Energy Informatics最新文献

The highly integrated source-grid-load-storage energy system has received increasing attention in energy transformation strategies. However, the current static network isomorphism algorithm for distributed machine learning cannot meet the energy exchange needs of the integrated energy system. To better solve the energy loss problem caused by energy trading in the power system, prevent the clean energy loss, and ensure the stable operation of the power system, a distributed dynamic network heterogeneous algorithm is designed on the basis of distributed machine learning. The proposed method uses a dynamic network to balance communication load among servers while solving the hidden state vector errors that cannot be corrected timely due to static network isomorphism. Compared with other methods with a sensitivity of 25%, the sensitivity level of the improved algorithm was above 75%. When the accuracy of other algorithms was 50%, the improved algorithm was above 80%. In the application experiment, the temperature reached 50℃ with the increase of the power. The humidity value always remained above 20. Therefore, the proposed algorithm has superior performance and good application effects, providing new ideas for energy trading in source-grid-load-storage energy systems.

This study presents a distributed electricity trading system using smart contracts to improve transaction efficiency and reduce costs in power markets. Three trading models are analyzed: centralized trading, blockchain-based decentralized trading, and smart contract-driven automated trading. The advantages and challenges of each model are examined, focusing on factors like node inclusion time, transaction costs, and price stability. The results show that the smart contract-driven model outperforms the others by increasing market efficiency, lowering transaction costs, and reducing price fluctuations. Through simulations and real-world analysis, this study provides support for using blockchain technology in power markets and offers practical advice for improving electricity trading systems. The findings suggest that the proposed system could greatly enhance transparency, efficiency, and cost-effectiveness in distributed energy markets, even in uncertain market conditions.

In the context of global climate change and sustainable development, enterprise digital transformation has become key to improving efficiency and competitiveness. Digital twin technology, as an emerging tool, enables real-time monitoring, prediction, and optimization by creating dynamic virtual models of real-world processes. This paper explores the impact of digital twin-based transformation on renewable energy investment decisions. Through empirical analysis of over 200 companies globally, the study finds that companies using digital twin technology exhibit higher accuracy and efficiency in renewable energy investment decisions. These companies show improved forecasting of energy consumption and investment returns, gaining a competitive edge. On average, these companies experience a 15% ROI increase for their renewable energy investments and enjoy a 20% acceleration in the decision-making process. Furthermore, the study delves into how the adoption of digital twin technology differs across various company sizes and industries, providing actionable insights and guidance for enterprises embarking on their digital transformation journey.

It is necessary to establish a sufficiently advanced Battery Management System (BMS) for safe driving of electric vehicles. Lithium-ion batteries have been widely used in electric vehicles due to their advantages of high specific energy and low-temperature resistance, so this paper takes lithium-ion batteries as the research object. BMS can monitor various status information of lithium-ion batteries in real-time, and the State of Charge (SOC) of lithium-ion batteries is a key parameter among them. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. However, the value of SOC cannot be directly measured. In order to more accurately estimate the SOC, this paper proposes a prediction method that combines an immune genetic algorithm, gated recurrent unit, and multi-head attention mechanism (MHA), using battery experimental data from the University of Maryland as the dataset. Compared with the traditional parameter optimization approach, this paper uses the immune genetic algorithm to find the optimal hyperparameters of the model, which on the one hand has a wider choice of parameters, and on the other hand has been improved for the genetic algorithm is easy to fall into the local optimal solution, so as to improve the SOC estimation accuracy of the GRU model. The model also incorporates a multi-attention mechanism to capture different levels of information, which enhances the expressive power of the model. The data preprocessing part adopts the sliding window technique, through which the original time series data is converted into several different training samples when training the machine learning model, as a way to increase the diversity of the dataset and improve the robustness of the model. Finally, the prediction performance of the fusion model proposed in this paper is verified by Pycharm simulation, and the average absolute error, root mean square error and maximum prediction error of the model are 1.62%, 1.55% and 0.5%, respectively, which proves that the model can accurately predict the SOC of lithium-ion battery. It is shown that the model can significantly improve the accuracy and robustness of SOC estimation, enhance the intelligence, real-time and interpretability of the battery management system, and bring a more efficient, safe and long-lasting battery management solution to the fields of electric vehicles and energy storage systems.

To improve the intelligent adjustment ability and energy consumption prediction accuracy of the fresh air system in hospital buildings, this study constructs an energy consumption prediction model based on the Back Propagation Neural Network (BPNN). Meanwhile, it introduces the Genetic Algorithm (GA) and Fuzzy Logic Algorithm (FLA) to optimize the BPNN, thus enhancing the model’s global search ability and robustness. By comparing the proposed optimized model with other models, the study analyzes the advantages of the proposed model in terms of prediction accuracy and convergence speed. Moreover, its practical effectiveness in energy consumption and operational cost optimization is evaluated. The results show that the Genetic Algorithm-Fuzzy Logic Algorithm-Back Propagation (GA-FLA-BP) algorithm performs the best in load prediction, with prediction errors typically below 1.5%, particularly on the 5th and 18th days, demonstrating exceptional performance. Compared to the GA-BP and FLA-BP models, the GA-FLA-BP algorithm exhibits stronger capabilities in handling complex data and uncertainty. Regarding energy consumption and electricity cost optimization, GA-FLA-BP also outperforms other models. Its energy consumption prediction accuracy is 91.5% and an electricity cost prediction accuracy is 90.8%, resulting in savings of 29.2% in energy consumption and 31.2% in costs. Although other algorithms show improvements, GA-FLA-BP remains significantly ahead. Furthermore, the GA-FLA-BP algorithm excels in robustness, consistency, time complexity, and real-time performance. This algorithm demonstrates the highest stability and consistency, the fastest processing speed, and the shortest response time, proving its superior performance in energy consumption management and cost optimization. This study enhances the intelligent adjustment capability of the fresh air system in hospital buildings by optimizing the energy consumption prediction model. Therefore, the study significantly reduces energy consumption and operational costs, improving the efficiency and economy of energy management.

The new power system puts forward higher requirements for the functionality, real-time performance and reliability of relay protection equipment. Therefore, this paper designs a monitoring platform for the operation of relay protection equipment at distribution network side under the background of new power system. The platform obtains the running state of relay protection equipment by establishing simulation models of different types of relay protection equipment on the distribution network side. The fault time, fault type and current action of relay protection equipment at distribution network side are analyzed to realize the monitoring of operation state. At the same time, the visual representation method of monitoring data based on three-dimensional parallel scattergram and human-computer interaction is adopted for human-computer interaction, and the electromechanical protection device is controlled to realize current quick-break protection and overcurrent protection. The experimental results show that the platform can effectively monitor the operation of relay protection equipment on the distribution network side in real time and accurately judge the action of the equipment, and the application effect is good.

Green finance plays a pivotal role in advancing sustainable urban development by enhancing energy efficiency and supporting low-carbon transitions. This study empirically demonstrates that green finance maturity (GFM), which reflects the development and effectiveness of green financial systems, has a significant positive impact on urban energy efficiency (UEE). Using panel data from Chinese prefecture-level cities spanning 2006 to 2021, the analysis shows that a one-unit increase in GFM improves UEE by 0.221 standard deviations. Mechanism analysis reveals that this effect is primarily mediated through technological advancements and improvements in innovation capacity. Further heterogeneity analysis highlights that GFM’s impact is more pronounced in non-resource-based cities and in regions characterized by advanced financial systems, greater global market integration, and higher levels of urbanization. These findings offer valuable, context-specific insights for policymakers seeking to leverage green finance maturity as a tool to promote sustainable urban development across diverse socio-economic and institutional settings.

With the acceleration of urbanization, urban waterlogging has become increasingly serious, posing new difficulties to the stability and safety of the power system. Given this, a new type of waterproof circular net cage is designed to ensure power supply and maintain voltage stability by switching to a bypass during floods. The improved non-dominated sorting genetic algorithm optimizes multi-source power supply systems. This results in the provision of innovative solutions for power systems and multi-source power supply collaborative control in flooded environments. Simulation experiments have demonstrated that, under conditions of mild flooding, the voltage of the ring net cage remained at approximately 400 V. In the case of severe flooding, the voltage of the ring net cage was switched to the bypass backup circuit and the voltage was maintained at around 220 V. The current changed with the load. The minimum comprehensive operating cost of the multi-source power supply system optimized based on the improved non-dominated sorting genetic algorithm was 1,453 yuan. Optimization strategies could reduce the unbalanced power of the system and increase the utilization rate of renewable energy to over 90%. The intelligent bypass net cage design has new features of automatic switching of bypass and maintaining voltage stability during floods. Combining an improved non-dominated sorting genetic algorithm for optimizing multi-source power supply systems can significantly reduce operating costs and greatly improve the utilization of renewable energy. The study provides an innovative solution for power systems in flood environments and theoretical support for multi-source power supply collaborative control technology.

The study employs Hebei Province as its research object and employs the weighting method for mathematical modeling to construct an energy structure optimization calculation model under carbon emission control. Secondly, a computable general equilibrium-based model is constructed for the purpose of assessing the impact of an optimal energy structure on the economic development of the province under different planning constraints. The results indicated that when the energy constraint increased from 0.8 to 1.2, the share of coal energy decreased to 61.19% and the share of petroleum energy decreased to 5.02%. The share of natural gas energy increased to 18.41% and the share of non-fossil fuel increased to 15.02%. The total cost of energy increased to 83.04 billion dollars and abatement cost decreased to 2.74 billion dollars. With the gradual completion of the planning constraints, the effect of emission reduction was gradually obvious, but the decline gradually decreased. While abatement costs could be decreased in tandem with rising energy costs, the macroeconomy and environment in the region suffered as a result of rising energy costs. The study indicates that in order to achieve sustainable regional economic development and align with the principles of ecological governance, it is essential to enhance energy management, actively advance the development of clean energy, and strive for equilibrium between economic growth, energy development, and ecological environmental protection. Concurrently, alternative energy sources must be identified through scientific and technological innovation in order to diminish reliance on fossil energy.