Buildings and their components account for a major amount of the overall global energy consumption. There is a rising demand to increase the end-use energy efficiency. Advanced automation and control for buildings and their components is one possibility how to achieve the desired goal of lower energy consumption. The model based predictive control approach as a special form of optimal control offers a good way to increase energy efficiency. This paper presents the employment of a model based predictive control algorithm for the energy efficient temperature control of a solar-thermal system consisting of a solar collector and a heat exchanger. The design of the controller is based upon a physical lumped model of the system components. In order to illustrate the potential of the model predictive approach for the use in building automation the comparison to a standard PI control approach is made where the energy consumption for both control concepts is analyzed.
Innovations in today’s energy grids are mainly driven by the need to reduce carbon emissions and the necessary integration of decentralized renewable energy sources. In this context, a transition towards hybrid distribution systems, which effectively couple thermal and electrical networks, promises to exploit hitherto unused synergies for increasing efficiency and flexibility. However, this transition poses practical challenges, starting already in the design phase where established design optimization approaches struggle to capture the technical details of control and operation of such systems. This work addresses these obstacles by introducing a design approach that enables the analysis and optimization of hybrid thermal-electrical distribution systems with explicit consideration of control. Based on a set of key prerequisites and modeling requirements, co-simulation is identified as the most appropriate method to facilitate the detailed analysis of such systems. Furthermore, a methodology is presented that links the design process with the implementation of different operational strategies. The approach is then successfully applied to two real-world applications, proving its suitability for design optimization under realistic conditions. This provides a significant extension of established tools for the design optimization of multi-energy systems.
Abstract-The implementation of components and systems of building automation, in particular for HVAC systems and renewables, opens the possibility to increase the energy efficiency of (clusters of) buildings even further than originally intended. By additionally implementing different control strategies, it is possible to use the thermal storage capacity of buildings to improve their electrical characteristic in order to better meet the needs of the electricity grid. However, it is usually hard to obtain the thermal storage parameters of a building, because they need to be computed in complex thermal simulations. In this paper we present a way to quickly estimate an approximation thereof and feed it to strategies of different complexity, dependent on the free resources of the building automation installation.
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