Sustainable Energy Grids & Networks最新文献_第2页

Multi-agent double time scale two critic deep reinforcement learning for voltage control in active distribution systems 基于多智能体双时间尺度双临界深度强化学习的有源配电系统电压控制

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.segan.2025.102077

Hafiz Mehboob Riaz, Malik Intisar Ali Sajjad

Active distribution systems (ADS) encounter significant challenges from severe voltage violations and increased power losses, driven by load variations and the intermittency of distributed and renewable energy sources (DRES). Such voltage violations can be mitigated by coordinating slow and fast voltage regulating devices on their respective time scales, considering their operational characteristics and response time. To address this, a multi-agent double time scale two-critic deep reinforcement learning (MA-DTTC-DRL) approach is proposed in this paper to meet the two objectives of volt/VAR control (VVC)—minimizing voltage violations and reducing power losses in ADS. The proposed method employs a multi-agent distributed control scheme by dividing the distribution network into sub-areas. Rather than combining two VVC objectives into a single critic per agent, this approach uses two centralized critics shared among all the agents, thereby reducing the learning complexity of DRL. The optimal set points of continuous agents including inverter-based distributed generators (IBDGs), and static VAR compensators (SVCs) are adjusted using the deep deterministic policy gradient (DDPG) method, while discrete actions of the capacitor agents are generated using reparameterization with Gumbel SoftMax distribution. The proposed method leverages centralized learning with decentralized execution to jointly manage continuous and discrete actions, enabling the coordinated control of various devices on the double time scale. The proposed method is validated on the modified IEEE 33-bus, 69-bus and 118-bus systems against two DRL methods, namely DDPG and soft actor-critic (SAC). Simulation results demonstrate that the proposed approach not only achieves enhanced voltage regulation and lower power losses but also exhibits faster convergence and improved learning stability compared to baseline DRL methods. Moreover, the centralized critic architecture offers substantial computational advantages, making it suitable for practical implementation in ADS.

由于负载变化以及分布式和可再生能源（DRES）的间歇性，主动配电系统（ADS）面临着严重的电压违规和电力损耗增加的重大挑战。考虑到它们的运行特性和响应时间，可以通过在各自的时间尺度上协调慢速和快速电压调节装置来减轻这种电压违规。为了解决这个问题，本文提出了一种多智能体双时间尺度双临界深度强化学习（MA-DTTC-DRL）方法，以满足电压/VAR控制（VVC）的两个目标——最小化电压违规和减少ADS中的功率损耗。该方法采用多智能体分布式控制方案，将配电网划分为子区域。这种方法不是将两个VVC目标合并为每个智能体的单个评论，而是在所有智能体之间共享两个集中的评论，从而降低了DRL的学习复杂性。采用深度确定性策略梯度（DDPG）方法对基于逆变器的分布式发电机（ibdg）和静态无功补偿器（SVCs）等连续型智能体的最优设定点进行调整，采用Gumbel SoftMax分布的重新参数化方法对电容型智能体的离散行为进行生成。该方法利用集中学习和分散执行的方法，对连续和离散动作进行联合管理，实现双时间尺度下各种设备的协调控制。在改进的IEEE 33总线、69总线和118总线系统上，采用DDPG和软actor-critic （SAC）两种DRL方法对该方法进行了验证。仿真结果表明，与基线DRL方法相比，该方法不仅实现了更强的电压调节和更低的功率损耗，而且具有更快的收敛速度和更好的学习稳定性。此外，集中式批评体系结构提供了大量的计算优势，使其适合在ADS中的实际实现。

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引用次数: 0

Low-carbon and robust economic scheduling of virtual power plants considering multiple uncertainties 考虑多重不确定性的虚拟电厂低碳稳健经济调度

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.segan.2025.102104

Yongcan Zhu, Naying Wei, Junjun Kang, Yi Tian

The uncertainties of distributed generation, power load, and electricity price forecasts pose significant challenges for optimal dispatching of load virtual power plants (VPPs). This study addresses these issues by introducing a two-stage robust economic optimization scheduling model based on information gap decision theory (IGDT). Initially, a deterministic VPP objective function is formulated to minimize operating and carbon trading costs while defining constraints for each participating element. Subsequently, a robust VPP scheduling model is developed using IGDT to quantify uncertainties in wind power, solar power, load, and electricity market price predictions. The Karush–Kuhn–Tucker conditions are applied to simplify the optimization model under both risk aversion and risk pursuit behaviors. The effectiveness of the proposed model is validated through a comparative analysis of VPP scheduling results and total costs across different scenarios using real VPP case studies. The results indicate that participation in the carbon trading market led to a reduction in carbon emissions by 17.34 %–23.57 %. The introduction of demand response averaged a 39.18 % reduction in system total costs. The risk pursuit model, considering multiple uncertainties, reduced the total costs by 25.46 %–30.00 %.

分布式发电、电力负荷和电价预测的不确定性对负荷虚拟电厂的优化调度提出了重大挑战。本文通过引入基于信息差距决策理论的两阶段鲁棒经济优化调度模型来解决这些问题。首先，制定确定性VPP目标函数，以最小化运营和碳交易成本，同时定义每个参与元素的约束。随后，利用IGDT建立了稳健的VPP调度模型，量化风电、太阳能、负荷和电力市场价格预测中的不确定性。应用Karush-Kuhn-Tucker条件对风险规避行为和风险追求行为下的优化模型进行简化。通过实际VPP案例，对比分析了不同场景下VPP调度结果和总成本，验证了该模型的有效性。结果表明，参与碳交易市场导致碳排放量减少17.34% ~ 23.57%。需求响应的引入平均降低了系统总成本39.18%。考虑多重不确定性的风险追求模型使总成本降低了25.46% ~ 30.00%。

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引用次数: 0

Dynamic pricing strategies for electric vehicle charging: Enhancing cost-reflectivity and revenue stability 电动汽车充电动态定价策略：提高成本反射性和收益稳定性

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-05 DOI: 10.1016/j.segan.2025.102082

Toni Simolin , Tim Unterluggauer , Mattia Secchi , Francesco Pastorelli , Mattia Marinelli , Pertti Järventausta

Public charging infrastructure is essential for accelerating electric vehicle (EV) adoption. Currently, in Europe, customers are often offered fixed charging prices, while the costs incurred by charging site owners (CSOs) vary significantly due to factors such as electricity prices and power grid tariffs. This paper proposes alternative pricing solutions to improve cost-reflectivity based on an analysis of the current pricing landscape and related scientific literature. Simulations are carried out, using Danish and Finnish charging session data of multiple locations and electricity price databases, to assess the impact of the proposed pricing solutions on CSO revenues and their potential implications for the charging service business model. The findings indicate that dynamic cost-reflective pricing enhances the stability of CSO revenues and allows users to optimise their charging decisions by providing transparency through precise hourly charging costs. Furthermore, the results show that the proposed dynamic pricing schemes provide a competitive economic advantage for the CSO over the competitors using the present pricing schemes. Additionally, the proposed pricing schemes lead to lower charging costs for 53–64 % of the users even if they do not alter their charging behaviour.

公共充电基础设施对于加速电动汽车的普及至关重要。目前，在欧洲，客户通常获得固定的充电价格，而充电站点所有者（cso）所产生的成本由于电价和电网关税等因素而差异很大。本文在分析当前定价格局和相关科学文献的基础上，提出了提高成本反射率的替代定价方案。利用丹麦和芬兰多个地点的充电时段数据和电价数据库进行了模拟，以评估拟议的定价解决方案对CSO收入的影响及其对充电服务商业模式的潜在影响。研究结果表明，动态成本反射定价提高了CSO收入的稳定性，并允许用户通过精确的小时收费成本提供透明度来优化他们的收费决策。此外，结果表明，所提出的动态定价方案为CSO提供了比使用现有定价方案的竞争对手更具竞争力的经济优势。此外，拟议的定价方案导致53-64 %的用户的收费成本降低，即使他们不改变他们的收费行为。

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引用次数: 0

Analysis of the iberian intraday market: Price dynamics, market participation, and balancing challenges 伊比利亚日内市场分析：价格动态、市场参与和平衡挑战

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.segan.2025.102072

Santiago Maiz , Raquel García-Bertrand , Luis Baringo , Tarek Alskaif

This paper presents an in-depth analysis of the intraday (ID) market within the Iberian electricity market. The study examines price dynamics and the participation of market agents across multiple trading sessions, including both the auction-based intraday (IDA) sessions and the continuous intraday (IDC) market. Additionally, it explores the intricacies of the balancing market, particularly in terms of managing untraded energy from various stages, including the day-ahead (DA) market, the IDA sessions, and the IDC market. Special attention is given to the recent reform of the discrete ID market, which transitioned from six daily sessions to three, as part of its integration into the European single intraday coupling (SIDC) framework. The work also investigates the evolution of price volatility as the delivery hour approaches, and studies market liquidity through two key indicators: the number of matched agents and the traded energy volume in each session. Overall, this research highlights the evolving structure and challenges of the Iberian ID electricity market, offering valuable insights for market participants and policymakers. The results contribute to a better understanding of how the ID market supports vRES integration and short-term system flexibility under increasing uncertainty.

本文对伊比利亚电力市场的日内（ID）市场进行了深入分析。该研究考察了多个交易时段的价格动态和市场代理的参与，包括基于拍卖的日内（IDA）交易和连续日内（IDC）市场。此外，它还探讨了平衡市场的复杂性，特别是在管理不同阶段的未交易能源方面，包括前一天（DA）市场、IDA会议和IDC市场。特别关注离散ID市场最近的改革，该市场从每日六个交易日过渡到三个交易日，作为其融入欧洲单一日内耦合（SIDC）框架的一部分。本文还研究了价格波动随交割时间的演变，并通过两个关键指标研究了市场流动性：匹配代理数量和每一时段的能源交易量。总体而言，本研究强调了伊比利亚ID电力市场不断变化的结构和挑战，为市场参与者和政策制定者提供了有价值的见解。研究结果有助于更好地理解在不确定性增加的情况下，ID市场如何支持vRES集成和短期系统灵活性。

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引用次数: 0

Low voltage topology mapping through network discovery events applied to AI-based digital twins 基于网络发现事件的低压拓扑映射应用于基于人工智能的数字孪生

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.segan.2026.102129

Mesfin Fanuel , Bassam Mohamed , Xavier Dominguez , Pablo Arboleya

Accurate knowledge of Low-Voltage (LV) distribution topology is critical for reliable operation, advanced monitoring and large-scale integration of distributed energy resources (DERs). In practice, topology records in GIS/NIS are frequently incomplete or outdated, while field verification remains costly. This paper presents a smart-meter (SM)–driven methodology for LV topology mapping that combines a data-trained surrogate model with physics-inspired sensitivity analysis. A feedforward Deep Neural Network (DNN), trained on historical SM measurements spanning diverse operating conditions (including DER-driven net generation), is used as a model-free digital twin to emulate customer-to-voltage relationships. Virtual Network Discovery Events (NDEs) are then generated by applying controlled perturbations within the surrogate to obtain voltage-response signatures that support topology inference without physical intervention. Phase groups are identified through dimensionality reduction and hierarchical clustering, and customer connectivity is inferred from the similarity structure of the resulting voltage-response signatures. The method is applied independently per feeder, enabling scalable execution across multi-feeder LV networks. Validation on six real feeders from an urban Spanish network demonstrates accurate voltage emulation and high-fidelity phase and topology reconstruction using only existing SM infrastructure.

准确的低压配电拓扑知识对于分布式能源的可靠运行、高级监测和大规模集成至关重要。在实践中，GIS/NIS中的拓扑记录经常是不完整或过时的，而现场验证仍然是昂贵的。本文提出了一种智能电表（SM）驱动的LV拓扑映射方法，该方法将数据训练的代理模型与物理启发的灵敏度分析相结合。前馈深度神经网络（DNN）经过不同运行条件（包括der驱动的网络生成）的SM历史测量训练，用作无模型数字双胞胎来模拟客户与电压的关系。然后，通过在代理内应用受控扰动来生成虚拟网络发现事件（nde），以获得支持拓扑推理而无需物理干预的电压响应签名。相位组通过降维和分层聚类来识别，客户连通性是从所得电压响应特征的相似结构中推断出来的。该方法可独立应用于每个馈线，从而实现跨多馈线LV网络的可扩展执行。对来自西班牙城市网络的六个真实馈线的验证表明，仅使用现有的SM基础设施就可以实现精确的电压仿真和高保真相位和拓扑重建。

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引用次数: 0

Short-term optimal scheduling of wind-solar-hydro-storage systems under extreme heat scenarios with uncertainty consideration 考虑不确定性的极端高温条件下的风能-太阳能-蓄能系统短期优化调度

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-08 DOI: 10.1016/j.segan.2025.102096

Mingyue Zhang , Yang Han , Te Zhou , Yongchao Sun , Huaiyu Zhang , Congling Wang , Fan Yang

Extreme heat events threaten power system reliability by reducing hydropower output and intensifying load peaks. This study proposes a short-term scheduling framework for wind-solar-hydro-storage systems under such conditions. A hybrid forecasting model integrating bidirectional temporal convolutional networks (BiTCN), bidirectional long short-term memory (BiLSTM) with attention mechanism, and quantile regression forest (QRF) is developed to jointly predict wind speed, solar irradiance, and power load, thereby providing probabilistic scenarios. Based on these forecasts, a two-timescale scheduling framework is established, where the day-ahead stage employs an ε-constraint multi-objective programming approach to balance hydropower regulation, renewable energy absorption, and output smoothness, while the intraday stage adopts a rolling chance-constrained model updated every 15 min. To enhance climate adaptability, two adaptive modules are incorporated: an ε-bound feedback mechanism based on plan deviations and a thermal correction model utilizing the human comfort index to adjust temperature-sensitive outputs. A case study conducted on the Xiluodu Hydropower Station in Sichuan Province, China, under the extreme heat conditions of summer 2022 validates the effectiveness of the proposed framework. Tested on the highly fluctuating wind-speed dataset, the proposed BiTCN-BiLSTM-AM model achieves an R² of 0.930, representing improvements of 0.032 and 0.039 over the TCN-LSTM-AM and Transformer models, respectively. In terms of dispatch performance, compared with no-storage and static-dispatch strategies, renewable utilization increases from 92.023 % and 93.692–100 %, with total generation gains of 102.489 MW and 117.101 MW. These results demonstrate that the proposed approach enables robust, adaptive, and climate-resilient scheduling for clean-energy-dominated power grids.

极端高温事件通过降低发电量、加剧负荷峰值等方式威胁着电力系统的可靠性。本研究提出了在此条件下的风能-太阳能-水力蓄能系统的短期调度框架。建立了双向时间卷积网络（BiTCN）、具有注意机制的双向长短期记忆（BiLSTM）和分位数回归森林（QRF）相结合的混合预测模型，联合预测风速、太阳辐照度和电力负荷，从而提供概率情景。在此基础上，建立了双时间尺度调度框架，其中日前阶段采用ε约束多目标规划方法平衡水电调节、可再生能源吸收和输出平滑性，日内阶段采用滚动机会约束模型，每15 min更新一次。为了提高气候适应能力，系统采用了两个自适应模块：基于平面偏差的ε界反馈机制和利用人体舒适度调节温度敏感输出的热校正模型。以2022年夏季极端高温条件下的中国四川省溪洛渡水电站为例，验证了该框架的有效性。在高波动风速数据集上进行测试，所提出的BiTCN-BiLSTM-AM模型的R2为0.930，比TCN-LSTM-AM和Transformer模型分别提高0.032和0.039。在调度性能方面，与无存储和静态调度策略相比，可再生能源利用率分别提高了92.023 %和93.692-100 %，发电总增量分别为102.489 MW和117.101 MW。这些结果表明，所提出的方法能够实现以清洁能源为主的电网的鲁棒性、适应性和气候适应性调度。

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引用次数: 0

Optimal capacity planning for grid‐connected power‐to‐hydrogen integrated energy system considering dynamic hydrogen production efficiency 考虑动态制氢效率的并网电氢一体化能源系统最优容量规划

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.segan.2026.102120

Jizhe Dong , Chongshan Xu , Meng Zhu , Yanbin Zhang , Zehao Zhao , Ziyang Hao , Shunjie Han

With the rapid expansion of hydrogen energy applications, the demand for high-precision modeling of hydrogen production systems has become increasingly urgent. In capacity planning for integrated energy systems (IESs), neglecting dynamic hydrogen production efficiency (DHPE) leads to planning results that deviate from the actual performance and impair the resource allocation. This paper proposes a capacity planning model for power-to-hydrogen IES that accounts for DHPE by incorporating the nonlinear relationship between the input power of an electrolyzer and its production efficiency. Additionally, a solution method is presented to address the problems of the model being unsolvable and having slow solution speed. Case studies, based on real operational and publicly available data, demonstrate that the DHPE model generates more reasonable planning solutions than the static hydrogen production efficiency model, and the operational levelized cost of hydrogen is reduced by approximately 0.3 %–3.9 %, while the renewable energy self-consumption increases by approximately 2.5 %–6.5 %.

随着氢能应用的迅速扩大，对制氢系统高精度建模的需求日益迫切。在综合能源系统容量规划中，忽略动态制氢效率会导致规划结果偏离实际性能，影响资源配置。通过考虑电解槽输入功率与其生产效率之间的非线性关系，提出了考虑DHPE的电制氢系统容量规划模型。此外，针对模型不可解和求解速度慢的问题，提出了一种求解方法。基于实际运行和公开数据的案例研究表明，与静态制氢效率模型相比，DHPE模型产生了更合理的规划方案，氢气的运行平准化成本降低了约0.3 % -3.9 %，而可再生能源自用增加了约2.5 % -6.5 %。

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引用次数: 0

A game-theoretic model for flexibility-constrained renewable energy communities in local energy trading with smart distribution networks 基于智能配电网的可再生能源社区柔性交易博弈模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.segan.2025.102105

Sahar Mobasheri , Masoud Rashidinejad , Amir Abdollahi , Mojgan MollahassaniPour , Sobhan Dorahaki

Renewable Energy Communities (RECs) play a critical role in advancing the energy transition towards a decentralized, distributed, and increasingly digitalized energy system. Through local energy trading within the distribution network, RECs have the potential to significantly enhance the flexibility of the energy system. This interaction, however, introduces complex challenges between REC operators and distribution network operators, necessitating robust analytical approaches. Leveraging Stackelberg game theory, this study models the hierarchical relationship between these entities, positioning the REC operator as the leader and the distribution network operator as the follower. To address the inherent uncertainties in renewable energy resources, Multi-Objective Information Gap Decision Theory (MO-IGDT) is employed, alongside flexibility constraints to ensure stability and efficiency in the system amidst fluctuations in REC output power. A bilevel optimization model, initially formulated as a mixed-integer linear program, is simplified into a single-level problem using Karush-Kuhn-Tucker (KKT) conditions. The findings underscore the benefits of integrating Community Energy Storage (CES) with renewable energy sources within an REC, demonstrating a 3.39 % increase in profits and a significant 51.23 % reduction in dependency on the upstream grid, highlighting the potential of RECs to enhance both economic and operational resilience in modern energy systems.

可再生能源社区（rec）在推动能源向分散、分布式和日益数字化的能源系统过渡方面发挥着关键作用。通过配电网内的本地能源交易，RECs有可能显著提高能源系统的灵活性。然而，这种相互作用在REC运营商和分销网络运营商之间引入了复杂的挑战，需要强大的分析方法。利用Stackelberg博弈论，本研究建立了这些实体之间的等级关系模型，将REC运营商定位为领导者，将配电网运营商定位为追随者。为了解决可再生能源资源固有的不确定性，采用多目标信息缺口决策理论（MO-IGDT），并结合柔性约束来保证系统在REC输出功率波动时的稳定性和效率。利用KKT条件将两层优化模型简化为单层优化问题，该优化模型最初是一个混合整数线性规划。研究结果强调了在REC内将社区能源存储（CES）与可再生能源相结合的好处，表明利润增加3.39% %，对上游电网的依赖显著减少51.23 %，突出了REC在提高现代能源系统的经济和运营弹性方面的潜力。

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引用次数: 0

A sequential conic programming algorithm for calculating voltage stability margins 计算电压稳定裕度的顺序二次规划算法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.segan.2025.102108

Long Fu , Gexiang Zhang , Jianping Dong , Gang Wang , Zhao Yang Dong , Yaran Li

With the increasing power demand and major blackout events, power systems are operating under more stressed conditions, approaching their stability limits. Voltage stability margin (VSM) characterizes a measure of distance to the power flow insolvability/infeasibility boundary that needs to be precisely calculated and effectively monitored, yet it can be challenging considering varying operational constraints and loading scenarios. Focusing on static power flow equations in this paper, a novel sequential conic programming (SCP) algorithm is proposed based on linear approximations of non-convex functions for an optimization-based VSM calculation. Compared with existing methods, the performance of proposed SCP is more robust against different operating scenarios where desired features of being initialization-free, exact, scalable, and applicable can be appropriately achieved. Multiple test cases validate the advantages and effectiveness of the proposed approach.

随着电力需求的增加和重大停电事件的发生，电力系统的运行压力越来越大，接近其稳定极限。电压稳定裕度（VSM）是一种距离潮流不可解/不可行的边界的度量，需要精确计算和有效监测，但考虑到不同的运行约束和负载情况，它可能具有挑战性。针对静态潮流方程，提出了一种基于非凸函数线性逼近的序贯二次规划（SCP）优化算法。与现有方法相比，所提出的SCP在不同操作场景下的性能更加健壮，可以适当地实现无初始化、精确、可扩展和适用的期望特性。多个测试用例验证了所提出方法的优点和有效性。

引用次数: 0

Enhancing grid resilience to extreme events: A synergistic framework integrating vegetation dynamics and microgrid capabilities 增强电网对极端事件的弹性：整合植被动态和微电网能力的协同框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-16 DOI: 10.1016/j.segan.2025.102094

Umar Salman , Zongjie Wang

Grid resilience against extreme weather events is critical for utilities and operators. Overhead distribution lines are particularly vulnerable due to secondary damage caused by falling trees or branches during such events. This paper proposes a vegetation dynamics integrated-resilience assessment framework incorporating microgrid capabilities to address these challenges. The methodology introduces a tree failure model that accounts for tree characteristics in assessing pole and line fragility. Grid resilience is evaluated under four extreme event scenarios, superstorms, hurricanes, earthquakes, and ice storms, considering both islanded and microgrid-operating conditions. Simulation case studies on an IEEE 69-node radial distribution system, performed using Monte Carlo simulations, have demonstrated the effectiveness of the vegetation dynamics integrated-resilience assessment framework in integrating vegetation dynamics for comprehensive vulnerability assessments of power systems. Across cases and events, distributed generation reduced EDNS by 60 %–100 % and LOLP by 60 %–95 %, with the largest gains in hurricane/earthquake conditions, underscoring the importance of DG siting relative to event centers and network bottlenecks. This approach provides practical insights for mitigating disruptions in power distribution systems caused by extreme events.

电网抵御极端天气事件的弹性对公用事业和运营商至关重要。在这种情况下，由于倒下的树木或树枝造成的二次损坏，架空配电线路特别脆弱。本文提出了一个结合微电网能力的植被动态综合恢复力评估框架来应对这些挑战。该方法引入了一个树木失效模型，该模型在评估杆和线的脆弱性时考虑了树木的特征。电网弹性评估在四种极端事件情景下，超级风暴、飓风、地震和冰暴，同时考虑孤岛和微电网的运行条件。通过对IEEE 69节点径向配电系统的蒙特卡罗模拟，验证了植被动态-恢复力综合评估框架在整合植被动态进行电力系统综合脆弱性评估方面的有效性。在案例和事件中，分布式发电将EDNS降低了60% - 100%，将LOLP降低了60% - 95%，在飓风/地震条件下收益最大，强调了DG选址相对于事件中心和网络瓶颈的重要性。这种方法为减轻极端事件造成的配电系统中断提供了实用的见解。

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引用次数: 0