Because of increased efforts to reduce CO₂ emissions a significant step in the development of small-scale (residential) biomass boilers for space heating has been achieved in recent years. Currently, the full potential for low-emission operation at high efficiencies, which is in principle possible due to optimized furnace geometries as well as combustion air staging strategies, cannot be exploited since there is still the need to enhance the controllers applied. For this reason, a model based control strategy for small-scale biomass boilers was developed and successfully implemented in a commercially available system. Thereby, appropriate mathematical models were developed for all relevant parts of the furnace and connected to an overall model subsequently used for the control unit design. The resulting controller is based on the input–output linearization and the state variables are estimated by an extended Kalman filter. Finally, the new control was implemented at a commercially available small-scale biomass boiler and the experimental verification showed a significant improvement of the operating behaviour in comparison to the conventional control.

The integration of solar thermal plants into district heating grids requires advanced control strategies in order to utilize the full potential in terms of efficiency and least operating effort. State-of-the-art control strategies cannot completely fulfil this since they are not able to consider the physical characteristics of the different components, nor do they take information on future conditions and requirements into account properly. A promising attempt for improvement is the application of model-based control strategies together with practicable forecasting methods for both the solar yield as well as the heat demand. This contribution will present the results of several projects performed on the development of suitable mathematical models, forecasting methods and control strategies relevant for the integration of solar thermal plants into district heating grids.

The use of renewable-energy-based heat producers within district heating grids is getting more and more popular. In order to benefit from the advantages and compensate for the different disadvantages of the various types of heat producers powered by renewable energy sources like biomass, solar energy or waste heat, a combination of these systems could be favoured over using, for example, only one main biomass-based boiler. Furthermore , in many cases, the additional use of buffer storages is necessary to fully benefit from the use of these kinds of heat producers. A major challenge with such multi-producer heating grids is the cost optimal management of all heat producers and buffer storages. Therefore , a high-level control strategy is necessary, which is able to plan ahead the use of slowly reacting and/or weather dependent heat producers while minimizing operational costs and pollutant emissions. This article shows the development of a linear model predictive controller (MPC) for a district heating grid with several (renewable) decentralized heat producers and heat storages. In order to provide the MPC with the required forecast of the future heat demand, an adaptive load forecasting method has been designed. Additionally, in order to be able to incorporate solar panels, the MPC needs to have a forecast of their possible future heat output. Therefore, a physically motivated solar yield forecasting method has been designed. The required prediction models for the MPC were represented by so-called mixed logical dynamical (MLD) system models. MLD system models combine the modelling power of discrete state system models (finite state machines) and discrete time system models by the extension of the regular linear state-space system model approach with integer and continuous auxiliary variables and linear inequality constraints. The occurrence of both integer and continuous variables within the resulting optimization problem of the MPC leads to a mixed-integer linear program (MILP), which can be solved efficiently using modern MILP solvers. The resulting control strategy is tested in a thermo-hydraulic simulation environment of an actual small-scale multi-producer district heating grid consisting of a medium-scale wood chip boiler with buffer storage, a solar collector with buffer storage and a high temperature heat pump, an oil boiler and 25 heat consumers. Additionally, a state observer was designed and connected with the MPC in order to detect control errors and to incorporate feedback from the heat producers and the buffer storages. The simulations have indicated that the designed MPC and the state observer work properly. Therefore, these elements have been implemented on-site on the actual heating grid, with the first test run scheduled for October 2017.
Modellprädiktive Regelung eines solar-und biomassebasierten Fernwärmenetzes | Request PDF. Available from: https://www.researchgate.net/publication/321314304_Modellpradiktive_Regelung_eines_solar-und_biomassebasierten_Fernwarmenetzes [accessed Feb 21 2018].

Scheitholzkessel sind die in Europa immer noch am stärksten verbreitete Form von Holz-basierten Zentralheizungssystemen. Der Bestand ist überaltert und weist die größten Anteile an den verursachten Schadstoffemissionen aus Festbrennstoffzentralheizungssystemen auf. Das Ziel des Projektes, die komplette Neuentwicklung einer modellbasierten Regelung für Scheitholzkessel mit Pufferspeichern und einer Solaranlage, stellte einen Technologie-sprung in Richtung einer drastischen Reduktion der Schadstoffemissionen (CO, org. C, Fein-staub) bei gleichzeitiger Erhöhung des Nutzungsgrades und Benutzerkomforts dar. Dabei erfolgte sowohl die übergeordnete Regelung des Zusammenspiels der Komponenten (Systemregelung) als auch die Regelung der einzelnen Komponenten (Feuerungsregelung, Hydraulikregelung) modellbasiert. Die neue Regelung basiert auf einer gezielten Interaktion mit dem Benutzer, in welcher der Benutzer zielgerichtet zum Nachlegen einer bestimmten Brennstoffmenge in einem bestimmten Zeitraum aufgefordert wird. Zusätzlich dazu werden alle Teilprozesse (Verbrennung des Scheitholzes, Übertragung der Wärme in den Pufferspeicher, usw.) modellbasiert und damit deutlich effizienter und genauer geregelt. Im Fall der Feuerungsregelung wurde zusätzlich zur modellbasierten Regelung von Vorlauf-temperatur und Sauerstoffgehalt auch eine innovative CO-l-Regelung eingesetzt, die basierend auf einer kontinuierlichen Schätzung der CO- l-Charakteristik unter Verwendung eines kombinierten Sensors zur Sauerstoffmessung und Detektion unverbrannter Kompo-nenten stets einen für den aktuellen Betriebszustand optimalen Sollwert für den Sauer-stoffgehalt vorgibt. Die laufende Anpassung des Sauerstoffgehaltes führt zu einer deutlichen Reduktion der Schadstoffemissionen (CO, org. C, Feinstaub). Zum Erreichen dieser Ziele wurden im Wesentlichen folgende Schritte durchgeführt:

• Experimentelle Untersuchung und Modellierung des Abbrandverhaltens von Scheitholz (inklusive der CO-l-Charakteristik)
• Entwicklung einer übergeordneten modellbasierten Systemregelung
• Entwicklung einer modellbasierten Feuerungsregelung (inkl. CO-l-Regelung) für einen effizienten und schadstoffarmen Betrieb des Scheitholkessels
• Experimentelle Bewertung des Potentials der modellbasierten Regelung
• Analyse der Anforderungen zur Anpassung der Regelung an andere Konfigurationen

Das beantragte Projekt leistete somit einen entscheidenden Beitrag zum Ausschreibungs-schwerpunkt „Effiziente und emissionsarme Klein- und Kleinstfeuerungen durch Integration einer intelligenten Verbrennungs- und Leistungsregelung“ und ging zusätzlich explizit auf die im Ausschreibungsleitfaden adressierte Verwendung von kombinierten Sensorsystemen wie CO- l-Sensorsysteme zur Verbrennungsregelung ein. Dabei ist insbesondere hervorzuheben, dass der durchdachte Ansatz das Sensorsignal zu Schätzung der CO- l-Charakteristik zu verwenden den wesentlichen Vorteil mit sich bringt, dass die exakte Messung der CO-Emissionen durch den Sensor nicht erforderlich ist, sondern es ausreicht, wenn dieser die Tendenzen richtig wiedergibt.

This paper proposes a novel design algorithm for nonlinear state observers for linear time-invariant systems. The approach is based on a well-known family of homogeneous differentiators and can be regarded as a generalization of Ackermann's formula. The method includes the classical Luenberger observer as well as continuous or discontinuous nonlinear observers, which enable finite time convergence. For strongly observable systems with bounded unknown perturbation at the input the approach also involves the design of a robust higher order sliding mode observer. An inequality condition for robustness in terms of the observer gains is presented. The properties of the proposed observer are also utilized in the reconstruction of the unknown perturbation and robust state-feedback control

Energy management systems aiming for an efficient operation of hybrid energy systems with a high share of different renewable energy sources strongly benefit from short-term forecasts for the heat-load. The forecasting methods available in literature are typically tailor-made, complex and non-adaptive. This work condenses these methods to a generally applicable, simple and adaptive forecasting method for the short-term heat load. From a comprehensive literature review as well as the analysis of measurement data from seven different consumers, varying in size and type, the ambient temperature, the time of the day and the day of the week are deduced to be the most dominating factors influencing the heat load. According to these findings, the forecasting method bases on a linear regression model correlating the heat load with the ambient temperature for each hour of the day, additionally differentiating between working days and weekend days. These models are used to predict the future heat load by using forecasts for the ambient temperature from weather service providers. The model parameters are continuously updated by using historical data for the ambient temperature and the heat load, i.e. the forecasting method is adaptive. Additionally, the current prediction error is used to correct the prediction for the near future. Due to their simplicity, all necessary steps of the forecasting method, the update of the model parameters, the prediction based on linear regression models and the correction, can be implemented and computed with little effort. The final evaluation with measurement data from all seven consumers investigated leads to a Mean Absolute Range Normalized Error (MARNE) of 2.9% on average, and proves the general applicability of the forecasting method. In summary, the forecasting method developed is generally applicable, simple and adaptive, making it suitable for the use in energy management systems aiming for an efficient operation of hybrid energy systems.

Fixed-bed biomass gasification coupled with internal combustion engines allows an efficient exploitation of biomass for the combined production of heat and power (CHP) at small scale with increased economic viability with respect to combustion-based CHP systems. The main barrier on the way towards a wider market distribution is represented by the fact that a robust practical operation of state-of-the-art fixed-bed biomass gasification systems is limited to very specific fuel properties and steady-state operation. The aim of this work is twofold. On the one hand, it presents the results of a series of test runs performed in a monitored commercial plant under different process conditions, in order to assess its behaviour during load modulation and fuel property variations. On the other hand, an in-house developed thermodynamic equilibrium model was applied to predict the behaviour of the gasification reactor. This gasification model could be used for the development of a model-based control strategy in order to increase the performance of the small-scale gasification system. To assess the general operational behaviour of the whole gasification system an experimental one-week-long test run has been performed by BIOENERGY 2020+ and the Free University of Bozen-Bolzano as round robin test. The plant has been tested under different operating conditions, in particular, varying the load of the engine and the moisture content of the feedstock. The outcomes shown in the present work provide a unique indication about the behaviour of a small-scale fix-bed gasifier working in conditions different from the nominal ones.

Workshop of the research project ÖKO-OPT-QUART (Ökonomisch optimiertes Regelungs- und Betriebsverhalten komplexer Energieverbünde zukünftiger Stadtquartiere)

The continuous increase of (volatile) renewable energy production and the development of energy-efficient buildings have led to a transformation of city districts’ energy systems. Their complexity has increased significantly due to the coupling of the different energy sectors like heating, cooling and electricity. Such complex multi-energy systems can be operated more efficiently and reliably if knowledge of their specific components (in terms of mathematical models) as well as knowledge of weather forecasts is incorporated in a high-level controller, which is typically referred to as an Energy Management System (EMS). However, still little comprehensive information on the costs and the practical advantages of such systems is available. For this reason, a simulation environment to estimate the real costs and advantages of the use of such an EMS is required. Consequently, this work focuses on the development of an EMS for future city districts’ energy systems and the development of a co-simulation environment in order to demonstrate the benefits of the use of the developed EMS in comparison to a conventional control strategy. The co-simulation is implemented with the aid of the co-simulation platform Building Controls Virtual Test Bed (BCVTB) and consists of the following parts: a non-linear, thermoelectric model and a control block containing either the conventional control strategy or the EMS. The thermoelectric model is built up using the well-established simulation tools TRNSYS and IDA-ICE, simulating the energy hub of the city district and the districts’ buildings, respectively. The control block is simulated using MATLAB, where IBM ILOG CPLEX is used for solving the resulting mixed-integer linear program (MILP) of the EMS. Finally, an economic model for financial (and ecological) assessment of the operation is simulated with the aid of the software package Dymola. To put the developed EMS and the co-simulation into practise a case study based on a new city district in Graz, Austria, which is currently in the planning stage, is carried out. The integration of the responsible planners and investors in the modelling process guarantees the models’ practical applicability. In the case study the performance of the originally planned conventional control strategy is compared with the performance of the developed EMS using annual simulations with a simulation time step of 1 minute, and a 24 hour prediction horizon and a 15 minute time step for the EMS. For a more robust and realistic comparison both control strategies are simulated for different scenarios considering current and future (2060) climate conditions, medium and high energy demands (load), ideal and real load prediction methods and varying import prices for electricity from the electricity grid. The results show that the use of the developed EMS strategy results in reduced annual total costs (considering operational and investment costs of additionally suggested distributed energy resources) in comparison to the conventional control strategy. Furthermore, the annual CO2-emissions could be reduced by increasing the self-consumption of the installed (renewable) energy resources and thus decreasing the necessary energy imports from the electricity and the heating grid.

Slides of the talk "Co-Simulation of an Energy Management System for Future City District Energy Systems"

Assessing the operational behaviour of biomass gasification systems is a crucial basis for further improvements in terms of operational behaviour and robustness in order to increase the technologies’ operational and economic viability. However, in most fixed-bed biomass gasification systems not all parameters required for the assessment can be measured directly. Typically, unknown parameters are determined by using as many balance equations as parameters have to be determined neglecting the additional information provided by other available but not chosen balance equations. Thus, these approaches do not incorporate all measurement data available resulting in a lack of reliability in their results. A detailed analysis of these approaches emphasises that even small deviations in the measurement data can lead to significant deviations in the calculated parameters, demonstrating that individual choices of equations can be highly sensitive regarding measurement uncertainties.

Therefore, an adjusted weighted least squares approach is developed utilizing an overdetermined system of equations incorporating all balance equations simultaneously. Thus, all measurement data available is taken into account, minimizing the influences of measurement uncertainties on the determined parameters. A comprehensive analysis shows that this approach is less sensitive to measurement uncertainties, allowing for a more reliable and accurate assessment of fixed-bed biomass gasifiers.

Keywords: fixed-bed, gasification, mass balance, performance assessment

Sustainable technologies can hardly compete with fossil-based technologies in terms of economic efficiency. One sustainable technology with special relevance due to its wide range of application and industrial readiness is biomass gasification using a dual fluidized bed (DFB). The economic challenges of a DFB gasification plant are addressed without constructional measures by adapting a current control strategy. This paper proposes a model-based control strategy aiming for increased economic efficiency of a DFB gasification plant considering exemplarily the “HGA Senden” in Ulm, Germany. A process analysis reveals high potential for improvement at the current control strategy for the synchronization of product gas production and utilization. A significant surplus of product gas is burned in an auxiliary boiler just for synchronization, and regular manual adjustments by the plant operators at the fuel feed are necessary. The model-based control strategy synchronizes by actuating the auxiliary boiler and the fuel feed simultaneously. The model-based control strategy is experimentally validated for over one month at the “HGA Senden” proofing a significant increase in economic efficiency. So, the economic efficiency of this technology for the sustainable production of energy and products is increased by model-based control.

A method for deriving kinetic models of gas–solid reactions for reactor and process design is presented. It is based on the nonparametric kinetics (NPK) method and resolves many of its shortcomings by applying tensor rank‐1 approximation methods. With this method, it is possible to derive kinetic models based on the general kinetic equation from any combination of experiments without additional a priori assumptions. The most notable improvements over the original method are that it is computationally much simpler and that it is not limited to two variables. Two algorithms for computing the rank‐1 approximation as well as a tailored initialization method are presented, and their performance is assessed. Formulae for the variance estimation of the solution values are derived to improve the accuracy of the model identification and to provide a tool for diagnosing the quality of the kinetic model. The methods effectiveness and performance are assessed by applying it to a simulated data set. A Matlab implementation is available as Supporting Information.

Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size is created in , therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach.

ie Biomasseverbrennung spielt eine zentrale Rolle bei der Bereitstellung von Wärme aus erneuerbaren Energieträgern. Konventionelle Biomasse-Brennstoffe werden jedoch aufgrund einer steigenden Anzahl stofflicher Verwertungsmöglichkeiten, wie z.B. der Umwandlung in Chemikalien, teurer und schwieriger zugänglich. Agrarbrennstoffe, die bisher nur selten oder gar nicht in Biomasse-Kleinfeuerungen eingesetzt wurden, stellen eine vielversprechende Alternative zu konventionellen Brennstoffen dar. Diese Agrarbrennstoffe, wie zum Beispiel Kurzumtrieb, Maisspindeln oder Stroh sind kostengünstig und in ausreichender Menge vorhanden. Der Einsatz von Agrarbrennstoffen in konventionellen Biomasse-Kleinfeuerungen ist jedoch aufgrund stark variierender Brennstoffeigenschaften mit erhöhten Anforderungen an das Verbrennungssystem verbunden. Erhöhte N, S, Cl, Alkalimetall- und Aschegehalte sowie niedrigere Aschenschmelzpunkte können zu aschebedingten Problemen (Ascheschmelze, Ascheablagerung und Korrosion) sowie erhöhten Konzentrationen von gasförmigen (CO, NOx, HCl und SOx) und partikelförmigen Emissionen bei der Verbrennung führen.

Ziel der in diesem Beitrag präsentierten Arbeiten war die Erhöhung die Brennstoffflexibilität einer handelsüblichen Biomasse-Kleinfeuerung um damit eine Verbrennung von Agrarbrennstoffen mit niedrigen Schadstoffemissionen und einem hohen Wirkungsgrad zu ermöglichen. Hierzu wurde eine modellbasierte Regelung entwickelt, welche insbesondere eine gezielte Einstellung des Luftverhältnisses in der Primärverbrennungszone ermöglicht und damit das Risiko der Ascheschmelze reduziert und Schadstoffmissionen verringert. Soft-Sensoren bestimmen relevante Brennstoffeigenschaften während des Betriebs, welche von der modellbasierten Regelung zur automatischen Anpassung an geänderte Brennstoffeigenschaften genutzt werden. Die modellbasierte Regelung wurde um eine CO-lambda-Optimierung ergänzt, welche auf Basis von Messwerten des Restsauerstoffgehalts und der CO-Emissionen den Wirkungsgrad der Verbrennung maximiert und gleichzeitig die Schadstoffemissionen verringert. Zur weiteren Verringerung von partikelförmigen Schadstoffemissionen wurde ein am Markt verfügbarer Elektrofilter adaptiert und nach dem Wärmeübertrager der Biomasse-Kleinfeuerung angebracht.

Dieses Verbrennungssystem wurde durch umfassende Testläufe mit begleitenden Emissionsmessungen sowie Brennstoff-, Staub- und Ascheanalysen bewertet. Der Einsatz der modellbasierten Regelung führte zu einem stabileren Betrieb bei allen Leistungen und für alle Brennstoffe. Der Elektrofilter zeigte sehr zufriedenstellende Abscheidegrade für alle untersuchten Brennstoffe und Anlagenleistungen. Dadurch konnte die Brennstoffflexibilität der handelsüblichen Biomasse-Kleinfeuerung erhöht und die Verbrennung von Agrarbrennstoffen ermöglicht werden.

The continuous increase of (volatile) renewable energy production and the coupling of different energy sectors such as heating, cooling and electricity have significantly increased the complexity of urban energy systems. Such multi-energy systems (MES) can be operated more efficiently with the aid of optimization-based energy management systems (EMS). However, most existing EMS are tailor-made for one specific system or class of systems, i.e. are not generally applicable. Furthermore, only limited information on the actual savings potential of the usage of an EMS under realistic conditions is available. Therefore, this paper presents a novel modular modeling approach for an EMS for urban MES, which also enables the modeling of complex system configurations. To assess the actual savings potential of the proposed EMS, a comprehensive case study was carried out. In the course of this the influence of different user behavior, changing climatic conditions and forecast errors on the savings potential was analyzed by comparing it with a conventional control strategy. The results showed that using the proposed EMS in conjunction with supplementary system components (thermal energy storage and battery) an annual cost savings potential of between 3 and 6% could be achieved.

A new model-based control strategy for biomass grate boilers is presented in this paper. Internal model control is used to control four outputs of the plant and to achieve a control structure with fewer control parameters needing to be experimentally tuned. A nonlinear state–space model describing the essential behaviour of the biomass grate boiler is used for controller design. The inverse system dynamics representing the main part of internal model control are designed with the help of this model. In doing so the properties of differentially flat systems are used. Due to a time delayed input, the inverse system is determined only for three input output channels. The stabilization of the inverse system dynamics, however, is a challenging task. A stabilization method with the help of the time delayed input is suggested and a stability analysis is given. The new control strategy has only three parameters to be tuned, representing a major reduction of complexity in comparison to existing model-based approaches. Finally, experimental results of the implemented control strategy on representative biomass grate boiler with a nominal capacity of 180 kW are presented and compared to an existing model-based control strategy based on input output linearization. The experimental evaluation proves that it is possible to operate the biomass boiler in all load ranges with high efficiency and low pollutant emissions.

Absorptionskälteanlagen können einen wesentlichen Beitrag zur Verringerung von CO2-Emissionen leisten, wenn Wärme aus regenerativen Energieträgern oder Abwärme aus industriellen Prozessen zum Antrieb verwendet wird. Absorptionskälteanlagen weisen bereits jetzt eine hohe Effizienz auf, bei veränderlichen Betriebsbedingungen kann diese je nach vorhandenen Stellgliedern weiter gesteigert werden. Dazu werden im Rahmen des Forschungsprojektes „Heat Pumping Systems Control (HPC)“ zwei Absorptionskälteanlagen – eine mit der Stoffpaarung Ammoniak/Wasser (NH3/H2O) und eine mit der Stoffpaarung Wasser/Lithiumbromid (H2O/LiBr) – untersucht, um für unterschiedliche Anwendungen optimale Betriebsstrategien zu entwickeln. Zur Berücksichtigung der Zustandsänderungen in der Absorptionskälteanlage, werden dynamische Simulationsmodelle in der Modellierungssprache Modelica entwickelt und mit Messdaten validiert.

Im Rahmen dieses Konferenzbeitrags werden Komponentenmodelle für die NH3/H2O-Absorptionskälteanlage und Simulationsrechnungen bei veränderlichen Randbedingungen präsentiert, sowie ein Vergleich mit Messdaten diskutiert.

The majority of renewable energy technologies are volatile in nature. External factors such as weather conditions lead to fluctuations in their produced electricity and heat. This results in a demand either not being covered or dissatisfied since too much electricity and heat is produced in the energy system. Although energy storages can counteract these fluctuations, renewable energy technologies that are capable of producing energy on demand are needed as well. As such, technologies based on the thermochemical conversion of biomass are especially relevant as they are considered to be CO2-neutral. Although most existing implementations are based on combustion of biomass, fixed-bed biomass gasification is of growing relevance due to higher overall efficiencies and low pollutant emissions. Currently, fixed-bed biomass gasifiers are usually operated at steady-state operation to produce the maximum amount of energy possible. This contribution investigates, whether they can be used as a technology for demand-oriented electricity and heat production

The operation characteristics of thermally driven absorption refrigeration systems (ARS) are strongly dependent on their hydraulic integration. Therefore, varying operating conditions of the hydraulic supply have a great influence on the behaviour of ARS and lead to dynamic operation, which can affect the efficiency and is largely unexplored so far. To enable a simple investigation of ARS integration considering their dynamic behaviour and to develop modern, efficiency-enhancing control strategies, dynamic simulation models of ARS are developed in Modelica Code.

In this paper, a dynamic simulation model of an ARS with the working pair ammonia/water (NH3/H2O) is presented. The parameterization and the physical correlations of selected components of the simulation model are described. Afterwards, the simulation model is verified by comparing simulation results with measurement data of the NH3/H2O-ARS. Finally, the capabilities of the simulation model are demonstrated by performing a simulation-based analysis of the temperature glide of the refrigerant in the evaporator.

Industrial plants using DFB biomass gasification are on the verge of profitability. These plants should be operated more economically in order to support the industrial applications for renewable technologies of this kind. Since some parts of such plants are typically difficult to control, a state-of-the-art control strategy is analyzed here in the context of its potential for increased economic efficiency. The DFB gasification plant “HGA Senden” in Ulm, Germany is considered on an exemplary basis here. A process analysis reveals a high potential in the synchronization of product gas generation and utilization. At the present time a relevant surplus of product gas is burned in an auxiliary boiler for synchronization purposes and regular manual adjustments at the fuel feed are necessary by the plant operators. For this synchronization a novel control strategy is developed that actuates the auxiliary boiler and the fuel feed simultaneously. The novel control strategy was experimentally validated for a period of over one month. Due to this long-term evaluation the fuel consumption was reduced by 5% and the manual adjustments of the fuel feed that were necessary on average every 30min were eliminated. As a result DFB gasification plants can be operated more economically by applying the novel control strategy for synchronization of product gas generation and utilization.

Long Term Validation of a New Modular Approach for CO-Lambda-Optimization

The optimization of existing biomass boilers in terms of efficiency and pollutant emissions is essential for their continued economic and ecological viability in future energy systems. These improvements are typically achieved by constructive changes which are expensive and can require prolonged downtimes. A well-known method for optimizing biomass boilers in terms of efficiency and pollutant emissions without constructive changes is the so-called CO-lambda-optimization. While multiple approaches for CO-lambda-optimization have been presented in literature, they are still rarely used in real biomass boilers. This is partly due to the fact that these approaches do not meet the requirements associated with their long-term operation in real biomass boilers. This contribution presents a new and modular approach for the CO-lambda-optimization which is specifically designed to meet these requirements. Particular emphasis in this contribution is laid on the long-term validation of the presented approach for CO-lambda-optimization at a medium-scale fixed-bed biomass boiler.

With the share of renewable energy sources increasing in heating and hot water applications, the role of hydraulic heat distribution systems is becoming more and more important. This is due to the fact that in order to compensate for the often fluctuating behaviour of the renewables a flexible heat transfer must be ensured by these distribution systems while also taking the optimal operating conditions (mass flow, temperature) of the individual components into consideration. This demanding task can be accomplished by independently controlling the two physical quantities mass flow and temperature. However, since there exists an intrinsic nonlinear coupling between these quantities this challenge cannot be handled sufficiently by decoupled linear PI controllers which are currently state-of-the-art in the heating sector. For this reason this paper presents a model-based control strategy which allows a decoupled control of mass flow and temperature. The strategy is based on a systematic design approach from models described in this contribution, which are validated by commercially available components from which most of them can be parametrized by the data sheet. The control strategy is designed for a typical hydraulic configuration used in heating systems, which will allow the accurate tracking of the desired trajectories for mass flows, temperatures and consequently heat flows. The controllers are validated experimentally and compared to well-tuned state-of-the-art (PI) controllers in order to illustrate their superiority and prove their decoupling of the control of mass flow and temperature in real world applications.

Modern central heating systems with logwood boilers are comprised of the boiler, a buffer storage and solar thermal collectors. Conventional control strategies for these heating systems do not coordinate the utilization of all components. This can lead to a sub-optimal operation of the entire heating system resulting in a loss of efficiency and increased pollutant emissions. This contribution presents a control strategy which considers all components of the heating system including the user and forecasts for the solar yield and heat demand. It determines and carries out an optimal operating strategy that improves the user utility and maximizes the heating system efficiency while also ensuring a clean and efficient combustion. The control strategy continuously learns the user behavior and instructs the user when to refill the logwood boiler and how much fuel to use. The new control strategy was verified through test runs performed at an experimental setup consisting of a commercially available logwood boiler with a nominal capacity of 28 kW , two buffer storages with a capacity of 1.5 m³ each and a heating device with a thermal output of up to 12 kW simulating a solar thermal collector. During these test runs, the CO emissions were reduced 93.6 %by in the main combustion phase, 7.1 % more solar yield was utilized, the buffer losses were reduced by - 16.9 % and the overall efficiency was increased by 3.1 % . Thus, the application of this control strategy resulted in a significantly improved user utility and heating system efficiency.

Integrated energy systems 1 couples power systems, district heating and cooling (DHC), and gas grids, thereby enabling the storage and distribution of energy across different infrastructure types. Supply and demand follow different patterns in these different domains, which can lead to synergies in generation, storage and consumption, if planned and managed as one energy system. An integrated approach has the potential to increase reliability, flexibility and supply safety and efficiency. Moreover, network coupling increases local utilization of renewables, avoiding problems in the distribution networks, as well as transmission losses. In addition, hybrid energy networks are a promising opportunity to manage and mitigate temporal imbalances of supply and demand in energy systems with a high share of volatile renewables, mainly PV and wind energy. The IEA DHC Annex TS3 provides a holistic approach for designing and assessing hybridization schemes, focusing on the district heating and cooling (DHC) networks and considering both technical (system configuration, operational strategy) and strategic aspects (business models, regulatory frame). These aspects will be discussed within the framework of the IEA DHC Annex TS3 in order to promote the benefits of DHC networks in an integrated energy system. Furthermore we can establish a common direction for the development and implementation of hybrid energy concepts. The IEA DHC Annex TS3 will connect existing national and international projects and thus benefit from interdisciplinary experience and exchange. The primary result of the IEA DHC Annex TS3 will be a guidebook including:  Analyses of available technologies and synergies / application areas  An overview of international case studies including simulation scenarios 1 Different alternative notations can be found in literature, e.g. multi-energy networks, hybrid energy networks, sector coupling, multi-domain networks, cross energy systems. However, since no standard definition is available, those notations are used synonymously.

Energy systems have increased in complexity in the past years due to the everincreasing integration of intermittent renewable energy sources such as solar thermal or wind power. Modern energy systems comprise different energy domains such as electrical power, heating and cooling which renders their control even more challenging. Employing supervisory controllers, so-called energy management systems (EMSs), can help to handle this complexity and to ensure the energy-efficient and cost-efficient operation of the energy system. One promising approach are optimization-based EMS, which can for example be modelled as stochastic mixed-integer linear programmes (SMILP). Depending on the problem size and control horizon, obtaining solutions for these in real-time is a difficult task. The progressive hedging (PH) algorithm is a practical way for splitting a large problem into smaller subproblems and solving them iteratively, thus possibly reducing the solving time considerably. The idea of the PH algorithm is to aggregate the solutions of subproblems, where artificial costs have been added. These added costs enforce that the aggregated solutions become non-anticipative and
are updated in every iteration of the algorithm. The algorithm is relatively simple to implement in practice, re-using almost all of a possibly existing deterministic implementations and can be easily parallelized.
Although it has no convergence guarantees in the mixed-integer linear case, it can nevertheless be used as a good heuristic for SMILPs. Recent theoretical results shown that for applying augmented Lagrangian functions in the context of mixed-integer programmes, any norm proofs to be a valid penalty function. This is not true for squared norms, like the squared L 2 -norm that is used in the classical progressive hedging algorithm. Building on these theoretical results, the use of the L 1 and L-infinity-norm in the PH algorithm is investigated in this paper. In order to incorporate these into the algorithm an adapted multiplier update step is proposed. Additionally a heuristic extension of the aggregation step and an adaptive penalty parameter update scheme from the literature is investigated. The advantages of the proposed modifications are demonstrated by means of illustrative examples, with the application to SMILP-based EMS in mind.

A key factor for the further distribution of biomass boilers in modern energy systems is the capability of changing the applied feedstock during normal plant operation. This is only possible with the application of advanced control strategies that utilize knowledge about the state variables and varying fuel properties. However, neither the state variables nor the fuel properties are measurable during plant operation and, thus, need to be estimated. This contribution presents a method for the simultaneous real-time estimation of the state variables and the fuel properties in fixed-bed biomass boilers which is a novel approach in the field of biomass boilers. The method bases on an Extended Kalman Filter using a nonlinear dynamic model and measurement data from the combustion process. The estimated variables are the masses of dry fuel and water in the fuel bed as well as the fuel's bulk density, water content, chemical composition and lower heating value. The proposed method is easy to implement and requires moderate computational effort which increases the potential of its application at actual biomass boilers. The proposed method is verified with simulation studies and by test runs performed at a representative small-scale fixed-bed biomass boiler. The estimation results show a good agreement with the actual values, demonstrating that the proposed method is capable of accurately estimating the biomass boiler's state variables and simultaneously its fuel properties. For this reason, the presented method is a key technology to ensure the further distribution of biomass boilers in modern energy systems.

A key factor for the further distribution of biomass boilers in modern energy systems is the capability of changing the applied feedstock during normal plant operation. This is only possible with the application of advanced control strategies that utilize knowledge about the state variables and varying fuel properties. However, neither the state variables nor the fuel properties are measurable during plant operation and, thus, need to be estimated. This contribution presents a method for the simultaneous real-time estimation of the state variables and the fuel properties in fixed-bed biomass boilers which is a novel approach in the field of biomass boilers. The method bases on an Extended Kalman Filter using a nonlinear dynamic model and measurement data from the combustion process. The estimated variables are the masses of dry fuel and water in the fuel bed as well as the fuel’s bulk density, water content, chemical composition and lower heating value. The proposed method is easy to implement and requires moderate computational effort which increases the potential of its application at actual biomass boilers. The proposed method is verified with simulation studies and by test runs performed at a representative small-scale fixed-bed biomass boiler. The estimation results show a good agreement with the actual values, demonstrating that the proposed method is capable of accurately estimating the biomass boiler’s state variables and simultaneously its fuel properties. For this reason, the presented method is a key technology to ensure the further distribution of biomass boilers in modern energy systems.

The flue gas mass flow is one of the fundamental quantities of the combustion process in biomass boilers. Since it directly relates to the enthalpy flow entering the heat exchanger, its knowledge is highly advantageous for a sophisticated load control of the biomass boiler. It also includes information regarding the primary and secondary air mass flows as well as the mass flows of potentially occurring leakage air and thermally decomposed fuel. However, in practical application it is not possible to obtain a reliable measurement of the flue gas mass flow. For this reason, this work presents a soft-sensor for the on-line estimation of the flue gas mass flow in biomass boilers. The approach is robust against fouling of the relevant boiler components and is based on standard measurements which are typically available in biomass boilers. In addition, the soft-sensor offers the possibility of monitoring the degree of heat exchanger fouling.

The viability of thermochemical energy storage for a given application is often determined by the reaction kinetics under process conditions. For high exergetic efficiency the process needs to operate in close proximity to the reaction equilibrium. Thus, accurate kinetic models that include the effect of the reaction equilibrium are required.

In the present work, different parametrization methods for the equilibrium term in the General Kinetic Equation are evaluated by modeling the kinetics of two reaction systems relevant for thermochemical energy storage (CaC2O4 and CuO) from experimental data. A non-parametric modeling method based on tensor decompositions is used that allows for a purely data driven assessment of different parametrization methods.

Our analysis shows that including a suitable equilibrium term is crucial. Omitting the equilibrium term when modeling formation reactions can lead to seemingly negative activation energies. Our tests also show that for formation reactions, the reaction rate decreases much faster towards the equilibrium than theory predicts. We present an empirical modeling approach that can predict the reaction rate of gas-solid reactions, regardless of the shortcomings of theory. In this way, non-parametric modeling offers a powerful tool for applied research and may contribute to the advancement of the thermochemical energy storage technology.

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.

Warum ist es sinnvoll, Wärmenetze zu verbinden?

• Erläuterung am Beispiel des Projekts Thermaflex
• Drei Wärmenetze bei Leibnitz in der Steiermark.
• Sind gewachsen und haben die Grenzen ihrer Nachbar-Wärmenetze erreicht.
• Die Wärmenetze werden durch zwei getrennte Eigentümer betrieben.

Overview on different approaches for the control of the heat distribution networks in case of the integration of large-scale solar thermal systems, and different possibilities for the reduction of the operating temperatures in DH systems.

Overview on the control of large-scale thermal plants, limited to plants feeding into DH networks as well as theirkey components, i.e. the actual collector circuit and the heat exchanger between primary and secondary circuit.

Durch die vermehrte Einbindung von Absorptionswärmepumpen und -kältemaschinen in bestehende und zukünftige Energiesysteme des Kälte- und Wärmesektors kann der Anteil erneuerbarer Energien deutlich gesteigert werden. Um dies erfolgreich umsetzen zu können, müssen die Betriebsstrategien und Regelungen dieser Systeme jedoch in der Lage sein, auch mit dynamischen und stark variierenden Betriebsbedingungen umgehen zu können. Dieser Herausforderung hat sich das von der FFG geförderte Projekt HPC – heat pumping system control gewidmet. Im Rahmen dieses Workshops sollen die Ergebnisse und deren Nutzen für die Praxis präsentiert und diskutiert werden.

Three estimators for the estimation of the flue gas mass flow in biomass boilers are presented and compared, namely a sliding-mode observer, a Kalman filter, and a so-called steady-state estimator. The flue gas mass flow is an important process variable in biomass boilers as it contains information about the supplied mass flows of air and decomposed fuel. It is also related to the generated heat flow. Furthermore, its knowledge may be exploited in model-based control strategies which allow one to keep pollutant emissions low, on the one hand, and to achieve high efficiency, on the other hand. However, due to fouling of the equipment over time, measurements and existing estimation methods are not suitable for long-term applications. The estimators proposed in this article are based on a dynamic model for gas tube heat exchangers. They are capable of handling the fouling of the heat exchanger and, additionally, they offer the possibility of monitoring the degree of fouling. By incorporating an additional differential pressure measurement and extending the aforementioned estimators, an improvement regarding the dynamic response and the estimation accuracy is achieved. The application of the estimators to real measurement data from both, a medium-scale and a small-scale biomass boiler, demonstrates their wide applicability.

Heat makes up the largest share of energy end-use, accounting for 50% of global final energy consumption in 2018 and contributing to about 40% of global carbon dioxide (CO2) emissions. Of the total heat produced, about 46% was consumed in buildings for space and water heating. Large-scale solar thermal systems provide a highly valuable possibility to increase the share of renewables in heating systems and to reduce carbon dioxide emissions. In this context, the worldwide number of large-scale solar heating systems has increased rapidly in the last couple of years, especially in China and European countries, e.g. in Denmark. This has led to the installation of about 400 large-scale solar thermal systems ( ≥ 350kWth, 500m²) by the end of 2019.
Unlike other heating systems, their main source of power (solar radiation) cannot be manipulated and is subject to changes on a seasonal as well as on a daily basis. That is why control systems play a very important role for the efficient operation of these systems. This thesis therefore focuses on the application of model-based control strategies, and the necessary preliminary work regarding modelling, in order to achieve an efficient control of large-scale solar thermal systems. Consequently, the thesis addresses three important aspects:
In the first main section, models of components of large-scale solar thermal systems are developed and validated. For the most important components (heat exchanger, solar collector and sensible heat storage), two models of different complexity, one simulation-oriented, one control-oriented, are developed. While the simulation-oriented models aim to model the physical behaviour very accurately in order to be used in simulation studies, control-oriented models aim to model the physical behaviour only as accurately as necessary in order to serve as a basis for model-based control strategies. All models are validated with measurement data from a typical solar system, and it is shown that they are sufficiently accurate for their intended purpose. The sum of the models provides a holistic view on all modelling aspects that have to be considered in large-scale solar thermal plants, and serves as a reasonable basis for model-based control strategies and accurate simulation studies of solar systems.
In the second main section, adaptive forecasting methods for the future solar heat production as well as the heat demand are developed and validated with measurement data and using real weather forecasts. These methods are important to most efficiently integrate and operate solar systems by better scheduling heat production, storage and distribution for the near future. In order to be used in real-world applications, the methods are developed with the goal to meet three important practical requirements: simple implementation, automatic adaption to seasonal changes, and wide applicability. The final long-term evaluation for half a year proves that the developed methods can forecast the solar heat production as well as the heat demand very accurately and outperform common forecasting methods, yielding results that are nearly twice as accurate.
In the third main section, model-based control strategies for the high-level as well as for the low-level control of solar thermal systems are developed and validated. For the high-level control an approach is presented which considers future information by using the developed forecasting methods. It achieves higher profits (plus 3 %) and leads to a more stable operation, compared to the existing commercial solution. For the low-level control, model-based control strategies based on the developed models for the heat generation and distribution are presented. The model-based control strategy for the heat generation considers the dynamic behaviour of the collector and especially considers the variable time-delay. This, compared to conventional control strategies, leads to a significantly better control performance in case of fluctuating solar radiation and changing inlet temperatures. The model-based control strategy for the heat distribution follows a modular approach which can be applied for several hydraulic settings, leading to an accurate and independent control of mass flow and temperature, and outperforms state-of-the-art control strategies. For both control levels, care was taken that the applied strategies can be used in real-world applications regarding their mathematical complexity and computational resources required.
In summary, this thesis presents a holistic approach regarding modelling (simulation-oriented models, control-oriented models and adaptive forecasting methods) and control aspects (high-level as well as low-level control) which can help to improve the efficiency of large-scale solar thermal plants on various levels, making them more competitive, and is furthermore essential for a successful integration of these plants in larger energy systems.

The growth of district heating and cooling (DHC) networks introduces the possibility of connecting them with neighbouring networks. Coupling networks can save costs by reducing operating hours of peak load or backup boilers, or free up production capacity for network expansion. Optimization-based energy management systems (EMS) already provide operators of individual DHC networks with solutions to the unit commitment and economic dispatch problem. They are especially useful for complex networks with multiple producers and integrated renewable energy sources, where incorporating forecasts is important. Time-dependent constraints and network capacity limitations can easily be considered. For coupled networks, a centralized optimization would provide a minimum with respect to an objective function which can incorporate fuel costs, operational costs and costs for emissions. However, the individual coupled networks are generally owned by different organizations with competing objectives. The centralized solution might not be accepted, as each company aims to optimize its own objective. Additionally, all data has to be shared with a centralized EMS, and it represents a single point of failure. A decentralized EMS may therefore be a better choice in a multi-owner setting. In this article, a novel decentralized EMS is presented that can handle multi-owner structures with cooperative and non-cooperative coupling. Each local EMS solves its own optimization problem, and an iterative Jacobi-style algorithm ensures consensus among the networks. The distributed EMS is compared to a centralized EMS based on a representative real-world example consisting of three coupled district heating networks operated by two companies.

The simultaneous operation of multiple connected heating networks can be handled by optimization techniques. However, a global optimum might not represent a good operating strategy if the networks belong to different owners and thus might habe competing interests. An approach from game theory then needs to be applied, which finds a generalized Nash equilibrium instead.

Overview on different approaches for supervisory control strategies,deciding on operating modes and set points for the controls of the different plants and componentsintegrated in solar thermal systems.

An extended version of a Simulink ^® -block providing on-line differentiation algorithms based on discretized sliding-mode concepts is presented. Based on user-specified settings it computes estimates of the time-derivatives of the input signal up to order ten. Different discrete-time estimation algorithms as well as optional filtering properties can be selected. The paper includes an overview of the implemented algorithms, a detailed explanation of the developed Simulink ^® -block and two examples. The first example illustrates the application of the toolbox in a numerical simulation environment whereas the second one shows results obtained via an electrical laboratory setup.

Biomassefeuerungen spielen eine Schlüsselrolle in der Energiewende hin zu einem vollständig erneuerbaren Energiesystem. Allerdings müssen sie sich zukünftigen Herausforderungen stellen, um weiterhin relevant zu bleiben. Einerseits müssen Biomassefeuerungen mit dem höchstmöglichen Wirkungsgrad arbeiten, um wirtschaftlich rentabel zu bleiben während sie gleichzeitig eine hohe Lastmodulationsfähigkeit aufweisen müssen, um für eine breitere Palette von Anwendungen eingesetzt werden zu können. Andererseits müssen Biomassefeuerungen immer strengere Grenzwerte für Schadstoffemissionen einhalten und gleichzeitig in der Lage sein, neue und alternative Biomassebrennstoffe mit geringerer Qualität zu verbrennen.

In dieser Arbeit wird eine modellbasierte Regelungsstrategie entwickelt, die es Biomassefeuerungen ermöglicht, all diese Herausforderungen zu meistern. Diese Regelungsstrategie besteht aus drei Teilen, einer Verbrennungsregelung, einem Zustands- und Parameterschätzer und einer Methode zur CO-lambda-Optimierung. Alle drei Teile werden in dieser Arbeit hergeleitet und im Detail diskutiert, insbesondere im Hinblick auf ihre Implementierung an realen Biomassefeuerungen. Darüber hinaus werden alle drei Teile der modellbasierten Regelungsstrategie durch Simulationsstudien sowie durch eine Implementierung in realen Biomassefeuerungen verifiziert.

Als Grundlage für die modellbasierte Regelungsstrategie wird ein mathematisches Modell abgeleitet, welches das dynamische Verhalten der Prozesse in der Biomassefeuerungen einschließlich des Einflusses der Brennstoffeigenschaften beschreibt. Die berücksichtigten Brennstoffeigenschaften sind die Schüttdichte und die chemische Zusammensetzung einschließlich des Wasser- und Aschegehalts sowie der untere Heizwert.

Die Verbrennungsregelung nutz die Stellglieder der Biomassefeuerung um dessen stabilen Betrieb zu gewährleisten und schnelle Laständerungen zu ermöglichen. Diese modellbasierte Regelstrategie berücksichtigt durch ihre Formulierung, die auf dem oben genannten mathematischen Modell basiert, explizit alle relevanten Brennstoffeigenschaften. Dadurch reagiert sie gezielt auf Änderungen dieser Brennstoffeigenschaften und kompensiert direkt deren Einfluss auf den Betrieb der Biomassefeuerung. Gleichzeitig weist sie eine einfache Struktur auf und ist daher leicht zu implementieren und zu warten. Diese modellbasierte Verbrennungsregelung wird sowohl in Simulationsstudien als auch durch Experimente nach einer Implementierung an einer realen Biomassefeuerung verifiziert.

Es wird ein kombinierter Zustands- und Parameterschätzer entwickelt, der gleichzeitig die Brennstoffeigenschaften, die anschließend von der Verbrennungsregelung verwendet werden, und die Zustandsgrößen der Biomassefeuerungen in Echtzeit schätzt. Er basiert auf einem erweiterten Kalman-Filter, der das in dieser Arbeit vorgestellte mathematische Modell verwendet. Diese Methode wird für verschiedene Brennstoffeigenschaften sowohl in Simulationsstudien als auch durch Messdaten aus realen Biomassefeuerungen verifiziert. Die Ergebnisse dieser Verifikation zeigen, dass diese Methode in der Lage ist, die Brennstoffeigenschaften und Zustandsgrößen auch bei Last- oder Brennstoffwechseln genau zu bestimmen.

Um einen Betrieb der Biomassefeuerung mit möglichst hohem Wirkungsgrad und möglichst geringen Schadstoffemissionen zu gewährleisten, wird eine Methode zur CO-lambda-Optimierung entwickelt. Diese Methode verwendet einen erweiterten Kalman-Filter in Kombination mit Messdaten des Sauerstoffgehalts und des CO-Gehalts des Rauchgases zur Bestimmung eines optimalen Luftüberschussverhältnisses für den aktuellen Zustand der Biomassefeuerung. Diese Methode wird an einer realen Biomassefeuerung in einer Langzeitvalidierung über mehrere Monate verifiziert und validiert. Während dieser Langzeitvalidierung führte die Anwendung dieser Methode zur CO-lambda-Optimierung zu einer Wirkungsgradsteigerung von 3,8 %, einer Reduktion der CO-Emissionen um durchschnittlich 200 mg/m³ sowie einer Verringerung der Gesamtstaubemissionen um durchschnittlich 19 %.

Zusammenfassend ermöglicht die in dieser Arbeit vorgestellte modellbasierte Regelungsstrategie es, Biomassefeuerungen mit den geringstmöglichen Schadstoffemissionen und dem höchstmöglichen Wirkungsgrad zu betreiben und dabei ein hohes Maß an Brennstoffflexibilität und Lastmodulationsfähigkeit zu erreichen. Darüber hinaus weist die Regelungsstrategie eine geringe Komplexität auf und ist leicht in realen Biomassefeuerungen zu implementieren und zu warten. Dies ermöglicht den breiten Einsatz dieser Regelungsstrategie an bestehenden und zukünftigen Biomassefeuerungen. Dies unterstützt die weitere Verbreitung von Biomassefeuerungen im Energiesystem, was zur Reduzierung der CO2e-Emissionen beiträgt und auch die verstärkte Nutzung anderer, volatiler erneuerbarer Technologien, wie z.B. solarthermischer Anlagen, ermöglicht.

Both the planning and operation of complex, multi-energy systems increasingly rely on optimization. This optimization requires the use of mathematical models of the system components. The model most often used to describe thermal storage, and especially in the common mixed-integer linear program (MILP) formulation, is a simple integrator model with a linear loss term. This simple model has multiple inherent drawbacks since it cannot be applied to represent the temperature distribution inside of the storage unit. In this article, we present a novel approach based on multiple layers of variable size but fixed temperature. The model is still linear, but can be used to describe the most relevant physical phenomena: heat losses, axial heat transport, and, at least qualitatively, axial heat conduction. As an additional benefit, this model makes it possible to clearly distinguish between heat available at different temperatures and thus suitable for different applications, e.g., space heating or domestic hot water. This comes at the cost of additional binary decision variables used to model the resulting hybrid linear dynamics, requiring the use of state-of-the-art MILP solvers to solve the resulting optimization problems. The advantages of the more detailed model are demonstrated by validating it against a standard model based on partial differential equations and by showing more realistic results for a simple energy optimization problem.

Modern buildings with floor heating or thermally activated building structures (TABS) offer a significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realized with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models describing the thermal characteristics of the individual thermal zones exist. Creating these by hand or from more detailed simulation models is infeasible for large numbers of zones; instead, they must be identified automatically based on measurement data. We present an approach using only open source tools based on the programming language Julia that allows to robustly identify simple thermal models for heating and cooling usable in MPC optimization. The resulting models are used in a distributed optimization scheme that co-ordinates the individual zones and buildings of a city quarter in order to best support an energy hub.

Modern buildings with floor heating or thermally activated building structures (TABS) offer a significant
potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realized with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models describing the thermal characteristics of the individual thermal zones exist. Creating these by hand or from more detailed simulation models is infeasible for large numbers of zones; instead, they must be identified automatically based on measurement data. We present an approach using only open source tools based on the programming language Julia that allows to robustly identify simple thermal models for heating and cooling usable in MPC optimization. The resulting models are used in a distributed optimization scheme that co-ordinates the individual zones and buildings of a city quarter in order to best support an energy hub.

Sector coupling is expected to play a key role in the decarbonization of the energy system by enabling the integration of decentralized renewable energy sources and unlocking hitherto unused synergies between generation, storage and consumption. Within this context, a transition towards hybrid energy networks (HENs), which couple power, heating/cooling and gas grids, is a necessary requirement to implement sector coupling on a large scale. However, this transition poses practical challenges, because the traditional domain-specific approaches struggle to cover all aspects of HENs. Methods and tools for conceptualization, system planning and design as well as system operation support exist for all involved domains, but their adaption or extension beyond the domain they were originally intended for is still a matter of research and development. Therefore, this work presents innovative tools for modeling and simulating HENs. A categorization of these tools is performed based on a clustering of their most relevant features. It is shown that this categorization has a strong correlation with the results of an independently carried out expert review of potential application areas. This good agreement is a strong indicator that the proposed classification categories can successfully capture and characterize the most important features of tools for HENs. Furthermore, it allows to provide a guideline for early adopters to understand which tools and methods best fit the requirements of their specific applications.

Ziel des Tasks ist es, Bioenergielösungen als flexible Ressource in einem dekarbonisierten Energiesystem herauszuarbeiten. Dabei sollen Typen, Qualität und Status von flexibler Bioenergie erhoben sowie Barrieren und Entwicklungsbedarf im Gesamtsystemkontext (Strom-, Wärme- und Transportsektor) identifiziert werden.

Most industrial fixed-bed biomass gasification systems usually operate at steady-state to produce the maximum amount of energy possible although they can principally modulate their loads to compensate for the fluctuations of other volatile renewable energy systems. To unleash their full load modulation capability, their typically traditional control strategies should be improved, their gas residence times affected by typically basic char removal strategies adjusted and any required manual interaction of an operator avoided. In this respect, a new controller for the char handling (accumulation and removal) of the reduction zone in a fixed-bed biomass gasifier of a representative industrial small-scale gasification system is developed and experimentally verified. This new controller consists of a recursive least-squares estimator for the flow resistance of the gasifier representing the amount of char inside and a switching controller for rotating a grate located at its bottom. The experimental verification reveals that only the traditional (pressure-based) controller requires manual adjustment of the thresholds. Moreover, the new controller (flow resistance based) significantly reduces the fluctuation range during partial load and stabilizes the temperature and pressure downstream the gasifier. This provides the basis for enhancing its fuel flexibility too and is an important feature for flexible operation in future.

Absorption heat pumping systems (AHPSs, comprising absorption heat pumps and chillers) are devices that mainly use thermal energy instead of electricity to generate heating and cooling. This thermal energy can be provided by, e.g., waste heat or renewable energy sources such as solar energy, which allow AHPSs to contribute to ressource-efficient heating and cooling systems. Despite this benefit, AHPSs are still not a widespread technology. One reason for this is unsatisfactory controllability under varying operating conditions, which results in poor modulation and partial load capability. Emloying model-based control is a promising approach to address this issue, which will be the focus of this  contribution.
First, a viable control-oriented model for AHPSs is developed. It is based on physical correlations to facilitate systematic adaptions to different scales and operating conditions and considers only the most relevant mass and energy stores to keep the model order at a minimum. The resulting model is mathematically simple but still has the structure of a nonlinear differential-algebraic system of equations. This is typical for models of thermo-chemical
processes, but is unfortunately not suitable for many control design methods. Therefore, linearization at an operating point is discussed to derive a model in linear state space representation. Experimental validation results show that the linearized model does have slightly worse steady-state accuracy than the nonlinear model, but that the dynamic accuracy seems to be almost unaffected by the linearization and is considered sufficiently good to be used in control design.
As a next step, the linearized model is used to design model-based control strategies for AHPSs. A special focus is put on redundantly-actuated configurations, i.e. configurations with more manipulated variables than controlled variables, which allows using additional degrees of freedom to extend the operating range of AHPS and hence improve their partial load capability. Two model-based control approaches are discussed: First, a linear model predictive control (MPC) approach is presented - a well-established and generally easy-to-parameterize approach, which, however, often results in high computational effort prohibitive to its implementation on a conventional PLC. Therefore, a second control approach based on state feedback is presented which is mathematically simple enough for implementation on a conventional PLC. It consists of an observer for state variables and unknown disturbances, a state feedback controller and, in case of redundantly-actuated configurations, a dynamic control allocation algorithm. Both approaches are experimentally validated and compared to a state-of-the art control approach based on SISO PI control, showing that the model-based MIMO control approaches allow for a wider operating range and hence better modulation and partial load capability compared to the SISO PI approach. This, in turn, reduces ON/OFF operation of AHPSs and also facilitates their integration into complex energy systems to generate heating and cooling in a ressource-efficient manner.

A process demonstration unit for a novel aqueous phase reforming (APR) process was built and scaled up by factor 666. The set-up of this demonstration unit was supported by virtual commissioning using a virtual test bed. By using virtual commissioning, it was possible to speed-up the commissioning and to support stable, reliable and continuous plant operation for 100h.

In various applications in the field of control engineering, the estimation of the state variables of dynamic systems in the presence of unknown inputs plays an important role. Existing methods require the so-called observer matching condition to be satisfied, rely on the boundedness e variables or exhibit an increased observer order of at least twice the plant order. In this article, a novel observer normal form for strongly observable linear time-invariant multivariable systems is proposed. In contrast to classical normal forms, the proposed approach also takes the unknown inputs into account. The proposed observer normal form allows for the straightforward construction of a higher-order sliding mode observer, which ensures global convergence of the estimation error within finite time even in the presence of unknown bounded inputs. Its application is not restricted to systems which satisfy the aforementioned limitations of already existing unknown input observers. The proposed approach can be exploited for the reconstruction of unknown inputs with bounded derivative and robust state-feedback control, which is shown by means of a tutorial example. Numerical simulations confirm the effectiveness of the presented work.

This article presents a new observer design approach for linear time invariant multivariable systems subject to unknown inputs. The design is based on a transformation to the so-called special coordinate basis (SCB). This form reveals important system properties like invertability or the finite and infinite zero structure. Depending on the system's strong observability properties, the SCB allows for a straightforward unknown input observer design utilizing linear or nonlinear observers design techniques. The chosen observer design technique does not only depend on the system properties, but also on the desired convergence behavior of the observer. Hence, the proposed design procedure can be seen as a unifying framework for unknown input observer design.