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The energy transition process that is being driven by the decentralization and electrification of energy systems impacts significantly on electricity distribution grids. The fast-evolving technical and policy landscape prompts distribution system operators (DSOs) to modernize their operational strategies. This underscores the critical significance of digitalization investments, particularly in optimizing grid performance, managing renewable energy integration, and meeting evolving consumer demands. Despite the expected gains from digital technologies, their deployment in power distribution grids remains limited and partial. This study comprehensively examines the barriers hindering the digitalization of distribution grids, including the technical, organizational, regulatory, economic and human factors. By combining insights from existing literature with interviews with European DSO representatives, we have ranked the barriers by order of significance and identified those that need priority action. We ultimately provide policy guidance with practical recommendations and associated measures to overcome them. The outcomes of our joint analysis inform DSOs, policy-makers and field experts, and serve to formulate detailed policy recommendations to accelerate digitalization.

Implementation of a new European Union directive on energy renovation in Denmark can be expected to have a strong impact on the country’s energy renovation requirements. Accordingly, an indicator framework has been developed with the aim of identifying and quantifying the energy renovation potential of a specific Danish municipality. We identified and selected four indicators—energy consumption, CO2 emissions, heating costs and current energy labels—specifically for detached dwellings. The physical renovation potential was then quantified based on the indicator results, and an average score for the four indicators was calculated for each of the 10,228 detached dwellings in the municipality. All four indicators were weighted equally in the calculations of the total renovation potential score. The methods used in this study can be applied more widely as they rank detached dwellings according to their renovation potential. However, we also found that more detailed data are necessary to determine the specific renovation potential of detached dwellings. A greater effort to collect more data on the part of the municipalities would improve the accuracy of analyses.

The introduction of smart meters and time-use survey data is helping decision makers to understand the residential electricity consumption behaviour behind load profiles. However, it can be difficult to obtain the actual detailed consumption data due to privacy issues. Synthesising residential electricity consumption profiles may be an alternative way to develop synthetic load profiles that initially starts by reviewing the existing synthetic load profile methods. The purpose of this review is to identify the recent methods for synthesising residential electricity load profiles by conducting a rigorous standalone literature review. This review study has been applied and presented transparently and is replicable by other researchers. The review has answered the following research questions: the definition, concept and roles of residential electricity load profile and synthesised data; recent approaches and methods; research purposes; applicable simulations and validation methods of the final selected studies. The results show that the most applied approach in modelling residential electricity load profiles is the bottom-up approach. As it is detailed, it suitable to reflect the local residential behaviour in electricity consumption. Consequently, it is more complex to develop and calibrate the model as identified in the results. Bottom-up models are more powerful in analysing energy consumptions that focus on behavioural patterns, dwelling profiles and control strategies.

Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes.

Data Centres serve as the foundation for digital technologies in the energy sector, enabling advanced analytics, optimization, and automation. However, their rapid growth can exert a substantial influence on the environment due to their energy consumption, water utilization, and production of electronic waste. This research begins with an energy overview of the setup and operations of data centres, highlighting their key components and infrastructure, and emphasizing their crucial role in managing energy resources and driving the energy sector’s digital technologies. Building upon this understanding, a holistic framework is proposed to tackle energy sustainability concerns in data centres, with a focus on energy-related aspects. The framework places emphasis on three primary sustainability metrics, namely energy efficiency, water consumption, and waste management. It underscores the significance of green building design principles and energy-efficient equipment as crucial constituents of sustainable data centre infrastructure. The framework delineates optimal energy operational best practices encompassing virtualization and consolidation, effective cooling tactics, and energy management and monitoring, all aimed at reducing energy consumption and enhancing energy performance. Furthermore, the framework emphasizes the significance of incorporating energy-related sustainability metrics into decision-making procedures and adhering to regulatory standards for energy efficiency. Through adherence to this framework, data centres’ environmental impact can be mitigated and a positive contribution towards a sustainable future can be made, particularly in the realm of energy conservation and optimization.

Understanding crowd behavior is crucial for energy demand-side management.Inthispaper,weemploythefluiddynamicsconcept potential flow to model the energy demand shift patterns of the crowd in both temporal and spatial dimensions. To facilitate the use of the proposed method, we implement a visual analysis platform that allows users to interactively explore and interpret the shift patterns. The effectiveness of the proposed method will be evaluated through a hands-on experience with a real case study during the conference demonstration.

Understanding energy consumption patterns is crucial for energy demand-side management. Unlike traditional data mining or machine learning-based methods, this paper presents visual analysis methods for exploring energy consumption data from spatial, temporal, and spatiotemporal dimensions, including variability, segmentation, and energy demand shifts. To support the proposed methods, we develop a visual analysis tool that allows users to explore consumption data and validate their hypotheses based on visual results through human–client–server interactions. In particular, we propose a novel potential flow-based method to model energy demand shift patterns and have integrated it into the proposed analysis tool. We comprehensively evaluate the proposed methods and the tool using real-world electricity consumption data from the Shanghai Pudong district, and compare with traditional data mining methods. The results demonstrated the effectiveness and superiority of the proposed visual analysis methods, including its ability to discover the spatiotemporal variability of energy demand, customer groups, and demand shift patterns across different geographical areas and time horizons. All results can be well explained by knowledge of the energy consumption in the study region.

In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex temporal correlations pose a great challenge for anomaly detection. Many traditional unsupervised anomaly detection algorithms such as cluster-based or distance-based models are not robust to noise and not fully exploit the temporal dependency in a time series as well as other dependencies amongst multiple variables (sensors). This paper proposes an unsupervised anomaly detection method based on a Variational Recurrent Autoencoder with attention mechanism. with “dirty” data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training. This paper makes a quantitative comparison with the VAE-based baseline approach and four other unsupervised learning methods, demonstrating its effectiveness and superiority. This paper further validates the proposed method by a real case study of detecting the anomalies of water supply temperature from an industrial heating plant.

District heating utilities have long used consumption data and prognoses to plan their production and meet the local heat demand. But to achieve more operational goals, the use of large amounts of existing data has been limited. The HEAT 4.0 project has therefore set this on the agenda and has successfully demonstrated new methods and an open digital platform where IT and OT can meet in new harmonies.

DTU Man er en af de 16 aktører, der har været involveret i det innovative projekt HEAT 4.0. DTU Man har forsket i forskellige vigtige aspekter af fjernvarmen. Disse aspekter dækker digitalisering og elektrificering af fjernvarme, forståelse af diskonteringsrentens indvirkning investering i fjernvarme og analyse af langsigtede udviklingsveje for fjernvarmesektoren.

DTU Man has used energy system investment optimization model Balmorel to study the future development trends for the district heating sector in Denmark. We have developed several new modules (add-ons) for Balmorel. These modules comprehensively model the essential dynamics of the heating sector, which include investment competition between heat savings measures, district heating expansion, and individual heating technologies. Thus, the model can now optimize investment into the whole energy system by taking into the abovementioned dynamics of the heating sector.

The aim of this study was to help FUJIFILM Diosynth Biotechnologies monitor their progress in different sustainability aspects and help optimize the decision-making within it. For the purpose, sustainability indicators were researched, adapted and developed specifically for the company. The result is a set of indicators that will allow the top management, the sustainability team and the engineering team to have an overview of sustainability and plan ahead based on which aspects need attention sooner and which ones can wait. Also, it aims to give information about the company’s sustainability performance to all employees that are curious, hopefully resulting in more awareness and new ideas to improve in all different areas of environmental sustainability. In the thesis, two different years are taken as an example of how the tool should be used, analyzing why an indicator has a certain rating and what it means in terms of the general health and progress in that aspect of sustainability. The tool resulting from the project can be useful as it is now but has potential to grow and improve with periodic revisits, readaptations, additions, and modifications, Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables::12.4 - Per a 2020, aconseguir la gestió ecològicament racional dels productes químics i de tots els residus al llarg del seu cicle de vida, de conformitat amb els marcs internacionals convinguts, i reduir-ne de manera significativa l’alliberament a l’atmosfera, a l’aigua i al sòl a fi de minimitzar-ne els efectes adversos sobre la salut humana i el medi ambient

The energy sector is in the process of opening its vast databases for research and development purposes. This builds on top of more specific decentralized data exchange between a few specific operators, such as a district heating company and an energy trader/broker. The connection is typically based on a secured full two way “normal” internet connection. Such a connection can be challenging for smaller projects since the initial investment and operation cost demands a corresponding higher value creation. Furthermore, the thought of an “open” connection to the internet can challenge many decision-making processes. Better safe than sorry seems to dominate the decision making-process. This project suggests a simple way to share data between a district heating company and potential users of the data.

Residential heating units (from district heating supply) are often optimized when installed but then not serviced thereafter. A service technician is only called in if there is an observable decrease in comfort level. Even if the unit is maintained regularly the technician only observes the actual values on analog meters – and make decisions based on these observations. It is rarely considered how the units operate seen from a supply side. It is possible to verify the operation of the unit by introducing online measurements at a service job and potential optimizations can be identified and documented. Valuable information about the transport time and cooling in the transmission system can be shared with the supplier at the same time.