Roozbeh Sangi

Dynamic Modeling, Simulation and Exergy Analysis of an Innovative Hydronic Heating System

In this study, dynamic modeling and simulation of a decentralized hydronic heating system has been performed. The simulated space heating system is a model of an innovative heating system of an apartment with a boiler that supplies heat for eight radiators which are served by distribution pipes. Eight small pumps, located at the outlet of the radiators instead of a central circulation pump, work according to demand, and supply each radiator with hot water only when required. A PID controller regulates the rotational speed of the pumps and consequently the power of the radiators to maintain the room temperature at the set point.To evaluate the performance of such a heating system, a model of the entire energy chain from generation to distribution is needed. Therefore, in this work, detailed sub-models for the hydraulic system have been developed and coupled with the model of a building to make a general model for simulating the performance of the whole system. The analysis has been carried out by simulation of the system using the object-oriented programing language Modelica. Dymola, which is a multi-domain modeling and simulation tool, has been used as simulation environment. The Modelica Standard Library 3.2 and the Modelica libraries for building simulation such as BaseLib, Database, Building and HVAC components libraries have been applied to simulate the hydraulic and thermal behavior of the system. In the next step, the system has been analyzed form exergy point of view and the simulation results from the dynamic model have been presented in an exergetic framework.Copyright

Simulation-based implementation and evaluation of a system of systems optimization algorithm in a building control system

The objective of this research is to evaluate the performance of a system of systems optimization algorithm, namely, L4G-PCAO, in building energy systems. Since the test bed of this research is an office building with more than two hundred occupiers, the heating and cooling demands of the building must always be fully satisfied. Consequently, changes in the currently-installed control system cannot be made forthrightly. Therefore, fresh ideas like implementation of new control strategies or optimization algorithms should be firstly put to the test via dynamic simulation, which makes engineers capable of examining new control and optimization strategies. The performance should then be analyzed and evaluated before implementing in the use case. This paper presents a strategy for simulation-based implementation of L4G-PCAO in a building energy system and also evaluates its performance. The results show that it is not only possible to conserve energy by applying this newly-developed optimization algorithm to existing control systems, but also it can shift the usage of energy sources in a more environment-friendly direction.

System of Systems theory as a new perspective on building control

Energy efficiency of buildings has been identified as a core element in facing the climate change and the scarcity of energy resources. The energy supply system for a building is, especially with view on thermal interrelations, a highly complex and heterogeneous system that emerges jointly with the building construction, the users behavior, the weather conditions and the surrounding energy and price policy to a real system-of-system. The building control system as the “intelligent instance” of the System of Systems (SoS) takes a special role in realizing the potential of the joint operating constituent systems due to the energy efficiency of the building or district. We apply SoS as an innovative engineering approach to a complex energy system.

A linear model predictive control for advanced building energy systems

Attempts to develop efficient and environmentally friendly building energy systems have led to modern complex energy concepts for buildings, which have consequently initiated a need for new control strategies for them. Model predictive control, which uses a system model to predict the future states of the system, offers a promising solution to this challenge. In this study, a model predictive control for modern complex building energy systems is developed. In order to keep the calculation times as short as possible, mixed integer linear programming is used to develop the model for the controller. The developed linear model predictive controller is evaluated by implementing it into a model of building using software-in-the-loop simulations. The building model represents a generic advanced building energy systems. The default controller of the case study is a mode-based controller, which is considered as the reference case for evaluation purposes. It is revealed that the developed linear predictive control could save up to 22, 5 % of energy consumption compared to the mode-based control.