Optimal Energy Research Articles

Восстановление лакового слоя изоляционных пальцев ТЭД с применением установки инфракрасного нагрева камерного типа

Purpose: based on an in-depth analysis of the causes of damage to the insulating fingers of traction motors of electric locomotives, it is necessary to develop a method for increasing their operational reliability. The most effective way to obtain the desired result is to use an improved technology for restoring the electrical insulating varnish layer of insulating fingers using thermoradiation heating. The implementation of a promising technology in depot conditions is possible with the use of a chamber-type infrared heating unit, the control unit of which contains the most optimal energy supply mode determined during experimental studies on a laboratory combined heating unit. Methods: the most significant results of the work were obtained thanks to experimental studies of the electrical and mechanical strength of the cured electrical insulating varnish layer of insulating fingers, in addition, in the process of determining the optimal energy supply mode, finite element modeling methods of thermal processes were used. Results: a design of a laboratory combined infrared heating unit has been developed, thanks to which the optimal drying mode of insulating fingers has been determined, embedded in the operation of an industrial chamber-type unit for drying a set of insulating fingers. Also, the dependence of the breakdown voltage and the hardness of the outer surface of the insulating finger, linking its mechanical and dielectric properties, was determined. Practical significance: the results of the work can be used to improve existing technologies for depot and factory repair of insulating fingers, in particular, to create a series of industrial infrared heating units in locomotive service depots. It is also worth noting that the obtained curve of the dependence of hardness and breakdown voltage, in the future, will allow assessing the electrical strength of insulating fingers indirectly by the hardness parameter of the outer varnish layer.

Oxygen-functionalization of unique hierarchical carbon nanosheet fishtail-like for synergistically enhanced capacitive performance supercapacitor

The optimization of high-performance supercapacitors with enhanced electrochemical properties using biomass-based activated carbon is a challenging task. To overcome this, a novel strategy was used to create functional nanocarbon with a hierarchical-nanosheet structure based on bio-waste of Clausena Excavata Burm F (CEBF) leaves. The precursor was optimized through different chemical impregnation concentrations without the addition of any other substances. This resulted in a unique hierarchical carbon nanosheet fishtail-like with a specific surface area of 828.679 m2 g−1. The high carbon content of CEBF (up to 88.58%) and 4.46% oxygen as heteroatoms showed a beneficial pseudocapacitance effect. The electrochemical properties of CEBF-activated carbon were excellent, with a specific capacitance of 248 F g−1. The optimal energy density reached 33.8267 Wh kg−1, and the power density was 4.755 kW kg−1 at 10 A g−1. These findings suggest that CEBF biomass has significant potential as a source of hierarchical carbon nanosheet that enhances high-performance electrochemical supercapacitors.

Controlled synthesis of the M doped (M = Fe, Cu, Zn, Mn)-Co4S3/Ni3S2 catalyst with high electrocatalytic performance for hydrogen evolution reaction in seawater and urea

Electrolysis of seawater is a promising method for producing hydrogen, but sluggish reaction kinetics and electrolyte containing corrosive ions limit its development. In this work, a series of M doped (M = Fe, Cu, Zn, Mn)-Co4S3/Ni3S2 catalyst has been prepared on Ni foam by hydrothermal method with excellent seawater and urea splitting performance. The unique 3D porous structure provides a large number of active sites for the catalyst, which has strong catalytic activity. Fe-Co4S3/Ni3S2 electrode showed excellent hydrogen evolution reaction (HER) catalytic activity in 1 M KOH + seawater and 1 M KOH + 0.5 M urea, driving a current density of 10 mA cm−2 at a low overpotential of 81 and 66 mV. In addition, Fe-Co4S3/Ni3S2 electrode also shows strong stability in 15 h stability test. The results show that although the impurity ions in seawater affect the activity of catalyst, the corrosion resistance of catalyst also improves the catalytic activity and stability. Density functional theory calculations show that this Fe-Co4S material exhibits the optimal Gibbs free energy of hydrogen, the introduction of this Ni3S2 material enhances the electrical conductivity of the material, and the synergistic catalysis of the two materials promotes the optimal hydrogen production performance of the Fe-Co4S3/Ni3S2 material. This work provides a novel understanding for the research of catalysts used in the electrolysis of seawater and urea.

Optimizing energy efficiency through building orientation and building information modelling (BIM) in diverse terrains: A case study in Pakistan

Building orientation plays a key role in enhancing the built environment's energy efficiency through the application of Building Information Modelling (BIM) and energy simulation. Due to their significant global energy consumption, buildings are the focus of energy optimization efforts. This study explores how building orientation, when combined with modern methods like BIM and energy modelling, affects energy usage and savings throughout Pakistan's numerous geographic terrains. The methodology is quantitative, beginning with an extensive review of literature on energy analysis, BIM applications, and urban building energy modelling. Autodesk Insight 360 is employed for energy simulations, which enables the precise prediction of energy consumption by considering various factors such as building orientation, window-to-wall ratios, shading, wall and roof construction, infiltration rates, lighting efficiency, occupancy controls, plug load efficiency, and HVAC systems. Energy simulation of the data indicates that optimizing building orientation alone can result in an average energy savings of 18 %, while combining orientation optimization with improvements in window arrangements and construction materials can achieve savings of up to 30 % over 30 years. The energy simulation results were validated through case study representing typical terrains and building types (commercial and residential), with simulated energy expenditures showing a high correlation (R2 = 0.92) with actual utility bills, adjusted for local energy rates. The findings of this study highlight substantial financial benefits, with potential annual savings ranging from $2500 to $4000 for residential buildings and $10,000 to $15,000 for commercial buildings, depending on building size and location. These results suggest broader applicability to diverse geographical areas and building types in future research. This research contributes significantly to energy-efficient building practices, paving the way for sustainable construction and reduced environmental impact across the varied landscapes of Pakistan.

Comparative analysis of direct coupling and MPPT control in standalone PV systems for solar energy optimization to meet sustainable building energy demands

Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's total annual energy consumption. The performance ratio and losses of the SAPV system with both direct coupling and an MPPT charge controller was compared. This comparative analysis aimed to evaluate the efficacy of two solar photovoltaic control methods—SAPV direct coupling and Maximum Power Point Tracking control—in optimizing energy harvesting from solar panels.

Recognizing social forestry's role in bioenergy optimization through geospatial fuzzy-multicriteria analysis

Current bioenergy development has emphasized on degraded land, since the sustainability of bioenergy in the forest sector remains a subject of debate related with emissions and deforestation risk. Thus, this study aims to open new perspectives of how degraded land and social forestry can be potentially combined to significantly impact the energy transition and environmental-societal enhancement. Considering sustainability of Bali as a small island with its unique customary governance structure, a model of biomass energy optimization using geospatial fuzzy-multicriteria analysis was developed to select potential green energy source sites. Firstly, potential degraded land and social forestry were mapped to identify potential feedstock, then normalized using Euclidian Distance and Fuzzy Logic based on identified five sustainability criteria. They are availability of raw material, road, port, transmission, and demand proximities. Meanwhile, using identified three restriction criteria, i.e. protected area, slope and land-use restrictions, a restriction map was developed. The two maps were then integrated using Geospatial-based multicriteria analysis, fuzzy logic and Analytical Hierarchy Process (AHP) weighting method, to further identify potential green energy source map. The integration shown a significant increase of 60 % in land availability for bioenergy development. Results of study recognized potential 36,527 ha of degraded land; 21,671 ha of social forestry; and 40 optimal locations for bioenergy facilities, considering various spatial and temporal criteria. To conclude, the identified 120 social forestry sites in Bali involving 78,385 household provide opportunity to a community based socio-economic coupled with revitalizing environment efforts, which lead to massive net zero emissions community participation. Further, the integration of social forestry and degraded land should be highly recommended to policy maker in bioenergy development.

Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm

The study addresses the integration of hybrid hydrogen (H2) and battery (BT) energy storage systems into a renewable energy microgrid comprising solar photovoltaic (PV) and wind turbine (WT) systems. The research problem focuses on improving the effectiveness and computational efficiency of energy management systems (EMS) while ensuring high system reliability. Despite the existing optimization methods for hybrid microgrids, challenges remain in optimizing energy storage and capacity planning in grid-connected microgrids. To solve this, we propose the use of the Levy Flight Algorithm (LFA) to optimize the capacities of PV, WT, H2 tanks, electrolyzers (EL), fuel cells (FC), and BT, which presents a complex nonlinear optimization challenge. The novelty of this study lies in integrating the LFA with a rule-based EMS, enhancing system reliability and efficiency. The proposed approach significantly reduces the annualized system cost (ASC) and the levelized cost of energy (LCOE). The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. The LFA achieves the lowest LCOE at $0.275/kWh, compared to $0.278/kWh with SSA, $0.289/kWh with GA, $0.280/kWh with PSO and $0.283/kWh with GWO. This research contributes to the broader scientific community by providing a more efficient approach to optimizing renewable energy microgrids with hybrid storage systems, thus promoting eco-friendly and cost-effective energy solutions. The proposed system design offers a pathway to future energy systems with high renewable integration, especially as technology advances and costs continue to decrease.

Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study

This research work introduces a novel approach to energy management in Smart Energy Systems (SES) using Deep Reinforcement Learning (DRL) to optimize the management of flexible energy systems in SES, including heating, cooling and electricity storage systems along with District Heating and Cooling Systems (DHCS). The proposed approach is applied on Meridia Smart Energy (MSE), a french demonstration project for SES. The proposed DRL framework, based on actor-critic architecture, is first applied on a Modelica digital twin that we developed for the MSE SES, and is benchmarked against a rule-based approach. The DRL agent learnt an effective strategy for managing thermal and electrical storage systems, resulting in optimized energy costs within the SES. Notably, the acquired strategy achieved annual cost reduction of at least 5% compared to the rule-based benchmark strategy. Moreover, the near-real time decision-making capabilities of the trained DRL agent provides a significant advantage over traditional optimization methods that require time-consuming re-computation at each decision point. By training the DRL agent on a digital twin of the real-world MSE project, rather than hypothetical simulation models, this study lays the foundation for a pioneering application of DRL in the real-world MSE SES, showcasing its potential for practical implementation.

DeepEMS: Multimodal optimal energy management of microgrid systems based on a hybrid multi‐stage machine learning model

AbstractThe effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs).

First-principles calculations to investigate physical properties of oxide perovskites LaBO3 (B[dbnd]Mn, Fe) for thermo-spintronic devices

Oxide perovskite LaBO3 was extensively examined using first principles computations with density functional theory. Various exchange-correlation functionals were applied to investigate several of its physical properties. The compound's stability was validated through energy optimization in both ferromagnetic and non-magnetic phases, revealing that the ferromagnetic phase is more energetically stable. With the optimized lattice parameter, we explored various electronic, mechanical, magnetic, and thermodynamic properties. According to the GGA + U approximation, LaMnO3 and LaFeO3 exhibit half-metallic and semiconductor characteristics, respectively. The elastic constants, along with the elastic moduli (Y, B, and G) and Vickers hardness (Hv) number, were calculated to assess the mechanical properties of both compounds. Our simulation confirmed the ductile nature of the material by analyzing the Cauchy pressure, Poisson's ratio, and Pugh ratio. Additionally, thermodynamic parameters, including thermal expansion, specific heat capacity, and Debye temperature, were computed using the quasi-harmonic Debye model. The study's findings suggest that these materials are suitable for thermo-spintronic devices.

Energy optimization and age of information enhancement in multi‐UAV networks using deep reinforcement learning

AbstractThis letter introduces an innovative approach for minimizing energy consumption in multi‐unmanned aerial vehicles (multi‐UAV) networks using deep reinforcement learning, with a focus on optimizing the age of information (AoI) in disaster environments. A hierarchical UAV deployment strategy that facilitates cooperative trajectory planning, ensuring timely data collection and transmission while minimizing energy consumption is proposed. By formulating the inter‐UAV network path planning problem as a Markov decision process, a deep Q‐network (DQN) strategy is applied to enable real‐time decision making that accounts for dynamic environmental changes, obstacles, and UAV battery constraints. The extensive simulation results, conducted in both rural and urban scenarios, demonstrate the effectiveness of employing a memory access approach within the DQN framework, significantly reducing energy consumption up to 33.25% in rural settings and 74.20% in urban environments compared to non‐memory approaches. By integrating AoI considerations with energy‐efficient UAV control, this work offers a robust solution for maintaining fresh data in critical applications, such as disaster response, where ground‐based communication infrastructures are compromised. The use of replay memory approach, particularly the online history approach, proves crucial in adapting to changing conditions and optimizing UAV operations for both data freshness and energy consumption.

Transforming Hydroponics with IoT for Sustainable Water Management

General Background: Urbanization is reducing agricultural land, necessitating innovative cultivation techniques like hydroponics to thrive in limited spaces. Specific Background: The study investigates the use of IoT technology in hydroponic systems, specifically focusing on remote monitoring and control of water pumps for efficient resource management. Knowledge Gap: Limited research has explored the use of IoT in optimizing water pump operations in hydroponic systems, focusing on energy management and remote accessibility. Aims: The research aims to create an IoT-based automated water pump control system for hydroponics, enabling remote monitoring and evaluating its energy efficiency and operational effectiveness. Results: The experimental setup, including an ESP32 microcontroller, ADS1115 module, solar charger controller, and submersible pump, demonstrated optimal performance with a time-based control strategy, achieving 96.7% current and 97% voltage accuracy. Novelty: The study showcases a successful model for integrating IoT in hydroponic systems, thereby enhancing sustainability and operational efficiency. Implications: The study underscores the potential of IoT technologies in enhancing water management in hydroponics, thereby providing a feasible solution to contemporary agricultural challenges in space-constrained environments. Highlights: IoT Integration: Remote control enhances hydroponic system monitoring and efficiency. Energy Optimization: Effective solar charging improves automated water pump performance. Real-time Monitoring: Enables accessible tracking from significant distances via smartphones. Keywords: Hydroponics, IoT, Water Management, ESP32, Energy Efficiency

Cost-effective and optimal pathways to selecting building microgrid components – The resilient, reliable, and flexible energy system under changing climate conditions

Amidst the changing climate conditions, electricity retailers-led demand response programs and the emergency backup power for possible grid outages are often discussed in isolation, although several underlying linkages exist, which are important for how the existing building stock evolves with the energy transition. This study navigates through the linkages while investigating the levelized cost of electricity (LCOE)-based building microgrid components and undertakes a comparative analysis of energy optimisation models with and without emergency diesel generators. It also examines the building energy system’s resilience, reliability, and flexibility by using OpenStudio and HOMER Grid to develop energy simulation and optimisation models and other probabilistic models for a chosen building archetype in Sydney, Australia. This study finds that excluding the emergency diesel generator will require a larger battery storage system, increasing LCOE from A$0.17/kWh to A$0.20/kWh across climate scenarios. However, the larger battery storage system increases the probability of surviving outages (> 4 h) by around 10 % across climate scenarios. The LCOE increases up to 45 % for outages up to 8 h across climate scenarios and up to 85 % for outages up to 12 h. Additionally, for the building archetype, the probability of grid purchase (below the current average) is 0.78 in 2030, which drops to 0.55 in 2050. The probability of grid sales (above the current average) also drops from 0.69 to 0.46. Thus, while the narratives around the building energy system’s flexibility overstate the electricity exchange between the building and the grid, this study finds that increasing the on-site production utilisation rate and larger battery throughput contributes to demand response application and flexibility.

Energy Optimization in Statistical AoI-Aware MEC Systems

Energy Optimization in Statistical AoI-Aware MEC Systems

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

Adaptive optimization of thermal energy and information management in intelligent green manufacturing process based on neural network

The study of thermal energy adaptive optimization has important theoretical and practical significance. The deep learning neural network model is used to monitor and analyze the thermal energy data in the manufacturing process in real time. Through the construction of adaptive optimization algorithm, the heat input and output are systematically evaluated and adjusted, and the heat distribution scheme is dynamically optimized according to environmental changes and production needs. At the same time, information management system is introduced to realize data summary, feedback and decision support. The experimental results show that the proposed method can effectively reduce the heat energy loss, optimize the heat energy utilization, and significantly reduce the overall energy consumption compared with traditional management methods. With real-time data updates, the system improves the flexibility and responsiveness of the production process, significantly improving manufacturing efficiency. The thermal energy adaptive optimization method based on neural network provides an effective solution for intelligent green manufacturing, which can not only optimize thermal energy management, but also provide a reference for other resource conservation and environmental protection.

Enhancing Hydrothermal Carbonization of Food Waste with Landfill Leachate: Optimization, Methane Recovery, and Sustainable Energy Generation

Enhancing Hydrothermal Carbonization of Food Waste with Landfill Leachate: Optimization, Methane Recovery, and Sustainable Energy Generation

Mainchain and Sidechain-involving H-bonded Asymmetric Polyimides Containing Rigid Amide and Flexible Ether Linkages

Hydrogen bonds (H-bonds), usually mainchain amide H-bonds, have been well recognized to play important roles in modulating the properties of polyimides (PIs) by strengthening macromolecular interaction. While the structure-properties relationships of symmetric mainchain amide H-bonded PIs have been extensively researched, the asymmetric H-bonded PIs, particularly those containing rigid-flexible backbone structure, and sidechain-involving H-bonds, have been rarely explored. Herein, starting from designed synthesis of diamine monomers, we present two series of asymmetric PIs containing rigid amide and flexible ether linkages that form mainchain amide-amide and amide-imide H-bonds, lacking or bearing pendent trifluoromethyls (-CF3) that form sidechain-involving amide-CF3 H-bonds. The presence of these three types of H-bonds were revealed by RDF simulation and confirmed by FTIR. Mainchain H-bonded PIs (PI-Ax) show higher Tg than sidechain-involving H-bonded PIs (PI-Tx), while PI-Tx series exhibit higher optical transparency and hydrophobicity, lower dielectric constant (ε') and better mechanical properties than PI-Ax. The general researches on the PIs’ solubility, WAXD spectra, thermal properties, fluorescence emission and optical transparency, the dihedral angles (φ), fractional free volume (FFV), theoretical gas transport properties, chain geometry optimization and HOMO-LUMO energy gap (ΔE) values have been comprehensively carried out from the aspects of both experiments and theoretical calculation.

Numerical investigation of dynamic gas–liquid separator by population balance model

Dynamic gas–liquid separator (DGLS) can efficiently separate gas and liquid phases and are widely used in aerospace, chemical, and petroleum engineering. The energy loss and separation efficiency within the DGLS are studied through the combination of numerical simulations and experiments. Three-dimensional transient Reynolds-Averaged Navier–Stokes equations were solved to analyze the fluid dynamics within the DGLS. The bubble aggregation and breakup in oil were simulated by using the population balance model. Experimental data were meticulously compared with numerical results to validate the accuracy and reliability of the numerical methods. The findings revealed a direct correlation between the inlet flow rate and various performance metrics of the DGLS. Specifically, as the inlet flow rate increased, the energy loss within the DGLS escalated, resulting in higher power consumption. The degassing rate of the DGLS exhibited a decreasing trend with increasing inlet flow rate, while the de-oiling rate showed an inverse relationship. The optimal performance of the separator was observed at an inlet flow rate of 140 m3·d−1, with ηg* and ηl* reaching 0.94 and 0.99, respectively. The relationship between the Qo and the η* and Po was fitted by a second-order polynomial. Moreover, the rotational speed of the DGLS demonstrated a positive correlation with energy consumption, accompanied by an increase in power output. However, the separation efficiency of the DGLS exhibited a non-linear relationship with rotational speed, peaking at a specific value before marginally declining. The optimization of degassing and dewatering rates occurred at a rotational speed of 2500 r·min−1. These findings underscore the importance of carefully adjusting operational parameters to achieve optimal performance and energy efficiency within DGLS.

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.