Reliable Power Research Articles

Deep eutectic solvent and laser-reduced graphene oxide: Synergistic effects in ternary nanocomposite supercapacitors

In recent years, flexible supercapacitors (FSCs) have gained considerable attention as reliable power supplies for wearable and portable electronic devices. However, optimizing both energy and power densities remains a challenge and requires effective enhancement of two key components: the electrolyte and the electrode. To address this, we have developed a new and efficient approach to FSC fabrication using a ternary nanocomposite of laser-reduced graphene@polyaniline/NiFe₂O₄(LrGO@Pani-NF) as the electrode and stable polymer hydrogels as the electrolyte. Also, we constructed FSC devices using a cross-linked poly (vinyl alcohol)/deep eutectic solvent (DES) gel electrolyte (PVA/ChCl/EG) and a PVA/Polyaniline/Nickel Ferrite gel electrolyte (PVA/Pani/NF). Among these devices, the LrGO@Pani-NF//LrGO@Pani-NF demonstrated a remarkable energy density of 48.4 Wh kg⁻¹ at 401.8 W kg⁻¹ and a long cycle life of 1000 cycles with 96.9 % capacity retention at 2 mA cm⁻². Furthermore, the supercapacitors assembled with the DES gel exhibited excellent stability at higher voltages, operating within a potential window of −3.0–3.0 V. This work not only enhances FSC electrochemical performance with graphene nanocomposite electrodes but also introduces a novel method for FSC design using conductive, stable gel electrolytes, thereby improving energy storage capabilities under harsh conditions.

Numerical method research and characteristics analysis on frozen start-up process of high-temperature heat pipe

Future space exploration technology requires a long-life and reliable power source that is not reliant on solar energy. Space micro-reactors are able to meet this need, with Heat Pipe Cooled Reactors (HPR) emerging as a notable type of space micro-reactor that has attracted widespread attention in recent years. The HPR utilizes high-temperature alkali metal heat pipes for heat transfer, which presents certain complexities due to the solid state of the alkali metals working medium at room temperature. This results in a three-phase transition during the high-temperature heat pipes start-up process, which significantly impacts the heat transfer characteristics and dynamic behavior of the HPR start-up process. Consequently, thorough research is necessary in this area. Numerical simulation is a crucial tool that can effectively analyze, predict, and guide experiments. This article utilizes the Finite Volume Method (FVM) to develop a simulation code for high-temperature heat pipe frozen start-up. Various physical models are integrated to describe different components of the heat pipe: the container is represented by a two-dimensional axisymmetric heat conduction equation, the wick region utilizes a Fixed Grid Method (FGM) to depict the melting process of the medium, and the vapor channel is described through a two-dimensional axisymmetric compressible laminar flow. The wick region and vapor channel are coupled through the evaporation and condensation of the medium. For the vapor channel, numerical methods such as SIMPLE and PISO are used for solving. Adaptive time step and OpenMP acceleration are employed in the code to enhance computational efficiency. Finally, by comparing the calculated results with experimental data, the feasibility and accuracy of the code are assessed, highlighting special phenomena during the start-up process. The findings confirm that the developed code accurately predicts parameter changes during start-up, and can serve as a heat pipe analysis module for multi-physics coupling analysis of HPR.

Smart grid management: Integrating hybrid intelligent algorithms for microgrid energy optimization

The increasing global demand for power drives the need for advancements in generation, control, and supply technologies. While bulk power generation remains cost-effective, it depends on transmission and distribution systems for delivery. Integrating distributed generators into the grid enhances reliability and direct power supply to consumers. Micro Grids (MGs) are a promising solution, offering smoother and more reliable operations. This study explores MGs, incorporating the latest loads and distributed generators to minimize operating costs for electrical power providers. This work utilizes intelligent algorithms, including the Firefly algorithm, Spider Monkey Optimization (SMO), and a hybrid approach combining both. The Firefly algorithm, inspired by swarm intelligence, mimics fireflies' light emission to optimize solutions. SMO, based on spider monkeys' social behavior, balances exploration and exploitation to achieve optimal results. A novel hybrid algorithm is developed to minimize MG operating costs by combining SMO's exploration with the Firefly algorithm's neighborhood optimization; enhancing navigation through the complex constraint solution space of MGs. Case studies on a modified IEEE 33-bus test feeder are conducted. The study compares three metaheuristic methods with non-convex solution spaces to MG operational algorithms. The results demonstrate the hybrid method's efficacy in determining the optimal location and size of real and reactive power support devices for MG operation. Numerical results show the hybrid algorithm's superiority over existing methods, reducing operating costs by 7.5 %, real power loss by 36.5 %, and reactive power loss by 1.7 %, and voltage deviation by 16.895. By considering MG constraints, the hybrid algorithm consistently delivers better outcomes, optimizing operating costs and ensuring efficient power supply. This study presents a novel MG optimization approach, enhancing future energy systems with improved solutions.

A Quantitative Comparison of 31P Magnetic Resonance Spectroscopy RF Coil Sensitivity and SNR between 7T and 10.5T Human MRI Scanners Using a Loop-Dipole 31P-1H Probe.

In vivo phosphorus-31 (31P) magnetic resonance spectroscopy (MRS) imaging (MRSI) is an important non-invasive imaging tool for studying cerebral energy metabolism, intracellular nicotinamide adenine dinucleotide (NAD) and redox ratio, and mitochondrial function. However, it is challenging to achieve high signal-to-noise ratio (SNR) 31P MRS/MRSI results owing to low phosphorus metabolites concentration and low phosphorous gyromagnetic ratio (γ). Many works have demonstrated that ultrahigh field (UHF) could significantly improve the 31P-MRS SNR. However, there is a lack of studies of the 31P MRSI SNR in the 10.5 Tesla (T) human scanner. In this study, we designed and constructed a novel 31P-1H dual-frequency loop-dipole probe that can operate at both 7T and 10.5T for a quantitative comparison of 31P MRSI SNR between the two magnetic fields, taking into account the RF coil B1 fields (RF coil receive and transmit fields) and relaxation times. We found that the SNR of the 31P MRS signal is 1.5 times higher at 10.5T as compared to 7T, and the power dependence of SNR on magnetic field strength (B0) is 1.9.

Optimal preventive maintenance: Balancing reliability and costs in the electricity market

This study addresses the challenge of achieving optimal preventive maintenance within power systems, aiming to balance reliability and costs effectively. The primary focus is on understanding the relationship between preventive maintenance expenditures and the failure rate of essential transmission components, particularly transformers in substations. We contribute to the existing literature by providing two key insights. Firstly, we establish a theoretical framework for determining the optimal level of preventive maintenance, which can be extended across various electricity facilities. By analyzing the relationship between preventive maintenance costs and failure rates, our goal is to identify the investment level that ensures a reliable power system while minimizing financial burdens. Secondly, we explore the functional form that accurately characterizes the link between preventive maintenance costs and failure rates. The diminishing marginal rate of failure, dependent on different functional forms, highlights the variability of optimal preventive maintenance expenses. Utilizing Lasso regression, we identify the functional form that optimally characterizes the relationship between outage occurrences and maintenance costs. These insights extend well beyond the confines of the Korean power industry, offering a sustainable approach to managing transmission facilities and enhancing the overall stability and efficiency of global electricity networks.

COMBINED SUPERCHARGING SYSTEM OF DOMESTIC HIGH-PRESSURE TWO-STROKE SPECIAL PURPOSE DIESEL

Modern trends in engine construction are inextricably linked to the improvement of power units by modernizing existing models. The introduction of a forced to 1100 kW diesel engine of the DN type allows to increase the efficiency and bring the equipment in line with modern requirements in a short time and with lower costs. The key method of improvement is the modernization of the supercharging system, which increases power without increasing the dimensions and weight of the engine, as well as improves technical and economic characteristics. A combined supercharging system with a low-pressure turbocharger (LPT) and a high-pressure drive compressor (HPT) is proposed, which work together or separately depending on the operating mode. The use of a mathematical model of the workflow made it possible to carry out a computational and experimental study of the proposed system in the modes of maximum power and maximum torque, confirming its high efficiency. The use of modern technologies and materials, as well as the introduction of innovative design and modeling methods, allows to create more reliable and durable power units. Modernization of the supercharging system is one of the most important directions in the development of modern engine construction, which allows to achieve an increase in the level of specific power, improvement of fuel economy and environmental characteristics of engines by ensuring optimal conditions for their operation in various operating conditions and in a wide range of operating modes. The paper presents the research results of the proposed two-stage supercharging scheme of the DN type diesel engine, which makes it possible to achieve a power of 1100 kW. It is shown that this scheme has advantages over the traditional one, as it makes it possible to provide a higher boost pressure in a wider range of crankshaft RPM, as well as to reduce power consumption from the crankshaft to drive the compressor at modes close to the nominal ones. These factors collectively lead to a significant improvement in the characteristics of the diesel due to the introduction of the two-stage supercharging system.

A Study the optimisation of smart grid power flow

Optimal Power Flow (OPF) is a fundamental concept in the management and operation of modern electrical power systems, particularly within the context of smart grids. As the demand for electricity continues to grow and the energy landscape evolves towards greater sustainability, the need for efficient, reliable, and adaptive power systems has become more critical than ever. The primary objective of OPF is to determine the most efficient way to operate the power system while satisfying various technical and operational constraints. This involves the optimization of several variables, including generator outputs, voltage levels, and power flows, to minimize costs, reduce losses, and enhance system reliability. Smart grids can enhance their resilience to disturbances, support the efficient use of renewable energy, and provide reliable electricity to consumers.

Investigating the influence of geometry on the performance of a wave-powered marine observation buoy: The wave-to-wire approach

As an important marine monitoring platform, marine observation buoys have been widely used in marine environmental monitoring, marine resource surveys, etc., attracting great attention worldwide. However, the lack of a reliable power supply is a crucial problem that seriously restricts buoys’ operation lifetime and carrying capacity. The floating buoy with an inbuilt wave energy harvester is a promising solution to the long-term and sustainable energy supply problem. This study develops a fully coupled wave-to-wire (W2W) mathematical model where the relevant friction parameters and power-take-off system parameters are identified from flume experimental results. Besides, the influence of geometry on the buoy’s electric power performance is investigated under regular and irregular waves, respectively. Numerical results imply that the cylinder-cone-shape buoy produces the most electric power when the three shapes have the same geometric sizes, while the cylinder-sphere-shape buoy performs the best W2W efficiency among the three shapes with the same mass. Moreover, a high W2W efficiency of up to 23.13% is gained under a regular wave. Finally, according to the similarity theory, the averaged electric power of a full-size buoy is estimated at 4.84kW in realistic sea states.

Intelligent hybrid deep learning models for enhanced shipboard solar irradiance prediction and charging station

Electrifying marine routes with solar energy significantly reduces ocean carbon emissions, playing a vital role in climate regulation. Solar irradiance forecasting ensures reliable power despite unpredictable sea weather, necessitating innovative model development. This research presents a forecasting model designed for the optimal placement of solar charging stations, providing hour-ahead solar irradiance predictions for onboard solar vessels. India (Jawaharlal Nehru Port Trust)– United States (Port of New York and New Jersey) sea route is selected to test our proposed forecasting model, as it aligns with busy shipping operations and long-distance trade requirements for electrification. Two distinct procedures by two case studies are used to select optimum data to predict the suitable locations for solar charging stations along the navigation route. In the first case study (Medium data analysis – MDA), the data are selected based on a statistical analysis of twenty years of hourly solar irradiance data from 201 locations. In the second case study (Large data analysis – LDA), two years of seasonal data from 201 locations are chosen and combined to form a large amount of hourly data. For both case studies, a hybrid solar irradiance forecasting model (ARIMA – Bi – LSTM) is developed by integrating the deep learning model with the time series model and fine-tuning them using optimization algorithms (PSO and GA). In MDA, the PSO optimization achieves mean absolute error (MAE) values of 0.32, 0.85, 1.83, 1.40, and 1.33 W/m2 for locations 1, 2, 3, 4, and 5, respectively, significantly outperforming the GA, which has MAE values of 1.36, 1.65, 3.25, 0.92, and 1.41 W/m2 for the same locations. In LDA, the PSO model achieves MAE values of 0.79, 0.91, 0.28, and 0.39 W/m2 for winter, rain, summer, and spring, respectively, also outperforming the GA, which has MAE values of 1.54, 2.17, 0.99, and 1.46 W/m2 for the same seasons. Comparatively, the LDA reinforces its best alignment to predict optimal charging station locations.

UNCERTAINTY EVALUATION USING LAW OF PROPAGATION AND MONTE CARLO SIMULATION METHODS WITH THE AUTORFPOWER MEASUREMENT SOFTWARE

RF power measurement is essential in RF and microwave metrology. For reliable and accurate power measurement, automatic measurement is preferred. A software application in C#, named AutoRFPower, was developed for automatic RF power measurement and uncertainty calculations at this study. According to the GUM document, this application is enhanced for uncertainty calculations by utilizing the Law of Propagation method and the Monte Carlo Simulation method. Trial measurements were performed at different RF power levels and frequencies between 50 MHz and 18 GHz using the AutoRFPower software. Law of Propagation and Monte Carlo Simulation uncertainty calculations were carried out by AutoRFPower based on the trial measurements and by the Oracle Crystal Ball simulation application. All measurements and their uncertainty calculations were compared with each other, and this study validated the uncertainty calculation of AutoRFPower. In addition, it was observed that in the Monte Carlo Simulation, uncertainty calculation results were non-symmetrical normal distribution, contrary to the assumption of symmetrical normal distribution according to the Low of Propagation method. Moreover, it has been observed that the statistical distribution of uncertainty changes depending on the dominant component of the parameters in the model function used for the uncertainty calculation with the Monte Carlo Simulation method.

An Innovative Energy Storage System Based on Phase Change Material and Solar Energy Integrated With an Air Handling Unit to Produce Heating and Cooling

ABSTRACTThis study investigates the potential of using phase change material (PCM) in a building using an air handling unit (AHU) assisted by solar energy. To further enhance the system, an energy storage system (ESS) can be considered. Implementing ESS would allow the captured solar energy to be stored efficiently, ensuring a continuous and reliable power source for cooling or heating the air, even during non‐sunlight hours. The air‐conditioned zone is considered to be 220 m2 in which 30 people work for 12 h a day. An energy analysis is conducted to evaluate the performance of the proposed system in terms of energy and exergy loss. The genetic algorithm (GA) method is used to optimize the system. The results indicate that the system, including PCM and solar system, can reduce energy consumption by up to 8.9% compared to conventional AHUs in the hottest month of the year (July). The results indicated that employing PCM leads to a decrease of 124 kWh in heat loss during January and 229 kWh in heat gain during July. Taking into account the beneficial impacts of both PCM and the solar system, the yearly assessment demonstrates a 2.69% reduction in power demand, equating to an energy saving of 679 kWh. Furthermore, PCM in the proposed system can be used in integration with AHUs based on renewable energy systems to store renewable energy for buildings.

The efficacy of a cellular approach to resilience-oriented operation of distribution grids based on eigenvectors of the Laplacian matrix

This study aims to address the following research query: In the event of an imminent disaster poised to impact distribution grids, what constitutes the optimal course of action for the distribution system operators to keep the lights on? To address this challenge, we propose a cost-efficient cellular model for enhancing the resilience of smart distribution grids. This model prioritizes resilience in the face of natural disasters or other disruptions that could impact service delivery. This method benefits both grid operators and consumers by ensuring reliable power supply while minimizing energy costs. Furthermore, the model's scalability allows it to be applied to distribution systems of varying sizes. The proposed method utilizes an innovative approach to form optimal cellular network configurations within the grid. As the first step in the formation of cellular topology for the grid, the eigenvectors of the Laplacian matrix of the grid will be used to decide on the optimal configurations. Subsequently, a bi-level mixed-integer linear programming model is proposed to decrease the network costs while simultaneously consider potential power transfer scenarios between the cells and the upstream network during both normal and emergency conditions. The researchers validated the effectiveness of the proposed method through simulations on an IEEE 33-bus test system. The results demonstrate outstanding performance, with a significant increase in the resilience index (96 %) and a substantial reduction in load-shedding costs (80 %), making the network considerably more robust.

The dual impact of AI and renewable energy in enhancing medicine for better diagnostics, drug discovery, and public health

This review paper discusses how Artificial Intelligence (AI) and renewable energy are shaping the field of medicine. It focuses on improving accuracy speeding up drug discovery and enhancing health efforts. AI technologies are changing diagnostics by identifying patterns that may be missed by human doctors leading to better clinical decisions and outcomes, for patients. At the time incorporating energy into healthcare facilities is promoting sustainability cutting costs and ensuring a reliable power supply essential for uninterrupted medical operations. The paper also explores how machine learning models are used in drug discovery to streamline target identification improve trials and reduce development timelines and expenses. Additionally, it looks at how AI can enhance health by using analytics for early disease detection and efficient resource allocation while supporting these technologies with renewable energy solutions. By combining the advancements in AI with the benefits of energy this approach presents an sustainable perspective, on medicine that emphasizes innovation and environmental responsibility. It stresses the importance of research and ethical considerations to harness its potential.

High-performance optical dialogue for power electronic system

High-accuracy signal and high-quality power transmissions play an essential role in power electronics systems. Nowadays, the severe electromagnetic interference (EMI) by the switching converter threatens the stable operation of conventional signal and power transmissions. To mitigate the effects of EMI, optical fiber is a better solution due to its unmatched isolation properties. Here, we perform a transmission configuration, named optical dialogue, based on a single fiber for simultaneous bidirectional analog optical signal (AOS) and analog optical power (AOP) transmission, to ensure EMI immunity. This optical dialogue transmission not only utilizes AOS to achieve high-accuracy signal transmission, but also utilizes AOP to achieve high-quality power transmission. We validate the proposed optical dialogue transmission using discrete off-the-shelf components, demonstrating the high frequency signal transmission and high efficiency power transmission. Our proposed optical dialogue transmission offers a high-performance optical solution for signal and power transmission, which is significant step forward in the development of more accurate, reliable, and safer power electronic systems.

Properties of a continuous optical discharge sustained by short-wave infrared laser radiation in high pressure argon

Abstract The paper is devoted to the experimental study of the characteristics of continuous optical discharge (COD) sustained by high power continuous wave laser radiation at a wavelength λ = 1.08 μm in high pressure argon. New data on COD threshold laser power dependence on argon pressure in the range 20-50 bar are obtained. COD threshold laser power is shown to be in good agreement with the data obtained by other authors and theoretical evaluations provided the contribution of plasma energy loss due thermal radiation is taken into account properly. A study of the convective plume oscillations around COD in argon has been carried out. It was found that in a pressure range 25-35 bar the growth of the laser radiation power leads to a decrease in convection oscillation frequency from 33 to 29 Hz, while the radius of the convective plume grows accordingly. The oscillation frequency ν and characteristic radius of the convective plume r0 were found to obey the similarity relation previously established in experiments with COD in xenon.

Technoeconomic analysis of solar PV electrification to remote areas of Dera Ghazi Khan: A case study

With the increase in population at an immense rate, electricity demand is growing exponentially. Researchers and policymakers are seeking alternating means of power generation to meet the load demand. These resources should be cost-effective, environmentally friendly and least carbon emissions. To mitigate the load demand Renewable Energy Sources (RES) are integrated into electrical networks. In this work, an off-grid solar photovoltaic (PV) system is designed for rural areas of Dera Ghazi Khan (DG Khan), Pakistan. These areas often lack access to reliable grid power. Installing PV systems in these areas can help provide a reliable source of electricity, reduce reliance on fossil fuels, and improve living conditions. This case study is simulated using PVsyst 7.2. of Roonghan village such as hospitals, shops, and residential houses for techno-economic analysis of off-grid solar PV. The economic viability shows that installing off-grid solar PV in DG Khan is much cheaper. The electricity taken from the grid that's almost 80.01%is expensive. This case study helps other researchers and policymakers to mitigate the electricity requirement of remote areas located far away from the national grid. To install a transmission line, having cost $26.98 corer but using an off-grid to provide electricity to the same area is $2.16 corer. Additionally, it integrates Battery Energy Storage (BES) with Renewable Energy Sources (RES) to achieve a 5 % annual energy cost reduction and enhanced self-sufficiency, filling a gap in existing literature.

Energy poverty and environmental sustainability in Sub‐Saharan Africa: Evidence from method of moments quantile regression

AbstractThe enormous ensembles of energy resources have elevated Sub‐Saharan Africa (SSA) to a prominent position in the world energy scene as a leading region in forming a sustainable energy future. However, over half of the population in the region lives without electricity and less than 20% of the population has access to clean fuels. The excessive reliance on fossil fuels such as coal, kerosene, and solid biomass has implications for environmental outcomes in SSA. Nevertheless, the environmental impact of energy poverty from the SSA perspective remains an under‐explored terrain in the research landscape. In this context, to address the pressing challenges of energy poverty and environmental sustainability, this study aims to explore the threat energy poverty poses on the ecological footprints, focusing on “land‐use, carbon footprint, forestry resources, and fishing ground” across 35 SSA countries using disaggregated data covering the period 2000 and 2021. This study employs the novel Method of the Moments Quantile Regression following the Stochastic Impacts by Regression on Population, Affluence and Technology model as the reference theoretical and analytical framework. The findings disclose heterogeneous effects of rural and urban energy poverty on ecological footprint items. When comparing the magnitudes, we found that rural energy poverty has a more detrimental impact on land‐use than urban energy poverty. Energy poverty is found to have no reliable power to explain the variation in carbon footprint and fishing ground. In the meantime, urban energy poverty exerts a positive effect on forestry resource sustainability. Surprisingly, population density is found to have a significant and desirable impact on land‐use. Based on the obtained results, numerous policy suggestions have been discussed along with some prospects for future research.

Operational assessment of solar-wind-biomass-hydro-electrolyser hybrid microgrid for load variations using model predictive deterministic algorithm and droop controllers

This study presents the operation and assessment of a solar-wind-biomass-hydro-electrolyser hybrid microgrid for different types of load variations by using a proprietary derivative-free algorithm (Model Predictive Deterministic Algorithm) and voltage IQ droop controller. The proposed hybrid microgrid integrates various renewable energy sources and an electrolyser to generate hydrogen and produce electricity. The objective is to optimize the microgrid’s performance and provide reliable power supply to meet varying load demands. The proprietary derivative-free algorithm is used to determine the optimal dispatch of power from each energy source to meet the load demand while minimizing the overall cost of energy. Additionally, a voltage IQ droop and voltage Q droop controller are employed to regulate the voltage, frequency and active power of the microgrid and maintain its stability during load variations. The proposed hybrid microgrid is assessed under different load scenarios for both grid tied and isolated modes. The results demonstrate that the proposed hybrid microgrid with the derivative-free algorithm and voltage droop controllers can effectively operate and provide reliable power supply under different load variations while maintaining the power system stability of the microgrid. Further, the mutual performances of the controller are compared to find the best controller for the specific microgrid. The findings of this study can contribute to the development of efficient and reliable hybrid microgrid systems for sustainable energy production and distribution.

Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events

With wind power penetration increases, accurate and reliable wind power forecasting is becoming gradually critical, and data driven model is a promising solution to implement this task. However, limited by deficient data samples under extreme conditions, scarcity of feature measurements fails to meet the number of training samples required, making the forecasting model exhibits low adaptability. This paper proposes a physics-informed reinforcement learning based method for probabilistic forecasting. Analytical physical expression of wind power output under extreme event is established to construct the error evaluation function for abrupt feature. Deep deterministic policy gradient-based quantile fitting model is then proposed, with abrupt feature embedded as the auxiliary input data for neural network. On this basis, parameter training technique under small data set is proposed to deal with the effect of extreme conditions on correction process of network. It extracts key historical transitions from experience replay pool to establish effective training samples, and model parameters are updated through the small-batch learning strategy to minimize long-term error feedback. Test result on the practical cold wave event of wind farms in China shows effectiveness of the proposed method.

Optimizing compressed air energy storage with organic Rankine cycle and ejector refrigeration for sustainable power and cooling provision

In the pursuit of sustainable energy systems, integrating storage technologies is crucial. Compressed air energy storage (CAES) emerges as a significant option for ensuring reliable power supply during peak hours. This study focuses on a configuration combining a CAES unit with two integrated organic Rankine cycle and ejector refrigeration units (ORCERC). The primary objective is to ensure adequate power supply during peak hours in the discharge period, while also providing cooling capacity to enhance system flexibility and efficiency. The proposed system is evaluated from thermodynamic, exergoeconomic, and exergoenvironmental perspectives to determine optimal operating conditions for the CAES unit. A fluid selection process identified a suitable zeotropic mixture, R141b/Hexane, as the working fluid for both ORCERCs. The results demonstrate a net power production of 20,749.91 kW and a cooling load of 1448.23 kW, with cost and exergoenvironmental impact rates of $1193.59/h and 143.42 Pt/h, respectively. Additionally, the configuration achieves an exergy round-trip efficiency of 65.85 % and a payback period of 2.88 years. Increasing the HTES temperature from 1150 to 1250 K improved both the round-trip efficiency (RTE) and exergetic round-trip efficiency (ERTE) from 52.22 % to 53.39 % and 62.98 %–63.71 %, respectively, while reducing the product cost and exergoenvironmental impact rate from $1128 to $1117/h and 134.35 to 127.93 Pt/h, respectively. Three triple-objective optimization scenarios were considered, yielding diverse outcomes, each contributing unique insights into the system's performance and potential improvements.