key Considerations • Running simulations for more than just current timestep • Cost of configuration change • Transient effects, energy storage Detailed Problem Definition

  13. Study Methodology Develop a control function in Matlab to act as the integrated control unit Make it general. Allow it to collect inputs, interface with TRNSYS models, use appropriate optimization strategy, find S, send outputs Integrated Control Unit Output Signals Input Signals Methodology

  14. Case Studies • Use a simple case study to develop and test the Matlab control module • Simplified Building (exists on paper only) • Then apply the method and module to more detailed case studies to further refine and test the approach • PWGSC Building (extant), with added cogeneration-desiccant system (not extant) • LEPTAB Building with solar-desiccant system in Chambery (extant) • Other possibilities • building-in-a-box, Annex20 Methodology

  15. Simplified Building Methodology: Simple Case Study

  16. Simplified Building • 2 elements, {x1,x2} • x1 has 3 possible states, x2 has 2 • possible system states M = 6 • t = 1 hr • integrated control unit must determine the system state S for that time period, S = {s1?, s2?} Methodology: Simple Case Study

  17. Simplified Building • Input signals C = {c1, c2, c3, … cl} • c1 = Toutdoor • c2 = Tindoor • c3 = solar radiation on south wall • c4 = electrical load • c5 = number of occupants in space • etc... • Find S to minimize Z = f(C,S) • Z = Total energy consumption • f(C,S) calculated by simulation Methodology: Simple Case Study

  18. Simplified Building Text Model Shading Input Window Input TRNSED Model Output Example Results Methodology: Simple Case Study

  19. Simplified Building • Determines what configurations to test • Runs simulation for each configuration • Modifies Input Files (txt files) • Calls TRNSYS • Reads Output Files (txt files) • Compares outputs, chooses best • Integrated Control Unit (Matlab) Integrated Control Unit Output Signals Input Signals Methodology: Simple Case Study

  20. Detailed Case Studies • PWGSC Building for Responsive Buildings Project • LEPTAB Building with Solar-Desiccant System • Other possibilities Methodology: Detailed Case Studies

  21. PWGSC Buildingwith Cogeneration-Desiccant Responsive Buildings Project Methodology: Detailed Case Studies

  22. PWGSC Buildingwith Cogeneration-Desiccant • More than 5 elements, {x1,x2,x3, ...} • x1 = generator dispatch level • x2 = desiccant dispatch level • x3 = room temperature setpoint • x4 = room humidity setpoint • x5 = light dimming control • etc... • Each xi has numerous possible states • t = 1 hr? 15 min? 5 min? • Inputs C will depend on building system • Minimize Z = Energy Cost ? Methodology: Detailed Case Studies

  23. LEPTAB Building with Solar-Desiccant System Methodology: Detailed Case Studies

  24. LEPTAB Building with Solar-Desiccant System • 4 operating modes (M=4) • Ventilation Mode (S1) • Direct Humidification (S2) • Indirect Humidif. (S3) • Desiccant Mode (S4) • Currently rule-based control if (time>9AM) & (TF-TE>1C) if (Troom>26C) Desiccant Mode else Indirect Humid. … Methodology: Detailed Case Studies

  25. Other possible case studies • ‘Building in a box’ • Controls researchers at PWGSC have a box that tests actual building control systems, but with the control signals going to and from computers instead of a building and occupants • Other buildings with desiccant systems in Germany that have already been modeled extensively, and for which I can get lots of data (part of IEA Annex 20 – Sustainable Cooling with TES) Methodology: Detailed Case Studies

  26. Expected Contributions • Codification of model-based approach • Application to desiccant systems • Consideration of future timesteps in determining state at current timestep Summary

  27. Expected Challenges • Dealing effectively with future states • Performance Mapping or ANN creation for buildings under consideration • TRNSYS modeling of desiccant systems Summary

  28. ?? Questions? Comments? Feedback? Thank You

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