Refine
Year of publication
Document Type
- Conference Proceeding (13)
- Article (reviewed) (12)
- Part of a Book (6)
- Contribution to a Periodical (2)
Conference Type
- Konferenzartikel (11)
- Konferenz-Abstract (1)
- Sonstiges (1)
Language
- English (33) (remove)
Is part of the Bibliography
- yes (33)
Keywords
- Haustechnik (4)
- MPC (3)
- Renewable Energy (3)
- Algorithmus (2)
- Batterie (2)
- Fotovoltaik (2)
- Photovoltaic (2)
- Smart Grid (2)
- Adaptive predictive control (1)
- Automation (1)
Institute
- Fakultät Maschinenbau und Verfahrenstechnik (M+V) (25)
- INES - Institut für nachhaltige Energiesysteme (13)
- CRT - Campus Research & Transfer (5)
- Zentrale Einrichtungen (2)
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (1)
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (1)
Open Access
- Closed Access (17)
- Open Access (9)
- Closed (4)
- Bronze (3)
- Gold (2)
Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.
Building energy management systems (BEMSs), dedicated to sustainable buildings, may have additional duties, such as hosting efficient energy management systems (EMSs) algorithms. This duty can become crucial when operating renewable energy sources (RES) and eventual electric energy storage systems (ESSs). Sophisticated EMS approaches that aim to manage RES and ESSs in real time may need high computing capabilities that BEMSs typically cannot provide. This article addresses and validates a fuzzy logic-based EMS for the optimal management of photovoltaic (PV) systems with lead-acid ESSs using an edge computing technology. The proposed method is tested on a real smart grid prototype in comparison with a classical rule-based EMS for different weather conditions. The goal is to investigate the efficacy of islanding the building local network as a control command, along with ESS power control. The results show the implementation feasibility and performance of the fuzzy algorithm in the optimal management of ESSs in both operation modes: grid-connected and islanded modes.
Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System
(2015)
Demand Side Management for Thermally Activated Building Systems based on Multiple Linear Regression
(2015)
Automation devices or automation stations (AS) take on the task of controlling, regulating, monitoring and, if necessary, optimising building systems and their system components (e.g. pumps, compressors, fans) based on recorded process variables. For this purpose, a wide range of control and regulation methods are used, starting with simple on/off controllers, through classic PID controllers, to higher-order controllers such as adaptive, model-predictive, knowledge-based or adaptive controllers.
Starting with a brief introduction to automation technology (Sect. 7.1), the chapter goes into the structure and functionality of the usual compact controllers using the application examples of solar thermal systems and heat pump systems (Sect. 7.2). Finally, the integration of system automation into a higher-level building automation system and into the building management system is described using specific application examples (Sect. 7.3).
The use of renewable energy sources for heating and cooling in buildings today offers the best opportunities to avoid the use of fossil fuels and the associated climate-damaging emissions. However, unlike fossil fuels, renewable energy sources such as solar radiation are not available at the push of a button, but occur uncontrollably depending on weather conditions, the location of the building and the time of year. Their use is free of charge. However, complex converters and systems usually have to be installed in order to use them. These must be carefully planned and operated in order to avoid unnecessary costs and to generate the maximum possible yield. The regenerative energy systems are usually integrated into existing conventional systems. When designing the control and regulation equipment, it is crucial to design the automation of the systems in such a way that primarily renewable energy sources are used and the share of fossil energy sources is minimized.