Energy Informatics最新文献

Photo thermal power generation, as a renewable energy technology, has broad development prospects. However, the operation and scheduling of photo thermal power plants rarely consider their internal structure and energy flow characteristics. Therefore, this study explains the structure of a solar thermal power plant with a thermal storage system and analyzes its main energy flow modes to establish a self-operation and low-carbon scheduling optimization model for the solar thermal power plant. The simulation results of the example showed that for the self-operating model oriented towards power generation planning and peak valley electricity prices, the existence of a thermal storage system could improve the power generation capacity and revenue of the photovoltaic power plant. For example, when the capacity of the thermal storage system was greater than 6 h, the penalty for insufficient power generation in the simulation result was 0 $, and the maximum increase in revenue reached 84.9% as the capacity of the thermal storage system increased. In addition, when the capacity of the thermal storage system increased from 0 to 8 h, the comprehensive operating cost decreased from 1635.2 k $ to 1224.6 k $, and the carbon emissions decreased from 26.4 × 10³ ton to 22.1 × 10³ ton. Compared with the existing literature, this study provides a more comprehensive and systematic solution through detailed energy flow analysis and optimization model. The research has practical and far-reaching significance for promoting the development of clean energy technology, improving the sustainable utilization of renewable energy, and optimizing the overall performance of the energy system.

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

Background

With the increasingly serious environmental pollution and natural environment damage, renewable energy such as solar cells have gradually become the key to change this situation. Therefore, the local abnormal diagnosis of the charge and discharge of solar cell capacitors is particularly important.

Objective

To extend the life of ultracapacitors by resolving the issue of their low detection rate and enhancing the capacity to recognize fault diagnosis factors. A novel approach to charging and discharging, as well as the diagnosis of local anomalies, is put forth, utilizing switching networks.

Methods

By controlling the capacitors of multiple solar cells and supercapacitors to work together, it is possible to compensate for the shortage of photovoltaic power. The performance of fault diagnosis is optimized by combining principal component analysis and binary K-means clustering, which completes the fault diagnosis of capacitors.

Results

The experimental results show that the research method can increase the maximum output power of photovoltaic by 32.9% under multi-layer shadows. In the charging state of the training set, the number of abnormal capacitors is 6, and the number of normal capacitors is 12, and both of them are in accordance with the preset value. The number of abnormal capacitors and normal capacitors in the discharge state is the same as that in the charging state, which is also 6 and 12.

Conclusion

The research method can effectively address the issue of unbalanced energy storage battery packs and minimize the impact of local shadows on photovoltaic systems. In comparison to fuzzy C-means clustering, this method requires fewer iterations, enables faster fault diagnosis, and produces more accurate clustering results. It can provide technical support for diagnosing local abnormalities in the charging and discharging of solar cell capacitors.

With the widespread application of drone technology, energy management of drone swarms has become a key challenge in ensuring sustained and efficient operations. To optimize the energy consumption of drone swarms, researchers have proposed the energy balance plane routing algorithm. This algorithm uses ultraviolet light for low visibility communication and has shown promising results in managing the energy of drone swarms. To address the issues of uneven cluster head distribution and reduced energy consumption in other algorithms, an improved non-uniform clustering energy balancing routing algorithm is proposed. Compared to existing algorithms, the improved non-uniform clustering energy balancing routing algorithm achieved the lowest average communication consumption among 5 nodes and prolonged node failure time. The performance of the research method has been verified through simulation experiments, which is of great significance in maintaining energy consumption balance and can improve the energy efficiency and sustainability of the network. This study can provide more effective solutions for the development and application of drone technology, promoting its widespread application and promotion in various fields.

Objective

Energy usage in has been increased due to the rising demand of cloud infrastructure. The government policy has been focused on building the green IT data center. The energy data need to be collected in order to monitor the energy usage. However, in an old typical data center, the building has been built with no support of such data collection. In this research, we aim to design the energy data collection system for our existing data center, a case study of data center in Thailand at the university. Based on the collected data, an energy usage monitoring system and prediction can be developed.

Methods

In the case study of Kasetsart University data center, the building and electric layouts were predetermined. The building layout and existing IT hardware were investigated. We designed the meter types and the number of meters to be installed for the building the energy data collection system. The corresponding database system was also designed for data logging, data visualization and analysis purpose.

Results

As a result, 25 installed meters along with the add-on network system were installed for logging data. A data usage example was demonstrated by building the data visualization and analysis. The presented 1 year dataset collected showed the changes of energy usages which can be used to compare with real activities happening in the campus. This encourages the integration of other related environment data such as outside temperature which may affect the electric billing cost. The dataset can be used for prediction of the electric usage; thus, the policy for reducing the electric billing cost could be established. In this paper, as a data note, we focus on the methodology of data collection required for data center.

As the boost of power electronics technology, the harmonic problem in the power system is becoming increasingly prominent. Fourier decomposition is performed on the load current in the power system, and components with a frequency that is an integer multiple of the fundamental wave are referred to as harmonic components. Harmonic control is essential to establish a safe and reliable power grid environment and provide high-quality and clean electricity to power users. The study focused on parallel active power filters, proposed a specific harmonic detection method on the grounds of synchronous harmonic rotating coordinate system, and developed a phase-locked loop design on the grounds of order generalized integrator. Meanwhile, a compensation current control method on the grounds of space vector pulse width modulation was introduced. The results showed that in the full compensation simulation experiment, the compensated A-phase grid side current waveform was significantly improved and presented a sinusoidal shape. After 0.05 s, the actual output compensation current closely followed the command current. Meanwhile, after compensation, the total harmonic distortion rate decreased from 26.58 to 3.06%. In specific harmonic compensation simulation experiments, when the sum of 5th, 7th, and 11th harmonic components was used as the command current for compensation, the distortion of the current waveform was improved after the load undergoes a sudden change. After compensation, the 5th, 7th, and 11th harmonic content significantly decreased, and the total harmonic distortion rate decreased to 4.08%. This indicated that the proposed phase-locked loop design and harmonic detection method for active power filters had high stability and effectiveness. The study’s primary contribution is to enhance the utilization efficiency of DC voltage and improve the dynamic response ability of current. Additionally, it offers a new method for reducing the impact of harmonics on the power grid and improving power quality. It provided an effective method reference for technological progress in related fields such as power electronics and control engineering.

Photovoltaic power generation is a promising method for generating electricity with a wide range of applications and development potential. It primarily utilizes solar energy and offers sustainable development, green environmental benefits, and abundant solar energy resources. However, there are many external factors that can affect the output characteristics of Photovoltaic cells and the effectiveness of the grid-connected control system. This study describes the introduction of Modular Multilevel Converter (MMC) technology into photovoltaic power generation systems to improve power generation efficiency. It proposes optimizing and improving the technology by adjusting the temperature and magnitude of lighting and combining traditional algorithms to propose a composite control algorithm. The photovoltaic power generation system employs the modular multi-level converter technology to enhance power generation efficiency alongside optimization and improvement. The temperature and size of light are regulated alongside the traditional algorithm to introduce the composite control algorithm. The improved composite algorithm surpasses the traditional one after experimental comparison of the results. The testing of a model photovoltaic power grid-connected system shows that the combination of modular multi-level converter technology and a photovoltaic grid-connected system, incorporating composite proportional integral control and quasi-proportional resonant control algorithms, yields improved results and feasibility. With rationality and effective control. The simulation results show that at 0.5 s, the light intensity suddenly increases from 750 to 1000 W/m², and the direct-current voltage suddenly increases for a short time, but then decreases rapidly and finally returns to a stable level close to the rated voltage. From this, it can be seen that when the light intensity continues to change, the voltage value on the direct-current bus side of this MMC grid-tied photovoltaic system can still be maintained close to the rated value, ensuring the operational stability of the entire system. Sensibly and effectively controlled. The implementation of MMC technology in photovoltaic power generation systems enhances power generation efficiency, whilst simultaneously supporting the advancement of photovoltaic power generation and contributing towards environmental protection in the long term.

In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues.

Under the rapid growth of Internet of Things technology, many households are moving towards smart solutions. Addressing the inflexibility of temperature control in traditional heating systems, this research focuses on designing an intelligent heating system. To enhance flexibility and intelligence, an intelligent heating system based on the Internet of Things and STM32 microcontroller is proposed. Furthermore, the study identifies limitations of traditional proportional-integral-derivative control methods and establishes an optimization control model for heating system output temperature based on the Dynamic Matrix Control algorithm. Results indicate that the system's web interface successfully draws temperature curves, displaying clear data on detected temperature and humidity. The output temperature optimization control model shows a temperature rise of 2 °C and a temperature control error index of 0.0543 during the initial heating stage, and a control error index of 0.0353 during the mid-heating stage when the valve relative opening is close to 0. And the temperature control effect is better than traditional PID control, fuzzy PID control, genetic algorithm based PID control, and predictive feedback predictive control, without obvious indoor temperature overshoot phenomenon, which has certain advantages. In conclusion, the proposed system and model exhibit favorable application outcomes, offering technological support for the intelligent management of heating systems.

The primary objective of this research is to explore the elements that shape the progression of digital technology in Sub-Saharan African nations. The study employs data obtained from 16 countries, covering the period between 2000 and 2020. Employing fixed effect panel regression analysis, our research indicates that various non-technological factors significantly impact digital technology development in the region. The results highlight that variables including general government final consumption expenditure, inflation rate, employment growth rate, financial development, ease of doing business index, logistics performance index, international migration, access to electricity, and access to safe drinking water have a positive impact on the development of digital technology. Conversely, international trade is identified as a negative influence, primarily due to insufficient infrastructural development. These findings underscore the significance of non-technological elements, encompassing aspects like globalization, economic conditions, favorable digital ecosystems, and the fulfillment of basic human needs, in shaping the landscape of digital technology in the region. The study, while acknowledging limitations in terms of selected indicators, years, and countries, emphasizes the need for broader investigations in future research. Practically, the study suggests that governments in the region should prioritize addressing these non-technological factors to fully leverage the potential of digital technology development. The originality and value of this research lies in its exploration of non-technological determinants, shedding light on their pivotal role in shaping the digital technology landscape in sub-Saharan Africa.