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Über zwei Jahrzehnte hat sich an der Hochschule Offenburg im Umfeld von Professor Elmar Bollin eine Forschungsgruppe etabliert, die die Bereiche Gebäudeautomation und nachhaltige Energietechnik zusammenführten. Anfänglich ging es darum die Potenziale der internetbasierten Wetterprognostik und modell-basierten Anlagensteuerung für die Verbesserung des Komforts und der Energieeffizienz im Gebäude zu nutzen. Im Rahmen von Forschungs- und Entwicklungsarbeiten mit Einsatz von dynamischen Gebäudesimulationen konnte schließlich ein Algorithmus gefunden werden, der es ermöglichte auf Basis von prognostizierter Außentemperatur und Sonneneinstrahlung den Energiebedarf eines Bürogebäudes für den Folgetag vorherzusagen. In Verbindung mit der Gebäudeautomation entstand so die adaptive und prädiktive TABS-Steuerung AMLR.

In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.

Inhalt der AMLR-Entwicklung war es, einen selbstlernenden bzw. adaptiven und vorausschauenden Algorithmus auf Basis der MLR-Methode zur Steuerung von TABS, den AMLR-Algorithmus, zu finden. Auf die Installation eines TABS-Rucklauftemperatursensors fur jede TABS-Zone sollte dabei aus Kostengrunden verzichtet werden. Dazu musste der lineare MLR-Zusammenhang um die Einbeziehung einer Raumtemperaturmessung erweitert und ein Zusammenhang zwischen der den TABS zugefuhrten Energie Qmc und der an die am Bauteil angrenzenden Raume ubertragenen Energie Qua hergestellt werden.

Die in Abb. 3.1 dargestellte TABS-Regelstrecke wurde mit Hilfe von Laborversuchen charakterisiert. In der Regelungstechnik wird das Verhalten von Strecken untersucht und durch Sprungantworten charakterisiert. Es wird zunachst angenommen, dass es sich bei TABS um ein lineares zeitinvariantes System (LZI) vom TYP PT1 handelt.

Die MLR (Multiple Lineare Regression) gehort zur Gruppe der Regressionsanalysen. Die Regressionsanalyse ist eine Methode um eine Beziehung zwischen unabhangigen Variablen Xp und abhangigen Variablen Yi durch die Bestimmung der unbekannten Parameter sp zu ermitteln. Im Gegensatz zur einfachen linearen Regression besitzt die Multiple Lineare Regression mehrere unabhangige Variablen. Bei der MLR wird davon ausgegangen, dass der Zusammenhang zwischen allen Variablen linear ist. Die allgemeine Form der MLR wird in (4.1) dargestellt.

The building sector is one of the main consumers of energy. Therefore, heating and cooling concepts for renewable energy sources become increasingly important. For this purpose, low-temperature systems such as thermo-active building systems (TABS) are particularly suitable. This paper presents results of the use of a novel adaptive and predictive computation method, based on multiple linear regression (AMLR) for the control of TABS in a passive seminar building. Detailed comparisons are shown between the standard TABS and AMLR strategies over a period of nine months each. In addition to the reduction of thermal energy use by approx. 26% and a significant reduction of the TABS pump operation time, this paper focuses on investment savings in a passive seminar building through the use of the AMLR strategy. This includes the reduction of peak power of the chilled beams (auxiliary system) as well as a simplification of the TABS hydronic circuit and the saving of an external temperature sensor. The AMLR proves its practicality by learning from the historical building operation, by dealing with forecasting errors and it is easy to integrate into a building automation system.

In this study, a high-performance controller is proposed for single-phase grid-tied energy storage systems (ESSs). To control power factor and current harmonics and manage time-shifting of energy, the ESS is required to have low steady-state error and fast transient response. It is well known that fast controllers often lack the required steady-state accuracy and trade-off is inevitable. A hybrid control system is therefore presented that combines a simple yet fast proportional derivative controller with a repetitive controller which is a type of learning controller with small steady-state error, suitable for applications with periodic grid current harmonic waveforms. This results in an improved system with distortion-free, high power factor grid current. The proposed controller model is developed and design parameters are presented. The stability analysis for the proposed system is provided and the theoretical analysis is verified through stability, transient and steady-state simulations.

There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I.

—Research in Microgrid has taken recently more attentions in the context of solving the conventional centralized power network constraints. Combining information and power technologies, the efficiency and reliability of the power network have been improved dramatically. As an additional advantage, including renewable energy sources and storage in to form distributed energy systems has proven its aptitude to solved some economic and technical penalties appearing in classical centralized systems. However, appropriate energy management algorithms in hybrid microgrids are primordial to reach the objectives mentioned previously, for which, this paper has given a significant contribution. This work proposes a practical and efficient solution to implement a real-time energy management policy on a microgrid prototype based on commercial products. This method is implemented and tested successfully to manage the power flow between 6.3 KWp photovoltaic system; 4.5 KW lead-acid batteries and 3.6 KW programmable loads. The objective is to maximize the use of renewable energy while driving efficiently the storage system. Another target summarized in operating the microgrid in island mode as much as possible to improve its reliability.

There is a growing trend toward the use of thermo-active building systems (TABS) for heating and cooling buildings, since these systems are known to be very economical and efficient. However, due to the large thermal inertia, the control of these systems turns out to be complicated and time-consuming in the parameterization. With standard control strategies, the required thermal comfort in buildings often cannot be met, especially if the internal heat sources are suddenly changed. This paper presents a novel adaptive and predictive computation method, based on multiple linear regression (AMLR) for the control of TABS. Through the self-learning effect, no parameterization of heating and cooling curves is necessary. The algorithm is compared to a conventional control strategy for TABS. In this simulation case, it turns out that the adaptive and predictive control strategy can achieve a saving of pump-running time of up to 81% while increasing thermal comfort. In addition, the algorithm is validated by a three-week laboratory experiment. With simple calculation approaches, this algorithm proves to be very practical and can easily be integrated into a building's automation system. Furthermore, it offers the possibility for load shifts from the use of the thermal mass of a building.

This work proposes and simulates a finite set model predictive control technique for the photovoltaic inverter. The power topology is a 30 KW PV array with two power conversion stages: DC/DC boost converter controlled with maximum power point tracking to extract the maximum available photovoltaic power and two-level voltage source inverter with passive filter to convert direct current to alternative current and inject it into a medium voltage grid via a transformer, a 30 KW lead-acid batteries system connected to the inverter DC bus through buck-boost converter. Two control levels appear for the inverter: a simple hysteresis-based power management which sends the power reference to the second level according to the battery state of charge value. The second level is a finite set model predictive controller which makes the inverter inject desired high-quality currents according to the reference. This technique doesn't need a modular for switching and the digital simulation with MATLAB/Simulink software proves the performance of MPC controller in term of injected currents quality with THD value of 0.91% compared to conventional hysteresis controller with THD of 1.57%.

Batteries are commonly used for energy storage in renewable energy systems. A microgrid is a small, independent or grid-connected energy system that enables the coupling and management of energy sources (photovoltaic, wind, grid), energy storage systems (batteries, supercapacitors) and different loads (water heating, electric car, etc.). Lithium-ion technology is today the most widely distributed battery technology for stationary energy storage. Unfortunately, the lifetime of state-of-the-art battery systems is considered still too low to make energy storage commercially viable. Aging on the cell level is a complex process depending strongly on operating conditions [1]. On the battery level, additional aging drivers can result from unbalanced cells or cells with different capacity. In the present study we investigate the aging behavior of a stationary storage system consisting of two lithium-ion battery systems embedded in a microgrid consisting of renewable energy sources (photovoltaic panels and wind turbine), real electrical loads (electric car, offices) and load simulators [2]. 28 commercial 180 Ah high-energy LFP/graphite prismatic cells were assembled into two 8 kWh stacks with different architectures, that is, serial and parallel connection of the cells. The individual cells were characterized under laboratory conditions before system assembly, and selection of the cells was carried out, providing cells of similar capacity to the serial configuration and cells with a large capacity spread to the parallel system. Thus, the influence of the stack architecture both on performance and on aging could be investigated. The stacks were operated in the microgrid, providing both stacks strictly with the same power demand. In regular intervals the individual cells were disassembled from the system to perform laboratory checkups that consisted of capacity, internal resistance and electrochemical impedance measurements. The capacity loss of individual cells and stacks was quantified by laboratory checkups and microgrid operation data, respectively. In parallel, two nominally identical cells were aged under laboratory conditions by continuous full cycling (100 % DOD) for a total of 1,200 cycles. These cells were investigated by the same checkups as the microgrid cells, performed at the beginning of life and at certain cycle times. By combining the laboratory and microgrid operation data, this study allows the comparison of stack-level aging as function of stack architecture and the comparison of cell-level aging under laboratory cyclic aging. Acknowledgements: The authors acknowledge the Federal Ministry of Education and Research (BMBF) for funding this work as part of the STABIL project (13FH004PX5). Reference s : [1] B. Weißhar and W. G. Bessler, “Model-based lifetime prediction of an LFP/graphite lithium-ion battery in a stationary photovoltaic battery system”, J. Energy Storage 14, 179-191 (2017). [2] D. Dongol, T. Feldmann, and E. Bollin, “An overview to the concept of smart coupling and battery management for grid connected photovoltaic system”, J. Electronic Science and Technology 13, 367-372 (2015). Figure 1

Fur die Energiewandlung stehen regenerative Energiequellen wie solare Einstrahlung, Erdwarme und Umgebungswarme zur Verfugung. Mit Hilfe spezieller Wandler konnen diese Ressourcen der Nutzung zugefuhrt werden. Dazu gehoren Sonnenkollektoren, Erdwarmesonden und Kaltemaschinen. Sie sammeln die Energie ein und konvertieren sie auf auf ein Temperaturniveau, das so hoch liegt, dass es fur die Warmenutzung geeignet ist. Bei den Kaltemaschinen wird zwischen elektrisch und thermisch angetrieben Maschinen unterschieden.

Die Nutzung erneuerbarer Energiequellen fur die Warme- und Kalteversorgung in Gebauden biete heute beste Moglichkeiten um den Einsatz fossiler Energietrager und die damit verbunden klimaschadlichen Emissionen zu vermeiden. Erneuerbare Energiequellen wie die solare Einstrahlung sind aber im Unterschied zu fossilen Energietragern nicht per Knopfdruck verfugbar, sondern fallen je nach Wetterbedingungen, Lage des Gebaudes und Jahreszeit unkontrolliert an. Ihre Nutzung ist kostenlos. Fur die Nutzung mussen aber in der Regel aufwandige Wandler und Systeme installiert werden. Diese mussen sorgfaltig geplant und betrieben werden um unnotige Kosten zu vermeiden und moglichst maximale Ertrage zu erwirtschaften. Die regenerativen Energiesysteme sind meist in vorhandene konventionelle Systeme eingebunden. Bei der Konzeption der Steuer- und Regeleinrichtung kommt es nun entscheidend darauf an, die Automation der Anlagen so zu gestalten, dass vorrangig erneuerbare Energiequellen zum Einsatz kommen und der Deckungsanteil fossiler Energietrager minimiert wird.

Automationsgerate bzw. Automationsstationen (AS) ubernehmen die Aufgabe, gebaudetechnische Anlagen und deren Anlagenkomponenten (z.B. Pumpen, Verdichter, Ventilatoren) basierend auf erfassten Prozesgrosen zu steuern, zu regeln, zu uberwachen und gegenfalls zu optimieren. Hierzu kommen eine breite Bandbreite an Steuerungs – und Regelungsverfahren zum Einsatz, beginnend bei einfachen Zweipunktreglern, uber klassische PID-Regler bis hin zu hoherwertigen Regelungen wie z.B. adaptive, modellpradiktive, wissensbasierte oder selbstlernende Regler.

The chapter is intended to introduce the predictive control based energy management strategy for the grid connected renewable systems in order to achieve an effective demand side management strategy. Grid connected Photovoltaic battery system as being popular and extensively used has been discussed in this chapter .Conventionally, battery storage has been used to store surplus energy produced and meet the load demand with this stored energy. However, such systems do not respond to the grid conditions and violate grid constraints of permissible grid voltage and frequency limits. The operation of the battery depends on the forecast of photovoltaic output and the load demand and as such a predictive control based energy management strategy is needed. A simple optimization problem for such scenarios has also been formulated in great detail to provide readers with an idea for solving such problems. The results of simulations are also discussed.

Solar energy converted and fed to the utility grid by photovoltaic modules has increased significantly over the last few years. This trend is expected to continue. Photovoltaics (PV) energy forecasts are thus becoming more and more important. In this paper, the PV energy forecasts are used for a predictive energy management system (PEMS) in a positive energy building. The publication focuses on the development and comparison of different models for daily PV energy prediction taking into account complex shading, caused for example by trees. Three different forecast methods are compared. These are a physical model with local shading measurements, a multilayer perceptron neural network (MLP), and a combination of the physical model and the neural network. The results show that the combination of the physical model and the neural network provides the most accurate forecast values and can improve adaptability. From April to December, the mean percentage error (MPE) of the MLP with physical information is 11.6%. From December to March, the accuracy of the PV predictions decreases to an MPE of 78.8%. This is caused by poorer irradiation forecasts, but mainly by snow coverage of the PV modules.

The complexity of sustainable energy systems for buildings services calls for more transparency of the processes which provide energy for the buildings heating, cooling and power needs. In the frame of applied scientific research at University of Applied Sciences Offenburg, different systems and even buildings in total have been monitored over years to analyse their performance and to optimize the system installations and operations. New EU regulations like EN 16001 require an effective monitoring and a continuous commissioning of the energy relevant systems to certificate sustainable processes. On the other hand, new operation tools are necessary to handle the volatility of renewable energy sources and the buildings demand. Predictive building automation has shown good results when applied for energy systems with high inertia. Operating large-scale solar thermal systems and sustainable buildings over long-term periods the University of Applied Sciences provided evidence that monitoring is an essential system tool for an energy and cost efficient operation of sustainable buildings.