Energy Informatics

Inhaltsverzeichnis

1. Frontmatter

2. IoT, Edge Computing, and Software Innovations in Energy

   1. Frontmatter

   2. Scheduling Electric Currents in Converter-Dominated Power Grids with Time-Slotted Energy Packets

      Klemens Schneider, Dominik Schulz, Daniel Helmig, Friedrich Wiegel, Veit Hagenmeyer, Marc Hiller, Martina Zitterbart

      Abstract

      Due to massive transformations in the energy sector, the power grid of the future requires new solutions. One possible novel building block of the future grid is the energy router. Energy routers are power electronic devices that can bridge previously unconnected parts of the power grid, in order to increase the use of power line capacity and to increase the power grid’s resilience. Through the massive use of energy routers, the power grid will cease its traditional, hierarchical structure and transform into a ‘network of cells’, which requires new control approaches. This paper advances the Energy Packet Grid, an architecture of a future power grid, structured as a network of cells. The architecture is inspired by selected Internet design principles. In the Energy Packet Grid, all major power flows should be organized as energy packet transfers, short-term contracts between power electronic devices. This paper proposes the Time-Slotted Energy Packet Transfer Protocol (TEPT), a scheduling protocol that conducts energy packet transfers. Its goal is to efficiently use the power line capacity, to achieve fairness among competing transfers, and to sustain the power grid’s safety in case of communication failures. We evaluated TEPT with our simulator and with our real-world power electronic devices, demonstrating TEPT ’s feasibility on hardware.

   3. Symbiosis: A Web-Based Decision Support Tool for Achieving Symbiosis in Industrial Parks

      Hampus Fink Gärdström, Henrik Schwarz, Bo Nørregaard Jørgensen, Zheng Grace Ma

      Abstract

      This paper presents Symbiosis, a web-based decision support tool developed to improve energy symbiosis within eco-industrial parks (EIPs). Symbiosis addresses the limitations of traditional desktop applications by offering a user-friendly, web-based interface that simplifies the modeling and visualization of energy flows among facilities. This tool is designed to be accessible to users with varying technical backgrounds. The effectiveness of Symbiosis was evaluated through a case study involving an industrial park with wind and photovoltaic energy sources and a greenhouse and brewery as energy consumers. The study assessed how replacing an existing greenhouse with a higher energy-consuming variant impacted the park’s energy balance. Symbiosis successfully modeled energy flows and visualized changes in energy surplus and deficit resulting from this replacement. The tool demonstrated its capability to provide insights into energy dynamics and optimize facility configurations to improve energy efficiency. The case study highlighted that Symbiosis is effective in visualizing and analyzing energy flows, supporting better decision-making for EIP management. However, it also revealed some limitations, such as performance issues with large datasets and the need for support for additional energy types. Future research should explore case studies in various EIP configurations and consider integrating optimization algorithms to enhance decision-making.

   4. A Cost-Effective Edge Computing Gateway for Smart Buildings

      Simon Soele Madsen, Benjamin Eichler Staugaard, Zheng Ma, Salman Yussof, Bo Nørregaard Jørgensen

      Abstract

      The retrofitting of existing buildings with building management systems presents significant challenges, primarily due to the need for labor and cost efficiency. Wireless technology offers a promising solution to these challenges by minimizing the need for extensive wiring and structural alterations. However, achieving retrofitting in a cost-effective manner necessitates the use of low-cost wireless technologies. This paper introduces a framework for constructing a Zigbee gateway using open-source tools combined with low-cost hardware. The proposed architecture addresses large-scale IoT deployments within the Zigbee ecosystem. By leveraging edge computing with the robustness and scalability offered by Zigbee technology, this architecture significantly reduces the economic barriers to retrofit buildings with building management systems. The results underscore the potential of open-source Zigbee technology in aligning with sustainability goals, providing a cost-effective pathway for retrofitting buildings into smart, energy-efficient living environments.

   5. Leveraging Internet of Things Network Metadata for Cost-Effective Automatic Smart Building Visualization

      Benjamin Eichler Staugaard, Simon Soele Madsen, Zheng Ma, Salman Yussof, Bo Nørregaard Jørgensen

      Abstract

      In recent years, the building sector has experienced an increasing legislative pressure to reduce the energy consumption. This has created a global need for affordable building management systems (BMS) in areas such as lighting-, temperature-, air quality monitoring and control. BMS uses 2D and 3D building representations to visualize various aspects of building operations. Today the creation of these visual building representations relies on labor-intensive and costly computer-aided design (CAD) processes. Hence, to create affordable BMS there is an urgent need to develop methods for cost-effective automatic creation of visual building representations. This paper introduces an automatic, metadata-driven method for constructing building visualizations using metadata from existing smart building infrastructure. The method presented in this study utilizes a Velocity Verlet integration-based physics particle simulation that uses metadata to define the force dynamics within the simulation. This process generates an abstract point cloud representing the organization of BMS components into building zones. The developed system was tested in two buildings of respectively 2,560 m² and 18,000 m². The method successfully produced visual building representations based on the available metadata, demonstrating its feasibility and cost-effectiveness.

   6. Process-to-Market: A Web-Based Evaluation Tool for Electricity Market Participation

      Henrik Schwarz, Hampus Fink Gärdström, Nicolas Fatras, Frederik Wagner Madsen, Bo Nørregaard Jørgensen, Zheng Grace Ma

      Abstract

      In deregulated electricity markets, large industrial consumers face significant challenges in participating effectively due to complexities in market regulations and diverse flexibility requirements. This paper introduces “Process2Market,” a web-based evaluation tool designed to assist large electricity consumers in the Nordics by leveraging the Process-to-Market Matrix Mapping (P2MM) model. The tool employs a Python-based optimization program integrated with a comprehensive questionnaire and market data analysis to evaluate market participation feasibility. By providing an accessible online interface, Process2Market facilitates broader engagement and understanding among stakeholders, including researchers and electricity consumers. A case study involving a hypothetical Power-to-Hydrogen (PtH) facility demonstrates the tool’s practical application, highlighting its potential to enhance grid stability through increased demand-side flexibility and optimized market participation. This paper contributes to the field by presenting a detailed methodology for developing a web-based tool, offering a practical application in a case study, and providing insights into the integration of industrial processes into electricity markets.

   7. IoT Based Smart Air Ventilation and Energy Management System

      Jetendra Joshi, Shreya Rao, Pratik Panda, Jagriti Bannerjee, Sriniket Mondal

      Abstract

      The integration of advanced ventilation systems with environmental air monitoring presents a major challenge in modern building management and urban development. The structure of buildings becomes more airtight, energy efficient indoor air quality (IAQ) becomes increasingly difficult. This paper investigates the need for ventilation strategies using real-time data and Internet of Things (IoT) technology to ensure a better environment. We introduce a decentralized smart ventilation system that employs control algorithms and localized sensor data to enhance ventilation performance. Our approach incorporates sensors in each room or zone to continuously monitor temperature, humidity, and occupancy levels. This data is transmitted to decentralized control units, which process the information to adjust ventilation settings according to the specific needs of each area. The above approach is useful and tested in the Earth air Tunnel cooling system on university premises.

3. Big Data Analytics and Cybersecurity in Energy

   1. Frontmatter

   2. Leveraging Open Data for Energy Source Selection in Bi-valent Industrial Processes

      Jonathan Sejdija, Florian Maurer, Ralf Schemm, Isabel Kuperjans

      Abstract

      The transition towards sustainable and efficient energy systems is a major challenge. This is particularly the case for industrial processes, where the choice of energy source has significant economic and environmental consequences.

   3. Legal Overview of Latest Developments in the Energy Sector Regarding Data Protection and Cybersecurity

      Bent Ole Gram Mortensen, Lisa Hjerrild

      Abstract

      This paper seeks to obtain a better understanding of the latest developments and purposes of the EU regulation related to data in the energy sector. It shows that EU cybersecurity measures has increased, giving a broader and stronger coverage although the regulation has a general aim of being at use for industry as well as for the people, which still is the intended purpose of the GDPR and NIS 2. It also argues that the importance of a well-functioning energy grid demands a strong protection from unauthored use or access to energy data or energy systems.

   4. Energy Data Collection Protocol: A Case Study on the ADRENALIN Project

      Balázs András Tolnai, Zheng Ma, Bo Nørregaard Jørgensen

      Abstract

      Effective data collection is crucial for the success of data science projects, ensuring that data is of high quality and sufficient quantity for practical applications. However, the process is often complex, time-consuming, and prone to yielding low-quality data if not well-organized. Despite its importance, standardized protocols for data collection are lacking, leading to inconsistencies across projects. This paper introduces a comprehensive data collection protocol aimed at streamlining the process and enhancing data quality. The protocol is exemplified through the ADRENALIN project, which focuses on developing advanced machine learning algorithms for smart control of heating and cooling systems. The case study demonstrates the protocol’s practical application, showcasing its effectiveness in overcoming common data collection challenges and ensuring reliable outcomes. By providing a structured approach, this protocol improves the consistency and comparability of datasets, facilitating better benchmarking and more accurate data-driven solutions, thus filling a critical gap in the literature and offering a valuable tool for researchers and practitioners.

   5. DataPro – A Standardized Data Understanding and Processing Procedure: A Case Study of an Eco-Driving Project

      Zhipeng Ma, Bo Nørregaard Jørgensen, Zheng Grace Ma

      Abstract

      A systematic pipeline for data processing and knowledge discovery is essential to extracting knowledge from big data and making recommendations for operational decision-making. The CRISP-DM model is the de-facto standard for developing data-mining projects in practice. However, advancements in data processing technologies require enhancements to this framework. This paper presents the DataPro (a standardized data understanding and processing procedure) model, which extends CRISP-DM and emphasizes the link between data scientists and stakeholders by adding the “technical understanding” and “implementation” phases. Firstly, the “technical understanding” phase aligns business demands with technical requirements, ensuring the technical team’s accurate comprehension of business goals. Next, the “implementation” phase focuses on the practical application of developed data science models, ensuring theoretical models are effectively applied in business contexts. Furthermore, clearly defining roles and responsibilities in each phase enhances management and communication among all participants. Afterward, a case study on an eco-driving data science project for fuel efficiency analysis in the Danish public transportation sector illustrates the application of the DataPro model. By following the proposed framework, the project identified key business objectives, translated them into technical requirements, and developed models that provided actionable insights for reducing fuel consumption. Finally, the model is evaluated qualitatively, demonstrating its superiority over other data science procedures.

   6. Detection of Municipal Heat Plan Documents Using Semantic Recognition Methods

      Nicolas Doms, Thorsten Schlachter

      Abstract

      Municipalities in Germany are required by law to prepare a report on municipal heat planning by June 2028 at the latest, depending on their population. Due to the federal structure of Germany in some federal states there exist different regulations and due dates. Most of the municipalities with already completed municipal heat plans have published them on their respective websites. Neither the heat plans themselves nor the location of publishing follow generic templates and are therefore presented in different formats, lengths and places. In order to gain an overview of these heat plans in an effort to coordinate heat planning across municipalities in Germany, a data set referring to the available heat plans shall be created and regularly updated.

      The first step is to use an internet search engine and a web crawler to identify candidate documents for a municipality’s heat plan. In a second step, the results are checked for plausibility, i.e. it is checked whether the candidate documents are actually municipal heat plans and whether they are assigned to the correct municipality. The third step involves semantic enrichment by a process that includes the normalization of time expressions to extract important information from the municipal heat plan documents that relate to time sequences, as well as the extraction of geographical units to link the document to the correct municipality.

      A Web interface provides access to the detected municipal heat plans for evaluation and research purposes.

4. Digital Twin Technology and Energy Simulations

   1. Frontmatter

   2. Challenges in Transitioning from Co-simulation to Practical Application: A Case Study on Economic Emission Dispatch in a Greenhouse Compartment

      Christian Skafte Beck Clausen, Sebastian Lehnhoff, Jan Sören Schwarz, Bo Nørregaard Jørgensen, Zheng Grace Ma

      Abstract

      Co-simulation is a widely applied method used to analyze the behavior of complex, interdisciplinary, and integrated cyber-physical control systems. Despite its prevalence, the transition from co-simulated control systems into practical applications is not discussed as much in the literature. This leaves a gap in the literature because practitioners may not be aware of these challenges. This paper aims to uncover and discuss some of the challenges that arise in the transition from a co-simulated control system to a practical application.

      A case study on economic emission dispatch in a Danish industrial greenhouse compartment serves as the fundament in studying these challenges. Economic emission dispatch is a method that can be used in a closed-loop arrangement to decrease costs and emissions of multiple energy production units. The case study is first implemented as a co-simulation which is subject to a subsequent practical implementation. The co-simulation implementation is governed by the open-source framework mosaik that is used extensively in smart grid applications. In contrast, the practical implementation is not governed by mosaik due to architectural design discrepancies. A key feature of the study is the use of software-in-the-loop, which means that the controller being tested is the actual software intended for deployment.

      The highlighted challenges include that the core abstractions (master algorithm, scenario-script, and protocol) of the co-simulation framework cannot be transferred to an operational context due to design discrepancies. Despite these challenges, the co-simulation can still serve as a baseline for comparing functional performance metrics during the transition.

   3. Multi-agent Based Simulation for Investigating Centralized Charging Strategies and Their Impact on Electric Vehicle Home Charging Ecosystem

      Kristoffer Christensen, Bo Nørregaard Jørgensen, Zheng Grace Ma

      Abstract

      This paper addresses the critical integration of electric vehicles (EVs) into the electricity grid, which is essential for achieving carbon neutrality by 2050. The rapid increase in EV adoption poses significant challenges to the existing grid infrastructure, particularly in managing the increasing electricity demand and mitigating the risk of grid overloads. Centralized EV charging strategies are investigated due to their potential to optimize grid stability and efficiency, compared to decentralized approaches that may exacerbate grid stress. Utilizing a multi-agent based simulation model, the study provides a realistic representation of the electric vehicle home charging ecosystem in a case study of Strib, Denmark. The findings show that the Earliest-deadline-first and Round Robin perform best with 100% EV adoption in terms of EV user satisfaction. The simulation considers a realistic adoption curve, EV charging strategies, EV models, and driving patterns to capture the full ecosystem dynamics over a long-term period with high resolution (hourly). Additionally, the study offers detailed load profiles for future distribution grids, demonstrating how centralized charging strategies can efficiently manage grid loads and prevent overloads.

   4. Leveraging Digital Twins for Sustainable District Heating: A Study on Waste Heat from Power-to-X Plants

      Magnus Værbak, Bo Nørregaard Jørgensen, Zheng Ma

      Abstract

      This paper investigates the integration of waste heat from a Power-to-X (PtX) plant into a district heating system (DHS) using a digital twin (DT) approach to optimize energy efficiency and manage complex system dynamics. Focusing on a case study of a new PtX plant in Kassø, southern Jutland, supplying waste heat to the district heating network of Aabenraa, this paper combines model-based and data-driven techniques through business ecosystem mapping and multi-agent-based simulation to evaluate integration viability and efficiency. The results indicate significant reductions in natural gas consumption and operational costs, demonstrating the feasibility of PtX waste heat integration. The DT model’s detailed simulations provide insights into optimizing operational strategies, balancing heat supply and demand, and ensuring system reliability. These findings underscore the critical role of DTs in advancing energy efficiency and decarbonization in urban heating systems.

   5. Hardware-in-The-Loop-Based Validation of Distribution System Control Applications with Grid Operators, Customer and Market Participants

      Marcel Otte, Carsten Krüger, Pratyush Das, Sebastian Rohjans, Sebastian Lehnhoff

      Abstract

      The flexibility potential of electric vehicles, heat pumps, photovoltaics, and storage systems allocated at customer premises raises attention to grid-serving and market-serving control applications. However, control applications vary as the kind of flexibility differs due to the complexity of interactions behind the meter, the actors involved, conflict of interests, regulatory requirements, and the number of systems to integrate. Consequently, the validation of applications across all phases of development reveals the potential bottlenecks of the application before deployment. This work presents the use cases for distribution system control applications and how they can be validated in a hardware-in-the-loop simulation environment. The validation environment covers the systems of a distribution system operator and its upstream grid operator, virtual field devices, a real-time grid simulation, the meter operator including the operation of an advanced metering infrastructure, as well as applications from market participants like aggregators. As a proof of concept, two use cases are selected to demonstrate how grid operator coordination and customer behaviour can be validated. Thereafter, it will be implied how the validation environment is suitable for future use cases, such as the coordination of grid-serving and market-serving control applications.

   6. Geospatial Semantic Enriched Digital Twin with Logical Reasoning Rules for Managing Control Loops

      Iqbal Shah, Ali Ghahramani

      Abstract

      This study introduces a framework for the development of a geospatial semantic enriched Digital Twins (DTs) with integrated logical reasoning rules, aimed towards addressing the challenges associated with the manual-intensive scripting for the development and upkeeping of traditional building control loops. By leveraging on geospatial information as a higher-level semantic, this framework enhances the integration with existing building ontologies, thereby facilitating the creation of a dynamic and responsive DT environment. An experimental trial involving 120 participants was conducted to validate the practicality and effectiveness of the geospatial semantic enriched DT with integrated logical reasoning rules, for the development of various human-centric user interfaces, providing options for the diverse range of user preferences. The results demonstrated strong preference for these adaptable DT interfaces. Moreover, the application of geospatial semantics with logical reasoning rules in selectively displaying only the nearby devices based on users’ locations not only enhances interface usability but also offers potential for reducing system misuse. These findings lay a robust foundation for future advancements in DT integration, emphasizing the transformative potential of integrating logical reasoning rules with geospatial semantics in the development of DTs.

   7. Data-Driven Digital Twin for Foundry Production Process: Facilitating Best Practice Operations Investigation and Impact Analysis

      Daniel Anthony Howard, Magnus Værbak, Zhipeng Ma, Bo Nørregaard Jørgensen, Zheng Ma

      Abstract

      In the context of increasing environmental concerns, the iron and steel industry faces large pressure to reduce its energy consumption and carbon footprint while maintaining economic viability. This paper explores the implementation of best practice operations within foundry processes, specifically induction furnace melting, to enhance energy and cost efficiency and reduce CO₂ emissions. A digital twin model is developed integrating discrete event simulation, system dynamics modeling, and symbolic regression to simulate the foundry production process and evaluate the impact of various operational practices. A large Danish foundry is used as a case study, providing data for induction furnace production incorporating various electricity market data sources. Symbolic regression models are deployed to accurately predict melt temperatures and energy requirements. Results indicate that adopting best practices can lead to significant savings - up to 21% in electricity costs and 14.2% in CO₂ emissions - while improving productivity. The study also highlights a point of diminishing returns at 65% adherence to best practices due to existing production schedules. Furthermore, the study demonstrates the digital twin’s potential as a decision-support tool in optimizing industrial process operations.

5. Energy Data and Consumer Behaviors

   1. Frontmatter

   2. Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications

      • Open Access

      Stefan Meisenbacher, Johannes Galenzowski, Kevin Förderer, Wolfgang Suess, Simon Waczowicz, Ralf Mikut, Veit Hagenmeyer

      Abstract

      Achieving net-zero carbon emissions necessitates the major transformation of electrical grids into smart grids. In this context, urban districts play a crucial role in the flexible balancing of electricity demand and supply, which involves solving decentralized optimization problems. Such optimization problems rely on forecasts of local demand and supply, and require the systematic orchestration of data streams using cloud services. At the same time, it is necessary to automate both the design and operation of forecasting models in such services to keep pace with the increasing need for such locally adapted forecasts. Therefore, this paper proposes an automation level taxonomy to communicate the scope of automation in time series forecasting. Furthermore, we demonstrate a forecasting service that is used in a downstream demand-side management application and realized in the real-world project Smart East in Karlsruhe, Germany. Finally, we analyze existing forecasting services in the literature, categorize them according to the proposed automation level taxonomy, and compare them with our implementation.

      PDF-Version jetzt herunterladen

   3. Enhanced Consumer Segmentation Through Load Profile Analysis Using Autoencoder and K-Shape Clustering

      P. Praveen, P. Balachandra, Pandarasamy Arjunan

      Abstract

      In India, residential consumers are traditionally segmented based on their monthly energy consumption. The adoption of Advanced Metering Infrastructure offers an opportunity to utilize detailed smart meter data for more precise consumer segmentation, helping electricity distribution companies to implement tailored demand response policies. This paper presents an analysis of consumer segmentation based on the clustering of load profiles from 84 residential buildings in India using smart meter data. Unlike existing methods that apply clustering algorithms directly, this research incorporates a novel improvement through a two-stage clustering process combining an Autoencoder network with the K-shape clustering algorithm. In the first stage, the Autoencoder transforms the input load profiles into a lower-dimensional latent space, effectively capturing the most significant features of the data. In the subsequent stage, the K-shape clustering algorithm is applied to these latent space vectors. This approach leverages the Autoencoder’s ability to reduce noise and highlight essential patterns, resulting in more accurate and efficient clustering of load profiles compared to using the K-shape algorithm alone. Our analysis demonstrates that the proposed two-stage clustering method enhances the performance of the traditional K-shape algorithm, as evaluated using clustering evaluation metrics such as the Calinski-Harabasz Index, Davies-Bouldin Score, and Silhouette Index. The results also indicate that rural consumers in Indian residential households can be effectively grouped into four distinct segments through this method. These clusters can be analyzed alongside the total load curve of a state or region for a given day to identify those contributing to peak demand. Subsequently, targeted price-based or incentive-based demand response schemes can be devised for these consumer clusters.

   4. Occupants Experiencing Energy Poverty: Where are They in Energy Datasets and Time Use Surveys?

      Marie-Pier Trépanier, Louis Gosselin

      Abstract

      Building operational data is widely used for the development and validation of models and good design practices. This paper aims to analyze the potential impact of inadequate representation of energy-vulnerable groups in residential building datasets and to demonstrate the diversity of occupancy behavior by analyzing national surveys. Emphasis is placed on the need to closely examine how these data sources can introduce bias, particularly in relation to the socio-economic realities of occupants. Biases can lead to policies that fail to address the needs of vulnerable populations, leaving gaps in our understanding of the real energy challenges these groups face. Based on an exploratory literature review, a reflection on the challenges associated with data collection, analysis, and use in this specific context is made. Furthermore, the diversity of occupant behavior is investigated using a Time Use Survey. The data were subjected to descriptive analysis, time-series K-means clustering, and decision tree classification to identify the socio-demographic patterns of occupants based on their activity and occupancy behaviors. Using the silhouette score and the elbow method, five clusters were identified. Based on the gain ratio (entropy) and maximum depth of the wanted decision tree, occupants’ characteristics were associated to different clusters. However, due to the heterogeneity of occupants and the complexity of human behavior, accurately representing activity occupancy behavior is challenging. This underscores the significance of diversity in datasets for accurately simulating building energy consumption.

   5. Extracting Daily Aggregate Load Profiles from Monthly Consumption

      Anmol Saraf, Anupama Kowli

      Abstract

      Consumer load profiling involves examining patterns of energy consumption using available data. With smart meter data available at (sub-)hourly intervals, it is possible to use the it to generate daily load profiles that capture the typical consumption behavior across a representative day. Previous work has shown how fine-grained smart meter data and monthly consumption data separated by time-of-use can be translated into representative load profiles for a given group of consumers. In this work, an approach for load profile generation is proposed which only uses monthly energy consumption data without breaking it down further based on time-of-use such as peak, off-peak and mid-peak hours. Performance of data-driven models using random forest, XGboost and multi-layer perceptron is studied by reconstructing the load profiles on a known smart meter dataset. A comprehensive assessment of how the shape of the reconstructed load profile compares with the actual profile is provided by evaluating amplitude errors, time shifts and slope variations. Our investigations provide insights on the role of the cluster size on load profile reconstruction: predictions of profiles for large cluster sizes show improved accuracy in terms root mean-square errors and less bias. By gaining insights into when and how energy is consumed, utilities can implement measures to reduce costs, mitigate peak demand and enhance overall energy efficiency.

6. Digitalization of District Heating and Cooling Systems

   1. Frontmatter

   2. Digitalization of District Heating: Transforming Heat Networks for a Sustainable Future

      Dietrich Schmidt

      Abstract

      The district heating and cooling sector is experiencing new challenges with the current transformation of energy systems. The required decarbonization will result in a more complex heat energy system, as a few large plants which utilize fossil fuels will likely be replaced, mostly by smaller production plants distributed around the system, that use renewable or waste energy sources. Furthermore, district heating and cooling systems must be operated more efficiently and flexibly to ensure a consistent and cost-effective thermal energy supply, as well as effective participation in the power system balancing market. Because of the necessary changes in the energy system, district heating becomes increasingly beneficial to both end users as well as other energy sectors, and the number of connections is increasing in many countries in conjunction with the phase-out of fossil fuels such as oil and gas for space heating and hot water supply. In this changed environment, increased adoption of digital technology in the district heating and cooling sector provides a chance to make systems smarter, more flexible, efficient, and reliable, hence accelerating the necessary integration of additional renewable and waste energy sources into thermal supply systems.

      However, many existing systems still lack a high level of digitalization. With more complexity, flexibility, more powerful tools, and approaches (and hence increased digitalization) will be required. Aside from technology, the integration of new digital business processes will make deployment easier. However, new concerns, such as data security and privacy, as well as questions concerning data ownership, must be addressed.

      This paper reflects on the results of the research conducted as part of the IEA DHC Annex TS4 project and the depiction thereof in the “Digitalisation of District Heating Systems – Optimised Operation and Maintenance of District Heating and Cooling Systems via Digital Process Management” guidebook [1].

      https://www.iea-dhc.org/the-research/annexes/2018-2024-annex-ts4

   3. Fault Detection in District Heating Substations: Overview of Real-Life Faults in Residential Heating Installations

      Anna Marszal-Pomianowska, Daniel Leiria, Hicham Johra, Michal Pomianowski, Imants Praulins, Justus Chigozie Abiodun Anoruo

      Abstract

      Heating, cooling and production of domestic hot water are the dominating energy uses in residential buildings. District heating (DH) provides the thermal energy for these uses to 65% of the Danish homes. However, 50–60% of the DH substations in these dwellings operate with an error, often leading to inefficient operations (i.e., high temperature or high-volume flow of the return heat-carrier fluid in the DH network). This hinders decarbonising the heating sector. The roll-out of smart heat meters allowed access to the hourly heat demand profiles of the DH customers. In turn, this unlocks the creation of data-driven methods for identifying faulty household systems. However, this cannot be done correctly without the knowledge of the fault types occurring in domestic installations. The present study analyzes 382 reports made by the DH technicians during onsite visits to the houses identified as “faulty” customers. The collected onsite information shows that more than 30% of faults stem from wrong control settings in the space heating or domestic hot water installations. These faults can be fixed with almost no additional cost to the building owner and with the immediate decrease of the return temperatures to the DH system.

   4. Multi-agent Based Modeling for Investigating Excess Heat Utilization from Electrolyzer Production to District Heating Network

      Kristoffer Christensen, Bo Nørregaard Jørgensen, Zheng Grace Ma

      Abstract

      Power-to-Hydrogen is crucial for the renewable energy transition, yet existing literature lacks business models for the significant excess heat it generates. This study addresses this by evaluating three models for selling electrolyzer-generated heat to district heating grids: constant, flexible, and renewable-source hydrogen production, with and without heat sales. Using agent-based modeling and multi-criteria decision-making methods (VIKOR, TOPSIS, PROMETHEE), it finds that selling excess heat can cut hydrogen production costs by 5.6%. The optimal model operates flexibly with electricity spot prices, includes heat sales, and maintains a hydrogen price of 3.3 EUR/kg. Environmentally, hydrogen production from grid electricity could emit up to 13,783.8 tons of CO₂ over four years from 2023. The best economic and environmental model uses renewable sources and sells heat at 3.5 EUR/kg.

7. Correction to: Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications

   • Open Access

   Stefan Meisenbacher, Johannes Galenzowski, Kevin Förderer, Wolfgang Suess, Simon Waczowicz, Ralf Mikut, Veit Hagenmeyer

   PDF-Version jetzt herunterladen

8. Backmatter