Energy Informatics最新文献

In the context of energy management during digital transformation, traditional energy storage devices face challenges in multi-source coordination and efficient management. The key issue for system optimization is how to stabilize the management of multiple energy storage units. To address this, the study innovatively proposes a Hybrid Energy Storage System integrating a Multi-Port Transformer and Direct Current Bus. By constructing multi-port control factors, the system achieves coordinated optimization of the energy storage units, through dynamic adjustment of multi-port control factors and energy conversion matrices, the system can flexibly allocate power output from various energy storage units according to load demands, ensuring stable system operation. Experimental results in a microgrid system show that the integrated control system has a response time of 2.3 ms under 80% load, significantly outperforming the Proportional Integral Control (8.7 ms) and during the energy storage unit switching process, the voltage fluctuation rate is only 0.8% with a switching time of just 1.8 ms, and system stability reaching 98.5%. Under high-load conditions, the energy conversion efficiency is 96.8%, and the power distribution error is only 1.2%. Compared to traditional energy storage devices, the initial investment cost of this device is reduced by 7.4%, and the annual maintenance cost is reduced by 21.7%. These results indicate that the improved hybrid energy storage device not only possesses excellent energy management capabilities but also significantly reduces operational costs and environmental impact. The study provides an efficient technical solution for managing complex energy systems, which is of great significance for promoting smart grid construction and achieving green, low-carbon goals.

The purpose of this study is to explore the impact of dynamic adaptation of corporate innovation culture and market demand on corporate sustainable development and the differences in corporate types and regions. The research sample covers 150 listed companies and 100 non-listed companies in eight industries and three economic regions: the eastern coastal area, the rise of central China, and the development of the western region from 2015 to 2020. The theoretical framework is constructed using the system dynamics model, and the empirical methods of multivariate regression analysis such as ordinary least squares, fixed effect model, and instrumental variable method are used for research. The main findings include that there is a significant positive synergistic relationship between corporate innovation culture and market demand, and there are differences in development models among enterprises of different types and regions. These results have important policy implications and can provide reference for the National Development and Reform Commission, the Ministry of Industry and Information Technology and other relevant departments to formulate policies such as industrial guidance and innovation incentives, help enterprises achieve sustainable development, and enhance the competitiveness of the national industry.

The paradigm shift towards Smart Grids, Smart Buildings, Smart Monitoring, and Operation has driven researchers to propose innovative solutions for designing and maintaining energy systems. Although the integration of Renewable Energy Sources (RES) supports sustainability goals, it also introduces vulnerabilities to unpredictable challenges such as grid stability, energy storage requirements, and infrastructure modernization. Machine Learning (ML) has emerged as a transformative tool to address these challenges, offering opportunities to enhance energy efficiency, and system design in alignment with Sustainable Development Goals (SDGs). The emphasis on these goals necessitates the study of new system designs that prioritize energy efficiency. Building on its proven success, researchers are increasingly adopting ML-driven approaches to accelerate advances in energy systems. This work presents a detailed review of current ML-driven research trends in energy systems, outlines the associated challenges, and provides potential research directions and recommendations. Unlike the existing literature, which focuses primarily on ML applications in the RES domain, this study offers a holistic perspective on ML-driven approaches across various aspects of energy systems, including energy policy and sustainability. It aims to serve as a comprehensive resource, bridging the gap between research advancements and practical implementations in energy systems through ML-driven innovation.

This study addresses the construction sector’s growing need for improved decision-making and reduced carbon emissions by integrating Lean principles into Building Information Modeling (BIM). A decision-support tool was developed using Python and RStudio to enhance stakeholder efficiency, reduce errors, and streamline communication. The tool combines Set-Based Design, Choosing By Advantages, and Big Room methods with Industry Foundation Classes (IFC) data to automatically generate and evaluate insulation options based on multi-criteria analysis. To test its adaptability and effectiveness, the tool was applied to two real-world case studies in different regions of France with distinct climatic conditions and project objectives. The first case study involved a mixed-use building in Rennes, where the objective was to enhance energy performance. The selected insulation material reduced heating needs by 13%, annual CO₂ emissions by 14%, and insulation costs by 45% over a 50-year period. The second case study focused on a residential building in Orléans, where the goal was to improve both energy efficiency and environmental impact. The tool achieved a 6% reduction in primary energy consumption, a 40% decrease in carbon footprint per (m^2) and a 6% reduction in annual CO₂ emissions. The tool’s ability to adapt to different building types and climatic conditions confirms its accuracy and reliability in optimizing energy performance and reducing environmental impact and project costs. This research provides a scalable tool for enhancing decision-making efficiency and improving building energy performance, environmental impact, and cost-effectiveness in construction projects.

Electric vehicles (EVs) are revolutionizing transportation, utilizing batteries as mobile energy storage to mitigate carbon emissions and fossil fuel depletion. Power utilities are increasingly employing EVs with dynamic energy storage for ancillary services such as frequency and voltage regulation. Additionally, EVs are utilized for dynamic damping services, where grid-connected EVs help mitigate frequency oscillations in weak grid conditions. This work presents a novel modified automatic voltage regulator (AVR)-integrated fuzzy logic-based control of EVs, incorporating a feedforward term to enhance damping services. A finely tuned AVR in a conventional generation improves synchronizing and damping torque for frequency oscillations. In this work, a modified AVR control loop is designed, combining the battery characteristics with linear controllers to generate additional damping vectors for frequency oscillations. Furthermore, an intelligent rule-based fuzzy logic (FL) controller is developed to replicate the traditional virtual synchronous control, enhancing the overall inertia and damping response. The proposed approach is validated using a modified IEEE 14-bus system under different case studies, such as load changes, EV variability, and integrated system dynamics. The results demonstrate superior performance over conventional droop control, achieving reduction in steady-state error, peak overshoot, and settling time. The comparative analysis validates the robustness and stability of the proposed control technique, marking a significant advancement in ancillary service support.

The task scheduling problem based on directed acyclic graphs (DAGs) has been proven to be NP-complete in general cases or under certain restrictions. In this paper, building upon existing scheduling algorithms, we introduce a static task scheduling algorithm based on directed acyclic graphs. By incorporating the proportion of task transmission delay as a guiding metric in the optimization process, processors can be prioritized for tasks with high latency, thereby improving computational efficiency. We first validate the theoretical feasibility of the algorithm using a theoretical case study and illustrate the algorithmic effectiveness using two real case studies, direct current (DC) model and alternating current (AC) model respectively. The research indicates that the scheduling algorithm proposed in this paper achieves an average scheduling length improvement of over 1.2% compared to the Heterogeneous Earliest-Finish-Time algorithm (HEFT) in topologies with high latency tasks. Additionally, the experiments show that the HEFT algorithm consumes 39.85us and the EMT-DM algorithm consumes 38.29us during simulation using DC, and the HEFT algorithm consumes 31.23us and the EMT-DM algorithm consumes 26.51us during simulation using AC, both of which are improved compared to the HEFT algorithm.

The transformation of power systems is accelerating due to the widespread integration of renewable energy sources (RES) and the growing role of inverter-based generations (IBGs). This shift has significantly reduced rotational inertia, increasing the system’s vulnerability to frequency fluctuations during disturbances. Consequently, the accurate and adaptive estimation of inertia has become crucial for maintaining frequency stability and grid reliability. Traditional estimation methods, though effective in certain scenarios, struggle to capture the non-linear and dynamic behaviors of modern power systems, necessitating the adoption of advanced solutions. This review comprehensively explores machine learning (ML)-based methodologies for inertia estimation, emphasizing their adaptability, scalability, and real-time capabilities compared to conventional approaches. The study categorizes ML techniques into supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL), highlighting their applications, advantages, and limitations. Advanced methodologies, such as hybrid and ensemble models, are examined for their effectiveness in overcoming challenges posed by noisy data, dynamic behaviors, and complex grid configurations. Some advanced techniques demonstrate proficiency in analyzing complex datasets and providing real-time insights into the evolving dynamics of inertia. In addition to evaluating existing approaches, the review identifies key research gaps and emerging trends, offering strategic guidance and important considerations for the development of innovative ML-driven inertia estimation methods. By addressing these challenges, this study aims to support the creation of adaptive and reliable tools that ensure effective grid management in an energy ecosystem increasingly dominated by RES.

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The integration of modern technology and architectural design in intelligent buildings has led to advancements in functionality and user experience. These developments have also contributed to the pursuit of environmental sustainability, energy conservation, and emission reduction through the implementation of advanced technological systems. Guided by the concept of green and low-carbon, intelligent building design emphasizes the full utilization of renewable energy while utilizing advanced algorithms to optimize energy scheduling in intelligent buildings, achieving green, low-carbon, energy-saving, and emission-reduction goals. Therefore, based on the concept of green and low-carbon, this study optimizes the renewable energy system, lighting control system, elevator control system, and air conditioning control system of intelligent buildings. The experimental findings, utilizing a paradigmatic intelligent office building in Shanghai as a case study, demonstrated that the solar wind complementary power generation system of the building attained an annual power generation of 609,380 kWh. This amount satisfied 60% of the building's electricity requirement, thereby signifying a substantial breakthrough in conventional building energy supply methodologies. The lighting system adopted intelligent time lighting dual-mode control, reducing energy consumption by 10.1%. The optimization of the elevator group control algorithm could achieve an average monthly power saving of 6100 kWh. The air conditioning system reduced energy consumption by 7238 kWh/month through a load forecasting model. The results showed that the intelligent building energy optimization system established in the study, through multi-system algorithm linkage, improved overall energy efficiency by 23% compared to traditional solutions. This method provides a reusable technical paradigm for smart city emission reduction.

Non-intrusive load monitoring (NILM) is a promising and cost-effective approach incorporating techniques that infer individual applications' energy consumption from aggregated consumption providing insights and transparency on energy consumption data. The largest potential of NILM lies in industrial applications facilitating key benefits like energy monitoring and anomaly detection without excessive submetering. However, besides the lack of feasible industrial time series data, the key challenge of NILM in industrial applications is the scarcity of labeled data, leading to costly and time-consuming workflows. To overcome this issue, we develop an active learning model using real-world data to intelligently select the most informative data for expert labeling. We compare three disaggregation algorithms with a benchmark model by efficiently selecting a subset of training data through three query strategies that identify the data requiring labeling. We show that the active learning model achieves satisfactory accuracy with minimal user input. Our results indicate that our model reduces the user input, i.e., the labeled data, by up to 99% while achieving between 62 and 80% of the prediction accuracy compared to the benchmark with 100% labeled training data. The active learning model is expected to serve as a foundation for expanding NILM adoption in industrial applications by addressing key market barriers, notably reducing implementation costs through minimized worker-intensive data labeling. In this vein, our work lays the foundation for further optimizations regarding the architecture of an active learning model or serves as the first benchmark for active learning in NILM for industrial applications.