Energy Conversion and Management-X最新文献_第6页

Chilled water-based hybrid cooling solution for data centers: A comprehensive survey of technologies, developments, and regenerative energy transitions 数据中心的冷冻水基混合冷却解决方案：技术、发展和可再生能源转换的综合调查

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-23 DOI: 10.1016/j.ecmx.2026.101612

Majid J. Almheiri , Haris M. Khalid , Abdulla Ismail , Asif Gulraiz , Zafar Said

Data centers face new cooling challenges due to the growth of digital infrastructure as well the rise in computing needs. This makes it urgent to find more sustainable ways to manage heat. To address this challenge, this proposed study surveys the chilled water-based hybrid cooling systems as a practical way to meet higher cooling demands while being environmentally responsible. To achieve this, the proposed research uses several methods: 1) it reviews cooling basics, 2) looks at recent technology, 3) studies control systems, and 4) examines real-world case studies. By reviewing current literature and industry examples, the proposed study highlights key performance and sustainability measures that could show the benefits of this choice towards hybrid cooling. Results show that combining water and air cooling with smart sensors, the Internet of Things (IoT), and artificial intelligence (AI) control can greatly improve energy efficiency. This would also make operations more resilient to climate change. The utilization of advanced chillers (chilled water-based hybrid cooling), heat exchangers, and phase-change materials helps transfer heat more efficiently, while using renewable energy can lower carbon emissions. Though there are challenges, such as saving water and working with older systems, the long-term savings and environmental gains make these hybrid systems more important for sustainable data centers. This proposed paper also offers useful insights for industry professionals who are working to adopt greener cooling solutions while keeping data centers reliable and high-performing.

由于数字基础设施的增长以及计算需求的增加，数据中心面临着新的冷却挑战。这就迫切需要找到更可持续的方式来管理热量。为了应对这一挑战，本研究将冷冻水基混合冷却系统作为一种实用的方式来满足更高的冷却需求，同时对环境负责。为了实现这一目标，拟议的研究使用了几种方法：1)回顾冷却基础知识，2)着眼于最新技术，3)研究控制系统，以及4)检查现实世界的案例研究。通过回顾当前的文献和行业实例，该研究强调了关键性能和可持续性措施，可以显示选择混合冷却的好处。结果表明，将水冷、风冷与智能传感器、物联网（IoT）和人工智能（AI）控制相结合，可以大大提高能源效率。这也将使企业更能适应气候变化。利用先进的冷却器（冷冻水基混合冷却）、热交换器和相变材料有助于更有效地传递热量，同时使用可再生能源可以降低碳排放。尽管存在一些挑战，比如节水和使用旧系统，但长期的节约和环境收益使得这些混合系统对于可持续的数据中心更加重要。本文还为正在努力采用更环保的冷却解决方案的行业专业人士提供了有用的见解，同时保持数据中心的可靠性和高性能。

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引用次数: 0

A direct upscaling probabilistic forecasting model for PV cluster power generation based on softDTW-(ClusterGAN-KShape)-AMQWavenet 基于软dtw -(ClusterGAN-KShape)- amqwaveenet的光伏集群发电直接升级概率预测模型

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-23 DOI: 10.1016/j.ecmx.2026.101618

Qing Li , Tianjiao Ma , Shumao Zheng , Yihui Lu , Zhaoxiang Deng , Fu Shen

The increasing penetration of photovoltaic (PV) power presents severe challenges to power system operation due to its inherent output uncertainty. To accurately quantify the uncertainty of regional PV generation, this paper proposes a novel integrated framework for direct multi-step probabilistic forecasting of PV cluster power. First, to address temporal misalignments in PV series caused by cloud movement, a differentiable soft Dynamic Time Warping (softDTW) method is introduced, enabling the joint and adaptive selection of the most representative station and key meteorological features, thereby ensuring the physical interpretability and representativeness of model inputs. Second, to overcome the limitations of single clustering methods in disentangling complex weather patterns, an improved hybrid clustering strategy that combines ClusterGAN and KShape is proposed. This strategy synergizes deep feature learning with shape-sensitive clustering to construct a condition-specific, highly discriminative weather-pattern dataset. Furthermore, an Attention-enhanced MQ-WaveNet (AMQWaveNet) probabilistic forecasting model is developed, where a multi-head attention (MHA) mechanism focuses on critical spatiotemporal information, and a residual-connected WaveNet encoder extracts multi-scale deep features, culminating in a dual-MLP decoder that directly outputs multi-step quantile forecasts. An empirical evaluation on 14 neighboring PV stations in a large-scale base in Xinjiang, China, demonstrates that: a) Under various weather conditions (sunny, cloudy, overcast/rainy), the proposed model reduces RMSE by an average of 15–25% compared to state-of-the-art benchmarks (e.g., TFT, DeepAR); b) Its Winkler Score is significantly lower than those of competing models under complex weather, proving superior uncertainty quantification; c) The method requires only key features from one representative station to achieve high-accuracy cluster forecasting, substantially reducing data dependency and model complexity, showing strong potential for practical deployment.

随着光伏发电的不断普及，其固有的输出不确定性给电力系统的运行带来了严峻的挑战。为了准确量化区域光伏发电的不确定性，本文提出了一种新的光伏集群电力直接多步概率预测集成框架。首先，为了解决云运动引起的PV序列时间失调问题，引入了一种可微软动态时间翘曲（softDTW）方法，实现了最具代表性的站点和关键气象特征的联合自适应选择，从而保证了模式输入的物理可解释性和代表性。其次，为了克服单一聚类方法在复杂天气模式分离中的局限性，提出了一种结合ClusterGAN和KShape的改进混合聚类策略。该策略将深度特征学习与形状敏感聚类相结合，构建特定条件、高度判别的天气模式数据集。此外，开发了一个注意力增强的MQ-WaveNet （AMQWaveNet）概率预测模型，其中多头注意（MHA）机制侧重于关键时空信息，残差连接的WaveNet编码器提取多尺度深度特征，最终形成双mlp解码器，直接输出多步分位数预测。对中国新疆大型基地14个相邻光伏电站的实证评估表明：a)在各种天气条件下（晴天、多云、阴天/雨天），与最先进的基准（如TFT、DeepAR）相比，所提出的模型平均将RMSE降低了15-25%；b)在复杂天气条件下，其Winkler评分显著低于竞争模式，证明其不确定性量化优于竞争模式；c)该方法只需要一个代表性站点的关键特征即可实现高精度的聚类预测，大大降低了数据依赖性和模型复杂性，具有很强的实际部署潜力。

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引用次数: 0

Development of a deep learning-based framework for operational optimisation of municipal solid waste incinerators 基于深度学习的城市固体垃圾焚烧炉运行优化框架的开发

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-23 DOI: 10.1016/j.ecmx.2026.101610

Xiaozhou Liu , Zhenming Wen , Taimoor Asim , Rakesh Mishra

Combustion efficiency of Municipal Solid Waste (MSW) incinerators depends on numerous operational parameters like air flowrates, boiler feedwater temperature, conveyer speed etc. Optimising these operational parameters can lead to higher efficiency, reduce emissions and maximise waste-to-energy conversion however, the complex interdependence of these parameters makes it difficult to identify the optimal conditions on which to run the power plant. In this study, we develop a Deep Learning (DL) based framework to optimise the operation of MSW incinerators. Historical operational data from a 600 tonne/day MSW incinerator has been collected and ranked based on feature importance using Gradient Boosting Decision Trees (GBDT). The dimensionally reduced dataset is used to train a Backpropagation Neural Network (BPNN) model, characterizing highly non-linear relationship between operational parameters and steam production from the MSW incinerator, achieving a mean relative error of 7.79% and prediction accuracy of 92.21%. Finally, Particle Swarm Optimization (PSO) is then employed to optimise the operational parameters. The optimisation process converged within 650 iterations (∼3 min), yielding increase in steam production from 2.7 t/t to 3.11 t/t waste, which is equivalent to 15.2% increase in the thermal efficiency of the MSW incinerator. The proposed DL-PSO framework enables automated optimisation of the operational parameters, minimising dependency on operator experience, providing a novel, practical and computationally efficient tool for enhancing the combustion performance of MSW incinerators and reducing emissions.

城市生活垃圾（MSW）焚烧炉的燃烧效率取决于许多操作参数，如空气流量、锅炉给水温度、输送机速度等。优化这些运行参数可以提高效率，减少排放，最大限度地提高废物转化为能源，然而，这些参数之间复杂的相互依存关系使得确定运行发电厂的最佳条件变得困难。在本研究中，我们开发了一个基于深度学习（DL）的框架来优化生活垃圾焚烧炉的运行。收集了600吨/天生活垃圾焚烧炉的历史运行数据，并使用梯度增强决策树（GBDT）根据特征重要性进行了排名。利用降维数据集训练了一个反向传播神经网络（BPNN）模型，该模型表征了运行参数与垃圾焚烧炉蒸汽产量之间的高度非线性关系，平均相对误差为7.79%，预测精度为92.21%。最后，采用粒子群算法（PSO）对运行参数进行优化。优化过程在650次迭代（~ 3分钟）内完成，产生的蒸汽产量从2.7 t/t增加到3.11 t/t废物，相当于城市生活垃圾焚烧炉的热效率提高了15.2%。拟议的DL-PSO架构可自动优化操作参数，减少对操作员经验的依赖，为提高都市固体废物焚化炉的燃烧性能和减少排放提供一种新颖、实用和计算效率高的工具。

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引用次数: 0

Enhancing Thermal and Exergy Efficiency of Evacuated Tube Solar Collectors Using Hybrid Fe2O3/MgO Nanofluid and Porous Medium Integration 利用Fe2O3/MgO纳米流体和多孔介质集成提高真空管太阳能集热器的热效率和火用效率

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-23 DOI: 10.1016/j.ecmx.2026.101611

T. Sathish , Jayant Giri , Bashar Tarawneh , Ali El-Rayyes , Rasan Sarbast Faisal , Likius Shipwiisho Daniel , Thandiwe Sithole , Kassian T.T. Amesho

Evacuated tube solar collectors (ETSCs) are advanced solar thermal systems that efficiently capture and utilize solar energy, even in low-temperature and diffuse radiation conditions. Integrating nanofluids enhances their thermal performance by improving heat transfer properties. This combination offers a promising solution for applications requiring high thermal efficiency and improved energy conversion. This investigation experimentally performed the ETSC function by combining hybrid nanofluid and porous material integration. For the hybrid nanofluid with a mix of iron oxide (Fe₂O₃) and magnesium oxide (MgO) nanoparticles in equal share (1:1 ratio). Moreover, the hybrid nanofluid was studied under various volume concentrations of about 0.5vol.%, 1vol.%, and 2vol.%. The result of the hybrid Fe₂O₃/MgO nanofluid at 2 vol.% experienced a maximum thermal parameter value with porous medium conditions. At 2 vol.% Fe₂O₃/MgO nanofluid concentration with porous medium, the fluid temperature peaked at 92.3°C, the heat gain reached 757.1 W, and the thermal efficiency improved to 86.9%. Also, the exergy efficiency increased to 19.2% under similar conditions. Moreover, the enviro-economic analysis was carried out and the energy output rose to 1697.5 kWh, achieving a CO₂ reduction of 22.7 tons. The study concluded that integrating hybrid Fe₂O₃/MgO nanofluid (2 vol.%) with the porous medium in evacuated tube solar collectors significantly enhanced thermal performance and highlighted its potential for sustainable and efficient solar energy applications. Combined Fe₂O₃/MgO hybrid nanofluid

真空管太阳能集热器（ETSCs）是一种先进的太阳能热系统，即使在低温和漫射辐射条件下也能有效地捕获和利用太阳能。集成纳米流体通过改善传热特性来增强其热性能。这种组合为需要高热效率和改进能量转换的应用提供了一个有前途的解决方案。本研究通过混合纳米流体和多孔材料的结合，实验实现了ETSC功能。在混合纳米流体中，氧化铁（Fe2O3）和氧化镁（MgO）纳米颗粒的比例相等（1:1）。此外，在0.5vol左右的不同体积浓度下，对混合纳米流体进行了研究。%, 1卷。%和2vol.%。多孔介质条件下，2 vol.% Fe2O3/MgO杂化纳米流体的热参数值最大。多孔介质中Fe2O3/MgO浓度为2 vol.%时，流体温度峰值为92.3℃，热增益达到757.1 W，热效率提高到86.9%。在同等条件下，其火用效率提高到19.2%。并进行了环境经济分析，发电量达到1697.5 kWh，实现二氧化碳减排22.7吨。研究表明，将Fe2O3/MgO混合纳米流体（2 vol.%）与多孔介质集成在真空管太阳能集热器中，显著提高了其热性能，并突出了其可持续和高效太阳能应用的潜力。复合Fe2O3/MgO杂化纳米流体

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引用次数: 0

Experimental investigation of the potential of wedge flaps for improving the aerodynamic performance of a straight-bladed vertical axis wind turbine 楔形襟翼改善直叶垂直轴风力机气动性能潜力的实验研究

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 10.1016/j.ecmx.2026.101613

Asmail A.M. Abdalkarem , Ahmad Fazlizan , Najm Addin Al-Khawlani , Wan Khairul Muzammil , Zambri Harun , Adnan Ibrahim

Vertical-axis wind turbines (VAWTs), such as the Darrieus configuration, offer a clean renewable energy source that can reduce reliance on fossil fuels. Compared with horizontal-axis wind turbines (HAWTs), VAWTs present several advantages. However, their performance is constrained by inherent limitations, including dynamic stall, wake rotation effects, and self-starting difficulties, which hinder their commercial viability. Passive flow control techniques, such as adding a wedge flap (WF) to the trailing edge of rotor blades, offer a potential solution. This study examines the performance of straight-bladed VAWTs (SB-VAWTs) with and without optimized WFs. Rotor blades were designed, fabricated, and tested in a wind tunnel under varying wind speeds and loading conditions. Results showed that adding a WF significantly enhances the power coefficient (C_p) across different wind speeds. Maximum C_p and electrical power output increased by 11% at 5 m/s and up to 20% at 13 m/s compared to clean VAWTs. Furthermore, C_p-TSR curves became flatter, indicating improved stability and reduced sensitivity to sudden wind speed changes. The WF demonstrates potential as a passive flow control device, enhancing VAWT performance while maintaining adaptability. With proper dimensioning, WFs could be integrated into new turbines or retrofitted onto existing ones, making them a promising option for renewable energy systems.

垂直轴风力涡轮机（VAWTs），如Darrieus配置，提供了一种清洁的可再生能源，可以减少对化石燃料的依赖。与水平轴风力机（HAWTs）相比，VAWTs具有许多优点。然而，它们的性能受到固有的限制，包括动态失速、尾迹旋转效应和自启动困难，这阻碍了它们的商业可行性。被动流动控制技术，如在转子叶片后缘增加楔形襟翼（WF），提供了一个潜在的解决方案。本研究考察了带和不带优化wf的直叶式VAWTs （SB-VAWTs）的性能。在不同的风速和载荷条件下，在风洞中设计、制造和测试了转子叶片。结果表明，在不同风速下，添加风场显著提高了风场的功率系数（Cp）。与清洁vawt相比，最大Cp和电功率输出在5m /s时增加了11%，在13m /s时增加了20%。此外，Cp-TSR曲线变得更平坦，表明稳定性提高，对突然风速变化的敏感性降低。WF显示了作为被动流量控制装置的潜力，在保持适应性的同时提高了VAWT的性能。如果尺寸合适，WFs可以集成到新的涡轮机中，也可以改装到现有的涡轮机上，这使它们成为可再生能源系统的一个很有前途的选择。

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引用次数: 0

Data Envelopment analysis applications for energy efficiency in power generation: A comprehensive review with bibliometric and content analysis 数据包络分析在发电能效方面的应用：文献计量学和内容分析的综合综述

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 10.1016/j.ecmx.2026.101590

Syarifa Hanoum , Mahmood Shubbak , Dewie Saktia Ardiantono , Jaroslaw Korpysa

As global electricity demand continues to rise, concerns over greenhouse gas emissions and climate change have intensified, making sustainability and efficiency in power generation critical priorities. Data Envelopment Analysis (DEA) has emerged as a powerful tool for assessing and improving energy efficiency while integrating environmental considerations. This study provides a comprehensive review of DEA applications in power generation, employing bibliometric and content analysis to identify key research trends, methodological advancements, and influential contributions. Analyzing 461 publications, our findings reveal DEA’s growing role in balancing efficiency with sustainability. The review highlights significant progress in advanced DEA models (including Network DEA, Fuzzy DEA, and hybrid approaches with methods such as the Malmquist Index and Stochastic Frontier Analysis) that enhance eco-efficiency assessment and inform sustainable energy transitions. International collaborations, particularly those led by China and the United States, underscore the global momentum in applying DEA to support emissions reduction, renewable energy integration, and carbon tax policy. Beyond efficiency benchmarking, DEA has evolved into a decision-support framework that links operational performance with broader sustainability outcomes, guiding policy interventions such as carbon pricing and renewable subsidy allocation. While sustainability remains a central focus, there is a need to explore the integration of DEA with emerging technologies, such as artificial intelligence and machine learning, to enhance predictive capabilities and efficiency assessments.

随着全球电力需求的持续增长，对温室气体排放和气候变化的担忧加剧，使发电的可持续性和效率成为当务之急。数据包络分析（DEA）已成为一种强大的工具，用于评估和提高能源效率，同时整合环境因素。本研究全面回顾了DEA在发电领域的应用，采用文献计量学和内容分析来确定关键的研究趋势、方法进步和有影响力的贡献。通过对461份出版物的分析，我们发现DEA在平衡效率与可持续性方面的作用越来越大。该综述强调了先进的DEA模型（包括网络DEA、模糊DEA和混合方法，如Malmquist指数和随机前沿分析）的重大进展，这些模型增强了生态效率评估并为可持续能源转型提供了信息。国际合作，特别是由中国和美国牵头的国际合作，突显了应用DEA支持减排、可再生能源整合和碳税政策的全球势头。除了效率基准之外，DEA已经发展成为一个决策支持框架，将运营绩效与更广泛的可持续性成果联系起来，指导碳定价和可再生能源补贴分配等政策干预。虽然可持续性仍然是一个核心焦点，但有必要探索DEA与新兴技术（如人工智能和机器学习）的整合，以提高预测能力和效率评估。

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引用次数: 0

A fuzzy two-phase model for renewable energy system optimization under uncertainty: from operational to strategic scenario planning 不确定条件下可再生能源系统优化的模糊两阶段模型：从运行情景规划到战略情景规划

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 10.1016/j.ecmx.2026.101600

Hamed Fazlollahtabar

Integrating volatile renewable energy into power grids requires robust optimization methods that avoid restrictive probabilistic assumptions. This study proposes a novel two-phase fuzzy model bridging operational planning and strategic market analysis. Phase 1 uses a Mamdani-type fuzzy inference system to generate realistic scenarios for solar/wind availability and demand. Phase 2 formulates a fuzzy linear program to minimize costs under uncertainty, the results of which inform a fuzzy Nash equilibrium model to analyze market participant strategies. Validated on a 10-node model with real California ISO data, our approach reduces system costs by 18.7% and maintains 99.2% reliability, outperforming stochastic and robust benchmarks. The model provides a practical decision-support tool for system operators and policymakers in renewable-dominated energy markets, demonstrating that fuzzy logic effectively captures real-world imprecision without complex data requirements.

将易变的可再生能源整合到电网中需要稳健的优化方法，以避免限制性的概率假设。本研究提出一种新的两阶段模糊模型，连结营运计划与策略性市场分析。第一阶段使用mamdani型模糊推理系统生成太阳能/风能可用性和需求的现实情景。第二阶段制定不确定条件下成本最小化的模糊线性规划，其结果为模糊纳什均衡模型提供信息，用于分析市场参与者的策略。在加利福尼亚ISO真实数据的10节点模型上验证，我们的方法降低了18.7%的系统成本，保持了99.2%的可靠性，优于随机和鲁棒基准。该模型为可再生能源主导的能源市场中的系统运营商和决策者提供了一个实用的决策支持工具，表明模糊逻辑在不需要复杂数据要求的情况下有效地捕获了现实世界的不精确性。

引用次数: 0

Security domain modeling and suppression of commutation failure for LCC-HVDC considering AC-DC coupling 考虑交直流耦合的LCC-HVDC安全域建模及换相故障抑制

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 10.1016/j.ecmx.2026.101608

Shoudong Xu , Jinxin Ouyang , Mingyu Pang , Taiyu Xiao , Chao Xiao

In line-commuted converter based high voltage direct current (LCC-HVDC), commutation failure (CF) is a main source of danger. The CF is usually suppressed by regulating the single electrical quantity. However, for the active and reactive power of inverter station, the combination of DC current and voltage, AC bus voltage, firing angle (FA) and extinction angle (EA) is the determining influence. The regulation of any electrical quantity leads to variations in power, thereby influencing the AC bus voltage and subsequently inducing variations in the DC current, DC voltage, and EA. The neglect of AC-DC coupling affects the suppression of CF and limits power transmission. The formulation of the active and reactive power of inverter station has been deduced. Considering the constraints imposed by DC current and firing advance angle (FAA), the power feasible range is established. The feasible range of AC bus voltage and power is built considering AC-DC coupling. The commutation security domain of inverter station to avoid CF is modeled. To maximize the active power, formulations of DC current and FAA are proposed. Taking into account the power transmission, an improved suppression method of subsequent CF is proposed. The CIGRE standardized model is utilized to verify the method.

在基于线路整流的高压直流变换器（lc - hvdc）中，整流失效（CF）是一个主要的危险源。通常通过调节单个电量来抑制CF。而对于逆变站的有功和无功功率，直流电流和电压组合、交流母线电压、发射角（FA）和消光角（EA）是决定性的影响因素。任何电量的调节都会导致功率的变化，从而影响交流母线电压，进而引起直流电流、直流电压和EA的变化。忽略交直流耦合会影响对CF的抑制，从而限制功率的传输。推导了逆变站有功功率和无功功率的计算公式。考虑直流电流和射前角（FAA）的约束，建立了功率可行范围。考虑交直流耦合，建立了交流母线电压和功率的可行范围。建立了避免CF的逆变站换相安全域模型。为了使有功功率最大化，提出了直流电流和等效空速的计算公式。考虑到功率传输，提出了一种改进的后续CF抑制方法。利用CIGRE标准化模型对该方法进行了验证。

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引用次数: 0

When does high-tech research investment enable green energy transition? 高科技研究投资何时能推动绿色能源转型？

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 10.1016/j.ecmx.2026.101598

Brahim Bergougui , Sudeshna Ghosh , Buhari Doğan , Ahmed Samour , Rabindra Nepal

The allocation of public budgets to research and development plays a vital role in advancing climate welfare and facilitating the energy transition. Energy transition focuses on sustainable development goal −7 (SDG-7). By discussing the importance of energy transition and research and development, the current study elaborates on the pathway to resilient energy. This study investigates the influence of public research and development budgets on energy transition in 20 leading sophisticated economies over the period from 1995 to 2022. Using the method of moment quantile regression (MM-QR), the findings reveal a positive association between public renewable energy R&D budgets and the Energy Transition Index (ETI) across all quantile distributions. The effect gets weakened slightly at higher quantiles. While energy efficiency RD&D budgets demonstrate a positive association, the results are statistically insignificant. Public spending on storage/other technologies and high-tech industry demonstrates a negative impact in the lowest quantile, transforming to a positive and significant effect in higher percentiles. The influence of control variables is further explored. Institutional quality and technological innovation exert a positive and significant effect on energy transition across all quantiles, while economic complexity demonstrates a negative impact, particularly pronounced in lower development stages. The study suggests that governments within these leading economies should prioritize public R&D budgets, particularly for low-cost renewable energy solutions across domestic, industrial, and transportation sectors. Furthermore, policies suggestions is towards carbon-free electrification, electric vehicle adoption, and hydropower generation can accelerate progress.

将公共预算分配给研发，在推进气候福利和促进能源转型方面发挥着至关重要的作用。能源转型的重点是可持续发展目标-7 （SDG-7）。本研究通过讨论能源转型和研发的重要性，阐述了弹性能源的途径。本研究调查了1995年至2022年期间20个主要发达经济体的公共研发预算对能源转型的影响。利用矩分位数回归（MM-QR）方法，研究结果表明，公共可再生能源研发预算与能源转型指数（ETI）在所有分位数分布上都存在正相关关系。在较高的分位数上，效果会稍微减弱。虽然能源效率研发预算显示出正相关，但结果在统计上不显著。存储/其他技术和高科技产业的公共支出在最低的分位数显示出负面影响，在较高的百分位数转化为积极和显著的影响。进一步探讨了控制变量的影响。制度质量和技术创新对所有分位数的能源转型都有显著的积极影响，而经济复杂性则表现出负面影响，在较低发展阶段尤为明显。该研究建议，这些主要经济体的政府应优先考虑公共研发预算，特别是在国内、工业和运输部门的低成本可再生能源解决方案上。此外，针对无碳电气化、电动汽车采用和水力发电的政策建议可以加速进展。

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引用次数: 0

Photovoltaic maximum power point tracking through reconfiguration and algorithm strategy: a comprehensive review 基于重构的光伏最大功率点跟踪与算法策略综述

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 10.1016/j.ecmx.2026.101604

YuYao Li , LiYa Xu

Solar photovoltaic (PV) system efficiency is highly dependent on Maximum Power Point Tracking (MPPT) technology. Currently, MPPT technology achieves performance optimization mainly through two core approaches: hardware-level reconfiguration strategy and software-level algorithm strategy, whose coordination is key to enhancing PV efficiency.

First, this paper constructs a dual-core classification system for topology reconfiguration and control algorithms, dividing 36 topology reconfiguration strategies into 2 major categories and 5 subcategories; 105 control algorithm strategies are classified into 4 types, among which intelligent algorithms are further subdivided into 8 subcategories.

Second, conduct a quantitative and qualitative comparative analysis focusing on core indicators such as tracking accuracy, dynamic response speed, local shading adaptability, computational complexity, and hardware cost. For example, results show that under local shading conditions, static topology reconfiguration strategies can reduce mismatch loss by up to 76.3%; compared with conventional algorithms, intelligent algorithms improve tracking efficiency by 10%-47%; hybrid strategies can achieve optimal balance of multiple performance indicators.

Subsequently, based on capacity scale, shading characteristics and adaptive algorithms, a three-dimensional classification model is established to realize precise matching of MPPT technologies with residential and large-scale grid-connected photovoltaic systems under steady-state or dynamic shading scenarios. This system addresses the lack of scenario pertinence in existing review literature and provides direct technical guidance for the selection of engineering solutions.

Finally, core bottlenecks of current MPPT technologies are clarified, and four future innovation directions are proposed: hybrid AI reconfiguration, dynamic cloud processing, standardized evaluation systems and scenario-adaptive engineering deployment, offering clear entry points for subsequent technological breakthroughs.

太阳能光伏发电（PV）系统的效率高度依赖于最大功率点跟踪（MPPT）技术。目前，MPPT技术主要通过硬件级重构策略和软件级算法策略两种核心方法实现性能优化，两者的协同是提高光伏发电效率的关键。首先，构建了拓扑重构与控制算法的双核分类体系，将36种拓扑重构策略划分为2大类5小类；105种控制算法策略分为4类，其中智能算法进一步细分为8个子类。其次，围绕跟踪精度、动态响应速度、局部遮阳适应性、计算复杂度、硬件成本等核心指标进行定量和定性对比分析。例如，结果表明，在局部遮阳条件下，静态拓扑重构策略可将失配损失降低76.3%；与传统算法相比，智能算法的跟踪效率提高10% ~ 47%；混合策略可以实现多个性能指标的最优平衡。随后，基于容量规模、遮阳特性和自适应算法，建立三维分类模型，实现MPPT技术与住宅和大型并网光伏系统在稳态或动态遮阳场景下的精确匹配。该系统解决了现有综述文献中缺乏场景针对性的问题，并为工程解决方案的选择提供了直接的技术指导。最后，明确了当前MPPT技术的核心瓶颈，提出了混合人工智能重构、动态云处理、标准化评估体系和场景自适应工程部署四个未来创新方向，为后续技术突破提供了明确的切入点。

{"title":"Photovoltaic maximum power point tracking through reconfiguration and algorithm strategy: a comprehensive review","authors":"YuYao Li , LiYa Xu","doi":"10.1016/j.ecmx.2026.101604","DOIUrl":"10.1016/j.ecmx.2026.101604","url":null,"abstract":"<div><div>Solar photovoltaic (PV) system efficiency is highly dependent on Maximum Power Point Tracking (MPPT) technology. Currently, MPPT technology achieves performance optimization mainly through two core approaches: hardware-level reconfiguration strategy and software-level algorithm strategy, whose coordination is key to enhancing PV efficiency.</div><div>First, this paper constructs a dual-core classification system for topology reconfiguration and control algorithms, dividing 36 topology reconfiguration strategies into 2 major categories and 5 subcategories; 105 control algorithm strategies are classified into 4 types, among which intelligent algorithms are further subdivided into 8 subcategories.</div><div>Second, conduct a quantitative and qualitative comparative analysis focusing on core indicators such as tracking accuracy, dynamic response speed, local shading adaptability, computational complexity, and hardware cost. For example, results show that under local shading conditions, static topology reconfiguration strategies can reduce mismatch loss by up to 76.3%; compared with conventional algorithms, intelligent algorithms improve tracking efficiency by 10%-47%; hybrid strategies can achieve optimal balance of multiple performance indicators.</div><div>Subsequently, based on capacity scale, shading characteristics and adaptive algorithms, a three-dimensional classification model is established to realize precise matching of MPPT technologies with residential and large-scale grid-connected photovoltaic systems under steady-state or dynamic shading scenarios. This system addresses the lack of scenario pertinence in existing review literature and provides direct technical guidance for the selection of engineering solutions.</div><div>Finally, core bottlenecks of current MPPT technologies are clarified, and four future innovation directions are proposed: hybrid AI reconfiguration, dynamic cloud processing, standardized evaluation systems and scenario-adaptive engineering deployment, offering clear entry points for subsequent technological breakthroughs.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101604"},"PeriodicalIF":7.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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