Towards interoperability testing of smart energy systems – an overview and discussion of possibilities

The project Integrating the Energy System (IES) Austria recognises interoperability as key enabler for the deployment of smart energy systems. Interoperability is covered in the Strategic Energy Technology Plan (SET-Plan) activity A4-IA0-5 and provides an added value because it enables new business options for most stakeholders. The communication of smart energy components and systems shall be interoperable to enable smooth data exchange, and thereby, the on demand integration of heterogeneous systems, components and services. The approach developed and proposed by IES, adopts the holistic methodology from the consortium Integrating the Healthcare Enterprise (IHE), established by information technology (IT) vendors in the health sector and standardised in the draft technical report ISO DTR 28380-1, to foster interoperable smart energy systems. The paper outlines the adopted IES workflow in detail and reports on lesson learnt when trial Integration Profiles based on IEC 61850 were tested at the first Connectathon Energy instalment, organised in conjunction with the IHE Connectathon Europe 2018. The IES methodology is found perfectly applicable for smart energy systems and successfully enables peer-to-peer interoperability testing among vendors. The public specification of required Integration Profiles, to be tested at subsequent Connectathon Energy events, is encouraged.

Population growth and city expansion in developing countries require traditional urban planning practices to be transformed in order to tackle climate change and follow Sustainable Development Goals (SDGs) agendas. Almost every expert in the urban sector believes that future cities should be sustainable, smart, and environmentally friendly, where energy is one of the most critical factors to achieve these goals. They also agree that smart and sustainable energy provision for cities requires a comprehensive and responsive legal and policy framework to be in place at the national level. However, this research’s findings reveal a lack of such frameworks for this group of countries. Considering the challenges and unique nature of Low-Income-Developing (LID) countries, there should be a framework based on the realities in these countries. In this research, key challenges of urban and energy sectors of LID countries, specifically Afghanistan, are identified, and a framework for the integration of sustainable and smart energy in the urban planning processes for LID countries is proposed. To make it easily replicable and adaptable for LID countries, the proposed framework is studied and analyzed around Afghanistan’s urban and energy sectors. This is one of the few frameworks of its kind for LID economies to the best of the authors’ knowledge. This framework lays a solid foundation for sustainable and smart energy integration in the urban planning process of developing countries. This study highlights that sustainable and smart energy systems could ensure climate change mitigation and economic growth enhancement but require close cross-sectoral coordination and policy maker’s commitments and involvement. This research will help many existing and emerging cities in the LID countries’ worldwide use and benefit from the proposed framework in their urban planning processes. It also enables policymakers, urban planners and designers, municipalities leadership, and other stakeholders of the urban, energy, and environment sectors to work together and make smart and rational decisions for the future of their cities and lead them towards smart and sustainable cities.

Due to the unsustainability of fossil fuels and the adverse impacts on the environment, along with the rapid growing energy demands, smart energy becomes a new trend. To achieve its carbon peak and neutrality targets in China, smart Energy is the main battlefield, electricity is the main force, and the construction of a new power system is an important content of the construction of smart energy system. The 2nd International Conference on Smart Energy and New Power Systems (SENPS2024) is to promote research and developmental activities in Smart Energy and New Power Systems by creating a platform to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will also make it an ideal platform for people to share views and experiences in related areas. With all contributions from the participants, 23 papers are final accepted from 88 submissions. Those accepted ones are well categorized into four sectors as follows: Smart Energy, Smart Grid, Power Electronics Technologies and New power system. We wish to show our sincere appreciations to Hunan University of Technology, Changsha Normal University, CRRC Zhuzhou Institute Co., Ltd which provided various platform to promote this conference with nice service. We are also thankful to the authors and reviewers for their contributions in this volume. We are also very much thankful to the organizations for contributing their time and expertise to maintain the quality of this proceeding. This time the conference service is supported by Digital Intelligence Academic Service Innovation Team from Changsha Normal University and funded by Research on the construction of international promotion platform for Chinese educational brands - A case study on the organization and management of international academic conferences from China Education Association for International Exchange. Last but not least, we especially thanks to the team of IOP Conference Publishing team for support to bring out these proceedings of this conference. we will feel honorable to have your continuous attention and attendance for our next edition of SENPS. List of Conference committee are available in this Pdf.

A bi-stage multi-objectives optimization of smart multi energy system with active consumers

To address the challenges of assessing information security risks in smart energy systems, this study proposes a multi-attribute decision support method based on interval type-2 fuzzy numbers (IT2TrFN). First, expert questionnaires were designed to gather insights from eight specialists in the fields of smart energy and safety engineering. Linguistic terms associated with IT2TrFN were employed to evaluate indicators, converting expert judgments into fuzzy numerical values while ensuring data reliability through consistency measurements. Subsequently, a decision hierarchy structure and an expert weight allocation model were developed. By utilizing the score and accuracy functions of IT2TrFN, the study determined positive and negative ideal solutions to rank and prioritize the evaluation criteria. Key influencing factors identified include the rate of excessive initial investment, regulatory stringency, information security standards, environmental pollution pressure, and incident response timeliness. The overall risk index was calculated as 0.5839, indicating a moderate level of information security risk in the evaluated region. To validate the robustness of the model, sensitivity analyses were conducted by varying IT2FWA (Weighted aggregated operator) and IT2FGA (Weighted geometric operator) operator selections and adjusting weight coefficients. The results reveal that key indicators exhibit high risk under different scenarios. This method provides an innovative tool for the scientific evaluation of information security risks in smart energy systems, laying a solid theoretical foundation for broader regional applications and the expansion of assessment criteria.

Post COVID-19 ENERGY sustainability and carbon emissions neutrality

Digital Twins are becoming central enablers of Europe’s digital and green transitions, yet their data-intensive and autonomous nature exposes them to one of the most complex regulatory environments in the world. This article presents a comprehensive scoping review of how six principal European digital laws—the General Data Protection Regulation, Data Governance Act, Data Act, Artificial Intelligence Act, NIS2 Directive, and Cyber Resilience Act—jointly govern the design, deployment, and operation of Digital Twin systems. Building on the PRISMA-ScR methodology, the study constructs a Unified Digital Twin Compliance Framework (UDTCF) that consolidates overlapping obligations across data governance, privacy, cybersecurity, transparency, interoperability, and ethical responsibility. The framework is operationalised through a Digital Twin Compliance Evaluation Matrix (DTCEM) that enables qualitative assessment of compliance maturity in research and innovation projects. Applying these tools to representative European cases in Smart Cities, Industrial Manufacturing, Transportation, and Energy Systems reveals strong convergence in data governance, security, and interoperability, but also persistent gaps in the transparency, explainability, and accountability of AI-driven components. The findings demonstrate that European digital legislation forms a coherent yet fragmented ecosystem that increasingly requires integration through compliance-by-design methodologies. The article concludes that Digital Twins can act not only as regulated technologies but also as compliance infrastructures themselves, embedding legal, ethical, and technical safeguards that reinforce Europe’s vision for trustworthy, resilient, and human-centric digital transformation.

In this study, we demonstrate that a digital colorimetric sensor platform can transform a conventional energy system, such as gas‐insulated switchgear (GIS) into a smart green energy system. Our sensor platform consists of a colorimetric sensor film (CSF), an array of silicon photodiodes, and a low‐power‐driven fiber optical photodiode with a wireless communication protocol integrated into an information network. The photodiode measures the transmitted light of the color‐variable CSF when exposed to sulfur dioxide (SO2) gas, a decomposition by‐product of the sulfur hexafluoride (SF6) gas in GIS. We report the electrical photocurrent measurement through the CSF can measure the concentration of SO2 gas via remote and real‐time monitoring and shows a comparable behavior to UV–Vis absorption measurement. The limit of detection of the sensor, 0.78 ppm is sufficient to analyze the GIS insulation deterioration allowing green energy systems.image

Integrating smart manufacturing ecosystems with industrial-grade smart energy and building automation systems enables real-time adaptation to changes in demands and factory conditions, the supply chain, and the needs of customers and society. However, integrating, managing, and controlling data exchange usually incurs high overheads in such a collaborative industrial environment. Smart home IoT technologies are a cost-effective solution for smart energy and building automation systems; they are not fully interoperable with industrial IoT technologies. This paper presents a mechanism to solve this interoperability problem using the Eclipse Arrowhead framework. The proposed solution provides a microservice-oriented architecture to develop protocol-specific smart adapter systems for the Arrowhead framework. These smart adapter systems provide seamless and highly scalable integrations between smart home and industrial IoT technologies. Our solution enables smart manufacturing ecosystems to meet Industry 5.0’s core values and reduce their carbon footprint to save the planet. We present the performance of our solution using an example from a real-world use case of a smart heating system scenario in a smart factory.

Smart energy systems (SESs), with integrated energy sectors, provide several advantages over single-sector approaches for the development of renewable energy systems. However, cross-sector integration is at an early stage even in areas challenged by the existing high shares of variable renewable energy (VRE). The promotion of cross-sector integration requires institutional incentives and new forms of actor participation and interaction that are suitable to address the organisational challenges of implementing and operating SESs. Taking as the point of departure an empirical case and its institutional context, this article presents an exploratory study of the ability of cross-sector consumer ownership at different locations in the power distribution system to address those challenges in Denmark. The methods comprise interviews of relevant stakeholders and a literature review. The results indicate that distant and local cross-sector integration will be necessary to reduce overinvestments in the grid and that consumer co-ownership of wind turbines and power-to-heat (P2H) units in district heating (DH) systems may provide advantages over common separate ownership with regard to local acceptance and attractiveness of investments. Several possibilities are identified to improve the current institutional incentive system in Denmark. Finally, the results suggest the relevance of analysing the possibility for single-sector energy companies to transition to smart energy companies.

With the deep integration of advanced information technologies such as artificial intelligence and traditional energy technologies, smart energy systems have been proposed as a method to provide the best solution for the coordination, balance, and control of the entire energy system. As a new way of energy balance and interaction in the user side energy market, peer-to-peer (P2P) electricity transaction can effectively promote energy sharing within the user group and improve the economic benefits of users participating in the energy market. Reinforcement learning (RL) is an artificial intelligence method in which agents continuously acquire relevant experience and knowledge during the interaction with the environment, automatically update their decision-making behavior, and achieve maximum return. It is suitable for P2P transaction decision analysis of small-scale users in the context of smart energy. Firstly, this paper establishes a P2P transaction model that includes a participant model, equipment model and price model. Secondly, the transaction problem is equivalent to a Markov decision process (MDP) and each learning element model is established. Then, the MDP problem is solved and analyzed using SARSA RL algorithm with average discrete processing. Finally, a case study of a community with multiple users is conducted to verify the effectiveness, economy, and security of RL method in solving energy storage action selection and transaction decision problems of energy storage users.

The transformation of traditional energy systems into smart energy systems has ushered in an era of efficiency, sustainability and technological growth. In this paper, we propose a new definition for “Quality of Energy Service” that focuses on ensuring optimal power-supply quality, encompassing factors such as availability, speed (i.e., the time to restore or adjust supply following interruptions or load changes) and reliability of supply. We explore the integration of advanced algorithms specifically tailored to enhance the Quality of Energy Services. By concentrating on key aspects—reliability, availability and operational efficiency—the study reviews how various algorithmic approaches, from machine learning models to classical optimisation techniques, can significantly improve power grid management. These algorithms are evaluated for their potential to optimise load distribution, predict system failures and manage real-time adjustments in power supply, thereby ensuring higher service quality and grid stability. The findings aim to provide actionable insights for policymakers, engineers and industry stakeholders seeking to advance smart grid technologies and meet global energy standards. Furthermore, we present a case study to demonstrate how these models can be integrated to optimise grid management, forecast energy demand and enhance operational efficiency. We employ multiple machine learning models—including Random Forest, XGBoost version 1.6.1 and Long Short-Term Memory (LSTM) networks—to predict future energy demand. These models are then combined within an ensemble learning framework to improve both the accuracy and robustness of the forecasts. Our ensemble framework not only predicts energy consumption but also optimises battery storage utilisation, ensuring continuous energy availability and reducing reliance on external energy sources. The proposed stacking ensemble achieved a forecasting accuracy of 99.06%, with a Mean Absolute Percentage Error (MAPE) of 0.9364% and a Coefficient of Determination (R2) of 0.998345, highlighting its superior performance compared to each individual base model.

In a smart energy system, power converters should be controlled by complex control software. However the efficient development of reliable control software is difficult. We have proposed to introduce a “model-based design” process using MATLAB/Simulink, established in the automotive and aerospace fields, to the development of power electronics systems. However, it was difficult in the past because calculating switching circuit as the plant model is difficult in MATLAB/Simulink due to the socalled stiff equation problem. Thus, in this study, we have developed a simulation environment in which the standard model-based design tool, MATLAB/Simulink, and the simulation engine for power switching circuits, SCALE, work cooperatively. SCALE was originally developed to solve stiff equation and efficiently simulate power switching circuits. This software environment enables us to design control algorithms with intuitive block diagram expression and test them in actual hardware, without writing a C program. Furthermore to improve this environment, a mechanism that handles the multiple sample rates was introduced. Smart energy systems that use several controllers with different sampling rates can be simulated in the proposed environment. Thus, we can develop control software for smart energy systems in a timely and cost effective manner.

Smart Energy Evolution Road-map Based On the Correlation Between Energy and Information

Smart energy systems and 4th generation district heating