Model-based control through co-simulation for intelligent Building Energy Management Systems design

Transcription

1 2013 Central European Symposium on Building Physics Model-based control through co-simulation for intelligent Building Energy Management Systems design Dr. Dimitrios V. Rovas Assistant Professor, Technical University of Crete, Greece Systems Integration Group, Fraunhofer Institute for Building Physics, Germany

2 Acknowledgments Paper Co-Authors G. I. Giannakis (TUC) G. D. Kontes (TUC) K. I. Katsigarakis (TUC) D. V. Rovas (TUC/FIBP) Work partially supported by the FP7 ICT Project PEBBLE: Positive Energy Buildings thru Better control decisions 2

3 Introduction Facts: Buildings are one of the major energy consumers world-wide; Most of that energy is used to foster comfortable internal conditions for occupants. Goal: Reduce energy consumption and/or improve comfort through efficient Building Energy Management System (BEMS) design. 3

4 Test case TUC Building Location and type of use: University Campus of Technical University of Crete; Technical Support Services office building. Weather Conditions: Hot summers; Humid winters; Long periods of sunlight. 4

5 Test case Objective PEBBLE System Objective: Minimize Energy Consumption; Maintain acceptable (thermal) comfort levels for the users. Methodology: Intelligent Control Decisions; ICT Solution. 5

6 Methodology Overview Properties Model-assisted Control in buildings Use a thermal model of the building Use weather and occupancy predictions Re-design the controller in pre-defined intervals Utilize a fast optimization algorithm with good generalization properties 6

7 Methodology Simulation Model Model design: EnergyPlus; Detailed thermal model; 19 thermal zones; External shading groups included; Natural ventilation; Activity data imported; HVAC system modeled. 10

8 Methodology Simulation Model Model validation Sensor Measurements occupancy windows operation thermostat setpoints Simulation Results temperature humidity consumption Sensor Measurements temperature humidity consumption 11

9 Methodology Simulation Model Initial thermal state of the building warming-up process Historical Sensor Measurements Perform warming-up before each simulation run Ability to use ideal system for speedup Number of warming-up days depend on the building construction and the simulation times 12

10 Methodology Optimization algorithm CAO-C: Stochastic optimization algorithm; Local optimization; Few iterations; Evaluate candidate controllers on simulation model. 13

11 Methodology Co-Simulation Co-Simulation Warning-up; Forecasts; Dynamical actuating schedules (controller). Weather Data Sensors Measurements Weather Data Sensors Measurements Thermal Model Warming-Up Phase Past Data BCVTB Warming-Up Phase Control Actions Forecast Phase Control Actions Optimization Algorithm Forecast Phase Thermal Comfort Energy Consumption Thermal Properties Thermal Comfort Energy Consumption Thermal Properties 14

12 Methodology Implementation concept Implementation concept 15

13 Heating Experiment The Control Problem Initial controller: central heating system for all campus buildings. Controllable elements: O5, served by T1 (controlled); O4, served by T2 (controlled) and T7 (always open). Radiator c.1 Capacity: 2245 W Radiator wc.1 Capacity: 1035 W Radiator office56.1 Capacity: 2523 W T1 Office: O4 Demand Side - Splitter Radiator office4.1 Capacity: 2326 W Radiator office13.1 Capacity: 2395 W Radiator office4.2 Capacity: 1860 W Radiator office11.1 Capacity: 942 W T2 T3 Demand Side - Mixer Radiator c.2 Capacity: 2453 W T4 Radiator c.3 Capacity: 1186 W Radiator c.4 Capacity: 870 W Radiator office1.1 Capacity: 1488 W Radiator office8.1 Capacity: 1488 W Radiator office2.1 Capacity: 1488 W Radiator office9.1 Capacity: 1488 W Radiator office3.1 Capacity: 1488 W Radiator office10.1 Capacity: 1488 W T5 T6 O1 O2 O3 O4 O5 Office: O5 Radiator c.5 Capacity: 1186 W Radiator office4.3 Capacity: 1488 W T7 16 Radiator c.6 Capacity: 1488 W Radiator office11.2 Capacity: 1488 W T8

14 Heating Experiment The Control Problem Goal: Minimize performance index, while satisfying comfort constraints. Performance Index: Thermal Energy Consumption. Comfort Constraints: Fanger PPD index < 15.3%; Higher Fanger values (violations) allowed for 15% of the occupied time. 17

15 Heating Experiment Experimental Setup Warming-up: 4 days; Use TUC initial controller for warming-up; Logged weather conditions; Logged occupancy. 20

16 Heating Experiment Experimental Setup Control Design: 1 day prediction horizon; 1h control design process; Reference occupancy patterns; Weather forecast; Control Application: Valve position every 30 minutes. 21

17 Heating Experiment Results Office O4 Operation: No valve opening for PEBBLE System, since second valve (T7) sufficient for heating demands of the space for the test days. Office O5 Operation: Valve open for less time, compared to initial controller. Savings: Approximately 55% savings for both zones 22

18 Additional Information Project Web Site: Contact: Dimitrios Rovas 23