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Institute
Das Wissen um den zukünftigen Wärmebedarf gewinnt, bei komplexer werdenden Fernwärmesystemen mit volatilen und heterogenen Erzeugungsanlagen, immer weiter an Bedeutung. Denn zur Vermeidung von ineffizienten und unwirtschaftlichen Betriebszuständen muss für eine passende Einsatzoptimierung eine möglichst präzise und zuverlässige Wärmelastprognose vorliegen. Maschinelle Lernverfahren, steigende Datengrundlagen und Verfügbarkeit von ausreichender Rechenleistung bergen hierbei erhebliches Verbesserungspotential. Das von der Hochschule für angewandte Wissenschaften Kempten durchgeführte Forschungsvorhaben „DeepDHC - Untersuchung und Weiterentwicklung modernster maschineller Lernverfahren für die hochgenaue Lastprognose in Fernwärmenetzen“ (FKZ: 03EN3017) befasste sich mit der Performance unterschiedlicher maschineller Lernverfahren zur Wärmelastprognose in Fernwärmenetzen. Unter Berücksichtigung unterschiedlicher Fernwärmenetztopologien, der Einbindung von Smart Meter Daten und der automatisierten Berücksichtigung von Veränderungen im Fernwärmenetz wurden hierbei besonders relevante Fragestellungen aufgegriffen und bearbeitet.
Past studies have used power plant dispatch models with fixed plant portfolios to demonstrate that flexible capture systems may allow operators to increase profits in current electricity markets by increasing plant output at times of high demand. Some of these studies have also speculated that flexible capture systems may be valuable in future electricity systems with high shares of renewable energy as they could allow fossil fuel plants with capture to rapidly respond to changes in residual load. However, few studies have actually examined the role that plants with flexible capture could play in future power systems. Thus, this study examines the role that power generation with flexible capture systems could play in a future European power system where 80% of generation (by energy) is supplied by renewables. The results show that conventional base- and mid-load capacity decreases while the peak-load capacity (i.e., open cycle gas turbines) increases. In European regions with high shares of renewables, the residual load duration curve become steeper, and hourly changes in residual load increase significantly and happen more frequently at low load levels. The shift towards peak capacity and general decrease in load factors places technologies with high capital costs at a relative disadvantage. In the scenario in which CO2 prices reach 650 per tonne CO2 in 2050, approximately one-fifth of the CCS capacity deployed is equipped with flexible capture. However, in a scenario in which CO2 prices reach € 100 per tonne CO2 in 2050, very little capacity is equipped with flexible capture systems as the cost of emitting CO2 offsets the value of flexibility.
Die Integration fluktuierender erneuerbarer Energien erfordert hoch flexible Kraftwerke, Energiespeicher und Power-to-X-Anlagen zur Netzstabilisierung und bedarfsgerechten Abdeckung der verbleibenden Netzlasten.
Im Projekt KWK-Flex untersucht die Hochschule Kempten mit den Partnern Fernwärme Ulm GmbH (FUG) aus Baden-Württemberg und der ZAK Energie GmbH (ZAK) aus Bayern die Entwicklung innovativer Methoden und Anlagenbetriebsstrategien zur flexiblen, stromgeführten Fahrweise von Kraft-Wärme-Kopplungs-(KWK)-Anlagen mit hohen Anteilen erneuerbarer Energien.
Die Analysen wurden unterstützt von der AGFW | Der Energieeffizienzverband für Wärme, Kälte und KWK e.V.
Keynote auf dem AGFW-Expertenforum "Fernwärme Digital" (18. - 19.04.2023, Frankfurt am Main):
Der Vortrag gibt einen Überblick über die aktuell zum Thema Digitalisierung in der Fernwärmeforschung laufenden Aktivitäten. Zudem werden die Forschungsprojekte deepDHC, KWKflex und HeatSHIFT der Hochschule Kempten zu diesem Thema vorgestellt.
KI-Anwendung in der Energietechnik: Einsatz maschineller Lernverfahren für die Wärmelastprognose
(2021)
Maschinelles Lernen gilt als eines der vielversprechendsten Teilgebiete der künstlichen Intelligenz (KI). Sein Einsatz hat in den vergangenen Jahren zu enormen Fortschritten sowohl in der Bild- und Texterkennung als auch bei Zeitreihenprognosen geführt.
Der Vortrag demonstriert dies am Beispiel von Wärmelastprognosen für die Fernwärmebranche. Dabei werden maschinelle Lernverfahren genutzt, um den Wärmebedarf in Fernwärme-netzen über mehrere Tage im Voraus hochgenau vorherzusagen. Auf diese Weise können Energieversorger den Einsatz ihrer Wärmeerzeugungsanlagen optimal planen. Beispielsweise können bei vorhersehbaren Lastspitzen Wärmespeicher frühzeitig mit erneuerbar erzeugter Wärme gefüllt und so der Betrieb fossiler Spitzenlastkraftwerke vermieden werden. Als Folge kann der Betreiber sowohl CO2-Emissionen als auch Emissions-, Brennstoff- sowie An- und Abfahrkosten einsparen.
Der Vortrag basiert auf Ergebnissen aus mehreren Forschungsprojekten, die an der Hochschule Kempten seit 2016 gemeinsam mit Fernwärmeversorgern durchgeführt wurden. Dabei wurden maschinelle Lernverfahren sehr unterschiedlicher Komplexität systematisch untersucht und bewertet – von „einfachen“ bis hin zu anspruchsvollen Verfahren aus dem Bereich des sogenannten „Deep Learning“. Die Wärmelastprognosen werden unter Verwendung historischer Betriebs- und Wetterdaten sowie von Wetterprognosen vollautomatisiert erstellt und dem Betreiber über eine Web-Schnittstelle zur Verfügung gestellt, die auch im Vortrag gezeigt wird.
Die vorgestellte Methode bietet erhebliche Einsparpotenziale für den Anlagenbetreiber. Sie ist zudem auch auf andere Branchen mit ähnlichen Zielgrößen bzw. Fragestellungen übertragbar.
Efficient operation of district heating networks requires a precise forecasting of the thermal loads and an optimised dispatch strategy for the available generation and storage portfolio. This paper presents a holistic modelling and optimisation approach: first, detailed process modelling and optimisation of power plants and thermal storages; second, a numerical model for dispatch optimisation; and third, machine-learning-based load forecasting. The work is based on operating data from the district heating network of the city of Ulm in Germany. The paper presents the modelling, validation and simulation results of stationary and instationary process simulation for a biomass-fired combined heat and power plant. The analysis identifies a potential to integrate additional renewable power by “power-to-heat” technologies into different parts of the process. The economic benefit is quantified by mixed-integer linear programming optimisation applied to the district heating network. In order to allow for real-time dispatch optimisation, a machine-learning-based thermal load forecasting method was developed and evaluated, based on a 72-h forecast horizon. In addition, the economic impact of prediction uncertainties is analysed with the numerical dispatch optimisation tool.
Temporal Fusion Transformer for thermal load prediction in district heating and cooling networks
(2022)
Accurate forecasting of thermal loads is a critical factor for operating district heating and cooling networks economically,efficiently and with minimized emissions. If thermal loads are known with high accuracy in advance, use of renewable energiescan be maximized, and fossil generation, in particular in peaking units, can be avoided. Machine learning has already provento be an efficient tool for time series forecasting in this context. One recent advancement in machine learning is the "TemporalFusion Transformer" (TFT), which shows especially good results in the area of time series forecasting. This paper examinesthe performance of TFT in the concrete context of thermal load forecasting for district heating and cooling networks. First,a brief summary of differences between TFT and other machine learning methods is given. Secondly, it is described how themethod can be adopted to train a machine learning model for thermal load forecasting. The data to train and evaluate the neuralnetwork is based on 8 years of hourly operating data made available from the district heating network of the city of Ulm inGermany. The presented technique is used to produce 72 hours of heating load forecasts for three different district heating gridsin the city of Ulm. The results are compared to forecasts of other machine learning methods that have been previously madeas part of the publicly funded research project "deepDHC", in order to evaluate if TFT is an improvement to further reduceforecasting uncertainties.
Precise forecasting of thermal loads is a critical factor for economic and efficient operation of district heating and cooling networks. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and – in combination with thermal storage units – fossil generation, in particular in peaking units, can be avoided. Machine learning has proven to be a powerful tool for time series forecasting, and has demonstrated significant advancements in recent years. This paper presents the scientific methodology and first results of the publicly funded research project “deepDHC”, which aims at a broad benchmarking of traditional and advanced machine learning methods for thermal load forecasting in district heating and cooling applications. The analysis covers autoregressive forecasting approaches, decision trees such as “adaptive boosting”, but also latest “deep learning” techniques such as the “long short-term memory” (LSTM) neural network. This work is based on data from the district heating network of the city of Ulm in Germany. First, different performance metrics for evaluating forecasting qualities are introduced. Second, approaches for data screening and results of a linear and non-linear correlation analysis are presented. Third, the machine learning tuning process is described. For thermal load forecasting, weather data are key input parameters. This work uses hourly weather forecasts from weather models provided by the German meteorological service. These weather data are updated automatically, and have been statistically corrected in order to represent very accurate point forecasts for up to ten days ahead. In addition, a user-friendly web interface has been developed for use by the district heating network operator. The performance of different machine-learning algorithms is compared based on 72 h heating load forecasts.
Diese Formelsammlung umfasst die Gebiete technische Thermodynamik, technische Strömungsmechanik, Wärmeübertragung und in geringerem Umfang auch die Stoffübertragung. Sie basiert auf den gleichnamigen Vorlesungen der Autoren an der Hochschule für angewandte Wissenschaften Kempten.
Systematisch gegliedert werden in der Formelsammlung grundlegende Berechnungsgleichungen, umfangreiche Stoffdaten sowie Lösungsdiagramme für wichtige Berechnungsfunktionen bereitgestellt. Aus Gründen der Kompaktheit und Übersichtlichkeit wird dabei weitgehend auf Erläuterungen von Gleichungen und Formelzeichen verzichtet. Die Formelsammlung eignet sich daher besonders als vorlesungs- oder prüfungsbegleitendes Hilfsmittel für Studierende in Ingenieurstudiengängen an Hochschulen oder Universitäten. Eine weitere Zielgruppe bilden Ingenieure mit entsprechenden Vorkenntnissen, die im Berufsalltag schnell auf wesentliche Gleichungen und Daten zugreifen möchten.
Natural gas-fueled combined cycle (NGCC) allows to reach the best performance among power plants fed by fossil fuels, but causes considerable CO2 emissions. With the aim of reducing greenhouse gases impact, NGCC could be integrated with post-combustion CO2 removal systems, typically based on chemical solvents like amines, that cause very large net efficiency penalties (about 9-12 percentage points at 90% overall CO2 capture). To reduce these high capture penalties, exhaust gas recirculation (EGR) has been studied. To further enhance the overall plant efficiency, the recovery of available low temperature heat from the solvent-based CO2 removal systems could be also performed. Low temperature heat is available in flue gas coolers (80-100 degrees C), in the amine reboiler water cooling (130-140 degrees C) and in the splitter condenser (100-130 degrees C). This waste thermal energy could be recovered by means of an Organic Rankine Cycle (ORC) that is able to convert heat into electricity efficiently even at comparably low temperatures. N-Butane was found to be as the most promising organic working fluid for the cycle operating temperatures and pressures. ORC produces additional electrical power improving the global performance of the power plant, for example, up to 1-1.5 percentage points in efficiency.
Load Forecasting in District Heating Systems Using Stacked Ensembles of Machine Learning Algorithms
(2021)
For district heating, heat demand forecasting is playing a key role for an optimised power plant dispatch. Machine Learning can help to significantly improve forecasts of thermal loads. The prediction quality of neural networks is higher than that of decision trees in most cases. However, compared to decision trees neural networks have weaknesses when extrapolating outside known ranges. This work presents a novel method called “Deep DHC” (Deep Learning for District Heating and Cooling), which combines these two approaches in order to benefit from strengths of both methods. On the one hand, the novel approach uses conventional decision tree based regression algorithms such as the AdaBoost and Random Forest, as well as artificial neural networks. In addition to common feed forward neural networks (FNN), a deep learning network structure, which consists of long short-term memory (LSTM) cells, is used for the first time. The LSTM method has already proven to be very powerful in modern speech recognition. In order to achieve best possible heat demand forecasts, the aforementioned methods for load forecasting are combined and weighted by an additional machine learning method. Results show that it is possible to achieve a further improvement in forecasting quality for district heating loads by purposefully combining individual forecasting methods. Hence, mean and absolute deviations are significantly reduced in comparison to the individual methods.
This paper presents a methodology to estimate the biomass energy potential and its associated uncertainty at a country level when quality and availability of data are limited. The current biomass energy potential in Colombia is assessed following the proposed methodology and results are compared to existing assessment studies.
The proposed methodology is a bottom-up resource-focused approach with statistical analysis that uses a Monte Carlo algorithm to stochastically estimate the theoretical and the technical biomass energy potential. The paper also includes a proposed approach to quantify uncertainty combining a probabilistic propagation of uncertainty, a sensitivity analysis and a set of disaggregated sub-models to estimate reliability of predictions and reduce the associated uncertainty. Results predict a theoretical energy potential of 0.744 EJ and a technical potential of 0.059 EJ in 2010, which might account for 1.2% of the annual primary energy production (4.93 EJ).
Hintergrund: Fluktuierende erneuerbare Energien erfordern flexible Kraftwerke zum Lastausgleich
Projektziele: Erhöhung der Flexibilität kommunaler Kraft-Wärme-Kopplungsanlagen (KWK) durch:
- Optimierte Anlagendynamik
- Wärmespeicherung
- Nutzung von Power-to-Heat
- Einbindung von Strommärkten
Vorgehensweise:
- Instationäre Prozesssimulation der Anlagen
- Nicht-lineare Optimierung der Anlageneinsatzplanung
- Demonstration durch Einbindung in die Leitstandstechnik
- Erstellung eines Betreiberleitfadens