Energy Utilization Research Articles

Stiffness characteristics of the lower extremities in Women’s Chinese Basketball Association competition athletes: normative values and position differences

PurposeHigher stiffenss is expected to augment performance by increasing the utilisation of elastic energy. However, excessive lower extremity stiffness increases the risk of bony injuries; while insufficient stiffness is associated with a higher likelihood of soft tissue injuries. Thus, there might be an ‘optimal’ stiffness value or range that allows for maximising athletic performance while simultaneously minimising risk of sports injury. Basketball players can be classified by position as centres, guards and forwards, with each position characterised by specific injury risks and exercise patterns. Therefore, this study aims to establish normative data for lower extremity stiffness characteristics of players in the Women’s Chinese Basketball Association (WCBA) and compare the characteristics based on position.MethodsA total of 124 WCBA athletes (over 70% of the WCBA teams) were recruited for this study, including 63 forwards, 22 centres and 39 guards. Stiffness was evaluated before and during the 2020–2021 WCBA season, which was averaged for data analysis. Quasi-static stiffness measurements of muscles and tendons were collected via a handheld myometer on seven sites of each leg. Vertical stiffness was also evaluated by OptoGait system.ResultsDescriptive statistics were used to establish the normative values of stiffness for forwards, centres and guards. The Kruskal Wallis test and post hoc Bonferroni pairwise comparisons found significant higher stiffness of the left patellar tendon (PT) in guards than centres (p = 0.004) and in guards than forwards (p = 0.012), right PT stiffness in guards than centres (p = 0.016) and in guards than forwards (p = 0.017), mean PT stiffness in guards than centres (p = 0.003) and in guards than forwards (p = 0.008); stiffness of the right soleus (SOL) in guards than forwards (p = 0.033), stiffness of the left biceps femoris (BF) in forwards than centres (p = 0.049) and in guards than centres (p = 0.038); and stiffness of the left vertical stiffness (hopping) in forwards than centres (p = 0.041).ConclusionForwards, centres and guards were characterised by significantly different stiffness values, which could be utilised for improvement of athletic performance and injury prevention.

Evaluating GHG Emissions and Renewable Energy Use in the Italian Energy Sector: Monitoring, Reporting, and Objectives

This study investigates the greenhouse gas (GHG) and renewable energy use reporting practices among thermal power plants (TPPs), waste incinerators (WIs), and hydropower plants (HPPs) in Italy, as reflected in their EMAS environmental statements. The analysis focuses on GHG emissions (Scope 1, 2, and 3) and renewable energy utilization reporting, and on the objectives set by the companies for reducing emissions and fossil fuels use. TPPs and WIs reported positive Scope 1 emissions extensively but reporting on Scope 2 and Scope 3 resulted inconsistent for all facilities. Negative emissions reporting was generally lacking, except for HPPs. Renewable energy use reporting was also limited, especially in TPPs and WIs, despite some facilities producing energy from renewable sources. The study also evaluated the objectives set by the companies on GHG reduction and renewable energy use increase, finding that GHG reduction was prioritized over renewable energy use. However, both were often a secondary goal integrated into planned operational improvements. The findings highlight that, to ensure transparency of sustainability data and the possibility of performances benchmarking in the energy production sector, there is the need for defining stronger reporting guidelines on GHG emissions, especially regarding Scope 3 emissions, and to prioritize increasing the share of renewable energy among strategic objectives. Future research should investigate factors affecting reporting behavior and the barriers to renewable energy adoption in fossil fuel-reliant sectors.

Network and Energy Storage Joint Planning and Reconstruction Strategy for Improving Power Supply and Renewable Energy Acceptance Capacities

The integration of distributed generation (DG) into distribution networks has significantly increased the strong coupling between power supply capacity and renewable energy acceptance capacity. Addressing this strong coupling while enhancing both capacities presents a critical challenge in modern distribution network development. This study introduces an innovative joint planning and reconstruction strategy for network and energy storage, designed to simultaneously enhance power supply capacity and renewable energy acceptance capacity. The proposed approach employs a bi-level optimization model: the upper level focuses on minimizing economic costs by determining the optimal locations and capacities of energy storage systems and the layout of network lines, while the lower level aims to maximize power supply and renewable energy acceptance capacities by optimizing line switch states. Additionally, this research quantifies the coupling relationship between these two capacities under uncertainty, providing a deeper understanding of their dynamic interaction. Advanced computational techniques, including Monte Carlo simulations and particle swarm optimization (PSO), are utilized to solve the model efficiently. Case studies demonstrate that the proposed strategy effectively enhances both power supply and renewable energy acceptance capacities. Furthermore, exploring the strong coupling relationship between these two capacities under various conditions not only optimizes the utilization of renewable energy in the power system and prevents resource waste, but also helps avoid the volatility impacts of renewable energy uncertainty on the power system in actual planning. Additionally, the network and energy storage joint planning and reconstruction strategy proposed in this study achieves cost minimization under the constraint of limited resources and simultaneously enhanced both capacities. The strategy provides feasible solutions for power grid planning in actual applications.

Constructing Donor‐Acceptor Covalent Organic Frameworks for Highly Efficient H2O2 Photosynthesis Coupled with Oxidative Organic Transformations

AbstractThe development of photocatalytic systems that enable the simultaneous production of H2O2 and value‐added organic chemicals presents a dual advantage: generating valuable products while maximizing the utilization of solar energy. Despite the potential, there are relatively few reports on photocatalysts capable of such dual functions. In this study, we synthesized a series of donor‐acceptor covalent organic frameworks (COFs), designated as JUC‐675 to JUC‐677, to explore their photocatalytic efficiency in the co‐production of H2O2 and N‐benzylbenzaldimine (BBAD). Among them, JUC‐675 exhibited exceptional performance, achieving a H2O2 production rate of 22.8 mmol g−1 h−1 with an apparent quantum yield of 15.7 %, and its solar‐to‐chemical conversion efficiency was calculated to be 1.09 %, marking it as the most effective COF‐based photocatalyst reported to date. Additionally, JUC‐675 demonstrated a high selectivity (99.9 %) and yield (96 %) for BBAD in the oxidative coupling of benzylamine. The underlying reaction mechanism was thoroughly investigated through validation experiments and density functional theory (DFT) calculations. This work represents a significant advancement in the design of COF‐based photocatalysts and the development of efficient dual‐function photocatalytic platforms, offering new insights and methodologies for enhanced solar energy utilization and the synthesis of value‐added products.

Constructing Donor-Acceptor Covalent Organic Frameworks for Highly Efficient H2O2 Photosynthesis Coupled with Oxidative Organic Transformations.

The development of photocatalytic systems that enable the simultaneous production of H2O2 and value-added organic chemicals presents a dual advantage: generating valuable products while maximizing the utilization of solar energy. Despite the potential, there are relatively few reports on photocatalysts capable of such dual functions. In this study, we synthesized a series of donor-acceptor covalent organic frameworks (COFs), designated as JUC-675 to JUC-677, to explore their photocatalytic efficiency in the co-production of H2O2 and N-benzylbenzaldimine (BBAD). Among them, JUC-675 exhibited exceptional performance, achieving a H2O2 production rate of 22.8 mmol g-1 h-1 with an apparent quantum yield of 15.7 %, and its solar-to-chemical conversion efficiency was calculated to be 1.09 %, marking it as the most effective COF-based photocatalyst reported to date. Additionally, JUC-675 demonstrated a high selectivity (99.9 %) and yield (96 %) for BBAD in the oxidative coupling of benzylamine. The underlying reaction mechanism was thoroughly investigated through validation experiments and density functional theory (DFT) calculations. This work represents a significant advancement in the design of COF-based photocatalysts and the development of efficient dual-function photocatalytic platforms, offering new insights and methodologies for enhanced solar energy utilization and the synthesis of value-added products.

A modified energy management strategy for PV/diesel hybrid system to reduce diesel consumption based on artificial protozoa optimizer

The photovoltaic (PV)/diesel hybrid system (PV/D-HS) combines solar PV panels with a diesel generator (DG) to meet energy demands, especially in industrial operations. This study introduces an improved energy management strategy designed to optimize the performance of PV/D-HS by reducing diesel consumption, increasing solar energy utilization, and minimizing environmental impact. The strategy dynamically manages power distribution between the PV panels and the DG, adapting to changing solar conditions and energy demands. Doing so reduces the system’s reliance on diesel, improves operational efficiency, and supports the integration of cleaner energy sources. Simulation results show significant improvements over traditional approaches: carbon emissions decreased from 62 kg/day with a standalone diesel generator to 38 kg/day, representing a 38% reduction. The solar energy fraction (SEF) increases from 12 to 35%, a 23% improvement in solar energy utilization. These results demonstrate the potential of the proposed strategy to enhance sustainability by lowering greenhouse gas emissions, reducing dependence on fossil fuels, and advancing global efforts to combat climate change. In addition to environmental benefits, the approach reduces operational costs and improves the system’s reliability, making it a practical solution for industrial energy needs.

Does renewable energy reduce energy intensity? A matter of income inequality

Abstract This study investigates the complex relationships between income inequality, renewable energy utilization, and energy efficiency across 104 countries from 2010 to 2020. By employing the Panel Threshold Model and Unconditional Quantile Regression based on the Re-centered Influence Function, the analysis focuses on the moderating role of income inequality in shaping the impact of renewable energy consumption on energy intensity. The results indicate a nonlinear relationship, driven by a single-threshold effect of income inequality. In contexts with low income inequality, renewable energy consumption significantly reduces energy intensity, demonstrating its potential to enhance energy efficiency. However, as income inequality increases, this positive association weakens, suggesting that income inequality can act as a barrier to achieving energy-efficient economies. The study further highlights substantial variations across regions and income levels. In high-income economies, greater financial resources enable more widespread adoption of renewable energy, mitigating the adverse effects of income inequality on energy efficiency. By contrast, in middle-income countries, severe income disparities erode the ability of renewable energy to contribute meaningfully to reducing energy intensity. These findings suggest that by incorporating equity considerations into energy strategies, nations can strengthen the synergy between renewable energy adoption and energy efficiency, fostering progress across diverse economies.

Advances in the Energy‐Saving Electro‐Oxidation of 5‐Hydroxymethylfurfural to 2,5‐Furandicarboxylic Acid

AbstractAs a pivotal bio‐based building block, 2,5‐furandicarboxylic acid (FDCA) holds immense and broad application potential in the chemistry industry. Its polymeric derivative, polyethylene furandicarboxylate (PEF), emerges as an appealing alternative to conventional petroleum‐based polyethylene terephthalate (PET). The electrochemical route for oxidizing 5‐hydroxymethylfurfural (HMF) into FDCA presents significant advantages over the thermochemical processes, without the requirements of high temperature, high pressure, chemical oxidants, and precious metal catalysts, featuring higher energy efficiency. Furthermore, the electrosynthesis of FDCA at the anode can be synergistically integrated with selective reduction reactions at the cathode, enabling the simultaneous production of two desirable value‐added products and further enhancing overall energy utilization efficiency. This work reviews the advancements in electrocatalytic HMF to FDCA (EHTF), encompassing catalyst design, reaction mechanisms, coupling strategies, and reactor configurations. It also indicates the challenges and opportunities of EHTF and provides insights into the future development directions.

Energy Scheduling Strategy for the Gas–Steam–Power System in Steel Enterprises Under the Influence of Time-Of-Use Tariff

Fully harnessing the inherent flexible adjustment potential of steel enterprises and fostering their interaction with the power grid is a crucial pathway to advancing green transformation. However, traditional research usually takes reducing energy consumption as the optimization goal, which limits the adjustment response capability, or ignores the storage and conversion constraints of secondary energy sources such as gas, steam, and electricity, making it difficult to fully explore and reasonably utilize the potential of multi-energy coordination. This study considers the production constraints of the surplus energy recovery and utilization system, establishes a collaborative scheduling model for a gas–steam–power system (GSPS) in an iron and steel enterprise, and proposes a demand response strategy that considers internal production constraints. Considering the time-of-use (TOU) tariff, iron and steel enterprises achieve a dynamic optimization adjustment range of electricity demand response through the conversion and storage process of gas, steam, and power. The adjustment capability of the GSPS reaches 26.94% of the initial electricity load, while reducing the total system energy cost by 2.24%. There is vast development potential of iron and steel enterprises participating in electricity demand response for promoting cost reduction and efficiency improvement, as well as enhancing the power grid flexibility.

Secure and energy-efficient inter- and intra-cluster optimization scheme for smart cities using UAV-assisted wireless sensor networks

In recent years, lightweight sensors have become essential for advancing technologies, particularly in wireless sensor networks (WSNs). A persistent challenge in WSNs is maintaining continuous operation while achieving balanced energy consumption across sensor nodes. Wireless mobile energy transmitters (WMETs) provide a promising solution by wirelessly recharging sensor nodes. However, most existing approaches fail to optimize WMET charging locations, resulting in energy imbalance and reduced network coverage. Furthermore, conventional clustering systems often overlook both inter- and intra-cluster energy balancing, degrading overall network performance. Security issues, such as insufficient data encryption, further exacerbate these challenges, leaving WSNs vulnerable to attacks. To address these gaps, we propose the secure and energy-efficient inter- and intra-cluster optimization scheme (SEI2), a novel WMET-based framework that ensures balanced energy utilization among cluster heads (CHs) and member nodes (MNs) while securing data transmission. The SEI2 system incorporates UAVs to dynamically recharge sensor nodes, determining optimal charging locations within clusters to maximize energy efficiency. Additionally, robust data encryption mechanisms are applied at both the CH and base station (BS) levels to safeguard transmitted data. Key parameters considered include node energy levels, data transmission rates, optimal charging locations, and encryption overhead. Experimental evaluations demonstrate that SEI2 improves network lifetime by 35%, reduces compromised data packets by 19%, enhances coverage time by 15%, and significantly minimizes energy variance by 62% for CHs and 88% for MNs. These results highlight SEI2’s potential as a comprehensive solution for extending WSN lifetimes, enhancing energy efficiency, and strengthening data security, making it particularly suitable for smart city applications.

Multiload Resonant Inverter With Boosting for All‐Metal Domestic Induction Heating System

ABSTRACTInduction heating (IH) systems have gained significant attention because of their high efficiency, rapid heating, versatility, and precise control. Induction cooking (IC) is one of the primary domestic applications of IH technology. Traditional IC systems typically employ single‐frequency inverters optimized for ferromagnetic (FM) material loads. This approach presents limitations when confronted with various cookware materials including nonferromagnetic (NFM) materials. To address the growing demand for multimaterial load IC systems with enhanced performance and flexibility, a multiload resonant inverter integrated with boosting is proposed with a double full‐bridge three‐leg architecture. The proposed system features three independent legs, each operating at a different frequency to power three different material loads. Integrating this topology with boost functionality enhances performance and optimizes energy utilization. Pulse frequency modulation (PFM) and asymmetrical duty cycle (ADC) control techniques are used to control the load powers independently and simultaneously. A detailed analysis of the inverter and its design procedure is presented. A 1.5‐kW hardware prototype is designed, and the experimental results are in good agreement with the simulation results.

Two-Stage Integrated Optimization Design of Reversible Traction Power Supply System

In a traction power supply system, the design of traction substations significantly influences both the system’s operational stability and investment costs, while the energy management strategy of the flexible substations affects the overall operational expenses. This study proposes a novel two-stage system optimization design method that addresses both the configuration of the system and the control parameters of traction substations. The first stage of the optimization focuses on the system configuration, including the optimal location and capacity of traction substations. In the second stage, the control parameters of the traction substations, particularly the droop rate of reversible converters, are optimized to improve regenerative braking energy utilization by applying a fuzzy logic-based adjustment strategy. The optimization process aims to minimize the total annual system cost, incorporating traction network parameters, power supply equipment costs, and electricity expenses. The parallel cheetah algorithm is employed to solve this complex optimization problem. Simulation results for Metro Line 9 show that the proposed method reduces the total annual project costs by 5.8%, demonstrating its effectiveness in both energy efficiency and cost reduction.

Blockchain-Enhanced Demand-Side Management for Improved Energy Efficiency and Decentralized Control

Blockchain technology introduces significant advancements in demand-side management (DSM) by enabling decentralized, transparent, and secure data handling within energy systems. This study explores a blockchain-based approach aimed at improving electricity efficiency through enhanced DSM strategies. We present a comprehensive framework that integrates blockchain technology with real-time data analytics to optimize energy consumption, stimulate consumer participation, and support efficient energy utilization. The experimental analysis demonstrates that blockchain integration significantly strengthens DSM operations by reducing operational costs, increasing participation in demand response programs, and enhancing grid stability. The findings highlight the effectiveness and potential of the proposed approach as a forward-looking solution for energy management systems.

Highly Transparent, Spectrally Selective Power-Generating Windows Based on WO3-x Nanorods and Carbon Dots for Full-Spectrum Utilization.

Near-infrared (NIR) shielding windows can selectively regulate excess solar radiation to reduce heating and cooling energy consumption in a built environment. However, the dissipation of ultraviolet (UV) and visible light into waste heat is inevitable, leading to inefficient solar energy utilization. Herein, a tandem spectrally selective power-generating (SSPG) window is developed by incorporating oxygen-deficient tungsten oxide WO3-x nanorod-based NIR shielding windows and red-emissive carbon dot-based luminescent solar concentrators (LSCs) to realize full-spectrum utilization. Semitransparent NIR shielding modules absorb NIR light to reduce indoor thermal radiation, while a semitransparent LSC coupled with a photovoltaic system converts UV and partially visible light into electricity. The SSPG window exhibits a visible light transmittance of up to 70.44% and power conversion efficiency of 0.31%, while effectively reducing the indoor temperature by 7 °C under sunlight irradiation. In addition, this SSPG window has good thermal-/photostability and excellent NIR shielding performance after heat treatment and UV irradiation. This work may provide a new avenue for the development of energy-saving windows.

Broadband Rectangular Microstrip Antenna with Slits for W-Band Applications

This work introduces a broadband rectangular patch antenna optimized for efficient data transmission in the W-band, particularly for 5G applications. By integrating two I-shaped slits with the radiating element, the antenna achieves an impressive performance, exhibiting wide bandwidth and excellent radiation characteristics. Utilizing Rogers RT5880 as the substrate material with a relative permittivity (εr) of 2.2, a small antenna with a size of 3.7 × 4.1 × 0.16 mm³ is realized. Extensive simulations are conducted using CST software in both frequency and time domains to optimize the antenna. The results show a notable 16% fractional bandwidth from 80.75 GHz to 94.79 GHz, with dual resonance frequencies at 84.5 GHz and 91.5 GHz, primarily a result of the incorporated slits. At 84.5 GHz, the antenna demonstrates an outstanding reflection coefficient of -66.37 dB, a Voltage Sanding Wave Ratio (VSWR) of 1.00096, a gain of 9.71 dBi, a directivity of 9.75 dB, and a high radiation efficiency of 91.8%. Similar trends are observed at 91.5 GHz, where the return loss remains at an impressive value of 55.92 dB and the VSWR maintains a very low value of 1.0032, indicating continued excellent impedance matching. While the gain (6.98 dBi) and directivity (7.05 dB) are slightly lower at this frequency, the radiation efficiency remains remarkably high at 94.9%, indicating efficient energy utilization. The wide bandwidth of the proposed design enables high data transfer rates, a crucial requirement for 5G networks. This translates to significant improvements in network capacity, allowing for more connected devices and data traffic. Additionally, the design exhibits excellent signal transmission characteristics, ensuring reliable data transfer. Finally, the antenna's compact size and efficient radiation have the potential to reduce power consumption in 5G devices, contributing to improved battery life and sustainability.

Design and optimization of distributed energy management system based on edge computing and machine learning

With the continuous growth of global energy demand and the rapid development of renewable energy, traditional energy management systems are facing enormous challenges, especially in the scheduling and optimization of distributed energy. In order to meet these challenges, edge computing and machine learning technology are widely used in the design and optimization of distributed energy management systems. This paper proposes a design scheme of distributed energy management system based on edge computing and machine learning, and optimizes it. The system reduces data transmission latency and improves energy scheduling efficiency by performing real-time data processing and analysis on edge devices. The experimental results show that the proposed system performs outstandingly in optimizing energy allocation, reducing energy consumption, and improving system response speed. Specifically, by using machine learning algorithms for dynamic scheduling of distributed energy resources, the system can achieve an energy utilization rate 12% higher than traditional scheduling methods, and reduce energy waste by 18% in the event of fluctuations in energy demand. In addition, the system response time has been improved by 30% compared to traditional cloud-based solutions. These optimizations not only reduce energy costs, but also effectively enhance the sustainability and intelligence level of distributed energy systems. The contribution of this research lies in the combination of edge computing and machine learning technology to achieve real-time optimal control of the distributed energy system, reduce the system’s computing load and delay, and improve the accuracy and flexibility of energy management through data-driven methods. Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.

The Role of Artificial Intelligence (AI) and Machine Learning (Ml) in the Oil and Gas Industry

Purpose: This study focused on the relevance of AI and ML in revolutionizing the Oil & Gas sector by innovation and rehabilitation. It investigated the role of AI and ML technologies in improving production efficiency, reducing environmental impact, and managing costs. Several end users noted considerable advancements in operation efficiency; Predictive analytics and real-time monitoring systems assisted in enhancing the effectiveness of predictive maintenance by as much as 40% lapse time. Thus, the digital twin technologies were discussed in the context of enhancing the design of production planning, as well as promoting more effective use of resources and their recycling. Methodology: The research applied integration of systematic qualitative methodologies to collect, analyze, and synthesize data from prior investigations. This methodology was designed to encompass the alignment of results, analysis, and conclusions within the literature findings. The application of Systematic Literature Review (SLR), Content Analysis, and Meta-Analysis of Qualitative Evidence effectively grounded the study objectives Findings: The Significance of AI is captured in the environmental management aspect of this study, whereby several companies’ emission control systems recorded a 30% improvement of Green House Gas (GHG), and the accuracy of compliance. Some of those that are of more economic advantage are the reduced cost of maintenance, low energy utilization and minimal wastage of resources. However, shortcomings like high implementation cost, integration difficulties, and infrastructural limitations remain some of the biggest threats to its adoption. Unique Contribution to Theory, Policy, and Practice: In addressing these challenges, this research suggests the promotion of partnerships, creating efficient innovations and establishing sustainable development initiatives. Thus, it offers valuable recommendations for policymakers, researchers, and other interested parties, stressing that AI and ML adoption demonstrate the potential to support operational excellence, environmentally sustainable practices, and increased profitability in the Oil and Gas industry.

An Improved Laxity based Cost Efficient Task Scheduling Approach for Cloud-Fog Environment

Task scheduling is critical in fog computing, as it has to assign workloads to fog nodes to save costs and execution times. This study emphasizes the allocation of jobs received from clients to suitable nodes through a proposed scheduling technique, which is deployed on layer 2 servers within a cloud-fog environment. Laxity-based Cost-efficient Task Scheduling (LCTS) is proposed for contemporary task scheduling difficulties, such as balancing cost and delay with optimal energy utilization. The results show that the proposed strategy decreased execution time and cost more than Round Robin (RR) and Genetic Algorithm (GA). Furthermore, the proposed method was less expensive than cloud-based IoT solutions. Compared to GA and RR, the simulation results showed that cost and execution time were reduced by 6.99%-17.36% and 4.58%-9.09%, respectively.

An Energy-Efficient Hybrid LEACH Protocol that Enhances the Lifetime of Wireless Sensor Networks

A Wireless Sensor Networks (WSN) comprises of little, low-power sensors, which have a low battery limit and are often utilized in unfavorable conditions. These sensors are data processing and networking devices. The battery life, processing power, communication range, and memory of sensors, which are tiny, self-contained devices, are just a few of their limits. They also have transceivers, which gather environmental data and transmit it to a base station. There are various strategies accessible to expand the existence of a WSN and diminish energy utilization. LEACH (Low Energy Adaptive Clustering Hierarchy) is a successful and widely used method. The protocol clusters the network in order to conserve energy The cluster head, which is a single-region indicative node, receives fusion cluster data once the sensor nodes are divided into clusters. In our study, we provide a novel method for reducing sensor node energy consumption and extending network lifetime. Our research aims to increase network lifespan by reducing energy consumption based on the restrictions of the LEACH and its related algorithms.

Optimal configuration of integrated energy system considering heat enhancement and combined operation of low head seawater pumped storage and reverse osmosis

The remote coastal areas face difficulties due to energy shortages and insufficient freshwater resources. To reduce the energy consumption of reverse osmosis desalination in coastal areas, this paper proposes an optimal configuration method of integrated energy system considering heat enhancement and combined operation of low-head seawater pumped storage and reverse osmosis. Firstly, the heat analysis of power to gas and the construction of the reverse osmosis temperature model considering heat enhancement are carried out, utilizing its reaction heat to preheat the reverse osmosis feed. Secondly, the reverse osmosis feed is supplied from the upper reservoir of the low-head seawater pumped storage, allowing the operating pressure of the reverse osmosis to be met by both the hydrostatic pressure of the upper reservoir and the high-pressure pump. Furthermore, the working efficiency of the reversible turbine is modeled by considering the operational characteristics of the seawater pumped storage. Then, a bi-level mixed-integer optimal configuration model with annual total cost and wind abandonment rate as evaluation indexes is established and solved. The results indicate that compared to the conventional system, the proposed system achieves a reduction of 4.30 % in annual total cost and 2.46 % in wind abandonment rate, resulting in more pronounced economic advantages and higher renewable energy utilization. Furthermore, the energy consumption of reverse osmosis is decreased by 26.62 %, and the energy utilization ratio of the seawater pumped storage is increased by 9.81 %, demonstrating superior performance in system energy efficiency.