Renewable Energy Focus最新文献_第2页

Assessing the social perception of agrivoltaic systems in vineyards. A case study of an integrated trellis-based configuration in South-eastern Spain 评估葡萄园中农业光伏系统的社会认知。西班牙东南部综合格子结构的案例研究

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-07 DOI: 10.1016/j.ref.2026.100812

Irene Arias-Navarro , Baltasar Miras-Cabrera , Carlos Toledo , Asunción María Agulló-Torres , Javier Padilla , África Martínez-Poveda , Francisco J. Del Campo-Gomis

Sustainable development is a key priority in addressing global challenges such as climate change and land degradation. In South-eastern Spain, the Murcia Region stands out for its agricultural significance and its exceptional potential for solar energy generation, owing to its semi-arid climate and high solar irradiance. However, the rapid deployment of ground-mounted photovoltaic systems has intensified competition for agricultural land, positioning agrivoltaics systems as a promising dual-use solution. This study explores the attitudes of wine tourists towards vertically integrated low-height agrivoltaics systems in vineyards, a sector of major economic and cultural relevance in the region. The methodology consisted of guided visits to pilot low-height vertically integrated agrivoltaics systems installed in vineyard trellises at several wineries, followed by administration of a structured ethically reviewed questionnaire. Statistical analysis reveals a predominantly positive social perception of agrivoltaics systems in vineyards, emphasising support for dual land use and a preference for the low-height system integrated into the trellises, due to its greater harmony with the wine-growing landscape. Differences in perception by gender, educational level and environmental awareness suggests the need for differentiated communication strategies in case actions are taken to further increase the favorable social perception observed. Overall, findings position agrivoltaics as a viable solution for reconciling agricultural and energy production in a context of climate change and resource scarcity.

可持续发展是应对气候变化和土地退化等全球挑战的关键优先事项。在西班牙东南部，穆尔西亚地区因其农业意义和太阳能发电的特殊潜力而脱颖而出，因为它的半干旱气候和高太阳辐照度。然而，地面安装光伏系统的迅速部署加剧了对农业用地的竞争，将农业光伏系统定位为有前途的两用解决方案。本研究探讨了葡萄酒游客对葡萄园垂直整合的低高度农业发电系统的态度，这是该地区主要的经济和文化相关部门。研究方法包括在指导下参观安装在几个酒庄葡萄园棚架上的试点低高度垂直集成农业光伏系统，然后进行结构化的道德审查问卷调查。统计分析显示，社会对葡萄园农电系统的看法主要是积极的，强调对双重土地使用的支持，以及对集成到棚架中的低高度系统的偏好，因为它与葡萄酒种植景观更加和谐。性别、教育水平和环境意识的感知差异表明，如果采取行动进一步增加所观察到的有利的社会感知，则需要采取不同的传播策略。总的来说，研究结果表明，在气候变化和资源稀缺的背景下，农业发电是协调农业和能源生产的可行解决方案。

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引用次数: 0

Efficient power conversion for renewable energy: Hybrid multi-output system with solar photovoltaic inputs and battery backup 可再生能源的高效电力转换：混合多输出系统与太阳能光伏输入和备用电池

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-07 DOI: 10.1016/j.ref.2026.100811

Shyamantak Raj Barman , Muzammil Ahmed , Anish Ahmad , Nabin Sarmah

Microgrids play a crucial role in the integration of solar photovoltaic (SPV) energy into power systems, but conventional microgrid architectures rely on multiple power converters, leading to increased system bulkiness, higher losses, and reduced efficiency. This paper presents a hybrid multi-input, multi-output single-stage power conversion system which is compact and capable of delivering both DC and AC outputs while integrating dual solar photovoltaic sources simultaneously. The proposed non-isolated switched-boost high-gain topology significantly reduces the number of power-processing stages and enhances reliability through the addition of a battery storage unit with a State-of-charge (SOC) based controller. Four distinct operating modes are implemented to dynamically balance power among the SPV sources, battery, and connected loads. Detailed theoretical analysis, time-domain simulations and real-time Hardware-in-the-Loop (HIL) are used to validate the system operations. Results confirm stable DC link regulation at 600 V along with well-controlled three-phase AC output with a 220 V phase peak, and robust performance under 50% load variation across all modes. These findings demonstrate that the proposed architecture offers an efficient, compact, and reliable solution for next-generation microgrid applications.

微电网在将太阳能光伏（SPV）能源整合到电力系统中发挥着至关重要的作用，但传统的微电网架构依赖于多个电源转换器，导致系统体积增加，损耗更高，效率降低。本文提出了一种多输入多输出混合单级电源转换系统，该系统结构紧凑，能够同时提供直流和交流输出，同时集成双太阳能光伏电源。所提出的非隔离开关升压高增益拓扑显著减少了功率处理阶段的数量，并通过添加带有充电状态（SOC）控制器的电池存储单元提高了可靠性。实现了四种不同的工作模式，以动态平衡SPV源，电池和连接负载之间的功率。详细的理论分析、时域仿真和实时硬件在环（HIL）验证了系统的运行。结果证实了在600 V时稳定的直流链路调节以及具有220 V相位峰值的良好控制的三相交流输出，以及在所有模式下50%负载变化下的稳健性能。这些发现表明，所提出的架构为下一代微电网应用提供了高效、紧凑和可靠的解决方案。

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引用次数: 0

Optimizing resource allocation and enhancing security in smart grid environments through a decentralized access control system with power theft detection mechanism 通过带窃电检测机制的分散式门禁系统，优化智能电网环境下的资源配置，增强安全性

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-30 DOI: 10.1016/j.ref.2025.100807

P. Mary Jyosthna , P. Srilatha , N. Raveendra

The smart grid upgrades an existing power grid with intelligence, such that data sharing can occur about things like customer data and energy consumption. However, several methods related to access management and theft detection currently exist, can be inflexible, have high computational costs, and their generalizations can be impaired by noisy sensor data. This work develops a secure, and efficient smart grid system that combines decentralized access control, and power theft detection. The major aim of the current technique is to develop a decentralized access control service with user revocation abilities while increasing smart grid security using information technology management. The method described in this paper will first create input data from the Theft Detection in Smart Grid Environment Dataset and process the data with a Fuzzy–Enhanced Kalman Filter (FEKF) to remove noise and outliers from the input data. The input data is sensed for power theft detection through the usage of a Quantum–enhanced Artificial Neural Network (QANN) that enables precise detection of illicit activity. To optimize resource allocation and access request routing, the Ship Rescue Optimization (SRO) algorithm is applied. The system is implemented and evaluated using the Python programming platform. When compared to the existing methods like African Vultures Optimization Algorithm (AVOA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm with Flower Mating Optimization (WOA–FMO), the proposed SRO achieves outstanding performance with a high accuracy of 98 %.

智能电网通过智能升级现有电网，这样就可以实现客户数据和能源消耗等方面的数据共享。然而，目前存在的几种与访问管理和盗窃检测相关的方法可能不灵活，计算成本高，并且它们的泛化可能会受到噪声传感器数据的影响。这项工作开发了一个安全、高效的智能电网系统，它结合了分散的访问控制和电力盗窃检测。当前技术的主要目标是开发具有用户撤销能力的分散访问控制服务，同时利用信息技术管理提高智能电网的安全性。本文所描述的方法将首先从智能电网环境数据集中的盗窃检测中创建输入数据，并用模糊增强卡尔曼滤波器（FEKF）处理数据，以去除输入数据中的噪声和异常值。输入数据通过使用量子增强人工神经网络（QANN）来检测电力盗窃，从而能够精确检测非法活动。为了优化资源分配和访问请求路由，采用了船舶救助优化算法。系统使用Python编程平台进行了实现和评估。与现有的非洲秃鹫优化算法（AVOA）、粒子群优化算法（PSO）、带花交配优化的鲸鱼优化算法（WOA-FMO）等方法相比，本文提出的SRO算法具有优异的性能，准确率高达98%。

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引用次数: 0

Enhancing photovoltaic system flexibility: a novel integrated converter with COA optimization approach 提高光伏系统灵活性：一种新型集成变流器的COA优化方法

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-28 DOI: 10.1016/j.ref.2025.100808

G. Madhusudanan , S. Padhmanabhaiyappan

This manuscript presents a Crayfish Optimization Algorithm (COA)-based strategy to enhance the performance of photovoltaic (PV) systems integrated with AC/DC microgrid converters. The proposed method adaptively tunes controller parameters to achieve high step-up voltage, reliable AC and DC outputs, and improved power quality. The COA efficiently optimizes the system, providing faster convergence compared to conventional approaches like Genetic Algorithm (GA) and Spotted Hyena Optimizer (SHO). Simulation outcomes establish that the proposed approach achieves the lowest error of 1.32 and THD of 2.56% outperforming existing approaches. These outcomes indicate that the proposed technique not only develops energy conversion efficiency but also ensures stable, reliable, and high-quality power delivery, which is crucial for modern microgrid operations. The improved performance is attributed to the COA’s adaptive parameter tuning and multi-objective optimization, which enable robust operation under variable conditions and enhance overall energy conversion. These results highlight the proposed method’s potential for efficient, reliable, and high-quality PV energy integration in modern distribution networks.

本文提出了一种基于小龙虾优化算法（COA）的策略，以提高与交/直流微电网集成的光伏（PV）系统的性能。该方法可自适应调整控制器参数，以实现高升压、可靠的交流和直流输出，并改善电能质量。与遗传算法（GA）和斑点鬣狗优化器（SHO）等传统方法相比，COA有效地优化了系统，提供了更快的收敛速度。仿真结果表明，该方法的最小误差为1.32，THD为2.56%，优于现有方法。这些结果表明，该技术不仅提高了能量转换效率，而且确保了稳定、可靠和高质量的电力输送，这对现代微电网的运行至关重要。改进的性能归功于COA的自适应参数调整和多目标优化，使其能够在可变条件下鲁棒运行，并提高了整体能量转换。这些结果突出了该方法在现代配电网中高效、可靠和高质量光伏能源整合方面的潜力。

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引用次数: 0

Integrating Null Energy Sellers for P2P trading: An economically viable and environmentally sustainable uniform pricing framework 整合零能源卖家P2P交易：一个经济上可行和环境上可持续的统一定价框架

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-15 DOI: 10.1016/j.ref.2025.100793

Nermish Mushtaq, Hassam Ishfaq, Iqra Nazir, Muqaddas Azad, Xuyang Shi, Waqas Amin

This paper proposes an innovative pricing and energy trading model for peer-to-peer energy markets integrating Null Energy Sellers (NES) and Participating Energy Sellers (PES) within a partnership framework. The model dynamically determines market operation modes either as buyers’ mode or sellers’ mode and calculates the final trading price (FTP) based on competitive interactions among energy sellers and consumers. A comprehensive financial analysis evaluates capital investment, net cash flow, net profit, payback period (PBP), and return on investment (ROI) for different classes of NES, demonstrating the model’s economic viability compared to state-of-the-art approaches. Moreover, the environmental and grid impacts of NES penetration levels are quantitatively assessed by analyzing reductions in CO

_{2}

emissions and grid stress, evidencing significant ecological benefits and enhanced grid stability with increasing NES integration. Extensive simulations over one year validate the model’s effectiveness in optimizing energy allocation, improving participant satisfaction, and fostering sustainable energy trading. The one-year simulation results reveal that under the proposed pricing model, the buyers’ annual energy bills can be reduced by approximately 9.7% to 18.56%. Conversely, the sellers’ revenues increase by about 8.08% to 15.90%. The proposed business model further shows that the capital invested in the renewable energy plant can be recovered within a payback period of approximately 5.7–6.8 years. Moreover, different levels of renewable energy penetration indicate that at 30% integration, significant reductions in CO

_{2}

emissions can be achieved, ranging from 31.4% to 65.81%. In addition, a 30% renewable energy penetration further reduces grid stress by approximately 23.8% to 39.9%. Overall, the proposed framework offers a balanced and competitive market environment, encouraging active participant engagement while contributing to environmental sustainability and grid resilience.

本文提出了一种创新的点对点能源市场定价和能源交易模型，该模型将零能源卖家（NES）和参与能源卖家（PES）整合在一个伙伴关系框架内。该模型动态地确定了市场运行模式是买方模式还是卖方模式，并根据能源卖方和消费者之间的竞争相互作用计算出最终交易价格。综合财务分析评估了不同类别的NES的资本投资、净现金流、净利润、投资回收期（PBP）和投资回报率（ROI），与最先进的方法相比，证明了该模型的经济可行性。此外，通过分析二氧化碳排放和电网压力的减少，定量评估了新能源网渗透水平对环境和电网的影响，证明了随着新能源网整合的增加，生态效益显著，电网稳定性增强。经过一年多的模拟，验证了该模型在优化能源分配、提高参与者满意度和促进可持续能源交易方面的有效性。为期一年的仿真结果表明，在该定价模型下，购买者的年度能源账单可减少约9.7%至18.56%。相反，卖方收入增长约8.08%至15.90%。拟议的商业模式进一步表明，投资于可再生能源工厂的资本可以在大约5.7-6.8年的投资回收期内收回。此外，不同的可再生能源渗透率水平表明，在30%的整合下，二氧化碳排放量可以实现显著减少，减少幅度从31.4%到65.81%不等。此外，30%的可再生能源渗透率进一步降低了约23.8%至39.9%的电网压力。总体而言，拟议的框架提供了一个平衡和竞争的市场环境，鼓励积极的参与者参与，同时有助于环境可持续性和电网弹性。

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引用次数: 0

Techno-economic analysis and machine learning integration for enhanced ammonia production 技术经济分析和机器学习集成提高氨生产

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-10 DOI: 10.1016/j.ref.2025.100805

Meryem Bahaj , Abdechafik EL Harrak , Hassan Naanani , Houssam Bouchouk , Abdessamad Faik

In 2023, around 72% of global hydrogen production came from natural gas via steam methane reforming, a highly energy-consuming process, emitting around 10 tons of CO₂ per ton of H₂ produced. This gray hydrogen can be partially decarbonized using carbon capture and storage technology, leading to blue hydrogen, which reduces CO₂ emissions by up to 90%. Alternatively, green H₂ is produced via water electrolysis using renewable energy sources. Finally, N₂ obtained through the cryogenic air separation technology interacts with H₂ in the Haber- Bosch process to produce NH₃. This study evaluates NH₃ purchase and import as well as different production scenarios in the case of Morocco at the Office Chérifien des Phosphates Group at Jorf Lasfar plant. It examines the gray ammonia process, blue ammonia, and green clean ammonia using water electrolysis. Detailed process models were provided using the Aspen software, including a techno-economic and sensitivity analysis to assess the feasibility of producing 4 tons of NH₃ daily. Finally, a gray ammonia plant was simulated, due to its extensive use and industrial relevance in Morocco, as well as its ability to generate large amounts of data for training and testing a machine learning model. The predictive model was trained to estimate energy consumption through equations that relate it to operating variables for each of the major energy-consuming units. By implementing optimization algorithms and thorough data analysis, energy consumption was successfully reduced by over 50% compared to the baseline process parameters.

2023年，全球约72%的氢气产量来自天然气，通过蒸汽甲烷重整，这是一个高能耗的过程，每生产一吨氢气排放约10吨二氧化碳。这种灰色的氢可以通过碳捕获和储存技术部分脱碳，从而产生蓝色的氢，从而减少高达90%的二氧化碳排放。另外，绿色氢气是通过使用可再生能源的水电解产生的。最后，通过低温空分技术得到的N2在Haber- Bosch工艺中与H2相互作用生成NH3。本研究以摩洛哥为例，在Jorf Lasfar工厂的磷酸组办公室评估了NH3的采购和进口以及不同的生产方案。它检查了灰色氨过程，蓝色氨和绿色清洁氨使用水电解。利用Aspen软件建立了详细的工艺模型，包括技术经济和敏感性分析，以评估日产4吨NH3的可行性。最后，由于灰氨工厂在摩洛哥的广泛使用和工业相关性，以及它能够生成大量数据用于训练和测试机器学习模型，因此对灰氨工厂进行了模拟。预测模型经过训练，通过将其与每个主要能源消耗单位的操作变量联系起来的方程来估计能源消耗。通过实施优化算法和全面的数据分析，与基线工艺参数相比，能耗成功降低了50%以上。

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引用次数: 0

Optimising solar energy communities in arctic micro-communities: addressing building azimuth challenges in Finnish Lapland 优化北极微型社区中的太阳能社区：解决芬兰拉普兰建筑方位角的挑战

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-08 DOI: 10.1016/j.ref.2025.100798

Vinay Shekar, Antonio Calò, Eva Pongrácz

The Energy Performance of Buildings Directive mandates solar photovoltaic installations on new buildings and requires buildings undergoing major renovation to meet their energy needs through significant renewable energy generation. Arctic micro-communities often face dispersed settlements, suboptimal building azimuths, and high heating demands. This paper examines the convergence of the mandate and challenges to determine whether cross-property energy community frameworks can overcome building azimuth constraints in Arctic regions, using three villages in Finnish Lapland: Sinettä, Vanttauskoski, and Vikajärvi. Using 3D building models created with SketchUp and Skelion, the solar energy generation potential was simulated using the NREL PVWatts and JRC PVGIS calculators. Economic viability was assessed through investment cost calculations, annual revenue projections, and payback period analysis. Two scenarios were compared: a traditional approach of installing solar on all roofs, versus a cross-property, energy-community-optimised approach focusing on installations on optimally oriented roofs with energy sharing. Results show that while Scenario (1) could generate nearly 1890 MWh annually, it incurs 8–12 % energy losses due to suboptimal azimuths, extending payback periods by 2–3 years; Scenario (2) achieves higher efficiency and improves economic viability with a lower payback period, despite lower total generation. The solar coverage of non-heating electricity ranges from 42 % to 60 %, but drops to 12–18 % when heating is included, emphasising the need for complementary heating solutions. This research concludes that cross-property energy community frameworks combining solar PV deployment with complementary heating solutions, supported by municipal “Champion” entities and solar-aware zoning for future developments, can effectively optimise Arctic solar deployment.

《建筑物能源性能指令》要求在新建筑物上安装太阳能光伏装置，并要求正在进行重大翻新的建筑物通过大量可再生能源发电来满足其能源需求。北极微社区经常面临分散的定居点、次优的建筑方位角和高供暖需求。本文以芬兰拉普兰的三个村庄（Sinettä、Vanttauskoski和Vikajärvi）为例，考察了任务和挑战的趋同性，以确定跨属性能源社区框架是否可以克服北极地区建筑方位角的限制。利用SketchUp和Skelion创建的3D建筑模型，使用NREL PVWatts和JRC PVGIS计算器模拟太阳能发电潜力。通过投资成本计算、年度收入预测和投资回收期分析来评估经济可行性。两种方案进行了比较：一种是在所有屋顶上安装太阳能的传统方法，另一种是跨物业、能源社区优化的方法，重点是在面向最佳方向的屋顶上安装能源共享。结果表明，虽然方案(1)每年可产生近1890兆瓦时，但由于次优方位角，它会产生8 - 12%的能量损失，将投资回收期延长2-3年；尽管总发电量较低，但方案(2)以较短的投资回收期实现了更高的效率和经济可行性。非供暖电力的太阳能覆盖率从42%到60%不等，但如果包括供暖，则下降到12 - 18%，这强调了补充供暖解决方案的必要性。这项研究的结论是，将太阳能光伏部署与互补供暖解决方案相结合的跨物业能源社区框架，在市政“冠军”实体和未来发展的太阳能意识分区的支持下，可以有效地优化北极太阳能部署。

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引用次数: 0

Evaluating the impact of a multi-objective trading decision optimizer on community energy markets performance 评估多目标交易决策优化器对社区能源市场绩效的影响

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-07 DOI: 10.1016/j.ref.2025.100791

Amin Zakhirehkar Sahih , Milad Ghasri , Ali Ahrari

This paper presents the first community-wide assessment of how prosumer decision-making optimizers affect local renewable energy markets. To capture realistic individual behavior, we develop a Multi-objective Trading Decision Optimizer (MO-TDO) that enables prosumers to schedule flexible loads by jointly considering electricity cost and convenience. Using this tool, we evaluate the broader impacts of MO-TDO adoption across three community-scale market-clearing mechanisms: the Uniform Price Double Auction (UPDA), the Innovative Coalition Business Model (ICBM), and the Hybrid Auction-Coalition (HAC). A discrete-event simulation of 100 Australian households is conducted under varying adoption rates, with outcomes measured in terms of community electricity bills, local matching efficiency, peak-load reduction, equity of profit distribution, and carbon emissions. Results show that increasing MO-TDO adoption consistently improves community outcomes across all markets. HAC most frequently achieves the lowest electricity bills, ICBM delivers the most significant peak-load reductions and maintains fairness in profit distribution, while UPDA provides only moderate cost benefits but greater inequality. By linking prosumer-level optimization with system-level outcomes, this study highlights how advanced decision-making tools can shape community-scale performance and provides actionable insights for policymakers and operators in designing local energy markets.

本文提出了产消决策优化器如何影响当地可再生能源市场的第一个社区范围的评估。为了捕获真实的个体行为，我们开发了一个多目标交易决策优化器（MO-TDO），使生产消费者能够在综合考虑电力成本和便利性的情况下安排灵活的负荷。利用这一工具，我们评估了在三种社区规模的市场清算机制中采用MO-TDO的更广泛影响：统一价格双重拍卖（UPDA）、创新联盟商业模式（ICBM）和混合拍卖联盟（HAC）。在不同的采用率下，对100个澳大利亚家庭进行了离散事件模拟，并从社区电费、当地匹配效率、高峰负荷减少、利润分配公平和碳排放等方面衡量了结果。结果表明，增加MO-TDO的采用持续改善了所有市场的社区成果。HAC最常实现最低的电费，洲际弹道导弹提供了最显著的峰值负荷削减，并保持了利润分配的公平性，而UPDA只提供了中等的成本效益，但更大的不平等。通过将消费者层面的优化与系统层面的结果联系起来，本研究强调了先进的决策工具如何影响社区规模的绩效，并为决策者和运营商设计当地能源市场提供了可操作的见解。

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引用次数: 0

Time-varying effects of policy uncertainty on supply chain market connectivity in Chinese photovoltaic industry 政策不确定性对中国光伏产业供应链市场连通性的时变影响

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-04 DOI: 10.1016/j.ref.2025.100792

Junhui Li , Yanqiong Zhao , Shiquan Dou , Yongguang Zhu , Deyi Xu

The transition to renewable energy is essential to address global environmental challenges. Central to this shift, the photovoltaic (PV) industry is vital for achieving low-carbon goals. Supported by government policies, China’s PV sector has led the world in newly installed capacity for a decade. However, the impact of policy uncertainty on the interconnected dynamics of the PV supply chain remains underexplored. This study uses a Time-Varying Parameter Vector Autoregression (TVP-VAR) model and Granger causality tests to analyze dynamic price dependencies within the Chinese PV supply chain. The results reveal midstream markets as net shock receivers, while upstream markets act as primary transmitters. Economic and trade policy uncertainties significantly and asymmetrically influence market connectivity, with economic policy uncertainty having a stronger impact. These findings highlight the critical role of policy frameworks in shaping supply chain dynamics and resilience. By offering a nuanced understanding of price interdependencies and temporal variations in spillovers, this research provides actionable insights for policymakers and stakeholders. It supports strategic decision-making to promote sustainable development and investment in China’s PV sector while addressing the challenges posed by policy-induced risks.

向可再生能源过渡对于应对全球环境挑战至关重要。作为这一转变的核心，光伏（PV）产业对于实现低碳目标至关重要。在政府政策的支持下，中国光伏行业的新增装机容量已经领先世界十年。然而，政策不确定性对光伏供应链互联动态的影响仍未得到充分探讨。本研究采用时变参数向量自回归（tpv - var）模型和格兰杰因果检验来分析中国光伏供应链的动态价格依赖关系。结果表明，中游市场是净冲击接受者，而上游市场是主要的发射器。经贸政策不确定性对市场连通性影响显著且不对称，其中经济政策不确定性影响更大。这些发现突出了政策框架在塑造供应链动态和弹性方面的关键作用。通过对价格相互依赖性和溢出效应的时间变化提供细致入微的理解，本研究为政策制定者和利益相关者提供了可行的见解。它支持战略决策，以促进中国光伏行业的可持续发展和投资，同时应对政策风险带来的挑战。

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引用次数: 0

Generalized model-predictive control for supercapacitor and superconducting magnetic energy storage systems 超级电容器和超导磁储能系统的广义模型预测控制

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2025-12-03 DOI: 10.1016/j.ref.2025.100795

Juan-Camilo Oyuela-Ocampo , Alejandro Garcés-Ruiz , Walter Gil-González

The integration of renewable energy sources requires efficient and reliable energy storage systems to stabilize grid operation and address the inherent variability of this type of generation. This study focuses on electric energy storage systems (EESS), which encompass supercapacitor energy storage (SCES) and superconducting magnetic energy storage (SMES). Leveraging their shared structural properties, it is possible to propose a unified modeling framework. A model predictive control (MPC) strategy is developed within this framework, offering precise regulation of active and reactive power while ensuring system stability. The proposed strategy incorporates a discrete bilinear model and a one-step control horizon to optimize performance under dynamic operating conditions. Numerical simulations demonstrate the proposed MPC approach’s effectiveness in reducing power oscillations, enhancing response dynamics, and maintaining grid stability in scenarios with variable loads, renewable energy fluctuations, and a three-phase fault in microgrid. The proposed control is compared to conventional strategies, showing superior performance with faster adaptation and fewer oscillations. Quantitative results based on standard performance indices (IAE, ITAE, ITSE,

T_{s}

, and

M_{p}

) further confirm the superior transient and steady-state behavior of the proposed MPC strategy. In addition, passivity and stability are formally guaranteed via the Lyapunov theorem.

可再生能源的整合需要高效可靠的储能系统来稳定电网运行，并解决这类发电的内在可变性。本研究的重点是电力储能系统（EESS），包括超级电容器储能（SCES）和超导磁能储能（SMES）。利用它们共享的结构属性，可以提出统一的建模框架。在此框架内开发了模型预测控制（MPC）策略，在确保系统稳定性的同时提供精确的有功和无功调节。该策略采用离散双线性模型和一步控制水平来优化动态工况下的性能。数值模拟结果表明，在负荷变化、可再生能源波动和微电网三相故障情况下，MPC方法在减少功率振荡、增强响应动力学和保持电网稳定性方面是有效的。与传统控制策略相比，该方法具有自适应快、振荡小等优点。基于标准性能指标（IAE、ITAE、ITSE、Ts和Mp）的定量结果进一步证实了所提出的MPC策略具有优越的瞬态和稳态性能。此外，通过李亚普诺夫定理正式保证了系统的无源性和稳定性。

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引用次数: 0