Energy Informatics最新文献

Modern power systems, referred to as cyber-physical energy systems (CPESs), are complex systems with strong interdependencies between power and information and communication technology (ICT) systems. CPESs also have dependencies between the essential grid services. For instance, coordinated voltage control depends on state estimation, which depends on measurement acquisition. Since the operation of CPESs is largely influenced by these grid services, assessing their performance is crucial for assessing the performance of a CPES. Most of these grid services are enabled by the ICT system, i.e., they rely to a high degree on ICT. Hence, properties such as availability, correctness and timeliness, which depend on the involved software, hardware and data of the ICT system, must be considered for assessing the performance of an ICT-enabled grid service. Disturbances and repairs in CPESs impact these properties, which can then propagate and affect the performance of a grid service as well as other dependent grid services. There is, therefore, a need to model the influence of the properties of software, hardware and data on ICT-enabled grid services for single services as well as across several services, resulting in a propagation of these parameters. Current literature lacks such a model, which can used not only to investigate but also to visualise the impact of these properties on the overall perfromance of a grid service as well as other dependent grid services. This paper proposes a meta model for assessing the performance of ICT-enabled grid services, which can be instantiated for different grid services considering their dependencies. A multi-dimensional operational state space, which serves as a visualisation of the performance of grid services in terms of their state trajectory, is also proposed in this paper. The contributions are then demonstrated by a case study with a state estimation service and the widely-used CIGRE medium voltage benchmark power grid augmented with an ICT system. Three scenarios with disturbances are presented to show the benefits of the contributions. Specifically, the performance of the state estimation service considering the disturbances is investigated using the meta model, and the change in performance is visualised as trajectories using the operational state space. These contributions enable new possibilities for planning and vulnerability analyses: property changes in parts of the ICT system can be simulated to investigate their consequences throughout the ICT-enabled grid services. A trajectory representing their performance can then be visualized in the state space based on which measures could be implemented to potentially improve the resilience of the service against the considered disturbances.

This research addresses the issue of State of Charge (SOC) prediction for electric vehicle batteries by employing a dynamic Kalman neural network model. The model is optimized using a Genetic algorithm to adjust the neural network weights. Additionally, a strategy involving support vector machines for model optimization is proposed. This strategy involves preprocessing the data, selecting appropriate kernel functions for training, and merging prediction results to enhance the stability of the model. Results indicated that the Dynamic Genetic Kalman Neural Network (DGKNN) model achieved the minimum prediction error percentage of only 0.1529% when the correction coefficient was set to 0.7. The DGKNN model consistently exhibited the lowest error percentage, average absolute error, mean square error, and root mean square error when handling small, medium, and large datasets. For instance, in the small dataset, the error percentage was only 0.1518, and the root mean square error was only 0.0604. The research findings demonstrated that the proposed model exhibited high real-time accuracy in predicting battery SOC, enabling real-time monitoring of battery operating parameters. The method proposed in this study can accurately predict the state of battery charge, extend the life of battery packs, and improve the performance of electric vehicles. It has important significance for promoting the development of the electric vehicle industry.

The reduction of fossil energy sources, the harmful environmental effects caused by high energy consumption, and the increase in the share of energy consumption in the building sector have increased the need to pay attention to building energy consumption. This study offers an intricate examination of a residential locality in Florida, with a particular emphasis on the architectural design of a building, issues related to the local environment and several possibilities for enhancing energy efficiency. It examines the influence of the environment in the area on architectural design and investigates two different possibilities for improving energy efficiency. The first scenario focuses on assessing thermal insulation and shading, while the second scenario envisions utilizing photovoltaic cells to achieve a zero-energy building. The proposed initiatives seek to optimize energy efficiency, save expenses, and foster environmental sustainability in the region. In this research, the total energy consumption of a building with residential use in the climate of the case study was validated by DesignBuilder^® simulation software, and the results obtained from the software. Then, using the standard of energy consumption of the building, various strategies for optimizing energy consumption have been simulated. Using energy simulation software, solutions for using external horizontal awnings and installing a thermal insulation sheet on the external wall of the building were investigated, which resulted in a reduction of 200 kWh of energy consumption compared to the normal state. Then, the building’s energy consumption intensity was calculated for each of the proposed solutions, and the building’s energy classification was determined with energy star and LEED standards.

This research takes a methodical look at how rising incomes and climate change affect energy use in six different divisions of Bangladesh. To investigate the indirect mechanism of income influence on the consumption of energy, this study employs indicators of industrial structure upgrading and urbanization in a novel way using the fixed effects model which has not been used so far in this kind of study. The results show that income affects energy use in two ways: directly and indirectly. The influence of income on the consumption of energy is inverted U-shaped and may be readily observed. Furthermore, by encouraging urbanization and upgrading of industrial structure, income can indirectly lower energy use. While energy consumption is negatively impacted by climate change, it is less severe than the effect on earnings. Furthermore, there are substantial geographical and temporal variations in the effect of wealth on energy use. Energy use decreases significantly as income rises over time. Income has a detrimental effect on the consumption of energy in the developed southern area. Energy usage is positively affected by income in the undeveloped northern area. In light of Bangladesh’s unique the consumption of energy profile, we must reject the “one size fits all” approach and instead concentrate on reducing wasteful spending in areas like income growth, industrial structure and urbanization, and while simultaneously increasing efficiency and precision in our aiming. This study aims to provide policymakers with fresh insights to inform decisions on energy production and consumption policies considering urbanization and industrial growth.

The Covid-19 pandemic has resulted in a permanent shift in individuals’ daily routines and driving behaviours, leading to an increase in remote work. There has also been an independent and parallel rise in the adoption of solar photovoltaic (PV) panels, electrical storage systems, and electric vehicles (EVs). With remote work, EVs are spending longer periods at home. This offers a chance to reduce EV charging demands on the grid by directly charging EV batteries with solar energy during daylight. Additionally, if bidirectional charging is supported, EVs can serve as a backup energy source day and night. Such an approach fundamentally alters domestic load profiles and boosts the profitability of residential power systems. However, the lack of publicly available post-Covid EV usage datasets has made it difficult to study the impact of recent commuting patterns shifts on EV charging. This paper, therefore, presents SPAGHETTI (Synthetic Patterns & Activity Generator for Home-Energy & Tomorrow’s Transportation Investigation), a tool that can be used for the synthetic generation of realistic EV drive cycles. It takes as input EV user commuting patterns, allowing for personalised modeling of EV usage. It is based on a thorough literature survey on post-Covid work-from-home (WFH) patterns. SPAGHETTI can be used by the scientific community to conduct further research on the large-scale adoption of EVs and their integration into domestic microgrids. As an example of its utility, we study the dependence of EV charge state and EV charging distributions on the degree of working from home and find that there is, indeed, a significant impact of WFH patterns on these critical parameters.

With the growth of the digital economy, the sustainable growth of rural energy has become crucial. However, traditional rural energy models have the drawback of not considering digital technology and renewable energy. Therefore, there is an urgent need for rational planning and development of rural energy. According to this, a multi-energy coupling model for rural energy systems was established by considering equipment capacity planning and operation scheduling optimization based on a multi-energy coupling structure. At the same time, considering the biomass resources in rural energy systems, an optimized configuration model for biomass coal-fired coupled power generation units was established. The results showed that the energy consumption cost in County A accounted for only 3.3%. County C focused mainly on tourism and emphasized economic efficiency, with investment costs 8.6% and 10.3% lower than other rural areas. The system utilized time of use electricity prices to optimize operation. The low storage stage was from 1:00 to 8:00, while the high incidence stage was from 12:00 to 14:00 and from 7:00 to 21:00. In the actual scenario, the multi-energy coupling model can be combined with intelligent technology to realize the real-time monitoring, prediction and optimal control of the energy system. Through the introduction of advanced digital technology, the model can be more flexible to deal with the diversified energy sources and complex operational scheduling situations involved in rural energy systems. This can improve the response speed and adaptability of the system, making the energy system more resilient and efficient.

Countries all over the world have been seeking ways and methods so that their electrical matrices can stand out using clean and renewable energy sources. In this context, this article presents a review with analysis of sector legislation on photovoltaic solar energy in Brazil. This study was grounded in four steps: (i) sample definition; (ii) theoretical basis; (iii) network analysis; and (iv) content analysis in two stages of research. Initially, a systematic literature review was carried out in order to map all the major and most cited works. The second stage consisted in reading and performing a critical analysis of government documents and reports from the energy sector in Brazil using a few bibliometric resources for such a purpose. Its results reveal that photovoltaic solar energy in Brazil has grown and expanded to different applications, since floating solar plants and subscription to solar energy are becoming increasingly attractive. Furthermore, a possible replacement of photovoltaic solar generation for thermoelectric plants has been investigated once there are a few positive aspects yet to be found thereof. As samples of the results obtained, we have that the replacement of works would allow the photovoltaic solar energy source to increase by 1% in the electrical matrix and would stop emitting 10,738,478 tons into the atmosphere, there would be a progressive decrease in the use of tariff flags (which affect directly to the final consumer) and a reduction in operating costs would also be achieved.

Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk.

Software-in-the-Loop (SIL) testing is an approach used for verification and validation in the energy sector. However, there is no comprehensive overview of the application, potential, and challenges of SIL within this sector. Therefore, this paper conducts a thorough scoping review of the existing literature within the scope of SIL and related in-the-loop approaches in the energy sector. A total of 88 full-text articles from four significant databases ACM, IEEE Xplore, Scopus, and Web of Science are analyzed and categorized to map the purpose, methods, architecture, interoperability and protocols, technologies, challenges, and limitations. The results present a grand perspective of in-the-loop across several domains followed by an analysis of SIL in the energy sector. The application domains carry characteristics from complex systems, systems-of-systems, cyber-physical systems, critical systems, real-time systems, and sociotechnical systems. The energy sector and the automotive industry are amongst the most applied domains. Within energy- and electricity systems, hardware-based in-the-loop paradigms are mostly applied for testing low-level signaling, and SIL is used for control strategy testing, optimization, dispatching, and experimentation. The examined SIL architectures have distributed-, real-time, and closed-loop properties, and are constrained by specialized simulation power hardware. Future research should address how to systematically develop SIL testing environments with guiding principles to support application development for the future digitalized energy system.

The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies.