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.

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.

Building and district energy management increasingly requires advanced control strategies. For several tasks that arise, Machine Learning (ML) and Artificial Intelligence (AI) have found widespread application, but often the interaction with existing elements and the integration into the productive system are done in a rather unsystematic way. Thus, one faces problems concerning interface design, data requirements and reliability/trustworthiness. In order to improve this situation, we outline the general control structure of such systems, review key concepts from ML/AI and discuss approaches for conventional high-level control. This is followed by a literature review on ML/AI applications for energy management. Based on the systematics and the literature findings, we establish a taxonomy for the integration of ML/AI methods for energy management, which leads as a main result to a comprehensive guide for such applications, taking into account both characteristics of the method and the affected elements of the system. This is supplemented by a quick guideline for choice of an appropriate method and for subsequent evaluation. The target readership are control engineers who would like to get a systematic overview of ways to integrate ML/AI methods in their work, and data scientists who want to get a better understanding for main tasks and challenges for applying their tools for control tasks.

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.

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.

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.

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.

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.

District heating (DH) networks have the potential for intelligent integration and combination of renewable energy sources, waste heat, thermal energy storage, heat consumers, and coupling with
other sectors. For growing networks in close geographical proximity, often the possibility arises to couple them using bidirectional heat exchangers, possibly unlocking synergies and reducing costs for the consumers. Each DH network may consist of producers, consumers and thermal energy storage (TES) devices. Often, each of the coupled DH networks will be already controlled via low-level controllers. Hence, a high-level control approach is needed, that coordinates the heat exchange between the
networks and takes renewable energy sources and the TES capacities in each network into account. These supervisory controllers are generally referred to as energy management systems (EMS).

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.

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.

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.

Retrofitting buildings with predictive control strategies can reduce their energy demand and improve thermal comfort by considering their thermal inertia and future weather conditions. A key challenge is minimizing additional infrastructure, such as sensors and actuators, while ensuring user comfort at all times. This study focuses on retrofitting with intelligent software, incorporating the users’ feedback directly into the control loop. We propose a predictive control strategy using an optimization-based energy management system (EMS) to control thermal zones in an office building. It uses a physically motivated grey-box model to predict and adjust thermal demand, with individual zones modelled using an RC-approach and parameter estimation handled by an unscented Kalman filter (UKF). This reduces deployment effort as the parameters are learned from historical data. The objective function ensures user comfort, penalizes undesirable behaviour and minimizes heating and cooling costs. An internal comfort model, automatically calibrated with user feedback by another UKF, further improves system performance. The practical case study is an office building at the ”Innovation District Inffeld”. Operation of the system for one year yielded significant results compared to conventional control. Thermal comfort was improved by 12% and thermal energy consumption for heating and cooling was reduced by about 35%.

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.

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.

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.

One of the main tasks for operators of medium- and large-scale biomass boilers is the continuous operational monitoring of these plants in order to assess their performance, detect errors and identify possibilities for operational optimization. However, due to the high complexity of this task, errors are frequently detected too late or not at all, which can lead to even more costly secondary errors. In addition, possibilities for optimization remain unused in many existing plants, resulting in unnecessary pollutant emissions and low efficiencies.
To assist operators in performing this task and to achieve a high level of automation, methods for the automated, model-based monitoring of such plants have been focus of recent research activities. In this contribution, we will discuss the numerous possibilities provided by the application of such methods in a practical context. For this purpose, we present selected results from previous activities, demonstrating how methods for model-based monitoring were applied at combustion plants and used to enable automated error detection and support operational optimization.

Exemplary result 1: We developed a soft-sensor which accurately estimates the non-measurable internal state of heat exchangers and implemented it at a large-scale combustion plant with a nominal capacity of 38.2 MW. This soft-sensor uses a dynamic mathematical model of the heat exchanger in combination with measured data to determine a new estimate for the heat exchanger’s internal state every second. Based on this estimate, the soft-sensor accurately detects fouling and determines the non-measurable flue gas mass flow in real time. The estimated flue gas mass flow was used in a model-based control strategy which resulted in significant improvements of the combustion plant’s operational behaviour and load modulation capabilities. These results are discussed in this contribution.

Exemplary result 2: We developed a method for the real-time estimation of non-measurable fuel properties, i.e. chemical composition, bulk density, lower heating value, in biomass boilers. These estimates were subsequently used in a model-based control strategy and enabled the improvement of the biomass boiler’s fuel flexibility. Results of this estimator achieved for different biomass fuels, e.g. poplar wood chips, corncob grits and standard wood pellets, are discussed in this contribution.
On the basis of these selected results, it will be examined which possibilities arise from the use of methods for model-based monitoring in biomass boilers and also how these results can be extended to other technologies such as biomass gasifiers.

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.

Open data in the renewable energy sector could contribute to a greener future and reduce carbon emissions. This study investigates existing open datasets in the solar thermal domain, challenges in publishing and using open data and its potential impact. The results support the common belief that open data is useful, offering considerable benefits for data re-users and the solar thermal community. While substantial open data in the solar thermal domain already exists, some datasets like plant statistics and cost data are hard to utilize due to licensing, accessibility, and quality issues. The study shows that data owners benefit less from publishing data compared to data re-users and service providers. While data sharing seems appealing to data owners, several barriers discourage data sharing. Suggestions to promote the use and publications of open data in solar thermal domain are discussed.

This article presents a general representation of the characteristic polynomial of the dynamic matrix for multivariable systems in Luenberger’s canonical forms. The characteristic polynomial is given by means of the determinant of a polynomial matrix of substantially lower order. Therein, the polynomial coefficients of the single elements are the coefficients of the corresponding blocks of the dynamic matrix. The proposed representation of the characteristic polynomial can be helpful for the design of state-feedback controllers and state observers which is demonstrated by a numerical example.

Heat is still the largest energy end-use, accounting for about 50% of global final energy consumption in 2022 and contributing to 40% of global carbon dioxide (CO2) emissions. Regarding the heat supply of buildings, district heating plays an important role and is well-established in many countries. However, most of the district heating networks worldwide are still operated with supply temperatures of 70-120°C (medium-high temperature) often produced by caloric power plants. Solar district heating (SDH) systems can be a valuable alternative to decarbonize these systems. How this can be done most efficiently is investigated within the task 68 Efficient Solar District Heating Systems of the International Energy Agency (IEA) from the technology cooperation program – solar heating and cooling (SHC). This contribution presents the latest results of the task regarding comparison of different collector technologies, important digitalization aspects, analysis of available funding schemes and latest efficient SHD installations.

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.

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].

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.

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.

Die saubere und effiziente Verbrennung von Müll stellt, insbesondere aufgrund der typischerweise stark inhomogenen Brennstoffzusammensetzung, eine große Herausforderung dar. Eine effektive Maßnahme dieser Problematik zu begegnen, ist der Einsatz geeigneter Methoden zur Prozessregelung. In diesem Artikel wird daher eine neue Methode zur modellbasierten Regelung des produzierten Dampfmassenstromes in einer Wirbelschichtmüllverbrennungsanlage vorgestellt. Diese beinhaltet als Herzstück einen Softsensor, welcher den Rauchgasmassenstrom aus vorhandenen Messgrößen online schätzt. Durch die zusätzliche Kenntnis dieser wichtigen Prozessgröße kann die Regelgüte deutlich verbessert werden. Zusätzlich zeigt sich eine stabilisierende Wirkung auf die Reaktorkopftemperatur, wodurch die thermische Belastung des Schamotts verringert werden kann.

A promising technology for the thermochemical conversion of a variety of feedstock into a valuable product gas is the advanced dual fluidized bed steam gasification process. Currently, a key challenge is the economically efficient operation of this technology in an industrial scale, which can barely be achieved due to various reasons. An asset for this would be the online knowledge of key performance indicators to quantify the process and its efficiency. However, many key performance indicators require the hardly measurable product gas water content at the gasification reactor’s outlet. Therefore, a method has been developed to estimate the product gas water content online in a model based way. Static and dynamic mass balances over the quench unit of the coarse product gas cleaning together with comparably cheap liquid level measurements are utilized. In addition to the product gas water content, the same method has been applied to the biodiesel scrubber unit of the coarse product gas cleaning to estimate the product gas volume flow. Measurement and model uncertainties are accounted for in a systematic way by two linear Kalman filters. The comparison of the estimated product gas water content with real measurement data from an advanced dual fluidized bed steam gasifier indicates a good performance of the presented method. The estimated dry product gas volume flow matches the measured dry product gas volume flow of an installed orifice plate on average well but fluctuates too much and cannot capture the required dynamics to render the orifice plate measurement obsolete. However, the estimated dry product gas volume flow can be used to track the pollution of the orifice plate.

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.

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.

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.

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.

Absorption heat pumping devices (AHPDs, comprising heat pumps and chillers) can provide heating and cooling in a resource-efficient manner. However, their perceived complexity has limited their widespread application. This contribution shows how mathematical models, systematically capturing this complexity, can be used for model-based control of AHPDs (on the device-level for model-predictive or state feedback control and on the system-level for optimisation-based energy management systems) to facilitate their integration into energy systems, and discusses an exemplary application to solar cooling systems.

Absorptionswärmepumpenanlagen (AWPA, beinhaltet Absorptionswärmepumpen und –kältemaschinen), sind Anlagen, die hauptsächlich thermische statt elektrischer Energie nutzen, um Wärme und Kälte zu generieren. Dadurch wird die Nutzung von Abwärme und erneuerbaren Energiequellen wie Solarenergie in Heiz- und Kühlsystemen erleichtert. Trotz dieses Vorteils ist der Einsatz von AWPA nach wie vor stark eingeschränkt. Ein Grund dafür ist das Fehlen von Regelungsstrategien, die eine zufriedenstellende Regelgüte über einen weiten Betriebsbereich, insbesondere unter Teillast, bieten. Deshalb befasst sich diese Arbeit mit der Entwicklung eines neuen, modellbasierten Regelungsansatzes für AWPA, die den Betriebsbereich durch den Einsatz von Mehrgrößen-Regelungsmethoden (multi-input-multi-output (MIMO) Regelungsmethoden) erweitern kann.



Zunächst wird ein geeignetes dynamisches Modell abgeleitet, das im modellbasierten Regelungsansatz verwendet werden soll. Es handelt sich um ein physikalisch basiertes Modell mit modularer Struktur, was eine systematische Anpassung an verschiedene AWPA erleichtert. Um die Anzahl der Zustandsvariablen niedrig zu halten, werden nur diejenigen Masse- und Energiespeicher berücksichtigt, die zu Zeitkonstanten und Totzeiten führen, die für die spätere Regelungsaufgabe relevant sind. Das entwickelte Modell ist mathematisch einfach, hat jedoch die Struktur eines nichtlinearen differential-algebraischen Gleichungssystems. Als solches ist es sehr gut als Simulationsmodell geeignet um verschiedene Regelungsstrategien in der Simulation zu testen, aber es ist zu komplex für viele modellbasierte Regelungsmethoden. Um eine noch einfachere Modellstruktur zu erhalten, wird das Modell an einem Betriebspunkt linearisiert, was auf ein Modell in linearer Zustandsraumdarstellung führt. Die entwickelten nichtlinearen und linearen Modelle werden experimentell validiert und mit zwei alternativen Modellierungsansätzen als Benchmark verglichen. Ein Vergleich zwischen dem abgeleiteten nichtlinearen Modell und den Benchmark-Modellen zeigt eine höhere Genauigkeit für das neue Modell, sowohl stationär als auch dynamisch. Ein Vergleich zwischen dem abgeleiteten nichtlinearen und dem linearisierten Modell zeigt, dass das linearisierte Modell zwar eine etwas schlechtere stationäre Genauigkeit aufweist, die dynamische Genauigkeit jedoch durch die Linearisierung nahezu unbeeinflusst zu sein scheint. Das vorgestellte neue linearisierte AWPA -Modell gilt daher als geeignet, als Grundlage für den Entwurf des modellbasierten Regelansatzes verwendet zu werden.



Als nächstes wird dieses Modell verwendet, um einen neuen modellbasierten Regelungsansatz für AWPA zu entwerfen. Der neue Regelungsansatz kann für verschiedene AWPA-Anwendungen und damit für verschiedene Regelungskonfigurationen verwendet werden, d.h., verschiedene Kombinationen von Stell- und Regelgrößen. Er kann auch für redundante aktuierte Konfigurationen mit mehr Stell- als Regelgrößen verwendet werden, was die Erweiterung des Betriebsbereichs einer AWPA ermöglicht. Der Ansatz besteht aus einem Beobachter für die Zustandsvariablen und unbekannte Störgrößen, einem Zustandsregler und, im Falle von redundant aktuierten Konfigurationen, einem Algorithmus zur dynamischen Stellgrößenverteilung. Der vorgeschlagene Regelungsansatz wird experimentell für zwei verschiedene Regelungskonfigurationen validiert und mit zwei Benchmark-Ansätzen verglichen – einem Eingrößen-PI-Regler (Single-input-single-output (SISO) PI-Regler), der den Stand der Technik repräsentiert, und einem modellprädiktiven Regelungsansatz (model predictive control, MPC) als alternative fortschrittliche Regelungsmethode. Die experimentelle Validierung zeigt, dass die beiden MIMO-Regelungsansätze (der vorgeschlagene Zustandsregler und der MPC-Ansatz) einen erweiterten Betriebsbereich und somit eine bessere Teillastfähigkeit im Vergleich zum SISO-PI-Regler ermöglichen. Während MPC durch die Notwendigkeit zur kontinuierlichen Lösung eines Optimierungsproblems im Allgemeinen eine vergleichsweise hohe Rechenleistung benötigt, ist der vorgeschlagene Zustandsregler-Ansatz mathematisch einfach genug, um auf herkömmlichen speicherprogrammierbaren Steuerungen für AWPA implementiert werden zu können. Er wird daher als vielversprechender neuer Regelungsansatz für AWPA betrachtet, der die Möglichkeit bietet, ihren Betriebsbereich zu erweitern und ihre Teillastfähigkeit zu verbessern, was wiederum eine einfachere Einbindung in moderne Energiesysteme ermöglicht und somit die Nutzung nachhaltiger Wärmequellen für Heizen und Kühlen erleichtert.

Optimization-based energy management systems (EMS) are a high-level control approach for energy systems like district heating networks. A descriptive model and objective function are required to solve an optimization problem and apply the resulting schedule in a receding horizon fashion. EMS for buildings require a simplified model of each thermal zone, and the objective function includes costs for heating and cooling, virtual costs, and a comfort model. Feedback from users is necessary since thermal comfort varies among individuals.

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.

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) use mainly thermal energy instead of electricity as the driving energy to provide resource-efficient heating and cooling when using waste heat or renewable heat sources. Despite this benefit, AHPDs are still not a very common technology due to their complexity. However, better modulation and part-load capability, which can be achieved through advanced control strategies, can simplify the use of AHPDs and help to better integrate them into complex energy systems. Therefore, this paper presents a new, dynamic model-based control approach for single-stage AHPDs that can extend an AHPD’s operating range by employing multi-input-multi-output (MIMO) control methods. The control approach can be used for different AHPD applications and thus control configurations, i.e., different combinations of manipulated and controlled variables, and can also be used for redundantly-actuated configurations with more manipulated than controlled variables. It consists of an observer for the state variables and unknown disturbances, a state feedback controller and, in case of redundantly-actuated configurations, a dynamic control allocation algorithm. The proposed control approach is experimentally validated with a representative AHPD for two different control configurations and compared to two benchmark control approaches – single-input-single-output (SISO) PI control representing the state-of-the-art, and model-predictive control (MPC) as an alternative advanced control concept. The experimental validation shows that the two MIMO control approaches (the proposed state feedback and the MPC approach) allow for a wider operating range and hence better part load capability compared to the SISO PI control approach. While MPC generally results in a comparably high computational effort due to the necessity of continuously solving an optimization problem, the proposed state feedback control approach is mathematically simple enough to be implemented on a conventional programmable logic controller. It is therefore considered a promising new control approach for AHPDs with the ability to extend their operating range and improve their part load capability, which in turn facilitates their implementation and thus the use of sustainable heat sources in heating and cooling systems.

Das im Projektantrag formulierte Ziel zum Arbeitspaket III enthält die systematische
Sammlung, Aufbereitung und Interpretation des Denkens sowie Verhaltens von Personen, die
im Microgrid handeln. Mittels qualitativer Befragungsmethoden werden Aspekte, wie der
erwartete Nutzen, die Akzeptanz, die erwartete Benutzerfreundlichkeit und die
Zahlungsbereitschaft – insbesondere in Bezug auf E-Mobilität und dazugehörigen
Lademöglichkeiten - erhoben.
Die Erkenntnisse aus diesem Arbeitspaket können verwendet werden für die Entwicklung der
theoretischen Planungsalgorithmen. Diese sollen die Effizienz der Einbindung von Gebäuden
in das Microgrid sicherstellen.

Energieflexibilität ist für viele Unternehmen ein neues Thema – gleichzeitig eröffnet sie span-nende Chancen, den Energieverbrauch effizienter zu steuern, Kosten zu senken und einen Beitrag zur Energiewende zu leisten.
Dieser hier vorliegende Leitfaden zeigt auf, was sich hinter dem Begriff Energieflexibilität ver-birgt, wo in Industrieunternehmen Potentiale liegen und wie erste Schritte in Richtung flexibler Energieanwendung aussehen können – und all das, ohne tiefgreifende technische Vorkennt-nisse vorauszusetzen. Ziel ist es, praxisnah zu informieren, Orientierung zu bieten und dabei zu unterstützen, Unternehmen fit für die Energiezukunft zu machen.

Buildings account for 30% of the globally consumed final energy and 19% of the indirect emissions, i.e., from the production of electricity and heat. Air-conditioned office buildings have an especially high energy footprint. Retrofitting buildings with predictive control strategies can reduce their energy demand and increase thermal comfort by considering future weather conditions. One challenge lies in the required infrastructure, i.e., sensors and actuators. Another challenge is about satisfying the comfort requirements of the users, getting their feedback and reacting to it. We propose a predictive control strategy, where an optimization-based energy management system (EMS) controls the thermal zones of such office buildings. The approach uses a mathematical model of the building within an optimization problem to predict and shift thermal demand. The individual thermal zones are modelled using a grey-box approach, where the simultaneous state and parameter estimation is handled by an unscented Kalman filter (UKF). This minimizes the needed effort for deployment of the system, as the parameters are learned automatically from historical measurement data. The objective function ensures the users’ comfort based on a comfort model, penalizes unwanted behaviour such as frequent valve position changes, and minimizes the costs for heating and cooling supply. Since the offices are typically shared by multiple users, the internal comfort model is calibrated based on their feedback. Each feedback is viewed as a measurement from the internal comfort model, and an UKF updates the parameters of the model, thus lowering or increasing the temperature setpoint of the zone controller in a robust manner. As a case study, an office building at the “Innovation District Inffeld” is considered. The proposed predictive control strategy, together with the user feedback, is implemented. A central information and communication technology (ICT) handles all communication with the building automation system. We developed a simple web-based feedback system with a five-point Likert scale for user feedback integration. The presented ideas are evaluated based on both a preliminary simulation study and potential evaluation using the building modelling software IDA ICE, and a real-world implementation. A key requirement was to limit the number of new sensors and actuators, thus focusing on how much can be achieved with a retrofit measure with minimal hardware, but intelligent software. The presentation will give, an overview of the developed methods and first results of the real implementation will be given.

Future hybrid energy systems require flexible technologies for compensating the volatile nature of most renewable energies. As such, fixed-bed biomass gasifiers are especially relevant as they allow a flexible production of heat, electricity and in a broader sense bio-based products (e.g. biochar). Thus, flexible fixed-bed biomass gasifiers will continuously become more relevant for a sustainable and highly flexible energy and resource system (bioeconomy).

However, due to their current economic dependency on specific feed in tariffs for the produced electricity, they are almost always operated at nominal load, to maximize the electricity production. Thus, their potential for flexibility has not been revealed up to now. Consequently, the currently applied control strategies are typically designed with the focus on steady-state operation. Any operation differing from nominal load typically requires manual interventions of the plant operators to avoid lower efficiencies or operational difficulties. Thus, currently applied control strategies do not allow a fully-automatic and flexible operation of the gasifiers.

To unleash the full potential of the gasifiers’ flexibility, new and more advanced control strategies able to handle varying operating conditions automatically are required. For this reason, this contribution aims for the development of a model-based control strategy, since it allows to explicitly consider all the correlations between the different process variables, and an efficient adaptation of the control strategy to different plants. The development was carried out on the basis of a representative industrial small-scale fixed-bed biomass gasifier operated as combined heat and power plant (CHP) with a nominal capacity of 300 kW_th and 150 kW_el. In this contribution we present the developed method as well as the practical verification of the model-based controller for the industrial small-scale fixed-bed biomass gasification plant.

The practical verification revealed a significant potential for flexibility increase by the new model-based control strategy in comparison to state-of-the-art control strategies. For example, the new controller performs a step-wise load change from 150 kW_el to 100 kW_el (-33%) within less than 2 min without affecting the gasification performance. The new control leads to a much more homogeneous gasification, in particular during partial load operation, and reduces the fluctuation margin of relevant process parameters to less than 1%. This controlled stabilization and homogenization of the gasification at different operating conditions is also a prerequisite for further future flexibilization measures, e.g. the extension of the feedstock variety (fuel flexibility) or increasing product flexibility.

Due to the modular and model-based design, the new control strategy can also be implemented on other fixed-bed gasifiers of the same type without requiring any structural modifications, by solely adjusting the model parameters appropriately. Furthermore, the new control strategy makes only use of sensors and actuators typically already available in state-of-the-art fixed-bed gasification systems. In conclusion, the model-based control strategy to be presented states a very important contribution towards flexible fixed-bed biomass gasification systems.

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.

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.

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 sustainable energy supply can only be achieved by a flexible, cross-sectoral energy system utilizing the specific advantages of the various renewable technologies. In this workshop possible roles of different technologies will be discussed based on a previous discussion of the users’ needs among the different sectors. In this a special focus should be given on the flexibility provision via the heating sector. By bringing together different users, representing municipal and industrial energy supply, and technological experts from different IEA Technology Collaboration Programmes (TCP) the workshop should support a holistic discussion.

List of presentations: 

• Wien Energie‘s vision of a sustainable energy and ressource supply of Vienna, Teresa Schubert, Wien Energie, Austria
• Digitalization of energy management systems – optimization of internal energy use as an industrial company, Maria Lechner, INNIO Jenbacher, Austria
• Flexible Bioenergy and System Integration, Elina Mäki, VTT Technical Research Centre of Finland, Finland Task Leader – IEA Bioenergy Task 44 Flexible Bioenergy and System Integration
• Use Case: Syngas platform Vienna for utilization of biogenic residues, Matthias Kuba, BEST – Bioenergy and Sustainable Technologies, Austria
• Transformation of District Heating and Cooling Systems towards high share of renewables, Ingo Leusbrock, AEE INTEC, Austria – Lead of Austrian delegation – IEA DHC Annex TS5 Integration of Renewable Energy Sources into existing District Heating and Cooling Systems
• Opportunities offered by long-term heat storages and large-scale solar thermal systems, Viktor Unterberger, BEST – Bioenergy and Sustainable Technologies, Austria Task Manager – IEA SHC Task 68 Efficient Solar District Heating Systems
• Possibilities through digitalization on the example of District Heating and Cooling, Dietrich Schmidt, Fraunhofer Institute for Energy Economics and Energy System Technology IEE, Germany – Operating Agent – IEA DHC Annex TS4 Digitalisation of District Heating and Cooling

List of contributing IEA Tasks:

• IEA Bioenergy Task 44 Flexible Bioenergy and System Integration
• EA DHC Annex TS5 Integration of Renewable Energy Sources into existing District Heating and Cooling Systems
• IEA SHC Task 68 Efficient Solar District Heating Systems
• IEA DHC Annex TS4 Digitalisation of District Heating and Cooling

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.

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.

The increasing share of volatile energy sources as well as variable demands provide challenges for the electrical power grid. To counteract these instabilities a balance between supply and demand needs to be established. In industrial processes, this can be achieved by coordinating the energy production with local storage and demand. Specifically, the optimized scheduling of batch production processes can avoid peak loads. A holistic approach for the control of industrial energy systems and production processes is needed to use this flexibility. This contribution presents an extension to a modular framework for optimization-based, predictive supervisory control of multi-energy systems providing the possibility to incorporate batch production processes, and a first study showing that shifting production processes can result in a more resource- and cost-efficient process.

Es wird ein ganzheitlicher Ansatz zum systemdienlichen Zusammenspiel dezentraler Energiesysteme (von Einfamilienhauesern bis zu Industriebetrieben) vorgestellt. Dieser beinhaltet ein generelles Softwareframework zum optimalen Betrieb dezentraler Multienergiesysteme und einen Ansatz zum automatisierten Aggregieren und systemdienlichen Verschieben von Bezugs- und Einspeiseleistungen.

Es wird ein ganzheitlicher Ansatz zum systemdienlichen Zusammenspiel dezentraler Energiesysteme (von Einfamilienhauesern bis zu Industriebetrieben) vorgestellt. Dieser beinhaltet ein generelles Softwareframework zum optimalen Betrieb dezentraler Multienergiesysteme und einen Ansatz zum automatisierten Aggregieren und systemdienlichen Verschieben von Bezugs- und Einspeiseleistungen.

FLEXcel is an Excel-based tool, developed within the FLEXcheck project, to support the quick estimation of industrial flexibility potentials. By combining a structured questionnaire with simplified calculation routines, FLEXcel enables companies to assess their technical flexibility using only limited process and energy data. The tool is designed as a low-threshold entry point for identifying shiftable loads and operational adjustments, providing decision support on whether more detailed analyses or investments in flexibility are warranted. With a clear focus on the Austrian market, FLEXcel is provided in German to ensure practical usability for local companies and stakeholders.

Industry is not only a big primary emitter of CO2 itself, but also a big consumer of electricity with a share of about 43% of electricity consumption in Austria in 2023. Therefore, providing the future electrical grid with storage capacities will be a big opportunity for industrial facilities, as industry has a unique capability of load management due to its diversity compared to households or other services. While major scientific projects have investigated this potential, there is a lack of incentives for industrial plants at various levels to foster the balancing of electricity supply.

In this paper we provide an overview regarding the state of research and the obstacles we need to overcome to close the implementation gap. We expand the state-of-the-art approach of assessing flexibility potentials by not only incorporating technical but also economic and organizational key performance indicators. With the proposed novel assessment strategy, we bridge the gap between top down and bottom-up approaches, aiming to facilitate practical and economically viable demand response measures in energy-intensive industrial processes. Our methodology is demonstrated through a case study based on real life industry application, with results validated against literature and expert discussions.

Fault-Detection (FD) is essential to ensure the performance of solar thermal systems. However, manually analyzing the system can be time-consuming, error-prone, and requires extensive domain knowledge. On the other hand, existing FD algorithms are often too complicated to set up, limited to specific system layouts, or have only limited fault coverage. Hence, a new FD algorithm called Fault-Detective is presented in this paper, which is purely data-driven and can be applied to a wide range of system layouts with minimal configuration effort. It automatically identifies correlated sensors and models their behavior using Random-Forest-Regression. Faults are then detected by comparing predicted and measured values.

The algorithm is tested using data from three large-scale solar thermal systems to evaluate its applicability and performance. The results are compared to manual fault detection performed by a domain expert. The evaluation shows that Fault-Detective can successfully identify correlated sensors and model their behavior well, resulting in coefficient-of-determination scores between R²=0.91 and R²=1.00. In addition, all faults detected by the domain experts were correctly spotted by Fault-Detective. The algorithm even identified some faults that the experts missed. However, the use of Fault-Detective is limited by the low precision score of 30% when monitoring temperature sensors. The reason for this is a high number of false alarms raised due to anomalies (e.g., consecutive days with bad weather) instead of faults. Nevertheless, the algorithm shows promising results for monitoring the thermal power of the systems, with an average precision score of 91%.

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.

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.