Renewable Energy Focus最新文献

Protection solutions and their performance assessment for MV embedded microgrid networks 中压嵌入式微电网的保护方案及其性能评估

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-22 DOI: 10.1016/j.ref.2026.100819

M. Aizaz Farid, Inam Nutkani, Lasantha Meegahapola, Nuwantha Fernando

Numerous innovative protection schemes involving a combination of overcurrent relays (OCRs) and directional overcurrent relays (DOCRs) have been investigated for a distribution network with distributed generators (DGs) and microgrids (MGs) governed by existing anti-islanding regulations. However, protection solutions for embedded microgrids (EMGs) have not been thoroughly investigated, where conventional overcurrent and islanding protection implemented at a single point-of-common-coupling (PCC) proves inadequate. The analysis in this paper shows that EMG supply reliability can deteriorate by up to 34% more than that of the distribution network itself. To address this issue, this paper proposes a suite of protection solutions designed for future embedded networks, aiming to minimize supply disruption through isolation of faulty network segments and maintaining supply to the rest of the network from the grid and EMG. The proposed protection solutions involve the addition of new DOCRs and their coordination among themselves and with the existing conventional OCRs to ensure adequate protection. A simple framework employing two strategies, based on feeders’ radial distance and connected load power is proposed for the placement of additional DOCRs, resulting in numerous solution scenarios (SSs) with different combinations of OCR and DOCRs. A comprehensive techno-economic performance analysis is conducted by estimating the improvement in reliability indices, the associated cost savings, and the deployment cost of each SS to determine its economic viability. The findings reveal that the most cost-effective solution have enhanced system reliability by up to 67%, demonstrating that the proposed simple yet practical framework provides highly effective and viable protection solutions for EMGs.

许多涉及过流继电器（ocr）和定向过流继电器（docr）组合的创新保护方案已被研究用于具有分布式发电机（dg）和微电网（mg）的配电网，这些配电网受现有反孤岛法规的约束。然而，嵌入式微电网（emg）的保护解决方案尚未得到彻底研究，在单共耦点（PCC）实施的传统过流和孤岛保护被证明是不够的。本文的分析表明，与配电网本身相比，肌电供电可靠性的下降幅度可达34%。为了解决这个问题，本文提出了一套为未来嵌入式网络设计的保护解决方案，旨在通过隔离故障网段和保持对电网和肌电图网络其余部分的供应来最大限度地减少供应中断。拟议的保护解决办法涉及增加新的区域保护区，并在它们之间以及与现有的常规区域保护区进行协调，以确保充分的保护。基于馈线的径向距离和连接的负载功率，提出了一个采用两种策略的简单框架，用于放置额外的docr，从而产生了许多具有不同OCR和docr组合的解决方案方案（ss）。通过评估可靠性指标的改进、相关的成本节约和每个SS的部署成本，进行全面的技术经济绩效分析，以确定其经济可行性。研究结果表明，最具成本效益的解决方案将系统可靠性提高了67%，表明所提出的简单实用的框架为肌电信号提供了高效可行的保护解决方案。

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引用次数: 0

Techno-economic analysis of hydrogen transport: comparison between tube trailer and pipeline 输氢技术经济分析：管式拖车与管道的比较

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-22 DOI: 10.1016/j.ref.2026.100820

Guillermo G. Griñán, Tomás Gómez-Acebo

Hydrogen logistics is a key enabler in the energy transition. This study presents a disaggregated, reproducible, and quantitative framework for the techno-economic analysis of hydrogen transport via tube trailers and pipelines, covering a broad range of delivery capacities (0.5–10,000 t/day) and distances (10–1,000 km). The model explicitly separates capital and operating costs for each subsystem, enabling transparent identification of the main cost drivers and robust comparison between technologies. Results show that tube trailer transport is dominated by vessel costs and is economically viable only for short-to-medium distances (up to 250–350 km) and low-to-moderate demands; notably, for demands above 5–10 t/day, road transport costs tend to stabilize. In contrast, pipeline transport is characterized by high initial capital expenditure at low capacities, with compression operating costs becoming dominant at high capacities (>500 t/day). Strong economies of scale allow pipelines to maintain costs below 2 €/kg for demands above 200 t/day across all distances considered. Comparative cost maps generated by the model clearly delineate the optimal operational regimes and transition zones for each technology. Sensitivity analysis demonstrates that material cost most strongly affects tube trailer economics, while labor and electricity cost are most influential for pipelines, particularly at high capacities. The reproducibility and transparency of the framework ensure that the techno-economic boundaries between transport modes are robust to plausible cost fluctuations. These findings provide actionable insights for infrastructure planners and policymakers, supporting the efficient and cost-effective integration of hydrogen into future energy systems.

氢物流是能源转型的关键推动者。本研究提供了一个分解的、可重复的、定量的框架，用于通过管道拖车和管道运输氢气的技术经济分析，涵盖了广泛的输送能力（0.5-10,000吨/天）和距离（10-1,000公里）。该模型明确地分离了每个子系统的资本和运营成本，使主要成本驱动因素的透明识别和技术之间的可靠比较成为可能。结果表明，管道拖车运输主要由船舶成本主导，仅在中短途（高达250-350公里）和低至中等需求时才具有经济可行性；值得注意的是，对于每天5-10吨以上的需求，公路运输成本趋于稳定。相比之下，管道运输的特点是在低容量时初始资本支出高，在高容量（500吨/天）时压缩运营成本占主导地位。强大的规模经济使管道在所有距离的需求超过200吨/天时，成本保持在2欧元/公斤以下。该模型生成的比较成本图清楚地描绘了每种技术的最佳操作制度和过渡区域。敏感性分析表明，材料成本对管道拖车经济性的影响最大，而人工和电力成本对管道经济性的影响最大，特别是在高容量时。框架的可复制性和透明度确保运输方式之间的技术经济界限对合理的成本波动具有稳健性。这些发现为基础设施规划者和政策制定者提供了可行的见解，支持将氢高效、经济地整合到未来的能源系统中。

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引用次数: 0

BC-AFBTM: bubble-net communication optimization based adaptive fuzzy neural network with bidirectional long short-term memory for power quality improvement 基于气泡网通信优化的双向长短期记忆自适应模糊神经网络改进电能质量

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-21 DOI: 10.1016/j.ref.2026.100818

Harshal Vitthalrao Takpire , Mukesh Kumar , Saurabh Sureshrao Jadhao

Power quality (PQ) is mandatory to ensure the safety, energy efficiency, performance of the equipment, environmental standards and production targets. However, overheating and breakdown of machines reduce the PQ, which increases the maintenance costs and reduces the lifespan of the machine. Conventional control strategies had poor dynamic performance and limited adaptability, while considering complex and non-linear conditions. Therefore, the research work implements the Bubble-net Communication optimization based Adaptive Fuzzy Neural Network-Bidirectional Long Short-Term Memory (BC-AFBTM) Model on the Unified Power Quality Conditioner (UPQC) device, to mitigate the harmonic issues such as sag, swell, interruptions and harmonic fluctuations, which contribute to enhance the PQ and stability performance of the power system. The BC-AFBTM model provides a precise compensating current and voltage signal to inject into the series and shunt converter for PQ issues mitigation. The BC algorithm optimizes the gain parameters of the converters to enhance the model performance. Eventually, the BC-AFBTM model outperformed the efficient results of injected reactive power, injected voltage, load voltage, power and reactive power, which exhibit the values are 49.01kVAR, 0.9656V_pu, 0.9953V_pu, 202.32KW and 231.78kVAR in load side, respectively.

电能质量（PQ）是保证设备安全、能效、性能、环境标准和生产目标的强制性指标。然而，机器的过热和故障会降低PQ，从而增加维护成本并降低机器的使用寿命。传统控制策略在考虑复杂非线性条件时，动态性能较差，自适应能力有限。因此，本研究工作在统一电能质量调节器（UPQC）设备上实现了基于气泡网通信优化的自适应模糊神经网络-双向长短期记忆（BC-AFBTM）模型，以缓解凹陷、膨胀、中断和谐波波动等谐波问题，提高电力系统的PQ性能和稳定性。BC-AFBTM模型提供精确的补偿电流和电压信号，注入串联和分流转换器，以缓解PQ问题。BC算法通过优化变换器的增益参数来提高模型的性能。最终，BC-AFBTM模型在负荷侧的注入无功功率、注入电压、负载电压、功率和无功功率的有效值分别为49.01kVAR、0.9656Vpu、0.9953Vpu、202.32KW和231.78kVAR，优于注入无功功率、注入电压、负载电压的有效值。

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引用次数: 0

Intelligent LFC for multi-microgrid system integrating ASIC and BESS 集成ASIC和BESS的多微网系统智能LFC

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-18 DOI: 10.1016/j.ref.2026.100817

Ghali Ahmad , Md Shafiullah , Mohamed Zaery , Mujahed Al-Dhaifallah , Mohammad A. Abido

High renewable penetration in multi‑microgrids reduces effective inertia and introduces fast, stochastic power imbalance, degrading load‑frequency control. This work proposes an adaptive artificial neural network-based proportional-integral-derivative secondary controller coordinated with an application‑specific integrated circuit and battery energy storage system in the power system having a conventional generator, PV, and wind systems. The application‑specific integrated circuit load has been utilized in a cryptocurrency mining system, allowing for the dynamic adjustment of power consumption based on generation levels. This approach stabilizes the system by using surplus power during periods of high generation and reducing demand during periods of low generation. The proportional-integral-derivative controller gains are pre-trained offline using a genetic algorithm sweeping across diverse operating scenarios and encoded into a feed-forward artificial neural network for online adaptation. Across all scenarios, the proposed controller consistently outperforms traditional methods such as genetic algorithm-based proportional-integral-derivative, particle swarm optimization-based proportional-integral-derivative, and gray wolf optimization-based proportional-integral-derivative controllers, achieving substantial reductions in error metrics such as Integral Square Error, Integral Absolute Error, and Integral Time Absolute Error by up to 28.95%, 16.85%, and 11.65% respectively. It has also achieved a lower rate-of-change of frequency, a 15% rise time, a 13% peak amplitude, a 7% settling time, and control effort for both loads, and the proposed scheme eventually enhances the system stability.

多微电网中的高可再生能源渗透率降低了有效惯性，并引入了快速、随机的功率不平衡，降低了负载频率控制。这项工作提出了一种基于人工神经网络的自适应比例-积分-导数二级控制器，该控制器与具有传统发电机、光伏和风力系统的电力系统中的特定应用集成电路和电池储能系统相协调。特定应用的集成电路负载已在加密货币挖矿系统中使用，允许根据发电水平动态调整功耗。这种方法通过在高发电量期间使用剩余电力和在低发电量期间减少需求来稳定系统。比例-积分-导数控制器增益使用一种遗传算法在不同的操作场景下进行离线预训练，并编码到前馈人工神经网络中进行在线适应。在所有场景中，所提出的控制器始终优于传统方法，如基于遗传算法的比例积分导数、基于粒子群优化的比例积分导数和基于灰狼优化的比例积分导数控制器，在误差指标（如积分平方误差、积分绝对误差和积分时间绝对误差）上分别大幅降低了28.95%、16.85%和11.65%。该方案还实现了较低的频率变化率、15%的上升时间、13%的峰值幅度、7%的稳定时间和两个负载的控制工作量，并最终提高了系统的稳定性。

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引用次数: 0

Sample-based robust unit commitment with scenario classification 具有场景分类的基于样本的鲁棒单元承诺

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-17 DOI: 10.1016/j.ref.2026.100814

Shuzhen Zheng , Hanjiang Dong , Jizhong Zhu , Haosen Yang , Shenglin Li

This paper proposes a two-stage distributionally robust optimization (DRO) model for unit commitment (UC) that incorporates representative scenarios. By constructing an ambiguity set that considers representative scenarios, the model effectively addresses the challenges posed by uncertainties in both power generation and load demand in UC problems. First, we formulate a two-stage DRO mathematical model for UC based on the Wasserstein distance. Then, we construct a classification-based ambiguity set with representative scenarios. Subsequently, we apply linear decision rules (LDR) and duality theory to reformulate the original model into a tractable mixed-integer linear programming (MILP) problem. Finally, we conduct a comprehensive case study on 14-bus, 30-bus, 57-bus, and 118-bus test systems, evaluating the model from four different perspectives. The results demonstrate that the proposed model, incorporating representative scenarios, exhibits superior stability and robustness.

本文提出了一个包含代表性场景的两阶段分布鲁棒优化（DRO）模型。通过构建一个考虑代表性场景的模糊集，该模型有效地解决了UC问题中发电和负载需求的不确定性所带来的挑战。首先，我们基于Wasserstein距离建立了UC的两阶段DRO数学模型。然后，我们构建了一个具有代表性场景的基于分类的模糊集。随后，我们应用线性决策规则（LDR）和对偶理论将原始模型重新表述为一个可处理的混合整数线性规划（MILP）问题。最后，我们对14总线、30总线、57总线和118总线测试系统进行了全面的案例研究，从四个不同的角度对模型进行了评估。结果表明，该模型具有较好的稳定性和鲁棒性。

引用次数: 0

An uncertainty microgrid system with the regulation potential of photovoltaic energy storage charging stations and its optimization algorithm 具有光伏储能充电站调节潜力的不确定微电网系统及其优化算法

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-14 DOI: 10.1016/j.ref.2026.100815

Ning Zhou, Jing Yao, Zhiwei Zhou

This study addresses the multi-objective optimization problem of multi-microgrid systems (MMGS) considering the uncertainties of renewable energy sources (RES) with respect to economy, security, and operational stability. A coordinated participation model integrating microgrid units (MGU), photovoltaic-storage-charging stations (PESCS), and distribution networks (DN) is constructed. In the model, the PESCS not only meets the charging demands of electric vehicles (EVs) but also supports DN voltage stability through coordinated charging and discharging. To effectively balance multiple objectives under RES uncertainties, a data-driven multi-objective fuzzy aggregation spider wasp optimization algorithm (DD-MOFASWO) incorporating fuzzy logic reasoning is proposed. The algorithm leverages the advantages of data-driven learning from historical data to specifically handle RES uncertainties in MGUs, generating partial dimensions of the initial-stage Pareto solution set. By dynamically maintaining an external archive through fuzzy aggregation crowding distance (FACD) and non-dominated sorting (NDS), the algorithm ensures population uniformity and diversity. Simulation results under four typical scenarios on the IEEE 33-bus system demonstrate that the proposed method significantly outperforms several classical multi-objective evolutionary algorithms in terms of convergence speed, Pareto front quality, and overall system operational performance, effectively validating the superiority of both the model and the algorithm.

考虑可再生能源（RES）在经济性、安全性和运行稳定性方面的不确定性，研究了多微电网系统（MMGS）的多目标优化问题。构建了微网单元（MGU）、光伏-储能-充电站（PESCS）和配电网（DN）的协同参与模型。在该模型中，PESCS不仅满足电动汽车的充电需求，而且通过协调充放电支持DN电压稳定。为了在RES不确定性条件下有效平衡多目标，提出了一种基于模糊逻辑推理的数据驱动多目标模糊聚集黄蜂优化算法（DD-MOFASWO）。该算法利用从历史数据中进行数据驱动学习的优势，专门处理mgu中的RES不确定性，生成初始阶段Pareto解集的部分维度。该算法通过模糊聚集拥挤距离（FACD）和非支配排序（NDS）对外部档案进行动态维护，保证了种群的一致性和多样性。在IEEE 33总线系统四种典型场景下的仿真结果表明，该方法在收敛速度、Pareto前质量和系统整体运行性能方面均明显优于几种经典多目标进化算法，有效验证了该模型和算法的优越性。

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引用次数: 0

Event-triggered neural network prescribed performance control for wave energy conversion system under input saturation 基于事件触发神经网络的波能转换系统输入饱和状态下的预定性能控制

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-11 DOI: 10.1016/j.ref.2026.100810

Shizhan Dong, Zhongqiang Wu

To solve the Maximum power point tracking (MPPT) control problem in wave energy conversion systems (WECS) under input saturation, an event-triggered neural network prescribed performance controller is designed. A structure of a direct-drive WECS with internal parameter changes and external disturbances is built, where these changes and disturbances are treated as lumped uncertainties. The auxiliary system is designed to solve the input saturation. The controller parameters are dynamically adjusted by an event-triggered mechanism to constrain control inputs and save communication resources. The radial basis function neural networks (RBFNN) are employed to approximate model uncertainties and disturbances, enhancing the robustness of the system. An asymmetric prescribed performance function is employed to constrain the state of the system within a prescribed range, ensuring the boundedness of the closed-loop stochastic nonlinear system. Simulation results show that the proposed method successfully realizes MPPT in the WECS under input saturation, internal parameter changes, and external disturbances.

为解决输入饱和情况下波浪能转换系统的最大功率点跟踪控制问题，设计了一种事件触发神经网络规定性能控制器。建立了具有内部参数变化和外部扰动的直驱WECS结构，将这些变化和扰动处理为集总不确定性。辅助系统的设计是为了解决输入饱和问题。通过事件触发机制动态调整控制器参数，约束控制输入，节约通信资源。采用径向基函数神经网络（RBFNN）逼近模型的不确定性和干扰，增强了系统的鲁棒性。采用非对称规定性能函数将系统状态约束在规定范围内，保证了闭环随机非线性系统的有界性。仿真结果表明，该方法在输入饱和、内部参数变化和外部干扰的情况下，成功地实现了wcs的最大功率跟踪。

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引用次数: 0

Control co-design under uncertainty for offshore wind farms: Optimizing grid integration, energy storage, and market participation 海上风电场不确定性下的控制协同设计：优化电网整合、储能和市场参与

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-10 DOI: 10.1016/j.ref.2025.100806

Himanshu Sharma, Wei Wang, Bowen Huang, Buxin She, Thiagarajan Ramachandran

Offshore wind farms (OWFs) are crucial for decarbonization, but their grid integration is often planned using sequential optimization, which leads to suboptimal performance by overlooking resource and market uncertainties. To address this, we propose a novel control co-design framework that simultaneously optimizes OWF design, control, and market participation decisions under uncertainty. Our approach includes optimal sizing of onshore energy storage and considers participation in both energy and primary frequency markets. Applying this framework to five U.S. west-coast OWF locations identified by the Bureau of Ocean Energy Management (BOEM), our results show that the optimized co-design solution increases market revenue by 3.2% while enhancing flexibility against wind resource uncertainties.

海上风电场（owf）对于脱碳至关重要，但它们的电网整合通常是使用顺序优化来规划的，由于忽略了资源和市场的不确定性，导致性能不佳。为了解决这个问题，我们提出了一个新的控制协同设计框架，该框架可以同时优化不确定性下的OWF设计、控制和市场参与决策。我们的方法包括陆上储能的最佳规模，并考虑参与能源和一次频率市场。将该框架应用于美国海洋能源管理局（BOEM）确定的五个美国西海岸OWF地点，我们的结果表明，优化的协同设计解决方案使市场收入增加了3.2%，同时提高了应对风力资源不确定性的灵活性。

引用次数: 0

Multidimensional effects of solar photovoltaics water pumping systems for rural agricultural activities: A system dynamics approach 农村农业活动中太阳能光伏水泵系统的多维效应：系统动力学方法

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-08 DOI: 10.1016/j.ref.2026.100813

Oliva Gonda , Erik Nuyts , Wilbard Kombe , Wim Deferme , Sarah Phoya , Griet Verbeeck

Alternative energy sources, including solar photovoltaics (PV), are fundamental for sustainable rural development, and their application in agricultural and other activities yields multiple effects. Studies indicate the numerous benefits of rural energy provision. However, the impact of solar PV water pumping systems (SPVWPS) on agricultural activities in rural communities is scarcely analysed. Building on scholars’ positive arguments on SPVWPS, this study answers the following questions: How do SPVWPS in agriculture relate to sustainable positive outcomes in rural communities? What are the challenges associated with solarisation in rural agricultural activities? A mixed-methods approach was employed, using questionnaire surveys among farmers and in-depth interviews with government officials and SPV developers in rural areas of the Iringa, Dodoma and Manyara regions in Mainland Tanzania. Causal loop diagrams (CLDs) were used to illustrate the dynamic multiple cycles of socioeconomic effects of the SPVWPS in rural areas. The results indicate that SPVWPS in agriculture increased agricultural productivity, which also positively impacts the well-being of rural communities. Therefore, efforts to energise rural communities should focus more on agricultural activities, which are the main economic drivers that enhance these communities’ social, economic and environmental well-being. Moreover, applying CLDs demonstrates the non-linear, dynamic and critical relationships that policymakers and solar PV developers should consider to enhance rural socioeconomic development.

替代能源，包括太阳能光电，是可持续农村发展的基础，它们在农业和其他活动中的应用产生多重效果。研究表明，农村能源供应有许多好处。然而，太阳能光伏水泵系统（SPVWPS）对农村社区农业活动的影响几乎没有得到分析。基于学者们对SPVWPS的积极论证，本研究回答了以下问题：农业中的SPVWPS如何与农村社区的可持续积极成果相关？在农村农业活动中与太阳能相关的挑战是什么？本研究采用混合方法，对坦桑尼亚大陆伊林加、多多马和曼亚拉地区农村地区的农民进行问卷调查，并对政府官员和特殊目的企业开发商进行深入访谈。利用因果循环图（CLDs）分析了农村特殊农业生产项目社会经济效应的动态多周期。研究结果表明，农业中的SPVWPS提高了农业生产力，这也对农村社区的福祉产生了积极影响。因此，振兴农村社区的努力应更多地侧重于农业活动，因为农业活动是增强这些社区社会、经济和环境福祉的主要经济驱动力。此外，cld的应用表明，决策者和太阳能光伏开发商应该考虑非线性、动态和关键的关系，以促进农村社会经济发展。

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引用次数: 0

Advanced decentralized control framework for voltage stability and proportional power sharing in hybrid AC-DC microgrid 交直流混合微电网电压稳定与比例功率共享的先进分散控制框架

IF 5.9 Q2 ENERGY & FUELS

Renewable Energy Focus

Pub Date : 2026-01-07 DOI: 10.1016/j.ref.2026.100809

Atul S. Dahane, Rajesh B. Sharma

A massive integration of hybrid AC-DC microgrids into modern power systems has paved the way for adequate robust decentralized control strategies. Proposed advanced decentralized control framework for hybrid AC-DC microgrids presents a solution to highly significant system voltage stability and proportional power sharing issues. The architecture combines Drop Control with Virtual Impedance, Model Predictive Control (MPC), Adaptive Droop-Based Power Sharing, and commonly Event-Triggered Control with Distributed Consensus Algorithm (DCA) for power dispatching in decentralized controllers. The proposed system operates very well and automatically adapts itself under dynamic circumstances for the distributed coordination without relying on a centralized architecture. The MPC model predicts a cost-optimized horizon for minimizing voltage deviation, while adaptive droop adjusts coefficients in real-time according to availability and load demand. Further, iterated local exchanges allow DCA to deliver distributed coordination, complementing the event-triggered logic that permits reductions by over 40% in updates for continuous-time methods. Simulation studies using a hybrid IEEE 39-bus system reveal that voltage deviation would always remain within ±1.2%, while accuracy in power sharing would remain above 97%, with response times under 32 ms, and control convergence within 50 ms. When benchmarked against existing methods of comparison, these performance parameters are still at least 20% better, which indicates strong improvements in scalability, efficiency, and responsiveness. Thus, the proposed framework holds the promise of becoming a credible response towards the next generation of adaptive and intelligent microgrids.

混合交直流微电网大规模集成到现代电力系统中，为适当的鲁棒分散控制策略铺平了道路。提出了一种先进的交直流混合微电网分散控制框架，解决了系统电压稳定性和比例功率共享问题。该体系结构结合了基于虚拟阻抗的下降控制、模型预测控制（MPC）、基于自适应下降的电力共享，以及基于分布式一致性算法（DCA）的事件触发控制，用于分散控制器的电力调度。该系统运行良好，能够在动态环境下自动适应分布式协调，不依赖于集中式体系结构。MPC模型预测了最小化电压偏差的成本优化水平，而自适应下垂则根据可用性和负载需求实时调整系数。此外，迭代的本地交换允许DCA提供分布式协调，补充事件触发逻辑，允许连续时间方法的更新减少40%以上。采用混合IEEE 39总线系统的仿真研究表明，电压偏差始终保持在±1.2%以内，功率共享精度保持在97%以上，响应时间低于32 ms，控制收敛在50 ms以内。根据现有的比较方法进行基准测试时，这些性能参数仍然至少提高20%，这表明在可伸缩性、效率和响应性方面有了很大的改进。因此，提出的框架有望成为对下一代自适应智能微电网的可靠响应。

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引用次数: 0