Energy Conversion and Management-X最新文献_第8页

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 : 2026-05-01 Epub 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。

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引用次数: 0

Leveraging renewable energy for mitigating greenhouse gas emissions in Iran 利用可再生能源减少伊朗的温室气体排放

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ecmx.2026.101632

Mahdi Jahandideh , Ali Mahmoudi , Saman Rashidi , Mohammad Sadegh Valipour , Saadat Zirak

The global challenge of dealing climate change and decreasing greenhouse gas emissions has prompted countries to explore effective methods, such as the utilization of renewable energy resources, which have lower emissions. This study investigates the potentials, and current situations for reducing greenhouse gas emissions through renewable energy resources in Iran. This study examines Iran’s energy consumption patterns, greenhouse gas emission profiles, and the present state and policies of renewable energy development by looking at both national energy data and foreign case studies. Key findings reveal that Iran reduced approximately 696,000 tons of CO₂ emissions in 2018 and saved 286 million m3 of fossil fuel in power plants. Iran’s installed renewable energy capacity as of 2021 was estimated to be 11,929 MW. Also, the increase of 7500 MW of renewable capacity by 2023 was targeted by the Ministry of Energy of Iran. In this regard, Iran is committed to a 4% greenhouse gas reduction by 2050, compared to 2010 levels. Also, considering that 560 million tons of CO₂ were produced in 2010, Iran committed to reducing 22.4 million tons. The study emphasizes how important it is to have supportive government policies, to invest in RE infrastructure, particularly in solar and wind energy, and the importance of strategic investment, supportive regulations, and international cooperation in advancing Iran’s transition toward a low-carbon economy.

应对气候变化和减少温室气体排放的全球性挑战促使各国探索有效的方法，例如利用排放较低的可再生能源。本研究调查了伊朗利用可再生能源减少温室气体排放的潜力和现状。本研究通过查阅国家能源数据和国外案例研究，考察了伊朗的能源消费模式、温室气体排放概况以及可再生能源发展的现状和政策。主要调查结果显示，伊朗在2018年减少了约69.6万吨二氧化碳排放量，并在发电厂节省了2.86亿立方米的化石燃料。截至2021年，伊朗的可再生能源装机容量估计为11,929兆瓦。此外，伊朗能源部的目标是到2023年增加7500兆瓦的可再生能源容量。在这方面，伊朗承诺到2050年将温室气体排放量在2010年的基础上减少4%。此外，考虑到2010年产生了5.6亿吨二氧化碳，伊朗承诺减少2240万吨。这份研究报告强调了制定支持性政府政策，投资可再生能源基础设施，特别是太阳能和风能的重要性，以及战略投资、支持性法规和国际合作对推动伊朗向低碳经济转型的重要性。

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引用次数: 0

Decarbonizing residential energy systems through integrated renewable and bioenergy pathways 通过综合可再生能源和生物能源途径使住宅能源系统脱碳

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2025-12-26 DOI: 10.1016/j.ecmx.2025.101491

Pooriya Khodaparast, Kimia Rasouli-Keshvar, Hamed Bayat, Masoumeh Bararzadeh Ledari, Farhad Maleki, Seyed Javad Hosseini, Abolfazl Jomehnejad, Ilya Qaheri, Maryam Fani

Residential energy analyses often optimize electricity and heat separately, masking their tight operational coupling and the cascading effects of technology choices across both domains. This study addresses that gap with a unified mixed-integer linear programming (MILP) framework that co-optimizes capacity sizing and hourly operation across photovoltaic panels and wind turbines (electric generation), geothermal and biogas systems (thermal generation), an air-source heat pump that couples power-to-heat conversion, and lithium-ion battery storage for a four-person dwelling. The model evaluates three policy scenarios designed to assess progressive decarbonization pathways: business-as-usual (BAU) to establish baseline performance, a 50% natural-gas capacity constraint aligned with European Union emission targets, and dual 70% constraints on gas capacity and CO₂ emissions addressing Iran’s energy challenges and ambitious net-zero commitments. Sensitivity analyses examine electricity price and carbon tax thresholds that drive technology transitions.

Under BAU, gas-based technologies dominate, yielding 11.66 kg CO₂ daily emissions. Imposing a 50 % gas constraint electrifies heat via the heat pump, reduces emissions by 54 % to 5.4 kg CO₂, and increases renewable penetration to 47 %. With dual 70 % constraints, renewables supply 95 % of total energy, grid imports decline by 73 %, and daily emissions fall to 0.93 kg CO₂ as battery cycling intensifies eight-fold. Battery storage mitigates short-term power variability, manages peak grid interactions, and enables load-shifting to periods of higher renewable availability, collectively enabling deeper decarbonization under stringent policy constraints. Economic sensitivity analyses reveal critical thresholds: renewables reach cost parity at €0.18 kWh⁻¹ grid electricity prices, and 57 % renewable penetration occurs at €120 tCO₂⁻¹ carbon taxation. By optimizing electricity, heat, and storage within a single framework, this study identifies practical policy levers—moderate pricing reforms coupled with storage incentives—for economically viable, net-zero-ready residential energy systems.

住宅能源分析通常分别优化电和热，掩盖了它们紧密的操作耦合和跨两个领域的技术选择的级联效应。这项研究通过统一的混合整数线性规划（MILP）框架解决了这一差距，该框架共同优化了光伏板和风力涡轮机（发电）、地热和沼气系统（发电）、耦合电热转换的空气源热泵和四人住宅的锂离子电池存储的容量大小和每小时运行。该模型评估了旨在评估渐进式脱碳途径的三种政策情景：常规业务（BAU）建立基准绩效，50%的天然气产能约束与欧盟排放目标一致，70%的天然气产能和二氧化碳排放双重约束，以应对伊朗的能源挑战和雄心勃勃的净零承诺。敏感性分析考察了推动技术转型的电价和碳税门槛。在BAU模式下，以天然气为基础的技术占主导地位，每天产生11.66千克的二氧化碳排放量。施加50%的气体限制，通过热泵加热，减少54%至5.4公斤的二氧化碳排放量，并将可再生能源的渗透率提高到47%。在70%的双重限制下，可再生能源占总能源的95%，电网进口下降73%，由于电池循环强度增加了8倍，日排放量降至0.93千克二氧化碳。电池储能减轻了短期电力的可变性，管理了电网的峰值相互作用，并使负荷转移到可再生能源可用性更高的时期，在严格的政策约束下共同实现了更深层次的脱碳。经济敏感性分析揭示了关键阈值：可再生能源在0.18千瓦时−1欧元的电网电价下达到成本平价，在120吨二氧化碳−1欧元的碳税下实现57%的可再生能源渗透率。通过在单一框架内优化电力、热能和储能，本研究确定了切实可行的政策杠杆——适度的定价改革与储能激励相结合——以实现经济上可行的、净零准备的住宅能源系统。

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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 : 2026-05-01 Epub 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

Multi-objective optimization of hybrid renewable energy systems for sustainable resource management and emission mitigation in climate-sensitive regions 气候敏感地区可持续资源管理和减排的混合可再生能源系统多目标优化

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2026-01-27 DOI: 10.1016/j.ecmx.2026.101630

Mohammad Alhuyi Nazari , Erkaboy Davletov , Sanjarbek Madaminov , Alisher Abduvokhidov , Mohammad Hossein Ahmadi

A multi-objective optimization framework is developed for designing a hybrid renewable energy system (HRES) for Hormuz Island, integrating photovoltaic, wind, lithium-ion battery, proton-exchange-membrane electrolyzer-tank-fuel-cell, and a standby diesel generator. Real meteorological and demand data are employed to minimize the Levelized Cost of Energy (LCOE) and Net Present Cost (NPC) while maximizing the Renewable Energy Fraction (REF) and system resilience. The optimization integrates multiple conflicting techno-economic and environmental objectives through a TOPSIS-guided multi-criteria framework, where a scalar closeness coefficient is used as the fitness function within the search process, without explicit Pareto-front construction. Results indicate that, compared with the diesel baseline, the optimized configuration achieves an LCOE of 0.139 USD kWh⁻¹, a 38.6 % reduction in NPC, a REF of 87 %, and nearly 89 % GHG mitigation. The discount rate exhibits the highest sensitivity, inducing ± 14.8 % variability in NPC and ± 9.2 % in LCOE, followed by battery CAPEX (±9.5 % NPC) and PV CAPEX (±7.1 %). Resilience evaluation under 10 % PV and 20 % hydrogen-storage perturbations confine the Loss-of-Load Probability to ≤ 2 %, confirming robust operation under adverse climatic fluctuations. Life-cycle assessment demonstrates approximately 20 % reduction in CO₂-equivalent emissions, while techno-economic analysis indicates about 15 % reduction in total energy cost relative to conventional diesel supply. The proposed configuration provides a replicable blueprint for off-grid, climate-vulnerable islands seeking reliable and low-carbon electrification pathways.

针对霍尔木兹岛集成光伏、风能、锂离子电池、质子交换膜电解罐燃料电池和备用柴油发电机的混合可再生能源系统（HRES），建立了多目标优化框架。使用真实的气象和需求数据来最小化平准化能源成本（LCOE）和净当前成本（NPC），同时最大化可再生能源比例（REF）和系统弹性。该优化通过topsis引导的多标准框架集成了多个相互冲突的技术、经济和环境目标，其中标量接近系数用作搜索过程中的适应度函数，没有明确的帕累托前结构。结果表明，与柴油基准相比，优化配置的LCOE为0.139 USD kWh−1，NPC降低38.6%，REF降低87%，温室气体减排近89%。贴现率表现出最高的敏感性，在NPC中引起±14.8%的变化，在LCOE中引起±9.2%的变化，其次是电池CAPEX（±9.5% NPC）和光伏CAPEX（±7.1%）。在10% PV和20%储氢扰动下的弹性评估将负载丢失概率限制在≤2%，确认了在不利气候波动下的稳健运行。生命周期评估表明，二氧化碳当量排放量减少了约20%，而技术经济分析表明，与传统柴油供应相比，总能源成本降低了约15%。拟议的配置为寻求可靠和低碳电气化途径的离网、气候脆弱的岛屿提供了可复制的蓝图。

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引用次数: 0

Natural resource rents, energy transition and socioeconomic factors to achieve sustainable development: Novel insights from Bangladesh 实现可持续发展的自然资源租金、能源转型和社会经济因素：来自孟加拉国的新见解

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ecmx.2026.101659

Muhammad Yousaf Raza , Mohammad Maruf Hasan , Qasim Javed

Realizing affordable and clean energy requires a global transition away from reliance on fossil fuels toward sustainable energy sources; however, the significance of natural resource rents (NRR) in this transition remains surprisingly underexplored. The aim of this study is toward analyzing the impact of eight natural resource factors in the Bangladesh perspective, and this study covers the period 1980–2022. Additionally, the ARDL model is employed. We employed different types of regression model evaluation metrics (i.e., RMSE, MAE, and MAPE) to solve the desired problem at hand. The outcomes show that (i) all the variables at the first difference represent the cointegration test, while the bounds test outcomes confirm that there is long-run cointegration and relationships among energy transition, energy import, natural gas, total natural resources, oil, and forest rents. (ii) The natural resource rents and economic growth factors show a positive relationship with energy transition in Bangladesh between 0.55% and 3.90% in the short run and between 0.05% and 7.69% in the long run. This suggests that even a 1% change in resource rent and economic growth factors can still lead to optimistic growth, driven by rapid economic growth and the intense use of natural resources, which provides advanced development for its energy sector. (iii) The prediction results provide RMSE higher than MAE, while MAPE for energy transition is calculated at 1.45%, suggesting that energy transition processing in Bangladesh can be properly adopted. Finally, based on a comprehensive analysis of Bangladesh’s energy transition, resource rents, and economic factors, the study suggests its contributions to resource governance and plans evidence-based pathways for rent management, import structure, and renewable energy transition with long-run environmental stability.

实现负担得起的清洁能源需要全球从依赖化石燃料转向可持续能源；然而，令人惊讶的是，自然资源租金（NRR）在这一转变中的重要性仍未得到充分探讨。本研究的目的是分析孟加拉国视角下八种自然资源因素的影响，本研究涵盖1980-2022年期间。此外，还采用了ARDL模型。我们采用不同类型的回归模型评价指标（即RMSE， MAE和MAPE）来解决手头的期望问题。结果表明：(1)所有变量在第一差处都代表协整检验，而边界检验结果证实了能源转型、能源进口、天然气、自然资源总量、石油和森林租金之间存在长期协整和关系。（ii）孟加拉国自然资源租金和经济增长因素与能源转型的短期正相关关系在0.55% ~ 3.90%之间，长期正相关关系在0.05% ~ 7.69%之间。这表明，即使资源租金和经济增长因素发生1%的变化，在经济快速增长和自然资源集约利用的推动下，仍然可以导致乐观的增长，这为其能源部门提供了先进的发展。（iii）预测结果提供的RMSE高于MAE，而能源转换的MAPE计算值为1.45%，表明可以适当采用孟加拉国的能源转换处理。最后，在对孟加拉国能源转型、资源租金和经济因素进行综合分析的基础上，本研究提出了其对资源治理的贡献，并规划了基于证据的租金管理、进口结构和长期环境稳定的可再生能源转型路径。

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引用次数: 0

How increased cycling costs due to PV and wind integration impact the merit order: Learning from an application to France 由于光伏和风能集成而增加的循环成本如何影响绩效顺序：从法国的应用中学习

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2026-02-02 DOI: 10.1016/j.ecmx.2026.101641

Samouro Dansokho , Alexis Tantet , Philippe Drobinski , Anna Creti

The large-scale integration of variable renewable energy (VRE), particularly wind and solar, requires greater operational flexibility from conventional generators. Yet, the associated cycling costs such as start-ups, shutdowns, and ramping remain insufficiently accounted for in long-term planning models. This paper develops a tractable methodology to quantify cycling costs with minimal data and computational needs by extending the e4clim investment–dispatch framework. Dispatchable producers are represented through a synthetic merit order mix, allowing cycling costs to be diagnosed ex post as additional variable costs proportional to start-up frequency and capacity. The approach is applied to mainland France under different levels of VRE penetration. Results show that while VRE integration reduces total system costs overall, start-up costs increase non-linearly with penetration and can reach up to 90% of VRE system value in extreme cases. Costs are unevenly distributed, with base load producers increasingly assuming flexibility needs at high VRE levels, while peaking units are less affected. Solar PV tends to induce roughly twice as many start-ups as onshore wind due to its diurnal profile. The analysis relies on simplifying assumptions and limited data, reflecting the interdisciplinary challenge of linking climate variability, electricity markets, and plant operation. As such, the method is best viewed as a qualitative tool to explore merit order disruptions and investment signals rather than a precise forecast for the French system.

可变可再生能源（VRE）的大规模整合，特别是风能和太阳能，需要传统发电机具有更大的操作灵活性。然而，在长期规划模型中，相关的自行车成本，如启动、关闭和坡道，仍然没有充分考虑到。本文通过扩展e4clim投资调度框架，开发了一种易于处理的方法，以最小的数据和计算需求来量化循环成本。可调度的生产者通过综合价值订单组合表示，允许循环成本事后诊断为与启动频率和容量成比例的额外可变成本。该方法适用于法国大陆不同水平的VRE渗透率。结果表明，虽然VRE集成总体上降低了系统总成本，但启动成本随着渗透呈非线性增加，在极端情况下可达到VRE系统价值的90%。成本分布不均匀，基本负荷生产商越来越多地承担高VRE水平的灵活性需求，而峰值机组受影响较小。由于太阳能光伏的昼夜分布，它往往会吸引大约两倍于陆上风能的初创企业。该分析依赖于简化的假设和有限的数据，反映了将气候变化、电力市场和电厂运行联系起来的跨学科挑战。因此，该方法最好被视为一种探索价值订单中断和投资信号的定性工具，而不是对法国体系的精确预测。

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引用次数: 0

Economic and management evaluation of vehicle-mounted photovoltaic–battery systems in electric vehicles under urban operating conditions 城市工况下电动汽车车载光伏电池系统经济性与管理评价

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2026-01-26 DOI: 10.1016/j.ecmx.2026.101628

Junfeng Niu , Nesrine Gafsi , Pooya Ghodratallah , Rabeb Younes , Mohamed Shaban , Narinderjit Singh Sawaran Singh , Amina Hamdouni

The rapid electrification of transportation demands intelligent coordination among heterogeneous energy subsystems within electric vehicles. This research establishes an analytics-driven management framework that unites photovoltaic generation, high-energy–density lithium-ion storage, and auxiliary fuel-cell support to achieve a balanced, sustainable, and economically viable propulsion system. Focusing on an urban case study in Xi’an, China, the model integrates real-time meteorological inputs and vehicle-operation data to dynamically regulate energy flows between PV modules and battery packs. A hybrid optimization layer couples techno-economic modeling with management-level decision analytics, allowing simultaneous assessment of power efficiency, operational scheduling, and lifecycle cost performance. Results show that the coordinated PV–battery strategy enhances driving range up to 61% while lowering equivalent energy cost and mitigating peak-load stress on urban charging infrastructure. Beyond the technical gains, the framework demonstrates how data-enabled decision mechanisms can inform managerial planning for fleet electrification and urban energy resilience. The study provides actionable insights for policymakers and industry practitioners seeking integrated strategies to strengthen the economic, environmental, and managerial dimensions of electric mobility, directly supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy.

交通运输的快速电气化要求电动汽车内部异构能源子系统之间的智能协调。本研究建立了一个分析驱动的管理框架，将光伏发电、高能量密度锂离子存储和辅助燃料电池支持结合起来，实现平衡、可持续和经济可行的推进系统。该模型以中国西安的城市为例，整合了实时气象输入和车辆运行数据，以动态调节光伏模块和电池组之间的能量流动。混合优化层将技术经济建模与管理层决策分析相结合，允许同时评估功率效率、操作调度和生命周期成本绩效。研究结果表明，在降低等效能源成本和缓解城市充电基础设施峰值负荷压力的同时，电动汽车的续驶里程提高了61%。除了技术进步之外，该框架还展示了数据驱动的决策机制如何为车队电气化和城市能源弹性的管理规划提供信息。该研究为寻求综合战略以加强电动交通的经济、环境和管理层面的政策制定者和行业从业者提供了可操作的见解，直接支持联合国可持续发展目标7关于负担得起的清洁能源。

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引用次数: 0

Explainable machine learning models for predicting current and voltage in photovoltaic systems 用于预测光伏系统中电流和电压的可解释机器学习模型

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2026-01-24 DOI: 10.1016/j.ecmx.2026.101627

Aditya Dinakar, D. Cenitta, R. Vijaya Arjunan, Venkatesh Bhandage, Krishnaraj Chadaga

Photovoltaic (PV) systems are responsible for the conversion of solar energy into electricity and with the rising usage of renewable energy, solar energy has emerged as one of the leading contributors. However, solar energy is dependent on various environmental conditions which raises the need for forecasting of the electricity produced. With the rise in the usage of machine learning (ML) there have been attempts to forecast the solar energy harvested by PV systems. In this study a robust framework is used to predict the current and voltage generated by a PV system. This study employs the use of feature selection using BorutaSHAP and Variance Inflation Factor (VIF) to train various ML models consisting of Linear Regression, tree-based models, TabNet and transformer-based models. These models were later interpreted using Explainable Artificial Intelligence (XAI) methods such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), Local Interpretable Model-agnostic Explanations (LIME) and Diverse Counterfactual Explanations (DiCE). The best performing model was TabPFN, a transformer-based model and it achieved an R-squared of 0.998 and 0.934 for current and voltage respectively. This study shows a strong performing and interpretable framework to predict the current and voltage of a PV system.

光伏（PV）系统负责将太阳能转化为电能，随着可再生能源的使用不断增加，太阳能已成为主要贡献者之一。然而，太阳能依赖于各种环境条件，这就需要对所产生的电力进行预测。随着机器学习（ML）使用的增加，有人试图预测光伏系统收集的太阳能。在这项研究中，一个稳健的框架被用来预测由光伏系统产生的电流和电压。本研究使用BorutaSHAP和方差膨胀因子（Variance Inflation Factor， VIF）的特征选择来训练各种ML模型，包括线性回归、基于树的模型、TabNet和基于变压器的模型。这些模型后来使用可解释的人工智能（XAI）方法进行解释，如SHapley加性解释（SHAP）、部分依赖图（PDP）、个体条件期望（ICE）、局部可解释模型不可知解释（LIME）和多样化反事实解释（DiCE）。表现最好的是基于变压器的TabPFN模型，其电流和电压的r平方分别为0.998和0.934。本研究展示了一个强大的执行和可解释的框架来预测光伏系统的电流和电压。

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引用次数: 0

Exploring the energy potential of agricultural and agroindustrial residues in michoacán: characterization to determine the feasibility of solid biofuels 探索能源潜力的农业和农业工业残留物michoacán：表征，以确定固体生物燃料的可行性

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-05-01 Epub Date: 2026-01-24 DOI: 10.1016/j.ecmx.2026.101626

Ricardo González-Cárabes , Luis Bernardo López-Sosa , Janneth López-Mercado , José Guadalupe Rutiaga Quiñones , Francisco Javier Reynoso Marín , Luis Fernando Pintor-Ibarra , Luis Ángel Ascencio de la Cruz , Mario Morales Máximo , Arturo Aguilera Mandujano , Saúl Leonardo Hernández-Trujillo

This research presents an analysis of the energy potential of 5 agricultural crop residues in the state of Michoacán, Mexico, considering their possible use as solid biofuels. This study consists of five phases: (a) Identification of agricultural areas and collection of residues of each of the crops, Persea americana Mill. (avocado), Saccharum officinarum L. (sugarcane), Lens culinaris Medik. (lentil), Zea mays L. (corn) and Mangifera indica L (mango); (b) processing of the residues for characterization; (c) physicochemical characterization of the collected residues using characterization techniques such as CHONS, polymeric compound composition, FTIR, ash microanalysis and calorific value, in addition to the proximate analysis of the residues by obtaining the moisture, ash, volatiles and fixed carbon contents; (d) determination of the energy potential (TJ/year); (e) dissemination of results. The results of this research show values for the crops analyzed in terms of ash contents lower than 10%, percentages of volatile matter higher than 70%, while fixed carbon values were lower than 21%, elemental analysis showed results for carbon higher than 40%, lower than 7% for hydrogen, higher than 47% for oxygen and for nitrogen lower than 2%, in terms of polymeric compounds showed values higher than 12% for cellulose, values higher than 8% for hemicellulose, and regarding lignin, values above 5% were reported. The calorific value values were estimated between 15. MJ/kg and 19.8 MJ/kg, with energy potential values that could, in their minimum production, eventually satisfy the energy demand for cooking of 30% of the rural sector of the state.

本研究分析了墨西哥Michoacán州5种农作物残留物的能源潜力，并考虑了它们作为固体生物燃料的可能性。这项研究包括五个阶段：(a)确定农业地区和收集每种作物的残留物。（牛油果），Saccharum officinarum L.（甘蔗），Lens culinaris Medik。（扁豆）、玉米（Zea mays L.）和芒果（芒果）；(b)对残留物进行表征处理；(c)除了通过获取水分、灰分、挥发物和固定碳含量对残留物进行近似分析外，还使用表征技术（如CHONS、聚合物化合物组成、FTIR、灰分微量分析和热值）对收集到的残留物进行物理化学表征；(d)确定能源潜力（TJ/年）；(e)传播结果。这项研究的结果显示值分析了作物的火山灰含量低于10%,挥发性物质的百分比高于70%,而固定碳值低于21%,碳元素分析显示结果高于40%,低于7%的氢、氧和氮高于47%低于2%,高分子化合物显示值高于12%的纤维素,值高于8%,半纤维素和木质素,超过5%的值被报道。热值值估计在15。MJ/kg和19.8 MJ/kg，其能量潜力值在其最低产量下最终可以满足该州30%农村部门烹饪的能源需求。

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引用次数: 0