−20 −10 0 10 20 Oversizing factor
Total energy savings (%)
Figure 2.18 – Heat pump optimal sizing for FLEOH distribution A with variable-speed heat pump (blue diamond), distribution A with two-speed heat pump (green square) and distribution B with variable-speed heat
pump (red circle)
2.5
Conclusion
This chapter described the adaptive grid search technique used to map the optimal heat pump perfor- mance as a function of the capacity and indoor and outdoor temperatures. The grid search algorithm was used to find optimal condenser and evaporator airflows and optimal subcooling at each operating point. It can also be used to optimize the heat pump performance in the economizer mode, when the outside temperature is lower than the water temperature. The optimized data for different heat pumps were used in the model predictive control by fitting polynomials to both compressor-on and economizer mode data, as described in more detail in Chapter 4. The optimization was performed for a specific heat pump geometry, but can also be done for other heat pumps by changing geometry of individual components. The impact of zero subcooling with respect to optimal subcooling, and fixed airflows with respect to opti- mal airflows was assessed for the single compressor machines. The results showed that the optimization of airflows has more significance than the subcooling optimization. It was also shown that the the heat pump designed for both sensible and latent cooling can under-perform when used for sensible cooling only, due to a low designed evaporator airflow compared to the optimal. The specific power at optimal fan speeds, as a function of capacity and indoor-outdoor temperature, was compared for R410A-, propane-, and ammonia-charged machines. Finally, the question of optimal sizing of optimally controlled variable- speed heat pumps was explored, and it was shown that modest oversizing is desirable. These findings suggest that the relative sizing of heat pump components (compressor, compressor motor, condenser and evaporator), as well as the sizing of the heat pump itself relative to design load, may benefit from a thorough reassessment of current practice.
Chapter 3
Building Model
When predicting a cooling strategy that results in a cost function reduction (e.g. the price of electricity, or the total electricity consumption) the thermal comfort for the occupants needs to be ensured. Therefore, the important component when developing the model predictive control (MPC) of a building mechanical systems is a model suitable of capturing thermal and hygric response of a building. Besides being accurate and computationally inexpensive, this model should be convenient to use with some of the existing optimization tools.
Many commercially available Building Energy Simulation Programs (BESP) offer more or less detailed simulation of a building’s dynamic behavior. EnergyPlus and TRNSYS are two widely used and compre- hensive BESPs. However, both programs proved to be challenging for the MPC application, since not all variables can be explicitly initialized at the beginning of a simulation. Before a simulation starts, a user must define the initial zone temperature, also assumed to be the initial wall temperature. The inability to explicitly define different wall initial conditions represents a problem for thermally activated building surfaces (TABS) and night precooling analysis since at the beginning of the next day’s optimization the thermal mass temperature can be significantly different than the room temperature.
To overcome the problem with initialization of variables, and also to reduce computational time, the alternative, data-driven (inverse) model was developed for dynamic optimization. TRNSYS model is still used to find appropriate coefficients for the inverse model and to represent a "virtual building". This chapter describes the TRNSYS model and the inverse model of the building zone, both implemented in the MPC algorithm described in Chapter 4. The models have been validated, the TRNSYS model using the experimental measurements and the inverse model using the TRNSYS model predictions. Although the TRNSYS model is more detailed and accurate, the inverse model is significantly faster and, therefore, more suitable for the optimization.
3.1
Literature review
There are numerous commercially available programs for whole building energy simulation such as DOE-2, DesignAdvisor, eQUEST, CAMSOL, SPARK etc. The complete list and overview of a program capabilities can be found in the Building Energy Software Tools Directory (EERE).
The two widely used programs developed to capture building’s transient behavior are EnergyPlus (Craw- ley et al., 1999) and TRNSYS (Transient System Simulation Program, Klein et al. 2010). They are often used for academic research, since they enable detail analysis of complex building systems. Numerous research papers can be found in the literature on their use for variety of building analyses, with TRNSYS more often used in Europe, and EnergyPlus in the U.S.
EnergyPlus is a free, stand-alone simulation program that has its roots in the BLAST and DOE 2 pro- grams. Although the program does not come with a "user friendly" graphical interface, there are many interfaces commercially available (CYPE-Building Services, Demand Response Quick Assessment Tool, DesignBuilder, Easy EnergyPlus, EFEN, AECOsim, Hevacomp, MC4 Suite, SMART ENERGY). The program is not originally designed for a detail analysis of building control systems, but can be linked to programs more suitable for system controls, such as MATLAB or Simulink. The connection can be done through Building Controls Virtual Test Bed (BCVTB) developed by Lawrence Berkeley National Laboratory. BCVTB is a software environment that allows expert users to couple different simulation programs for co-simulation. Some of available options include connections between EnergyPlus, MAT- LAB, Radiance (ray-tracing software for lighting analysis), and BACnet stack (allows data exchange with BACnet compliant Building Automation Systems).
BCVTB also enables the connection between EnergyPlus and the recently developed simulation environ- ment Modelica (Fritzson et al., 2013). Modelica is an equation-based language with open source library, and is aimed to be used for system analysis in different industries (e.g. building industry or automotive companies). It is developed for large models and control systems, including mechanical, electrical, and hydraulic control. Systems can be described through differential, algebraic, and discrete equations, and no particular variable needs to be solved manually because the Modelica tool solves all equations in parallel.
TRNSYS is another comprehensive program developed to describe building’s dynamic behavior. It has a "user friendly" graphical interface called Simulation Studio, and the library that consists of a variety of components, such as a multi-zone building, heat exchangers, pumps, controls etc. The program is very modular, allowing a user to write its own components, or modify the existing ones. To avoid the use of several programs, which would add to complexity and increase computational time, TRNSYS is chosen for the MPC simulation as the program that is highly modular and more suitable for the analysis of building system controls. This chapter will describe in more detail TRNSYS Type 56 for a multi-zone building, which will be implemented in the MPC algorithm. The type is used in combination with the inverse model and optimization function to capture thermal and hygric response of the room with TABS and VAV system.
Due to a lack of programs that combine airflow, heat transfer, and moisture transfer processes in build- ings, Annex 41 of the International Energy Agency (IEA) had the goal of stimulating the development of information and analytical tools. Rode and Woloszyn (2007) gave a detailed overview of the IEA project, description of common exercises, advances in simulation programs, and papers published on the topic. Abadie and Mendes (2006) analyzed both heat and moisture transfer problem with two distinct groups of BESPs and compared them to the known analytical solution. The first, BES 1, was a program that used the response factor method developed by Stephenson and Mitalas (1971). This method is used by both EnergyPlus and TRNSYS building model. The second, BES 2, was a program that used the finite volume method. BES 1 was shown to be 3 times faster than BES 2 for the heat transfer, and 80 times faster for