A Modelica-based model library for building energy and control systems, M Wetter

Tags: Modelica, room temperature, package, model developer, Modelica Fluid, differential equations, mass flow rate, component models, functional requirements, model developers, partial model, fluid flow, partial models, system model, United States Government, Lawrence Berkeley National Laboratory, Michael Wetter, Model Library for Building Energy, California Digital Library University of California, Michael Publication, Building Technologies Department Environmental Energy Technologies Division, The Regents of the University of California, building energy, control systems, modeling and simulation, algebraic equations, hydronic heating system, thermostatic radiator valves, model errors, packages, heat transfer, volume flow rate, temperature, medium models, boolean variables, The boiler, DP, heating system, real variables
Content: Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory Title A Modelica-based Model Library for Building Energy and control systems Permalink https://escholarship.org/uc/item/2q18r7jg Author Wetter, Michael Publication date 2010-03-31 peer reviewed
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A Modelica-based Model Library for Building Energy and Control Systems Michael Wetter, Lawrence Berkeley National Laboratory July 2009
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A MODELICA-BASED MODEL LIBRARY FOR BUILDING ENERGY AND CONTROL SYSTEMS Michael Wetter Simulation Research Group, Building Technologies Department Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA
the functional requirements of such applications. This pa-
This paper describes an open-source library with com- per deals with the second development. Typical functional ponent models for building energy and control systems requirements include:
that is based on Modelica, an equation-based objectoriented language that is well positioned to become the standard for modeling of dynamic systems in various industrial sectors. The library is currently developed to sup-
1. Faster implementation of models for equipment, systems and control algorithms, at different levels of abstraction.
port computational Science and Engineering for innovative 2. Means for implementing models by tool users in addi-
building energy and control systems. Early applications tion to tool developers.
will include controls design and analysis, rapid prototyp- 3. Ability to share models among users.
ing to support innovation of new building systems and the 4. Ability to model continuous time dynamics (for physi-
use of models during operation for controls, fault detec- cal processes), discrete time systems (for discrete time
tion and diagnostics.
controls) and state events (such as for switching con-
This paper discusses the motivation for selecting an trols).
equation-based object-oriented language. It presents the 5. Extraction of subsystem models for use in isolation
architecture of the library and explains how base models from the total system model (such as for validation, for
can be used to rapidly implement new models. To demon- more refined analysis, for model reduction, or for use in
strate the capability of analyzing novel energy and control operation).
systems, the paper closes with an example where we com- 6. Use of simulation models in conjunction with nonlinear
pare the dynamic performance of a conventional hydronic programming algorithms that require the cost function
heating system with thermostatic radiator valves to an in- (such as energy use) to have little numerical noise. This
novative heating system. In the new system, instead of a is important to efficiently solve optimal control prob-
centralized circulation pump, each of the 18 radiators has a lems that may involve state trajectory constraints and
pump whose speed is controlled using a room temperature hundreds of independent parameters that define the con-
feedback loop, and the temperature of the boiler is con- trol function.
trolled based on the speed of the radiator pump. All flows are computed by solving for the pressure distribution in the piping network, and the controls include continuous and discrete time controls. INTRODUCTION
From these functional requirements result several characteristic features of the software architecture that such a modeling and simulation environment should have. They include:
To significantly reduce greenhouse gas emissions asso- 1. Object-oriented modeling to facilitate code reuse. ciated with building operations, development of building 2. Use of an equation-based language to allow more natusimulation programs along two parallel tracks is needed: ral modeling. First, the usability of existing building simulation pro- 3. Use of a standardized modeling language.
grams needs to be improved so that they can better support 4. Support for interfacing Computational Models with real
the design of efficient buildings on a large scale. Second, experimental facilities at the component and whole
to accelerate the innovation of new HVAC components, building level.
systems and control algorithms, a modeling and simula- 5. Support for hierarchical model composition to allow
tion framework needs to be developed that better meets managing the complexity of large systems.
6. Use of a model connectivity framework that allows a model builder to assemble models in a similar way to what an experimenter would do on a workbench (cf. the object-oriented modeling paradigm summarized in Cellier 1996). 7. Use of symbolic algebra tools to reduce the dimensionality of the coupled systems of equations that need to be solved for simultaneously. 8. Use of numerical solvers that can solve stiff differential equation systems (which require implicit solvers with adaptive step size, cf. Hairer and Wanner 1996) that may contain boolean variables. This list illustrates that such a computational environment needs to simultaneously satisfy new requirements with regard to graphical modeling environments, modeling language, symbolic and numerical methods, and code translators (that convert a model description into an executable program). It is not likely that these requirements can be met by an incremental evolution of an existing building simulation program, which typically contains hundreds of thousands of lines of procedural code that mix program statements describing the physics with program statements for control algorithms, data management and numerical solution methods. Such program code does not allow use of code generators that use symbolic algebra for reducing the dimensionality of the coupled equation systems, for automatic differentiation and for index reduction of differential algebraic equations. It also makes it impractical to use modern solvers that analyze equation systems for events and differentiability. Both measures are used in modern system simulation programs to increase computational efficiency and robustness. To realize a modeling and simulation environment that can meet the above needs, we believe it is most efficient to start with a new approach that builds on the latest advances in system modeling and simulation. However, such a new environment for modeling and simulation can still be used in conjunction with existing building simulation programs using cosimulation, for example using the Building Controls Virtual Test Bed (Wetter and Haves 2008). Clearly, the development of such a computational environment requires expertise from various Research Disciplines such as computer science (for language design and code generation), mathematics (for symbolic and numerical methods) and engineering (for creating modeling libraries). Hence, a tool for such systems should not be developed by the building simulation community in isolation, but rather together with other industrial sectors to share resources. This is the approach that is followed by the Modelica consortium which has been developing the Modelica modeling language since 1997. Modelica
is an equation-based, object-oriented language for modeling of systems that are described by algebraic equations, differential equations, and difference equations, and that may contain real variables, integers, boolean variables and strings (Mattsson and Elmqvist 1997). Models written in the Modelica language cannot be executed directly. Rather, a simulation environment translates a Modelica model into an executable program. Several commercial and freely available modeling and simulation environments for Modelica that support textual and graphical modeling exist. IDA ICE 4.0, to be released in spring 2009, appears to be the first building simulation program that will support Modelica.1 For a list of Modelica modeling and simulation environments, see http://www.modelica.org/tools. While the performance and price of the different tools vary, there has been significant progress in the development of these tools over the last few years, and significant investments have been made in Modelica. For example, in three European ITEA projects (EUROSYSLIB, MODELISAR and OPENPROD), about 54 Million Euros for 370 person years are invested to further develop Modelica, Modelica tools, Modelica libraries and related technology.2 However, what is missing in Modelica is a comprehensive library for building energy and control systems. Thus, LBNL started an open-source development effort with the aim of filling this gap. While the library is currently used within LBNL projects, it is our intention to broaden the development effort and collaborate with other developers to create an open-source Modelica library that meets the need for simulation-based innovation in building systems. This paper presents the current design of the library, which is available free of charge, including its source code, from http://simulationresearch.lbl.gov. TERMINOLOGY To facilitate the discussion of our model library, we will first introduce some terminology. For a more detailed discussion see Tiller (2001) and Fritzson (2004). In Modelica, a general object is called a class, which is typically restricted by the model developer. Frequently used restricted classes are a model, a connector, a block and a function. (There are other class restrictions, but these will suffice for our discussion.) A model typically contains time-dependent variables and parameters, which are time-independent. An equation section is used to declare algebraic and differential equations that relate parameters, variables and their time derivatives. The equations are acausal and a Modelica translator sorts and in- 1See http://www.equa.se/news/2008_16.html. 2See http://www.modelica.org/publications/newsletters/ 2009-1.
verts them when generating executable code. To expose tor, or they may implement new models by using object-
interface variables, a model can contain instances of a re- inheritance of an existing model. For model developers,
stricted class called a connector. Connectors cannot con- the Buildings library contains partial models that imple-
tain equations. For example, the Modelica Standard Li- ment basic functionalities, such as access to states at the
brary 3.0 defines a connector for a heat port, which has component ports or conservation equations for the fluid
variables for temperature and heat flow rate. Similarly, a streams, with a variable, say Q flow for a heat input into
connector for an analog electrical port contains variables a medium, which a model developer needs to assign when
for voltage and electrical current. These connectors de- implementing a model. Using such a partial model, a
clare the variables for heat flow rate and current as flow model developer can implement a complete component
variables, which will cause a model translator to automat- model with a small set of equations. For example, an ideal
ically impose conservation equations when multiple con- heater or cooler with no flow friction is completely defined nectors are connected with each other. In contrast to a by the code3
model, a block requires the causality of its variables to be
declared. Blocks are typically used to model signal flows 1 model H e a t e r C o o l e r P r e s c r i b e d
such as in a control algorithm. Modelica function objects 2 e x t e n d s F l u i d . I n t e r f a c e s .
map inputs into outputs and contain an algorithm section 3 P a r t i a l S t a t i c T w o P o r t H e a t M a s s T r a n s f e r ;
with procedural code. Functions cannot have memory and 4 p a r a m e t e r Model i ca . S I u n i t s . H eat F l ow R at e
they cannot contain differential equations. Functions can 5 Q f l o w n o m i n a l
be recursive, and they can call other functions that may be 6 " Heat f l o w r a t e a t u=1" ;
implemented in Modelica, C or Fortran. A model, connec- 7 8
Modelica . Blocks . Interfaces . RealInput u "Control input";
tor, function or block can be declared to be partial. Partial 9
classes cannot be instantiated. The partial keyword is typ-10 Q flow = Q f l o w n o m i n a l u ;
ically used to force a model developer to provide a com-11 mXi flow = z e r o s ( Medium . nXi ) ;
plete implementation before instantiating the class. For12 end H e a t e r C o o l e r P r e s c r i b e d ;
example, the Standard Modelica Library implements for
one-dimensional heat transfer elements the partial model Element1D that defines two heat port connectors called port a and port b, variables for T and Q and the equations T = Ta - Tb, Q a = Q and Qb = -Q where the subscripts refer to the port names. The model is declared partial because the equation that relates the temperatures with the heat flow rate is not declared at this level of the object inheritance as it is different for heat conduction, radiation or convection. To group similar classes, classes are stored in a package in a tree-like hierarchy. For example, the Modelica Standard Library contains the package Modelica.Electrical which contains the packages Analog and Digital.
Library developers will typically develop the base models that can be used by model developers, such as PartialStaticTwoPortHeatMassTransfer in the example above. For the Buildings library, basic models of the Modelica Fluid library have been used and customized for buildings applications. Developing base models requires a comprehensive understanding of Modelica and of the application domain to ensure that the models will be computationally efficient and have a high degree of reusability. Reusing modeling concepts from Modelica Fluid allowed us to implement the Buildings library using the best practices that have been developed over the last six years by the Modelica Fluid working
group. By providing the partial models, ready-to-use base
classes are provided to model developers so they can focus
Users of the Buildings library can loosely be classified on higher level model implementations.
into model users, model developers, and library develop-
Model users will typically graphically compose system models using models that are already available in the Buildings library, the Modelica Fluid library (Casella et al. 2006) and the Modelica Standard Library. For model users, we are working towards creating a comprehensive set of component models that will allow modeling a variety of building energy and control systems.
When browsing the model library, a user is exposed to the class package view. To implement new models, the object-inheritance view is also of importance to understand what models can be reused. After a short discussion of the Modelica Fluid library on which our library is based, we will describe both views.
Model developers will typically copy and modify exist-
ing component models, using a graphical and textual edi- 3For brevity, annotations have been omitted.
Controls -- Continuous Discrete SetPoints Fluid -- Actuators -- Dampers Motors Valves Boilers Chillers Delays HeatExchangers MassExchangers Media MixingVolumes Movers Sensors Storage HeatTransfer Utilities -- Diagnostics IO Math Psychrometrics Reports
in conjunction with the actuators. In Fluid.Delays, there is a transport delay model that can be used in fluid flow systems. A dynamic boiler model is in Fluid.Boilers and different heat and mass exchanger models can be found in Fluid.HeatExchangers and in Fluid.MassExchangers. Various medium models are implemented in the package Media, such as for dry air, moist air and water. These medium models augment the medium models that are already available from Modelica.Media. Fan and pump models are stored in Fluid.Movers. Sensors that can be connected to a fluid stream are stored in Fluid.Sensors. The package Fluid.Storage contains models of stratified storage tanks. The package Utilities contains psychrometric models and blocks to format and print results to files. In the future, an interface will be added that allows linking Modelica models to the Building Controls Virtual Test Bed (Wetter and Haves 2008), and hence to EnergyPlus. Most packages include a package called Examples. The example files in these packages are used to illustrate the model use and to conduct unit tests. Currently, there are around 60 example files.
Class Inheritance
Figure 1: Package structure of the Buildings library.
Only the major packages are shown.
We will now explain how some models are imple-
mented in the library. While a comprehensive explanation
of the whole library implementation is outside the scope of
Modelica Fluid Base Library The Modelica Fluid library contains component models for one-dimensional thermo-fluid flow in networks of pipes. Version 1.0, on which our Buildings library is currently based, was released in January 2009. It is in-
this paper, we include this section to illustrate how objectoriented modeling allows reusing the same base classes for various model implementations. While using objectoriented class definitions requires more planning when designing a library, it provides the following advantages:
tended to become part of the Modelica Standard Library. It provides models that demonstrate how to implement fluid flow component models that may have flow friction, heat
1. The same code is used in many models which makes it more likely to detect model errors.
and mass transfer. The models demonstrate how to deal 2. Code is easier to maintain since features that are shared
with difficult design issues such as connector design, han- by different models can be declared once and propa-
dling of flow reversal and initialization of states in a com- gated by object-inheritance, as opposed to being copied
putationally efficient way. While many models of this li- into different source code sections.
brary can be used for our application domain, we provide 3. Complex models can be implemented using a series of
in the Buildings library models that reuse and augment models of increasing complexity. This facilitates con-
models from Modelica Fluid where applicable.
ducting unit tests for isolated model features, thereby
increasing the chance to detect model errors earlier
Packages of the Buildings Library
when they are easier and cheaper to fix.
The Buildings library is organized into the packages 4. Connectors and variables of similar models share the
shown in Fig. 1. Components in these packages augment same name if they are declared in a common base
components from the Modelica Standard Library and from class. This facilitates post-processing of simulation re-
the Modelica Fluid library.
sults. For example, because of object-inheritance, a
The package Controls contains models of controllers user knows that a flow resistance element always has
that are frequently used in building energy systems. The a public variable dp that reports the pressure drop.
package Fluid.Actuators contains models of valves 5. Inside a system model, component models can be con-
and air dampers, as well as of motors that can be used strained to belong to a certain base class. They can
PartialTwoPortTransport PartialResistance FixedResistanceDpM PartialActuator PartialTwoWayValve TwoWayExponential TwoWayQuickOpening
PartialDamperExponential Exponential VAVBoxExponential
Figure 2: Object-inheritance for pressure drop elements with two fluid ports.
then be redeclared to assign an instance of a particu- curve that relates mass flow rate with pressure drop. Given
lar model inside a system model. This allows treating a nominal mass flow rate m 0 and a corresponding pressure instances of component models in a similar way to a drop p0, the model assigns k = m 0/ p0. There are also parameter, thereby allowing changing the behavior of a parameters that allow a model user to specify where the
model. For example, a model for heat transfer in a wall transition between turbulent and laminar flow occurs. In
can be propagated into a building heat transfer model, contrast to this model, the model PartialActuator does
thereby allowing the creation of a building model with not define how k is computed, because different actuators
different model structure as described in Wetter (2006). require different equations. Instead, it simply instantiates
We will now illustrate how object-inheritance was used to implement two-way valves and air dampers. Figure 2 shows the object-inheritance tree. For the base model, we used the partial model PartialTwoPortTransport from the library Modelica Fluid. This partial model can be used to implement models that transport a fluid between two ports while conserving enthalpy, mass and species concentration. It defines two instances of a fluid port which are called port a and port b. It also defines a variable that requires a model user to declare with what medium this model is used (such as dry air, moist air or water). The model also implements the enthalpy balance as 0 = Ha + H b, the mass flow rate balance 0 = m a + m b, the species flow rate balance 0 = m X,a + m X,b and the pressure balance p = pa - pb. Note that how p is computed as a function of the flow rate is not yet specified, since the equation will be different for different models. Next, there is a model called PartialResistance. This model implements a function that computes the mass flow rate as a function of pressure drop, m = f (k, p). The function f (·, ·) is an approximation to m = sign(p) k |p| with regularization near zero for numerical reasons and to capture the laminar flow region. How k is computed is not specified at this level of the objectinheritance tree.4 There are two different models for specifying k. The model FixedResistanceDpM is a model for a fixed flow resistance in which the user can specify the point on the
a connector for an input signal whose value is equal to the actuator opening, with y = 0 defined as closed and y = 1 defined as open. Next, the model PartialActuator implements a partial model for a damper, i.e., the model PartialDamperExponential, and a partial model for a two-way valve, i.e., PartialTwoWayValve. The model PartialTwoWayValve defines that a valve implementation needs to specify a flow function (y) = k(y)/k(y = 1) that relates the valve opening y with the actual flow coefficient k(y) and the flow coefficient for a fully open valve, k(y = 1). It also specifies a parameter for the valve leakage l, i.e, l = k(0) so that (0) = l/k(y = 1). All these partial models are stored in packages called BaseClasses that a typical model user does not need to browse when assembling a system model. Next, there is a package called Valves with the model TwoWayLinear which implements the linear characteristics (y) = l + y (1 - l), and the models TwoWayEqualPercentage and TwoWayQuickOpening that implement valve opening characteristics for equal percentage and for quick opening valves. There is also a package called Dampers that implements models for an air damper and a variable air volume flow box with exponential damper opening characteristics based on the partial model PartialDamperExponential. For example, the implementation of the two-way valve with linear opening characteristics is as follows:
4We used mass flow rate instead of volume flow rate as this leads to 1 model TwoWayLinear "Two-way v a l v e w i t h simpler equations. However, it would be easy to implement a model in 2 l i n e a r f l o w c h a r a c t e r i s t i c s "
which a user can specify the volume flow rate instead of the mass flow 3 e x t e n d s B a s e C l a s s e s . P a r t i a l T w o W a y V a l v e ;
4 equation
Figure 3: Schematic view of the DP system. Each radiator has a pump in its return pipe. 5 phi = l + y (1 - l ); 6 end TwoWayLinear ; For brevity, the documentation has been omitted in the above code. The documentation is html formatted text that can be translated into a documentation that displays textual documentation together with the Modelica code. Similar object-inheritance trees are used to implement other models such as for three-way valves, for heat exchanger models and for measurement sensors. APPLICATION We will now show simulations that compare a conventional hydronic space heating system with thermostatic radiator valves (TRV system) to a hydronic space heating system with decentralized pumps at each radiator (DP system). The DP system is similar to the system Geniax, which the company Wilo presented to the European market in March 2009. Wilo reports that promises of the Geniax system include about 20% reduction in heating energy use and faster room temperature change during and after night setback. Fig. 3 shows schematically the DP system, with a pump at each radiator outlet. The TRV system has the same configuration, except that there is one central circulation pump at the boiler outlet, and thermostatic radiator valves are used for each radiator instead of the radiator pumps. We modeled both systems using the Modelica libraries Buildings 0.5.0, Modelica Fluid 1.0 and the Modelica Standard Library 3.0. The models were built and simulated in the Modelica modeling and simulation environment Dymola 7.1. Our system was a model of a hydronic heating system of a building with three floors. Three vertical distribution pipes served 18 radiators. All mass flow rates were computed based on the pressure distribution in the piping network, which depends on the pump curves, the flow friction in the individual branches and the pump speed. All pumps had variable frequency drives that can reduce the pump speed to one third of the nominal speed. Below that value, the pumps were switched off. The heat losses of
the rooms were modeled using a Finite Volume method to solve for the transient heat conduction through walls and floors, which we selected to be lightweight constructions. There was also steady-state heat transfer to the outside to account for heat losses due to ventilation and heat conductance through the window. In every other room, we added convective and radiative heat gains during the day to resemble people and solar heat gains. The room air was modeled as completely mixed with one state variable. In the TRV system, each radiator had a thermostatic valve with a proportional band of 0.5 K. The boiler set point was computed as a function of the outside temperature, using a heating curve with night setback that corresponds to a reduction of the room temperature from 20 C to 16 C. In the early morning, the heating curve was increased to allow faster recovery from the night set back temperature. The centralized pump had a variable frequency drive that regulates the pump head. In the DP system, each radiator had a pump that varied its speed to draw as much water as needed for tracking the room temperature setpoint. The control sequence specification was not available from the manufacturer. Based on the available literature (Baulinks 2009), we implemented the following control algorithm. A proportional controller determined the speed for each radiator pump based on the current room temperature control error. The room set point was 20 C during the day and 16 C during the night. To keep the boiler temperature as low as possible (for example to maximize the efficiency of a condensing gas boiler), the boiler temperature setpoint was adaptive based on the room temperature control error. In both systems, the boiler temperature setpoint was tracked using a P-controller with hysteresis. The hysteresis was used for switching the boiler on and off. The boiler switches off if the output signal y of the boiler controller is y < 0.3. If y > 0.5, the boiler switches on and then modulates between 0.3 y 1. A time relay was used to avoid excessive short cycling at very low load. All circulation pumps could reduce their speed to 30% of the nominal speed. Below this threshold, the pump switched off and remained off until its controller requested 50% of the nominal pump speed. Fig. 4 is a view of a subset of the system model as displayed by the graphical model editor of Dymola. Each icon encapsulates a model, which may encapsulate additional models to enable a hierarchical model definition. In Fig. 4, on the left are input signals for the room temperature setpoint and the outside air. Next, there are vertical lines to connect fluid ports at the bottom and top of the floor. (For the top floor of the building, the model translator will set the mass flow rates in these pipe segments to zero, as the top ports are not connected.) In the left
Figure 4: View of the two-room model in the graphical model editor of Dymola. pipe, we placed a model that computes flow friction. The grey boxes in the fluid lines are finite volume models for the radiators. To the right of the radiators are the circulation pumps, and on top of the radiators are the room models. The room models contain finite volume models for computing the transient heat flow through the building constructions. Input to the room models are the outside temperatures and heat gains. The heat gains were defined by a time table for the left room, but they were set to zero for the right room. The red connection lines connect the room models to the radiators. They equate the temperatures and balance the convective and radiative heat flows, respectively, between radiator and rooms. There is also a heat flow connection between the rooms for interzonal heat transfer. Above the room models are the pump controllers. This two-room model is then instantiated nine times to form a three-storey house with three vertical distribution lines, and the distribution lines are connected to a plant model that contains the boiler and the centralized system controller. The total system model is composed of 2400 component models that form a differential algebraic equation system with 13,200 equations. After the symbolic manipulations, there were 8700 equations with 300 state variables. Building the system models for the TRV and the DP systems, including the models for the room, the radiator, the boiler and a first version of the controllers, took about a week of labor. Fig. 5 shows the trajectories computed by the two system models. In the TRV system, the radiator valves open at night since the room temperature falls below their set point temperatures of 20 C. This causes the radiators to release heat to the room, although at a lower rate because of the lower supply water temperature. However, in the DP system, the radiator valves and the boiler switch off while the room temperature is above the night setback
T [°C]
T [°C]
T [°C]
T [°C]
Boiler set point, supply and return temperatures 60 40 20 0 2 4 6 8 10 12 14 16 18 20 22 24 Room temperatures 20 18 0 2 4 6 8 10 12 14 16 18 20 22 24 Boiler and radiator valve signals 1 0.5 0 0 2 4 6 8 10 12 14 16 18 20 22 24 Normalized radiator mass flow rates 2 1 0 0 2 4 6 8 10 12 14 16 18 20 22 24 (a) TRV system Boiler set point, supply and return temperatures 60 40 20 0 2 4 6 8 10 12 14 16 18 20 22 24 Room temperatures 20 18 0 2 4 6 8 10 12 14 16 18 20 22 24 Boiler and radiator pump signals 1 0.5 0 0 2 4 6 8 10 12 14 16 18 20 22 24 Normalized radiator mass flow rates 2 1 0 0 2 4 6 8 10 12 14 16 18 20 22 24 (b) DP system Figure 5: Comparison of the dynamic system response of the TRV and DP systems for a lightweight building. The lower three subfigures show the trajectories of the four rooms that are closest and farthest away from the boiler, with the solid lines corresponding to the rooms with heat gains.
temperature, which causes a larger reduction in room temperature at night. CONCLUSIONS
REFERENCES Baulinks. 2009, February. Wilo startet in ein "neues Zeitalter der Heizungssysteme". http://www.baulinks.mobi/news/2009/0204.htm.
Model-based system-level analysis of the dynamic performance of building energy and control systems promises to reduce both research and development expenditures and time to market of new systems. Such a research and development process requires a flexible modeling and simulation environment that allows users to rapidly add new models of physical equipment and of continuous and discrete time controls. We showed how object-oriented equation-based modeling allows addressing some of the requirements that model-based system-level analysis imposes on the modeling and simulation environment. To better support this process, we started the development of a library of component models for building energy and control systems. The models are developed using Modelica, an open-source modeling language that has considerable support in the system-simulation community, as well as in various industrial sectors. This broad support allows sharing resources for the development of tools that are common across many engineering domains, as well as sharing domain-specific models within the building simulation community. We discussed the software architecture of our opensource Modelica library of component models for building energy and control systems. We also demonstrated how the models can be used to compare the dynamic performance of a hydronic heating system, with circulation pumps at each radiator, to a conventional hydronic heating system with thermostatic radiator valves. Modeling both hydronic systems, including implementing dynamic models for a boiler, a radiator and a simplified room with transient heat conduction took about a week of labor. This is considerably shorter than it may have taken with many conventional building simulation programs, as modeling pressure driven flows and testing different local loop and supervisory control algorithms are often outside their capabilities. Technical challenges remain, however, in the numerically efficient and robust simulation of such systems, and in the creation of libraries with robust models. These items are the subjects of future research and development.
Casella, Francesco, Martin Otter, Katrin Proelss, Christoph Richter, and Hubertus Tummescheit. 2006, September. "The Modelica Fluid and Media Library for Modeling of Incompressible and Compressible Thermo-Fluid Pipe Networks." Edited by Christian Kral and Anton Haumer, Proc. of the 5th International Modelica Conference, Volume 2. Modelica Association and Arsenal Research, Vienna, Austria, 631­640. Cellier, Francёois E. 1996. "Object-Oriented Modeling: Means for Dealing With System Complexity." Proceedings 15th Benelux Systems and Control Conference. Mierlo, The Netherlands, 53­64. Fritzson, Peter. 2004. Principles of Object-Oriented Modeling and Simulation with Modelica 2.1. John Wiley & Sons. Hairer, E., and G. Wanner. 1996. Solving ordinary differential equations. II. 2nd. Springer series in computational mathematics. Berlin: Springer-Verlag. Mattsson, Sven Erik, and Hilding Elmqvist. 1997, April. "Modelica ­ An international effort to design the next generation modeling language." Edited by L. Boullart, M. Loccufier, and Sven Erik Mattsson, 7th IFAC Symposium on Computer Aided Control Systems Design. Gent, Belgium. Tiller, Michael M. 2001. Introduction to Physical Modeling with Modelica. Kluwer Academic Publisher. Wetter, Michael. 2006, September. "Multizone Building Model for Thermal Building Simulation in Modelica." Edited by Christian Kral and Anton Haumer, Proc. of the 5-th International Modelica Conference, Volume 2. Modelica Association and Arsenal Research, Vienna, Austria, 517­526. Wetter, Michael, and Philip Haves. 2008, August. "A modular building controls virtual test bed for the integration of heterogeneous systems." Proc. of SimBuild. IBPSA-USA, Berkeley, CA.
This research was supported by the Assistant Secretary for Energy efficiency and Renewable Energy, Office of Building Technologies of the U.S. Department of Energy, under Contract No. DE-AC02-05CH11231.

M Wetter

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Title: A Modelica-based Model Library for Building Energy and Control Systems
Author: M Wetter
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