Energy Management Applications Research Articles

Phonon diffraction and interference using nanometric features

Phonon diffraction and interference patterns are observed at the atomic scale, using molecular dynamics simulations in systems containing crystalline silicon and nanometric obstacles, such as voids or amorphous inclusions. The diffraction patterns due to these nano-architectured systems of the same scale as the phonon wavelengths are similar to the ones predicted by the simple Fresnel–Kirchhoff integral. The few differences between the two approaches are attributed to the nature of the interface and the anisotropy of crystalline silicon. Based on the wave description of phonons, these findings can provide insights into the interaction of phonons with nano-objects and can have applications in smart thermal energy management.

R&D and Application of Smart Park Energy Control Platform Combined with MLM Technology

Abstract As the core of park intelligence, the smart park energy control platform realizes the efficient management and optimal use of park energy by integrating and applying IoT technology. This study focuses on the research development and application of a smart park energy management and control platform using IoT technology, firstly constructing the IoT smart park platform, designing the login module, the park information module, and the energy consumption analysis module, and describing the steps of the smart park energy collection algorithm, and then designing the model of the energy prediction and planning algorithms, i.e.In the construction of the LSTM heating temperature prediction model and the GRU network modeling. Testing and research can be done by using a smart park as an example. The empirical study shows that the construction of the IoT smart park platform can improve the accuracy of monitoring, and the difference between the actual collection results and the system monitoring results is no more than 0.03 kW. After optimizing the configuration, the park’s energy supply system can meet the electric heating and cooling loads with optimal efficiency. Through the comprehensive evaluation of the operation of the extensive energy information monitoring platform, the environmental pollutant emissions are reduced by about 20%, and the platform has a remarkable effect on energy saving and emission reduction.

Double emulsion microencapsulation of ionic liquids for carbon capture.

Microencapsulation of pristine core liquids in polymer shells has critical applications in thermal energy storage and management, targeted drug delivery, and carbon capture, among others. Herein, we report a novel encapsulation approach based on a double emulsion soft-template to produce microcapsules comprised of an ionic liquid (IL) core in a degradable polymer shell. We demonstrate the production of [IL-in-oil1]-in-oil2 (IL/O1/O2) double emulsions, in which the oil interphase (O1) contains a CO2-derived polycarbonate bearing vinyl pendant groups, tetrathiol small molecule crosslinker, and photoinitiator; upon irradiation of the double emulsion under low shear, thiol-ene crosslinking of the loaded species results in the formation of a robust shell around the pure IL droplets. The core-shell structures have enhanced physisorption for CO2 uptake compared to the bulk IL, which is consistent with the combined capacity of the IL/shell alone and demonstrates more rapid uptake due to an enhanced gas-liquid interface. This approach to microencapsulation of functional liquids offers researchers a distinct route to fabricate composite architectures with a pristine core for applications in separations, transport of cargo, and gas uptake.

Research on key technology and system application of energy management coupled with liquid cooling and intelligent control algorithm

With the continuous progress and innovation of science and technology, the demand and requirements for energy in modern society are getting higher and higher. The system application of liquid cooling and intelligent control algorithm coupling energy management can adapt to this demand and provide more efficient, accurate and intelligent energy management solutions. On the basis of a brief introduction to the current energy problems, the principle of liquid cooling technology and intelligent control algorithm are expounded, the design and key technologies of liquid cooling system and intelligent control algorithm are analyzed. The liquid cooling system based on fuzzy control is studied. The liquid cooling system based on fuzzy control can quickly and accurately respond to equipment temperature changes, avoid equipment damage due to overheating, and improve energy utilization efficiency and reduce energy waste. On the basis of a brief introduction to the current energy problems, this study describes the principle of liquid cooling technology and intelligent control algorithm, focuses on the design and key technologies of liquid cooling system and intelligent control algorithm, and studies the liquid cooling system based on fuzzy control. The liquid cooling system based on fuzzy control can quickly and accurately respond to equipment temperature changes, avoid equipment damage due to overheating, and can improve energy utilization efficiency and reduce energy waste.

Development of hybrid MgO/GO modified microencapsulated phase change material for thermal energy management: An experimental approach

The microencapsulation technique addresses the concerns of phase change material (PCM) leakage, transportation challenges, and limited efficacy in thermal energy management applications. Nevertheless, the organic shell material has lower thermal conductivity, and significant leakage issues. To improve the ability of heat transfer as well as leakage prevention, a novel form of microencapsulated phase change material (MEPCM) with paraffin core, and melamine formaldehyde (MF) shell modified by hybrid magnesium oxide (MgO), and graphene oxide (GO) thermal conductivity enhancer (TCE) interface is fabricated through the process of in-situ polymerization. The results revealed a well-developed TCE interface layer in between the core, and the shell of the modified spherical microcapsules. The modified MEPCMs exhibited a maximum encapsulation rate of 88.0 %, and 177.84 J/g latent heat of melting. The thermal stability of the MEPCMs is increased by 50 °C to 70 °C, and the thermal conductivity is remarkably elevated to 0.44 W/mK. The leakage rate is significantly reduced by 98.58%. The excellent thermal energy storage/release performance of the modified MEPCMs is attributed to the TCE interface, which serves as an efficient medium for thermal conduction. The modified MEPCMs endure exceptional physical, and thermo-chemical properties, that are relevant for thermal energy management applications.

Dynamic Threshold Fine-Tuning in Anomaly Severity Classification for Enhanced Solar Power Optimization

This study explores an innovative approach to anomaly severity classification within the realm of solar power optimization. Leveraging established machine learning algorithms—including Isolation Forest (IF), Local Outlier Factor (LOF), and Principal Component Analysis (PCA)—we introduce a novel framework marked by dynamic threshold fine-tuning. This adaptive paradigm aims to refine the accuracy of anomaly classification under varying environmental conditions, addressing factors such as dust storms and equipment irregularities. The research builds upon datasets derived from Errachidia, Morocco. Results underscore the effectiveness of dynamically adjusting severity thresholds in optimizing anomaly classification and subsequently improving the overall efficiency of solar power generation. The study not only reaffirms the robustness of the initial framework but also emphasizes the practical significance of fine-tuning anomaly severity classification for real-world applications in solar energy management. By providing a more nuanced perspective on anomaly detection, this research advances our understanding of the intricate precision required for optimal solar power generation efficiency. The findings contribute valuable insights into the broader field of machine learning applications in renewable energy, offering a pathway for the refinement of existing frameworks for enhanced sustainability and operational effectiveness

Application and Challenges of Coalitional Game Theory in Power Systems for Sustainable Energy Trading Communities

The role of prosumers is changing as they become active and empowered members of the grid by exchanging energy. This introduces bidirectional power flow and other challenges into the existing power systems, which require new approaches capable of dealing with the increased decentralization and complexity. Such approaches rely on game-theoretic models and mechanisms to analyze strategic decisions in competitive settings. More specifically, a coalitional game can encourage participants to trade energy with one another and obtain fair and sustainable outcomes. Therefore, the contents of this work address the coalitional game for sustainable energy trading, as well as the challenges associated with its application in power systems. This is achieved by identifying literature works that successfully implemented coalitional games in energy trading and management applications while providing an overview of solution concepts and discussing their properties and contributions to sustainability. Moreover, this work also proposes conditions that peer-to-peer energy trading should satisfy to be considered sustainable. Finally, a case study is presented to demonstrate how a coalitional game and various solution concepts can be successfully implemented to ensure the benefits and stability of cooperation in power systems. The weighted Shapley value is proposed to allocate profits among communities according to their level of sustainability.

Driving Condition Recognition Combined With Stochastic Prediction and Machine Learning and Its Application in Energy Management of Medium Fuel Cell Trucks

Driving Condition Recognition Combined With Stochastic Prediction and Machine Learning and Its Application in Energy Management of Medium Fuel Cell Trucks

FlexNet: A warm start method for deep reinforcement learning in hybrid electric vehicle energy management applications

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded ‘warm start’ method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications.

Strategic Energy Management and Carbon Footprint Reduction in University Campuses: A Comprehensive Review

This review paper offers a detailed analysis and summary of current studies related to energy management and carbon footprint reduction in university campuses. Using insights from different research papers, the review highlights the necessity for strategic and sustainable energy management approaches. The research reviewed explores a wide range of methodologies, including the examination of energy consumption, the identification of areas for improvement, the application of energy management and optimization techniques, and the use of renewable and sustainable energy strategies. It is evident from the review that successful energy management and carbon footprint reduction depend on energy management systems, integration of renewable energy, encouraging behavioural changes, and efficient resource use. The review paper is divided into six sections: understanding and enhancing energy consumption, Techniques for Energy Management and Optimization, random and systematic approaches to energy management, Energy Management Systems and their Applications, approaches for sustainable and renewable energy and carbon footprint reduction techniques. The review also identifies areas of future research in the field. It aims to serve as a valuable guide for university administrators, energy managers, and researchers seeking to implement sustainable energy practices and reduce carbon emissions on university campuses.

Developing characteristic performance maps of silicon-based solar cells for efficient energy management applications

Developing characteristic performance maps of silicon-based solar cells for efficient energy management applications

Experimental evaluation of different macro-encapsulation designs for PCM storages for cooling applications

Extensive research has been conducted on utilizing phase change materials for cooling applications, making it one of the most explored techniques in this domain. This research paper presents a comprehensive performance evaluation of a latent heat thermal energy storage unit featuring three distinct macro-encapsulation designs for phase change materials. The study aims to assess the thermal performance, efficiency, and practical applicability of these macro-encapsulation designs in a storage system. The PCM macro-encapsulation designs under investigation include cylindrical and rectangular shapes, each possessing different geometry. Two different configurations have been considered in this study. One configuration contains same PCM mass in order to have similar storage capacity while the other configuration has maximum PCM mass that can be inserted inside the tank. The used phase change material is a salt hydrate with melting temperature of 17 °C. The experimental setup consists of a controlled test rig that simulates real-world conditions and enables the comparative analysis of the three designs. Key performance parameters such as the charging and discharging time, temperature profiles, heat transfer rate, and energy storage/retrieval rates are measured and analysed. The results obtained from the experimental study provide valuable insights into the thermal behaviour, energy storage capacity, and overall effectiveness of the three macro-encapsulation designs. It is important to mention that use of an encapsulation design is highly dependent on application. The findings of this study contribute to the understanding of the impact of different macro-encapsulation designs on performance of thermal energy storage units. The results serve as a basis for optimizing macro-encapsulation designs, improving the efficiency and reliability of latent heat storage systems, and promoting their wider adoption in various energy management applications.

Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems

In the current energy usage scenario, the demands on energy load and the tariffs on the usage of electricity are two main areas that require a lot of attention. Energy forecasting is an ideal solution that would help us to better understand future needs and formulate solutions accordingly. Some important factors to investigate are the quantity and quality of smart grids as they are significantly influenced by the transportation, storage, and load management of energy. This research work is a review of various machine learning algorithms for energy grid applications like energy consumption, production, energy management, design, vehicle-to-grid transfers, and demand response. Ranking is performed with the help of key parameters and is evaluated using the Rapid Miner tool. The proposed manuscript uses various machine learning techniques for the evaluation of power quality performance to validate an efficient algorithm ranking in a grid-connected system for energy management applications. The use of renewable energy resources in grid-connected systems is more common in modern power systems. Universally, the energy usage sector (commercial and non-commercial) is undergoing an increase in demand for energy utilization that has substantial economic and ecological consequences. To overcome these issues, an integrated, ecofriendly, and smart system that meets the high energy demands is implemented in various buildings and other grid-connected applications. Among various machine learning techniques, an evaluation of seven algorithms—Naïve Bayes, artificial neural networks, linear regression, support vector machine, Q-learning, Gaussian mixture model, and principle component analysis—was conducted to determine which algorithm is the most effective in predicting energy balance. Among these algorithms, the decision tree, linear regression, and neural networks had more accurate results than the other algorithms used. As a result of this research, a proposal for energy forecast, energy balance, and management was compiled. A comparative statement of various algorithms concludes with results which suit energy management applications with high accuracy and low error rates.

A Self‐Powered Power Management Circuit for Triboelectric Nanogenerators Utilizing Gas Discharge Tube

AbstractSpark discharge is essential for extracting energy from triboelectric nanogenerators (TENGs). However, the lack of universal technical standards for most discharge switches limits their widespread application. Herein, a buck circuit utilizing gas discharge tube (GDT) as discharge switches is presented. The GDT switch significantly enhances the instantaneous power to 30 mW, 15 times higher than the direct method. When VGDT (the breakdown voltage of GDT) ≤1000 V, it can be integrated with an LC converter, forming the GDTx‐Buck circuit with a maximum energy conversion efficiency of 92.2%. When VGDT >1000 V, GDT can combine with a transformer, forming the GDT‐nT circuit, which enables higher voltage power management. The results demonstrate GDT's universal applicability, reducing circuit complexity during design. In practical applications, the circuit can illuminate 1250 LEDs at an ultra‐low operating frequency of 0.6 Hz, generating electromagnetic waves during GDT breakover, which proves valuable for self‐powered wireless applications. Additionally, the circuit drives parallel calculators and thermometers at 2 Hz in micro‐nano energy applications and provides continuous power to high‐power fire sensors. Its high integration and simple design render it highly promising for large‐scale energy management applications based on TENGs.

Fabrication and investigation of paraffin based shape-stable composite phase change materials with styrene ethylene butylene styrene as supporting skeleton for low temperature thermal energy storage and management

Paraffin based composite phase change materials (PCMs) have gained intensive attentions in low temperature thermal energy storage (TES) and management application domains because of its suitable melting temperature range and advanced thermoproperties. Seeking decent structural supporting materials (SSM) and the associated manufacturing approaches that capable of fabricating paraffin composites at appropriate temperatures to avert the thermal decomposition of the paraffin particularly for low-molecule ones are in high demand. Herein, we developed a low-molecule paraffin (RT18HC) composite through a simple blending approach by using styrene-ethylene/butylene-styrene (SEBS) as SSM, and showed that such SEBS-paraffin composites could be able to fabricate at a low operation temperature of 80 °C, significantly lower than that required for the preparation of conventional high density polyethylene (HDPE)-paraffin composites. To enhance the composite effective thermal conductivity, a micro-sized graphite power as enhancer was also involved over the fabrication process. Owing to the desirable solubility of paraffin in SEBS, a crosslinking structure inlaying paraffin and graphite was formed, endowing the composite with ability to maintain the structure stabilization and eliminate the graphite sedimentation over the repeated thermal cycles. 10 wt% of SEBS gives the composite optimum formulation at which 10 wt% graphite can be involved, and also an excellent shape stability and thermal adjustment ability can be achieved.

A hybrid model based on LSTM neural networks with attention mechanism for short-term wind power forecasting

Wind power plants have gained prominence in recent decades owing to their positive environmental and economic impact. However, the unpredictability of wind resources poses significant challenges to the secure and stable operation of the power grid. To address this challenge, numerous computational and statistical methods have been proposed in the literature to forecast short-term wind power generation. However, the demand for more accurate and reliable methodologies to tackle this problem remains. In this context, this paper proposes a new hybrid framework that combines a statistical pre-processing stage with an attention-based deep learning approach to overcome the shortcomings of existing forecasting strategies in accurately predicting multi-seasonal wind power time series. The proposed ensemble model involves a data transformation stage that normalizes the data distribution, along with modeling and removing multiple seasonal patterns from the historical time-series. Considering these results, the proposed model further incorporates an LSTM Recurrent Neural Network (RNN) model with an attention mechanism, for each month of the year, to better capture the relevant temporal dependencies in the input residuals sequence. The model was trained and evaluated on hourly wind power data obtained from the Spanish electricity market, spanning the period from 2008 to 2019. Experimental results show that the proposed model outperforms well-established DL-based models, achieving lower error metrics. These findings have potential applications in energy trading, grid planning, and renewable energy management.

Enhancing Energy Storage and Utilization of Triboelectric Nanogenerator by a Synchronized Charge Accumulation Circuit

As an energy harvesting technology, triboelectric nanogenerator (TENG) plays an increasingly important role in achieving the goal of green, low-carbon, and renewable development. In practical application, a power management circuit that matches the TENG with the load is also necessary. In this work, a synchronized charge accumulation circuit (SCAC) is designed, which improves the output current of TENG and the charging rate of capacitor, achieving efficient energy storage. The experimental results demonstrate that the energy conversion efficiency of SCAC is calculated to be 88.89%, the output peak current reaches the mA level (from 1 to 12 mA), the maximum output peak current is 240 times higher than the original current of TENG, and can light up 1280 parallel LEDs, compared with the original signal, the number of lights is increased by 6 times. At the same time, when charging the capacitor, the charging rate with SCAC is 10 times that without SCAC, this charging rate can stably and continuously supply power to the hygrothermograph and calculator. This study improves the energy storage rate of TENG and provides an effective method for the energy management and practical application of TENG.

Chloroplast-granum inspired phase change capsules accelerate energy storage of packed-bed thermal energy storage system

Packed-bed thermal energy storage (PBTES) systems utilizing phase change capsules have found extensive applications in thermal energy harvesting and management to alleviate energy supply-demand imbalances. Nevertheless, the sluggish thermal charging rate of phase change materials (PCMs) capsules remains a significant impediment to the rapid advancement of PBTES. Here, bionic PCMs capsules are proposed by mimicking the internal and external structure of chloroplast-granum. The heat transfer and flow characteristics of the bionic PCMs capsules in the packed-bed are analyzed by experiments and numerical simulations. The results illustrate that the chloroplast-fin type PCMs capsule exhibits significantly faster heat storage compared to the sphere type PCMs capsule. This improvement is attributed to the bionic folded shape and inner membrane structure, which generate multiple local vortices to enhance heat convection, and shorten the heat transfer distance between the capsule wall and center PCMs to facilitate heat conduction. The PCMs capsules are further filled into the packed-bed in a staggered arrangement, resulting in increased heat transfer area and enhanced disturbance flow as compared to an aligned arrangement. Consequently, the melting time of the packed-bed filled with chloroplast-fin type capsules is reduced by 33.2%, meanwhile the average exergy storage rate and exergy efficiency are enhanced by 48.4% and 8.3%, respectively, compared to the packed-bed filled with sphere type capsules. This research offers a novel approach for designing high-performance PBTES system utilizing bionic capsules.

Phase Stability of Hexagonal/Cubic Boron Nitride Nanocomposites.

Boron nitride (BN) is an exceptional material, and among its polymorphs, two-dimensional (2D) hexagonal and three-dimensional (3D) cubic BN (h-BN and c-BN) phases are most common. The phase stability regimes of these BN phases are still under debate, and phase transformations of h-BN/c-BN remain a topic of interest. Here, we investigate the phase stability of 2D/3D h-BN/c-BN nanocomposites and show that the coexistence of two phases can lead to strong nonlinear optical properties and low thermal conductivity at room temperature. Furthermore, spark-plasma sintering of the nanocomposite shows complete phase transformation to 2D h-BN with improved crystalline quality, where 3D c-BN possibly governs the nucleation and growth kinetics. Our demonstration might be insightful in phase engineering of BN polymorph-based nanocomposites with desirable properties for optoelectronics and thermal energy management applications.

Research on energy management of hydrogen electric coupling system based on deep reinforcement learning

In this paper, a deep reinforcement learning-based energy optimization management method for hydrogen-electric coupling system is proposed for the conversion and utilization and joint optimization operation of hydrogen, wind and solar energy forms considering information uncertainty on the demand side of smart grid. Based on the wind energy, photovoltaic energy generation and load forecast information, the method uses deep Q network to simulate the energy management strategy set of the hydrogen-electric coupling system, and obtains the optimal strategy through reinforcement learning to finally realize the optimal operation of the hydrogen-electric coupling system based on the demand response. Firstly, based on the energy management model, a research framework and equipment model for integrated energy systems is established. On the basis of fundamental theories of reinforcement learning framework, Q-learning algorithm and DQN algorithm, the empirical replay mechanism and freezing parameter mechanism to improve the performance of DQN are analyzed, and the energy management and optimization of integrated energy system is completed with the objective of economy. By comparing the performance of DQN algorithms with different parameters in integrated energy system energy management, the simulation results demonstrate the improvement of algorithm performance after inheriting the set of strategies, and verify the feasibility and superiority of deep reinforcement learning compared to genetic algorithm in integrated energy system energy management applications.