Digitalization for Energy Systems

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases. Full article

Increasing levels of distributed generation (DG), as well as changes in electricity consumption behavior, are reshaping power distribution systems. These changes might place particular stress on the secondary low-voltage (LV) distribution systems not originally designed for bi-directional power flows. Voltage violations, reverse power flow, and congestion are the main arising concerns for distribution system operators (DSOs), while observability in these grids is typically nonexistent or very low. The present paper addresses this issue by developing a method for nodal voltage estimation in unbalanced radial LV grids (at 0.4 kV). The workflow of the proposed method combines a data-driven grey-box modeling approach with generalized additive models (GAMs). Furthermore, the proposed method relies on experimental data from a real-world LV grid in Denmark and uses data input from only one measuring device per feeder. Predictions are evaluated by using a test data set of 31 days, which is more than twice the size of the training data set of 13 days. The prediction results show high accuracy at root mean squared errors (RMSEs) of 0.002–0.0004 p.u. The method also requires a short computation time (14 s for the first stage and 2 s for the second stage) that meets requirements for the practical, real-time monitoring of DSO grids. Full article

Sorts of Li-ion batteries (LIB) have been becoming important energy supply and storage devices. As a long-standing obstacle, safety issues are limiting the large-scale adoption of high-energy–density batteries. Strategies covering materials, cell, and package processing have been paid much attention to. Here, we report a flexible sensor array with fast and reversible temperature switching that can be incorporated inside batteries to prevent thermal runaway. This flexible sensor array consists of PTCR ceramic sensors combined with printed PI sheets for electrodes and circuits. Compared to room temperature, the resistance of the sensors soars nonlinearly by more than three orders of magnitude at around 67 °C with a 1 °C/s rate. This temperature aligns with the decomposition temperature of SEI. Subsequently, the resistance returns to normal at room temperature, demonstrating a negative thermal hysteresis effect. This characteristic proves advantageous for the battery, as it enables a lower-temperature restart after an initial warming phase. The batteries with an embedded sensor array could resume their normal function without performance compromise or detrimental thermal runaway. Full article

For this study, we conducted a systematic review of the literature on digitalization in energy production, distribution, and consumption over a sufficiently long period in order to reveal the trends and particularities of this phenomenon at the sectoral level. For the systematic review of the literature, representative articles on the subject indexed in the Web of Science and Scopus databases were selected using the PRISMA 2020 flow diagram. As a result of the systematic review of the literature, a significant number of articles on the subject of digitalization in the energy sector were found—both over the entire period considered and especially in the last five years—indicating the magnitude of the digitalization process in this field. The impacts of digitalization in the energy production, distribution, and consumption sectors materialized in the aspects of health, safety, and environmental improvement; process improvements; and cost reductions. The most important technologies used in the digitalization process include data mining and machine learning, smart grid/smart metering/smart home, Internet of Things, cybersecurity, and automation solutions (e.g., robotics, drones, and distribution automation). Full article

In traditional energy production at large-scale, conventional methods are being used, including fossil fuels. This in turn leads to greenhouse gas emissions (e.g., carbon dioxide or CO₂) that cause environmental concerns, but also those traditional methods rely on traditional distribution systems, which are burdened with high transmission losses. This paper focuses on a new concept in the energy sector that undergoes transformation from a traditional centralized system to a decentralized one. In reaching sustainability goals, such as net-zero emissions, the energy sector is incorporating renewable energy sources into the energy system. This requires transformation that combines big conventional energy producers with multiple small- and large-scale energy producers (rooftop photovoltaic panels, wind farms and solar plants) in one system. This enormous transformation is a difficult task, but with recent advancements in information and communication technologies, digitalization, the Industry 4.0 paradigm and Internet of Things technology, it is feasible to achieve. This paper provides a review based on keyword bibliometric analysis, and although it cannot be considered exhaustive or conclusive, it provides a picture of the current international research. Full article

The digital twin has emerged as a crucial technology for smart production. The Asset Administration Shell (AAS) is a standard tool that can support the digital representation of a process plant. We present a method to use AAS coupled with ISO 15926 to facilitate the exchange of maintenance data in process plants. To accomplish this, the operation and maintenance (O&M) system framework employed in process plants is defined. Information requirements are derived based on this framework, and a maintenance data structure is designed. Along with this, reference data are applied to identify the types of equipment and properties of each equipment type using ISO 15926. According to the pre-designed data structure, a neutral format based on AAS is developed to exchange maintenance data among software systems in O&M. The neutral format is verified through a test case of exchanging maintenance data (equipment condition and health status data) in terms of applicability to O&M systems in the field and compliance with the AAS meta-data model. Full article

Over the last few decades, energy efficiency has received increasing attention from the Architecture, Engineering, Construction and Operation (AECO) industry. Digital Twins have the potential to advance the Operation and Maintenance (O&M) phase in different application fields. With the increasing industry interest, there is a need to review the current status of research developments in Digital Twins for building energy efficiency. This paper aims to provide a comprehensive review of the applications of digital twins for building energy efficiency, analyze research trends and identify research gaps and potential future research directions. In this review, Sustainability and Energy and Buildings are among the most frequently cited sources of publications. Literature reviewed was classified into four different topics: topic 1. Optimization design; topic 2. Occupants’ comfort; topic 3. Building operation and maintenance; and topic 4. Energy consumption simulation. Full article

The study addressed broad aspects related to digital technology platforms and renewable energy sources, including the integration of these systems and concepts. The main objective was to identify the implementation environment for a digital technology platform of renewable energy sources (RES) based on business and consumer feedback. This gives an insight into whether there is a favourable environment for implementing a RES digital technology platform. The study was based on research carried out using computer-assisted telephone interview (CATI) and computer-assisted web interview (CAWI) methods. Additionally, an alternative model of attitudes towards digital technology platforms (DTPs) built using CATREG (categorical regression) analysis was also referred to. The study found that currently, there is a positive attitude among companies, including those which install RES systems, as well as among consumers towards the implementation of DTP-based RES projects. This attitude is driven by the many benefits that can be achieved by using these platforms. However, there are some obstacles to the implementation of a digital RES platform. These relate to cyber security concerns, including computer or internet failures. However, the obstacles are not crucial for the practical implementation of the discussed platform. Full article

As the natural gas pipeline network becomes larger and more complicated, a stricter requirement of computation efficiency for the large and complicated network transient simulation should be proposed. The adaptive time step method has been widely used in the transient simulation of natural gas pipeline networks as a significant way to improve computation efficiency. However, the trial calculation process, which is the most time-consuming process in time step adjustment, was used to adjust the time step in these methods, reducing the efficiency of time step adjustment. In order to reduce the number of trial calculations, and improve the calculation efficiency, an improved adaptive time step method is proposed, which proposes the concept of energy number and judges the energy number of the boundary conditions after judging whether the variation of the pipeline state is tolerable. A comparison between the adaptive time step method and the improved adaptive time step method in the restart process of natural gas pipelines and an actual operation of the XB section in China shows the accuracy, effect, and efficiency of the improved adaptive time step method. The results show that with the same accuracy, 27% fewer trial calculation processes and 24.95% fewer time levels are needed in the improved time step method. Full article

It is well known that power plants worldwide present access to difficult and hazardous environments, which may cause harm to on-site employees. The remote and autonomous operations in such places are currently increasing with the aid of technology improvements in communications and processing hardware. Virtual and augmented reality provide applications for crew training and remote monitoring, which also rely on 3D environment reconstruction techniques with near real-time requirements for environment inspection. Nowadays, most techniques rely on offline data processing, heavy computation algorithms, or mobile robots, which can be dangerous in confined environments. Other solutions rely on robots, edge computing, and post-processing algorithms, constraining scalability, and near real-time requirements. This work uses an edge-fog computing architecture for data and processing offload applied to a 3D reconstruction problem, where the robots are at the edge and computer nodes at the fog. The sequential processes are parallelized and layered, leading to a highly scalable approach. The architecture is analyzed against a traditional edge computing approach. Both are implemented in our scanning robots mounted in a real power plant. The 5G network application is presented along with a brief discussion on how this technology can benefit and allow the overall distributed processing. Unlike other works, we present real data for more than one proposed robot working in parallel on site, exploring hardware processing capabilities and the local Wi-Fi network characteristics. We also conclude with the required scenario for the remote monitoring to take place with a private 5G network. Full article

The use of big data leads to higher demands for hyperscale data centers (HDCs) in terms of the scale and quantity required for data storage and processing. Before the construction of an HDC, it is necessary to comprehensively analyze the economic budget according to the energy requirements and potential energy cost. We propose a global energy consumption prediction framework based on the power usage effectiveness (PUE) calculation that considers all heat sources and power consumption. The framework integrates physical models and a statistical framework that combines IT equipment energy consumption and data center energy consuming predictions. Furthermore, the framework provides a method to calculate the carbon emissions and electricity cost of the data center. Using hourly meteorological data as climate parameters, combined with a limited range of energy parameters, the annual PUE values of 60 regions were estimated, and a further analysis of the Carbon Usage Effectiveness (CUE) and electricity costs in China was conducted as an example. Based on experimental validation and an evaluation of real-time data, our framework can predict the overall energy consumption of HDCs effectively, filling a gap in HDC research in the Asia-Pacific region and providing a basis for HDC feasibility analysis. Full article