Using Simulation for Optimized Building Operation

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simulation in building automation and how it can assist optimization of building ... many tools on the market that are used for building simulation, e.g. TRNsys, ...
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Using Simulation for Optimized Building Operation G. Zucker, C. Hettfleisch AIT - Austrian Institute of Technology, Energy Department, Vienna, Austria

ABSTRACT: In addition to all measures which lead to the sustainable operation of a building already in the planning and construction phase, building management is the key for optimum operation. This, however, has to happen between various building industries and physical domains, which have different demands regarding optimization. Building simulation provides a solution that allows predicting the thermal behavior of a building as well as the electrical consumption and especially the dependencies between these domains. We now face the question of how buildings can modeled best and what new opportunities for optimization arise to make the best use of energy in the future. KURZFASSUNG: Neben allen Maßnahmen, die schon bei der Planung und im Bau zum nachhaltigen Betrieb eines Gebäudes führen, ist das Gebäudemanagement der Schlüssel für optimalen Betrieb. Das allerdings muss im Spannungsfeld unterschiedlicher Gewerke und physikalischer Domänen passieren, was hinsichtlich der unterschiedlichen Optimierungsforderungen ein schwieriges Unterfangen ist. Einen Ausweg liefert die Simulation von Gebäuden, die es ermöglicht, das thermische Verhalten des Gebäudes vorherzusagen, den elektrischen Verbrauch zu bestimmen und vor allem die Abhängigkeiten zwischen diesen Domänen abzubilden. Wir stehen heute vor der Frage, wie man Gebäude simulationstechnisch besser erfassen kann und welche neuen Möglichkeiten zur Optimierung für das Gebäude man damit schaffen kann, damit in Zukunft die vorhandene Energie noch effizienter genutzt werden kann.

1. INTRODUCTION Thermal building simulation tools that are available today are the foundation of optimizing building operation towards different goals. Building automation allows controlling the building performance by giving an overview about the consumption of electric and thermal energy. Since many contemporary building are also equipped with sustainable energy sources like solar thermal, photovoltaic, geothermal systems or wind turbines, the range of possible energy flows and operation scenarios has greatly increased. This paper takes a look into the role of simulation in building automation and how it can assist optimization of building operation.

2. OPTIMIZATION GOALS Having the flexibility of a building automation system at hand gives the building operator the possibility to monitor, control and optimize the building. The primary task is to maintain user comfort in terms of climate and lighting (aside of many other user related constraints and requirements that need to be obeyed). In the following we assume that user related constraints and requirements are always the topmost priority and must always be maintained, since we do not cover emergency energy supply shortage scenarios as they happen e. g. in the United States. In such cases systems have to be taken offline in order to maintain sanity of the electric grid – an action that is until today not necessary in Austria.

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Thus the first optimization goal is energy efficiency. The building management system shall prevent unnecessary use of energy within the building. Most of the energy used in a building is used for heating and especially cooling, therefore efficient climate control has the highest potential for increasing energy efficiency. A building with its thermal masses can also be modeled as a storage for thermal energy. Since the building couples electric systems with thermal systems it is possible to load it with thermal energy, thus influencing the load profile of the building i. e. the electric consumption over a day. Systems that couple electric and thermal systems are, for example, heat pumps, direct electric heating and CHPs (combined heat and power units). Looking at the building from the outside, that is, the electric grid, we see another optimization goal: the building is able to support the grid by “peak clipping” i. e. avoiding electric energy consumption in times when the grid is at full capacity. Such optimization prevents blackouts in short term operation but also delays necessary grid upgrading and extensions, thus saving costs for the grid operator. Sustainable energy sources in a building also provide potential for optimization. Again the electric grid is the relevant factor, since photovoltaic systems can feed into the grid (up to now the market does not offer suitable solutions for feedback into the thermal grid – although research is ongoing). An optimization scenario for sustainable electric energy production is to optimize for maximum self-usage of the produced electricity. Thus the grid is relieved from energy feedback from the building. Another optimization is to use CHPs for peak clipping in the electric grid: at times of high electric consumptions the CHP also runs at maximum load, thus increasing the share of self-produced electricity. The thermal energy, which is produced simultaneously has to be either consumed or stored in storage tanks – which is a task for the building management system: to ensure that thermal storage and consumption can account for the produced heat. A final motivation is cost optimization. Depending on the tariff structure and the possibility of participating in the electricity market (e. g. by grouping multiple building together and acting as a virtual plant) it is possible to exploit thermal storage capacity of buildings for trading energy, a factor that may become relevant in the future.

3. BUILDING SIMULATION METHODOLOGY Within the scope of this paper only thermal simulation and the correlation to electric consumption and production is relevant, other domains shall not be considered. Still there are many tools on the market that are used for building simulation, e.g. TRNsys, EnergyPlus, Dymola and many more (see [Tool08] for a more comprehensive list). For the focus of this paper – using simulation during building operation – the requirements are given by the integration of the simulation tool into daily operation. In the first step a thermal model of the building is needed. This model represents the thermal properties of a building including thermal conductance of the building envelope and internal structures, window areas and sizes for thermal loads by solar radiation and other relevant parameters. Depending on the complexity of the model the accuracy of the simulation data increase, but so does the effort of model creation. In the next step the energy systems of the building are modeled. These include all systems that consume energy, produce energy or are responsible for energy flow into or out of the building, including HVAC systems (heating, ventilation, air condition) and renewable energy sources like solar thermal, photovoltaic, geothermal systems and wind turbines. The behavior of the control systems also has to be modeled for the simulation – at least partly. If the building management system controls the setpoints of, for example, room controllers, it is necessary to include these controllers in the simulation. If simulations are used to

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try out different control strategies without actually affecting the building, then also the control strategies of the building management system have to be included in the model, where they can then be modified. 3.1 DYNAMIC SIMULATION For the integration of thermal simulation into building management the models of buildings and energy systems have to be dynamic, that is, the models have to be based on the physiccal properties of the components e. g. by evaluating the differential equations that describe the physical relations; cumulative energy balance of the annual energy consumption and production is insufficient. Differential equations are typically done within the simulation tool by a solver, which is proprietary to the tool. An important issue here is the minimum timestep of simulation and the possibility for flexible time steps: while a resolution of 15 minutes is sufficient for thermal energy flow modeling in a building, the controls of the building management systems may require higher resolution to reflect the actual behavior of the controller. Think, for example, of a simple on-off controller that switches the heating depending on the current room temperature: these devices have a hysteresis to avoid switching too often. If the simulation timestep is too coarse then the simulation may deviate strongly from the actual situation in the room, because the energy flow in the simulation does not match the behavior of the onoff controller. 3.2 EFFICIENT MODELING Looking at the complete task we see that a lot of effort has to be put into modeling: the building physics for thermal building behavior, the energy systems and the controls all have to be modeled. In order to be efficient this should be done in only one tool, otherwise we have to ensure that the different simulation tools can be coupled and can cooperate. While it is clear that a decently designed model of the building will yield accurate results, we face the problem that the current work flow in designing, creating and operating a building does not cover costs for expert modeling efforts to create all of the above. In order to be successful modeling has to become cost effective (or it has to be legally enforced by, for example, requiring a “function and yield control” for buildings). One way to reduce costs for modeling is to reduce the accuracy of the models by reducing the complexity of the thermal building model. Work is ongoing to find a good tradeoff between accuracy and modeling effort, e.g. by moving to electrical equivalences for thermal models and reducing the number of parameters that are required to create a model. Ideally, a coarse model would consist of few – or only one – thermal conductance and thermal capacity that describe the whole building. Much research has been done on model reduction to reduce the modeling (and computational) effort see, for example, [Gru01] and [Thro01]. For thermal building simulation a model of third or second order appears to be appropriate. Another helpful development that supports modeling is the introduction of new tools. Such a tool is Google Sketchup [Goo8], which eases the creation of 3D building models. TRNSYS offers a plug-in to conveniently use models created in Google Sketchup for thermal building models. Thus the time that the modeling expert has to invest to create a model, define the thermal zones and interconnect them, can be greatly reduced.

4. MODEL-BASED BUILDING MANAGEMENT Building management and controls today relies on experience: linear controllers (P- or PIcontrollers) and control strategies are set up based on empiric rules to enable stable operation and ensure user comfort. Not much is done with regard to energy efficiency or other optimization goals. Taking the experience into the controls requires modeling and simulation.

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The goal is to use the model of the building (the “plant” in terms of control theory) and the systems to get an estimate about the building status without actually measuring it. A first benefit of having a thermal simulation of a building is the ability to do offline optimizations: by modifying control strategies and using the model to try them in a simulation it is possible to find the optimum strategy. Usually such a simulation run is done for a whole year and the overall performance of the building is assessed. In case a quick but more coarse solution is satisfactory it may suffice to simulate only significant days throughout the year (i. e. average summer and winter days and days during the change between heating and cooling period. Modifications can be as easy as changing on/off times or duty cycles or the energy systems, but may also involve new control strategies or the exchange of controllers. A more systematic way that does not require that much experience when doing offline optimization is to use a tool to automate optimization. Such a tool is GenOpt [GenOpt10] that uses genetic algorithms and other optimization algorithms to automatically find the best solution. The challenge for the user is to define what “optimal” actually means. This is done by defining an objective function that includes all necessary parameters to optimize (e. g. minimum use of energy, maximum use of renewable energy sources etc.). The problem breaks down to defining all requirements and all constraints mathematically. Once this is done, GenOpt runs unattended until optimization is finished. A simulation of the building behavior yields another benefit: the ability to make estimations about the behavior of the building in the near future. The simulation of the building can predict energy flows within the building (and between building and environment) before they actually happen. Systems with large inertia (e. g. concrete core activation for room heating and cooling) and therefore large time delays can thus be operated in an optimized way, overshoots can be avoided and efficiency increased. Aside of giving the possibility of optimizations the simulation can also be enhanced by additional information: using weather prediction data that are available for the next day the simulation can derive the thermal losses or gains over the day. This information can be used to modify the control strategies of the heating or cooling systems. In [Hett10] the authors show that the usage of weather prediction combined with thermal simulation has a considerable potential for energy saving in buildings. The consequent continuation of this approach is a fully automated building management system that employs thermal simulation and adapts control strategies automatically. The building management system can access the thermal simulation and assess the impact of the current control strategy; if necessary it can make modifications for better operation. Model predictive control (MPC) is based on this approach. The controller uses the integrated model to predict behavior until the prediction horizon and use the predicted behavior for its control strategy. MPC is also capable of integrating predicted data like the aforementioned weather prediction, but also occupancy schedules which influence the inner thermal loads of the building. While the methodology and mathematical foundation for MPC is well-known and established in other domains, building management has not yet profited much from this approach. First products have reached the market, but we are far from proper market penetration. It is expected that this changes once the market offers incentives for buildings with predictive building management systems. Applications are the focus of ongoing research and include the aforementioned optimizations towards energy efficiency, peak clipping or load shifting for the electric grid or other optimization like the use of self-produced renewable energy, which is, for example, state-aided in Germany. Without a smart building automation system that is able to predict and optimize its consumption for the next day it is not possible to benefit from such incentives.

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5. CONCLUSION Building management is an excellent platform for employing simulation based optimization. The management systems today use IT infrastructure and standardized protocols, which allows for seamless integration of thermal simulation. Still this type of optimization has until today not broadly disseminated into the market. One reason is the considerable costs for modeling, which need to be reduced in order to be successful. In terms of research this cost reduction may be achievable by finding ways to create adaptive building models. Such models would be coarsely initialized with the most important building parameters and then adapt to the actual building behavior during operation. Another possibility is that the thermal models emerge as by-products of the design process. Since buildings are created in a 3d modeling tool anyway it may become a feasible solution to use these models as the foundation for simulation-based building operation.

REFERENCES [GenOpt10] GenOpt – Generic Optimization Program http://simulationresearch.lbl.gov/GO/, document date April 26, 2010 [Goo8] Google Sketchup 8, http://sketchup.google.com/, accessed on Oct 10, 2010 [Gru01] Gruber, P.; Gwerder, M.; Tödtli, J. (2001): Predictive Control for Heating Applications, CLIMA 2000 World Congress, 15.-18. September 2001, Neapel [Hett10] Hettfleisch, C., Ledinger, S., Zucker, G., „Energiesparpotenzial eines Passivhauses unter Berücksichtigung von Wetterprognosen“, in Proceedings of the BauSIM 2010, 22.24. Sept. 2010, Wien [Thro01] Thron, U. (2001): Vorausschauende selbstadaptierende Heizungsregelung für Solarhäuser, Dissertation, Fachbereich Maschinenbau, Universität Hannover [TRNSys] TRANSSOLAR Software - TRNSYS Overview, http://www.transsolar.com/__software/docs/trnsys/trnsys_uebersicht_en.htm, accessed on Oct 10, 2010 [EPlus] Building Technologies Program – EnergyPlus Energy Simulations Software, http://www.energyplus.gov/, document date: September 10, 2010 [Tool08] Building Energy Software Tools Directory, http://apps1.eere.energy.gov/buildings/ tools_directory/subjects.cfm/pagename=subjects/pagename_menu=whole_building_analy sis/pagename_submenu=energy_simulation, document date: August 13, 2008