Energy Informatics最新文献

Under the background of global energy transformation and the integration of digital economy, energy enterprises’ digital investment faces the challenges of uncertain return cycle and lack of data asset pricing mechanism. By constructing a system dynamics model, this paper reveals the dynamic mechanism of data-driven digital investment decision-making of energy enterprises. The research shows that: the value of data assets forms a self reinforcing cycle through the return reinvestment loop, and its scale expansion is regulated by the dynamic balance between the cost constraint and the value inhibition loop; The improvement of market risk perception, the robustness of the trading market, the increase of energy policy intensity and the weakening of peer competition can significantly improve the cumulative profits of enterprises; Adaptive investment strategy has more advantages than fixed investment strategy, but the timing of strategy transformation needs to be accurately controlled. The simulation results provide a basis for enterprises to optimize the data investment path. It is suggested to build a data-driven dynamic investment system, deepen the operation of data assets, and call on the policy side to improve the data factor market system and incentive measures, so as to jointly promote the strategic transformation of energy enterprises to data centers.

This paper proposes an integrated knowledge graph-based power dispatching model for emergency response, combining Markov chain-based text preprocessing, entity-extracted knowledge graph construction, and case-based reasoning optimization - a novel approach that enhances both real-time decision-making and system security. First, a Markov chain-based method effectively removes redundant information from power anomaly event texts, improving entity extraction accuracy. Subsequently, a knowledge graph is constructed to precisely identify key entities, enabling the creation of a structured power emergency plan database. Finally, case-based reasoning matches real-time anomalies with historical cases, facilitating the rapid generation of optimal dispatching schemes. The experiments demonstrate that the proposed model achieves high efficiency (with an average dispatching time < 50 s) and reliability (exhibiting a failure blowout rate below 0.1%), thereby significantly improving power grid safety. The proposed framework advances intelligent power system dispatching by integrating text analytics, knowledge representation, and adaptive reasoning.

Achieving Sustainable Development Goal 7 (SDG7: Affordable and Clean Energy) and Sustainable Development Goal 13 (SDG13: Climate Action) requires advancing renewable energy systems with enhanced sustainability and resilience. Traditional Photovoltaic (PV) planning often focuses on average energy output, overlooking critical metrics such as consistency, variability, and long-term performance. This study analyzes three consecutive years (2017–2019) to assess the impact of climate variability on the energy trends of three PV technologies, fixed PV, Concentrated PV (CPV), and Dual Axis Tracking PV (DATPV), across six global cities. Sustainability scores were calculated using a GIS-based metric that captures energy consistency, intermonthly variability, and climatic adaptability, providing a technical evaluation of long-term system stability under varying weather conditions. The results reveal Cairo and Riyadh as top performers, achieving sustainability scores of 0.87 and 0.70, respectively, for fixed PV in 2019. In Madrid, DATPV systems excelled with sustainability scores reaching 0.39 in 2019, leveraging abundant solar resources. Meanwhile, Beijing’s fixed PV systems demonstrated exceptional stability, maintaining scores of 0.58 across all years, reflecting the region’s consistent solar conditions. By integrating sustainability metrics, this study offers a comprehensive framework for evaluating PV systems under changing climatic conditions, advancing SDG7 by ensuring reliable energy access and SDG13 by promoting resilient, climate-adaptive renewable energy solutions.

Amidst growing environmental imperatives, digital technologies have emerged as pivotal enablers of sustainable transformation in the logistics sector, particularly by improving energy efficiency and reducing greenhouse gas emissions. Despite increasing recognition of their importance, the concrete mechanisms and pathways through which digitalization drives energy conservation and emission reduction at the enterprise level remain insufficiently understood. Addressing this substantive gap, this study aims to systematically elucidate how digital technologies empower logistics enterprises to achieve low-carbon transformation. Using SF Holding—a leading digitalized logistics firm in China—as a representative case, we develop and empirically validate an integrated framework encompassing green innovation, energy substitution, and operational efficiency. Employing Grey Relational Analysis, we quantitatively investigate how six key factors—R&D investment, cumulative granted patents, newly granted patents, new energy vehicle adoption, photovoltaic power generation, and enterprise digitalization degree—impact two core environmental performance indicators: greenhouse gas emission intensity and energy consumption intensity. The results demonstrate that cumulative technological capability and the degree of enterprise digitalization are especially influential in promoting emission reduction and energy efficiency. By clarifying the micro-level mechanisms—such as technological accumulation, clean energy integration, and operational optimization—this study advances theoretical understanding of digitalization-driven green transformation in logistics and offers actionable insights for both policymakers and industry practitioners seeking to foster low-carbon logistics through digital innovation.

When it comes to smart power grid (SPG) reliability and energy balancing, multi-objective energy optimization is a must. Uncertainty and several competing criteria on the demand and generation sides make multi-objective optimization difficult. Selecting a model capable of resolving scheduling issues related to loads and dispersed energy sources is, therefore, essential. This study details a concept for optimizing the SPG’s operating cost and pollutant emissions using renewable electricity. Renewable energy sources, such as solar photovoltaic and wind power, are inherently unpredictable and subject to change. Uncertainty around renewable energy is handled by the suggested approach via the use of a probability density function (PDF). In order to address a multi-objective optimization (MOCO) issue, the model that was built relies on a MOCO method. A benchmark model for energy management and control is used to verify the performance of the suggested model, which is a multi-objective deep reinforcement learning (DRL) method. According to the results, MOCO reduces operating costs by 15% and environmental emissions by 8%. The results show that compared to the comparison models, the proposed model achieves the aims better.

This paper presents a new and multidisciplinary systematic analysis of floating photovoltaic (FPV) systems that integrates recent advances in computational modelling and intelligent optimization to address persistent issues with performance, hydrodynamics, and adaptability. The review is organized according to five main goals: (i) to publish experimental and empirical results in FPV literature; (ii) to develop a unified computational approach that combines CFD and ML; (iii) to assess system improvements through multi-scale hydrodynamic modelling and AI-driven adjustments; (iv) to introduce the Bidirectional Conceptual Feedback Loop (BCFL) as a dynamic optimization model; and (v) to develop a scalable, climate-resilient FPV model for the global energy transition. Scopus, Web of Science, Google Scholar, ScienceDirect, SpringerLink, and Taylor & Francis were the sources of 404 research publications in all. 189 high-impact publications were found through a careful curation of online databases, with a focus on computational innovations, machine learning (ML)-based optimization, and hydrodynamic analysis. Following a strict inclusion and exclusion process and using Mendeley reference management software to remove duplicate records during the screening stage, authors evaluated a collection of high-impact literature, technology developments, and verified empirical data related to mooring systems, wave-wind interactions, structural stability, predictive analytics, and digital twin environments. According to the synthesis, real-time adaptation, predictive defect detection, and optimized energy yield are made possible by the clever fusion of CFD and ML, especially in dynamic aquatic environments. In order to meet the demands of both climate resilience and the scaling of renewable energy, FPV platforms must become cyber-physical, self-optimizing systems. This paper introduces a paradigm shift by using a methodical and theoretical approach to review and incorporate empirical research, advanced simulation, and AI-driven system intelligence. Future FPV development can be revolutionized by the proposed BCFL paradigm, which makes it easier to move from isolated innovation to integrative, flexible, and globally replicable FPV system design.

To address the “secondary dispatch” problem in alpine virtual power plants caused by uncertainties in decentralized resource allocation, we develop an algorithm for aggregating dispatch parameters of distributed resources to achieve real-time load-demand matching. Based on alpine power generation resources, we design a specialized virtual power plant structure and analyze its market trading applications. For the actual operation of the decentralized resources in the alpine virtual power plant, we determined the power provided by the alpine virtual power plant to the electric power system as well as the adjustable power capacity and other scheduling parameters, and then designed the dispatch model objective with the decentralized resource power and scheduling parameters based on the imitator dynamic algorithm. The model incorporates constraints based on these parameters to enable effective aggregation of adjustable power ranges for both individual resources and the entire virtual power plant, while ensuring compliance with all power constraints. This approach enhances scheduling flexibility and resolves the grid-side secondary dispatch issue. An improved ant colony algorithm based on continuous optimization was used to solve the aggregation parameters. Experimental results demonstrate superior solution performance, with the aggregated parameters increasing wind farm planned output by over 12 MW across different periods. This significantly boosts power delivery to the main grid, provides more stable supply, and improves virtual power plant revenue.

With the increasing share of distributed renewable energy sources the need arises to store excess energy and/or to shift demands to match the given supply. To coordinate multiple suppliers and demands in a local energy-system different control approaches can be used. This publication introduces a framework called MASSIVE that aims to coordinate multiple participants in a district energy-system. The energy-system is controlled in a distributed way by using a multiagent approach that is scheduled by a market-mechanism. This market-mechanism allows to coordinate many individual agents with only few restrictions by using pricing mechanisms. This offers an incentive for the agents to adapt their power consumption to best match the forecasted power supply. However, the agents are free to follow this incentive or ignore it depending on the value of the incentive. The individual agents are flexible in the internal approach to forecast power supply or demand, allowing easy development of agents using individual algorithms. The coordination takes place using a market-mechanism that is similar to the day-ahead market. It, however, is run multiple times a day to form a rolling horizon, making it less sensitive to forecasting errors. The market approach furthermore exhibits a nearly linear scalability with regard to the duration of the market clearing. On the used computer, the creation and solving of the linear optimization-problem is performed in less than one minute for approximately 1500 participating agents. Therefore, this approach is capable of real-time use and can be used in real-world applications.

The rapid growth of photovoltaic (PV) installation poses a major challenge for the energy transition in Germany. A key concern is that the increasing number of PV systems can create overloads in the low voltage grid, particularly in areas with high concentrations of installations. To better estimate the adoption of industry sized PV systems, a recommendation framework is introduced to assess the probability of adoption for specific companies. The presented framework utilizes openly available data and a hierarchical clustering approach to predict the likelihood of PV adoption for a company. Predicting PV adoption for companies allows identification of potential bottlenecks in the energy grid. As a recommendation system, it can be leveraged to promote PV systems more effectively, targeting areas with high adoption potential and optimizing grid infrastructure planning. In order to achieve that, openly available data sources have been acquired through web scraping. Company data then have been clustered using a hierarchical agglomerative approach. The recall value for the installation prediction showed an average performance of 0.75, which is found sufficient for an elaborated estimate of PV adoption.

This research focuses on investigating predictive analytics for renewable energy systems, specifically developing advanced forecasting models for solar photovoltaic (PV) power generation and non-dispatchable load consumption. To address the challenges associated with the intermittent and variable nature of solar energy, an innovative hybrid model is proposed. Specifically, this research integrates the K-nearest neighbor (KNN) classification method and genetic algorithm (GA) to optimize a backpropagation neural network (BPNN). This novel approach significantly enhances the precision of short-term solar photovoltaic power generation forecasting, enabling more accurate predictions of power output. This study proposed a prediction algorithm for non-dispatchable loads based on an online learning long short-term memory (LSTM) network. The algorithm determines whether to update parameters in the LSTM network through an online learning strategy by evaluating the root mean square error (RMSE) between prediction results and actual power consumption. The KNN-MBP algorithm reduces the RMSE by 50.36% compared to the MBP algorithm through weather classification. The KNN-GA-MBP algorithm demonstrates the best prediction performance among the three algorithms, with an RMSE of only 0.39 kW, this represents a 43.37% improvement in RMSE over the KNN-MBP algorithm and a 71.89% improvement over the MBP algorithm.