Energy Informatics最新文献

In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.

In order to cope with the increasing energy demand and achieve the “double carbon “goal of China’s 14th Five-Year Plan,” combined with hydrogen energy storage technology, it has the characteristics of zero pollution, high efficiency and rich source. In the context of reducing energy consumption and the vigorous development of hydrogen energy storage technology, a multi-objective optimization configuration model with economy, energy consumption index and carbon emission index is proposed, which takes into account the working characteristics of the hydrogen energy storage system, and the exothermic heat release from the electrolysis tanks and fuel cells when they are working to provide the loads with an additional heat source of the Combined Cooling, Heating and Power (CCHP) system, to reduce energy consumption and carbon emission. Finally, taking a region as an example, a multi-objective optimization algorithm based on decomposition is used to solve the model, so as to obtain a series of alternatives with better optimization effect. At the same time, the two-way projection method based on interval intuitionistic fuzzy information is used to make decisions, and the scheme that optimizes the economy, energy consumption index and carbon emission index is obtained, which verifies the feasibility of the system proposed in this paper.

Air conditioning, as an essential appliance in daily life, has the function of ensuring comfortable room temperature, but it is also accompanied by a large amount of power consumption. Consequently, the study suggests an energy consumption prediction model based on improved genetic algorithm—least squares support vector machine—to accurately predict the energy consumption of building heating, ventilation, and air conditioning. This model uses the improved genetic algorithm for regularization parameter and kernel parameter optimization to prevent overfitting and underfitting issues. According to the testing results, the least squares support vector machine, an upgraded genetic algorithm, may accomplish convergence faster than other algorithms, taking only 0.2 milliseconds to finish. In addition, the average relative error of the improved genetic algorithm- least squares support vector machine did not exceed 0.6%. In the energy consumption prediction for the whole year of 2022, the average error of the improved genetic algorithm-least squares support vector machine was only 2.0 × 10⁶ kWh, and the prediction accuracy could reach up to 97.2%. The above outcomes revealed that the energy consumption prediction model can accurately predict the air conditioning energy consumption, which provides a strong support for the control and optimization of the air conditioning system.

In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection.

In response to the safety and stability issues of current electric vehicle charging connection devices, this study proposes a charging system planning for electric vehicles with different capacity charging piles based on the user behavior characteristics of electric vehicles and Monte Carlo methods. It is found that the predicted results under the set management strategy are most consistent with the trend of actual load changes. Moreover, in the prediction of weekly load, the research strategy has better performance than traditional unmanaged strategies. Under the research scheme, the average charging speed of charging piles with capacity of A and B in the peak period was 41.4 min/ and 18.8 min/, respectively, which increased by 29.3% and 11.7% respectively compared with 58.6 min/ and 21.3 min/ in the normal period. The total economic cost of the research plan was 4.871 million yuan, which was 67.0 million yuan and 3.833 million yuan lower than the control methods 1 and 2, respectively. The total number of charging stations of types a and b that need to be purchased for the research method decreased by 18.47% and 63.24% compared to the comparative method 3. The results indicate that the research method significantly improves the utilization rate of charging stations in the electric vehicle charging system. This study has important application value in the intelligent management of electric vehicle charging systems.

Amidst the increasing complexity of microgrid optimization, characterized by numerous decision variables and intricate non-linear relationships, there is a pressing need for highly efficient algorithms. This study introduces a tailored Mixed Integer Nonlinear Programming (MINLP) model that optimizes the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESS) while incorporating Distributed Flexible AC Transmission System (D-FACTS) devices. To address these challenges, a novel approach based on the Large-Scale Two-Population Algorithm (LSTPA) is proposed. The model's effectiveness was evaluated using a 33-node microgrid, where the proposed method achieved a total purchased energy of 1.2 MWh, a voltage deviation of 0.0357 p.u, and a CPU time of 551 s, outperforming traditional methods like NSGA-II, PSO, and JAYA. Additionally, in a 69-node microgrid, the approach resulted in a total purchased energy of 0.3 MWh and a voltage deviation of 0.0078 p.u. These results demonstrate the superior performance of the proposed method in terms of energy efficiency, voltage stability, and computational time, advancing the efficiency of microgrid management.

Energy management in datacenters is a major challenge today due to the environmental and economic impact of increasing energy consumption. Efficient placement of virtual machines in physical machines within modern datacenters is crucial for their effective management. In this context, five algorithms named CNN-GA, CNN-greedy, CNN-ABC, CNN-ACO and CNN-PSO, have been developed to minimize hosts’ power consumption and ensure service quality with relatively low response times. We propose a comparative approach between the developed algorithms and other existing methods for virtual machine placement. The algorithms use optimization algorithms combined with Convolutional Neural Networks to build predictive models of virtual machine placement. The models were evaluated based on their accuracy and complexity to select the optimal solution. The necessary data is collected using the CloudSim Plus simulator, and the prediction results were used to allocate virtual machines according to the predictions of the models. The main objective of this research is to optimize the management of Information Technology resources within datacenters. This is achieved by seeking a virtual machine placement policy that minimizes hosts’ power consumption and ensures an appropriate level of service for users' needs. It considers the imperatives of sustainability, performance, and availability by reducing energy consumption and response times. We studied six scenarios under specific constraints to determine the best model for virtual machines’ placement. This approach aims to address current challenges in energy management and operational efficiency.

Cloud computing is the paradigm for delivering streaming content, office applications, software functions, computing power, storage, and more as services over the Internet. It offers elasticity and scalability to the service consumer and profit to the provider. The success of such a paradigm has resulted in a constant increase in the providers’ infrastructure, most notably data centers. Data centers are energy-intensive installations that require power for the operation of the hardware and networking devices and their cooling. To serve cloud computing needs, the data center organizes work as virtual machines placed on physical servers. The policy chosen for the placement of virtual machines over servers is critical for managing the data center resources, and the variability of workloads needs to be considered. Inefficient placement leads to resource waste, excessive power consumption, and increased communication costs. In the present work, we address the virtual machine placement problem and propose an Imitation-Based Optimization (IBO) method inspired by human imitation for dynamic placement. To understand the implications of the proposed approach, we present a comparative analysis with state-of-the-art methods. The results show that, with the proposed IBO, the energy consumption decreases at an average of 7%, 10%, 11%, 28%, 17%, and 35% compared to Hybrid meta-heuristic, Extended particle swarm optimization, particle swarm optimization, Genetic Algorithm, Integer Linear Programming, and Hybrid Best-Fit, respectively. With growing workloads, the proposed approach can achieve monthly cost savings of €201.4 euro and (hbox {CO}_2) Savings of 460.92 lbs (hbox {CO}_2)/month.

Biomass-based hydrogen production is an innovative approach for realizing carbon-neutral energy solutions. Despite their promise, both structures differ in terms of the biomass energy domain, which is at the entry point of the technology, and the hydrogen energy domain, which is at the exit point of the technology. In this study, we conducted structural and predictive analyses via cross-domain bibliometric analysis to clarify the differences in the structures and perspectives of researchers across domains and to suggest ways to strengthen collaboration to promote innovation. Our study revealed that the hydrogen energy domain has a balanced impact on realizing a hydrogen society using biomass-based hydrogen production technology, while the biomass energy domain has a strong interest in the process of processing biomass. The results reveal that different communities have different ideas about research, resulting in a divide in the areas to be achieved. This comparative analysis reveals the importance of synergistic progress through interdisciplinary efforts. By filling these gaps, our findings can lead to the development of a roadmap for future research and policy development in renewable energy and highlight the importance of a unified approach to sustainable hydrogen production. The contribution of this study is to provide evidence for the importance of cross-disciplinary cooperation for R&D directors and policy makers.

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.