Optimization of Building Energy Management Systems Matthias Franke and Jürgen Haufe EnTool 2013 Symposium, Workshop & Summer School, 13.06.2013
Agenda Motivation and Challenges BEMS Optimization: online vs. offline Holistic Building models Simple Examples Optimization algorithms and tool coupling Conclusion & Outlook, Matthias Franke
Building Energy Consumption Challenges Buildings in the EU still cause about 40% of the primary energy consumption. The reduction of the primary energy consumption by passive means is technically nearly exhausted. Up to 70% of energy consumption could be saved by intelligent, cross-trade energy-aware control systems (EN 15232) Holistically optimized control systems require novel design methodologies and platform architectures. 3
Building Energy Management System (BEMS) What is a BEMS? Building BEMS Occupants Building Services Energy sources Energy consumers Energy storages Local control units Building physics Sensors Set points Monitoring Reporting Controlling 4
Building Energy Management System (BEMS) Automation Pyramid and BEMS ERP Contracting, Controlling, Energy demand planning Management Monitoring, Reporting, Alarming Automation System Components and local controls Building Energy Management System Cross-trade control Energy-aware optimization Field Communication, Sensors, Actuators 5
Building Energy Management Systems Optimization Approach Goals Reduction of energy consumption Maintain or improve user comfort level Lower overall costs Approach Investigation of a model of the complete building system (models for all essential components, as detailed as necessary) Identification of energy critical parameters by sensitivity analysis Simulation based optimization of energy consumption two possible ways: online -- offline 6
Building Energy Management Systems (BEMS) Optimization approach - online Building Occupants Building Services Energy sources Energy consumers Energy storages Local control units Building physics Sensors Set points BEMS Model Predictive Control Building model is simulated and optimized online 7
Building Energy Management Systems (BEMS) Optimization approach - offline Energy consumption, Human comfort, CO2 emission, Optimizer Holistic Building Model Control Settings Building Occupants Building Services Energy sources Energy consumers Energy storages Local control units Building physics Sensors Set points BEMS Parametric Mappings and Monitoring Reporting State-machines Controlling 8
Building Energy Management Systems (BEMS) Optimization approaches online vs. offline Online Optimization is executed at runtime Only short prediction horizon (up to a few days) Model accuracy can be improved according to measurements during operation System configuration at commissioning stage Offline Optimization is executed before installation Long simulation periods (usually four seasons) Model accuracy is fixed at design stage System configuration at design stage 9
Agenda Motivation and Challenges BEMS Optimization: online vs. offline Holistic Building models Simple Examples Optimization algorithms and tool coupling Conclusion & Outlook
Holistic Building Model Modeling Language & Libraries Object-oriented, multi-domain modeling language: Existing libraries should be used as much as possible: Green Building (EA Systems Dresden GmbH, ITI GmbH) Human Comfort Library (XRG Simulation GmbH) Modelica Building Library (Lawrence Berkeley National Laboratory) BuildingSystems Library (Lehrstuhl für Versorgungsplanung und Versorgungstechnik, Universität der Künste, Berlin) Extensions can be made easily because of the modularity of Modelica 11
Holistic Building Model Building physics Building model as simple as possible (only energy flows shall be considered) Zones or rooms consist of 7 temperature and 7 moisture states Weather and radiation model 12
Holistic Building Model Models of building services and equipment Storages water storage tanks (stratified) concrete core activation Sources solar thermal collectors ground heat condensing boilers Equipment / Consumers radiators floor/panel heaters domenstic hot water supply sunblinds ventilation systems local controllers 13
Holistic Building Model Occupancy modelling Only number and time of present persons is considered Constant heat gain per person (typically 80W) Comfort is determined by temperature (radiation is not taken into account) and CO2 (no humidity) 14
Holistic Building Model BEMS modelling Building Occupants Building Services Energy sources Energy consumers Energy storages Local control units Building physics Sensors Set points BEMS Parametric Mappings Building and model is simulated and optimized online State-machines 15
Building Energy Management Systems BEMS modelling: inputs and outputs sensor data comfort settings User profiles Weather data BEMS Parametric Mappings and State-machines control settings 16
Building Energy Management Systems Modelling Room control according to guideline VDI3813/14 17
Building Energy Management Systems Modelling State machine model 18
Building Energy Management Systems Modeling State machine model State machines are modelled with Modelica 3.3 19
Agenda Motivation and Challenges BEMS Optimization: online vs. offline Holistic Building models Simple Examples Optimization algorithms and tool coupling Conclusion & Outlook
Example of BEMS optimization Optimization of a simple heating system Optimization target: Energy consumption Constraint: Comfort Optimization variables: Supply temperature (Flow speed) Duration of nightly temperature reduction Lead time 21
Example of BEMS optimization Optimization of a simple heating system Outdoor temperature BEMS supply temperature Desired temperature flow speed start of night reduction Occupancy data lead time 22
Building and Equipment Models Example of a Building Model (Green Building Library) BEMS 23
Sample Building Fan-Coils Chiller Heatpump Source: P. Stenzel, U. Donath 24
Devices of Building Services Fan-Coils Chiller Heatpump Source: P. Stenzel, U. Donath 25
Pipe & Instrumentation Diagram Source: U. Donath 26
Hierarchical Control Control Loops for Air Conditioning Room 1 Room 12 Fan Coil 1 Fan Coil 12 Pump BEMS Fan Coil Speed Level T ai, W ai FC Speed i T w supply CH Load Chiller Load Level Energy Source Chiller Global BEMS Loop Control Loop for Cooling Water Production Source: U. Donath
Agenda Motivation and Challenges BEMS Optimization: online vs. offline Holistic Building models Simple Examples Optimization algorithms and tool coupling Conclusion & Outlook Fraunhofer IIS
Optimization algorithm Tool Coupling Weather Occupancy profiles Optimization algorithm optimization variables ff(xx) value of cost function FMU Wrapper Calculation of Cost function BEMS parameters FMI outputs FMU (Holistic Building Model) GenOpt OptiY Own implementation (PSO) for co-simulation 29
Agenda Motivation and Challenges BEMS Optimization: online vs. offline Holistic Building models Simple Examples Optimization algorithms and tool coupling Conclusion & Outlook
BEMS Optimization Conclusion & Outlook Building automation systems are getting too complex for easy manual tuning -> optimization is needed Online & offline optimization approaches are possible Even for simple systems there is a huge potential for optimization Required model accuracy for energy optimization is still under investigation, no final results yet Modelica library for BEMS models is in development 31
BEMS Optimization Next steps Application in demo buildings Validation of models Quantification of energy savings 32
Design of Energy Managements Sytems in Buildings Project enermat Concept System and component models as executable specification Verified and optimized system and component models System Model Development System Simulation Formal Verifikation Optimization Control models Component Model Library Control Algorithms Building Services and Equipment Building Physics Operating Scenarios - Desired Comfort Levels - Government Energy Directives - Environmental Scenarios - Building Occupancy Scenarios Generation of BEMS Code Control Code Room Monitoring Multimedia Virtual Commissioning No Correct? Yes Streckenmodelle Open and closed loop control Communication network Sensors and actuators Water Installation Lightening Electric Installation HVAC Installation Test and commissioning Energy Storages Energy Sources 33
Novel BEMS Platform Architectures Project SEEDS Building Energy Management System Optimization of Control Settings Potential Control Settings Predicted Comfort and Energy Values Prediction of Comfort Variables and Energy Consumption Control Settings Sensor Values Comfort Settings Environmental Values Historical Data Data Management and Process Interface Control Settings Sensor Values Comfort Settings Environmental Values 34
Thank you Matthias Franke Jürgen Haufe Fraunhofer Institut Integrated Circuits IIS Design Automation Division EAS Zeunerstraße 38 01069 Dresden www.eas.iis.fraunhofer.de
Optimization Algorithms Particle Swarm Optimization (PSO) 36
Fraunhofer IIS Design Automation Division EAS Founded 1992 Location Dresden Employees approx. 90 Budget 8,3 Mio. approx. 20 % basic governmental funding approx. 80 % project financed Director Dr. Peter Schneider 37
Fraunhofer IIS Design Automation Division EAS Profile One of the largest research institutions in the field of design automation in Europe Microelectronics and heterogeneous systems Focus on all aspects of functional design Challenges Mastering the growing complexity of electronic systems Closing the gap between manufacturing and system design Comprehensive consideration of different physical domains 38