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A novel industrial load scheduling model to balance scheduling with virtual power plant regulation requirements 一种新的工业负荷调度模型，以平衡调度与虚拟电厂的调节需求

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-25 DOI: 10.1016/j.segan.2025.102109

Sheng Xian Cao, Lin Yue, Gong Wang, Gui Chao Duan, Kun Li, Jun Li, Ying Zhe Kang, Hui Jing Sun

Within virtual power plants (VPPs), large industrial loads function as key controllable loads (CL), and precise load quantification is pivotal to achieving efficient VPP operation. For industrial loads subject to heterogeneous scheduling problem (SP) constraints alongside uncertain renewable outputs, production scheduling and energy use decisions are strongly coupled, and demand response (DR) execution can conflict with pre-established production plans. There is an urgent need to establish a VPP-oriented unified constraint set and feasible region. However, the coordination challenges of production scheduling optimization, economic performance improvement, and flexible participation in VPP operation remain insufficiently addressed. Accordingly, this paper proposes an industrial load scheduling model for virtual power plants (ILS-VPP), an industrial load scheduling model that reconciles factory-level scheduling with VPP-level regulation requirements. First, to address the difficulty of VPP participation under flexible job shop scheduling problem (FJSSP) constraints, we embed FJSSP into the optimization framework and unify the feasible region with participation constraints. Second, to overcome the limited adaptability of DR responses, we design a co-optimization mechanism that integrates production scheduling with DR. Third, to balance economic benefits and completion deadlines under high uncertainty, we develop a three-stage robust optimization (RO) strategy grounded in multi-polyhedral uncertainty sets, chance-constrained programming, and the

ϵ

-constraint method. An improved football team training algorithm (FTTA) is employed to solve the model, enhancing convergence stability and solution-set quality. A case study over a 90-day operating horizon shows that the proposed model improves economic performance by 21.3 %, can supply 11,003 kWh of energy to the VPP, achieves a load flexibility index (LFI) of 79.8 %, and increases the load factor (LF) from 0.708 to 0.807.

在虚拟电厂（VPP）中，大型工业负荷是关键可控负荷（CL），而精确的负荷量化是实现VPP高效运行的关键。对于受异构调度问题（SP）约束以及不确定可再生输出约束的工业负荷，生产调度和能源使用决策是强耦合的，并且需求响应（DR）的执行可能与预先建立的生产计划相冲突。迫切需要建立面向vpp的统一约束集和可行域。然而，在VPP操作中，优化生产调度、提高经济效益和灵活参与的协调挑战仍然没有得到充分解决。在此基础上，提出了虚拟电厂工业负荷调度模型（ILS-VPP），这是一种协调工厂级调度和vpp级调节需求的工业负荷调度模型。首先，针对柔性作业车间调度问题（FJSSP）约束下VPP参与困难的问题，将FJSSP嵌入到优化框架中，统一了具有参与约束的可行域；其次，为了克服生产调度响应的有限适应性，设计了一种集成生产调度和生产调度的协同优化机制。第三，为了平衡高不确定性下的经济效益和完工期限，我们开发了基于多多面体不确定性集、机会约束规划和ϵ-constraint方法的三阶段鲁棒优化（RO）策略。采用改进的足球队训练算法（FTTA）对模型进行求解，提高了收敛稳定性和解集质量。90天运行周期的实例研究表明，该模型提高了21.3%的经济效益，可为VPP提供11,003千瓦时的能源，实现了79.8%的负荷灵活性指数（LFI），并将负荷系数（LF）从0.708提高到0.807。

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引用次数: 0

Capacity allocation of pumped hydro storage under marketization process: A transitional strategy 市场化进程下抽水蓄能容量配置：一种过渡性策略

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-25 DOI: 10.1016/j.segan.2025.102107

Yizhou Feng , Zhi Wu , Chen Chen , Liang Ma , Wei Gu , Suyang Zhou

To address the challenges posed by renewable energy integration in power systems, China is advancing the development of Pumped Hydro Storage (PHS). However, the rapid growth of PHS installations, coupled with strict regulations and a high reliance on capacity compensation, has led to increasing financial burdens on other utilities. One solution is to reduce PHS’s capacity compensation through its marketization. To this end, a ‘partial-regulated dispatch’ mechanism is proposed as a transitional strategy for gradual marketization. Also, an operational policy analysis framework is proposed based on evaluating dispatch mechanisms and business models. The dispatch mechanism evaluates the capacity support PHS provides to the power system, while the business models focus on enhancing PHS profitability to reduce the dependency on capacity compensation while ensuring long-term economic sustainability. Furthermore, the flexibility of PHS is introduced into the capacity compensation to incentivize PHS to support the power system during transitional stages. This flexibility is mathematically defined using the discrete Minkowski sum, considering both the vibration characteristics of individual units and the unit-commitment of PHS as a whole. The case study shows that through partial-regulated dispatch, PHS can reduce its reliance on capacity compensation by nearly 50 % while ensuring its regulatory service via flexibility compensation. This policy effectively balances economic viability with system support capabilities. Moreover, flexibility compensation provides PHS operators with a risk mitigation strategy in the complex power market environment. Under an appropriate operational strategy and policy incentives, flexibility can be enhanced by nearly 30 % in a fully marketized scenario, thereby contributing to both system stability and operational efficiency.

为了应对可再生能源在电力系统中的整合所带来的挑战，中国正在推进抽水蓄能（PHS）的发展。然而，小灵通安装的快速增长，加上严格的法规和对容量补偿的高度依赖，导致其他公用事业的财务负担不断增加。解决方案之一是通过市场化降低小灵通的容量补偿。为此，建议建立“部分管制调度”机制，作为逐步市场化的过渡战略。同时，提出了基于调度机制和业务模型评估的操作策略分析框架。调度机制评估小灵通为电力系统提供的容量支持，商业模式侧重于提高小灵通的盈利能力，以减少对容量补偿的依赖，同时确保长期的经济可持续性。此外，在容量补偿中引入小灵通的灵活性，以激励小灵通在过渡阶段支持电力系统。这种灵活性在数学上是用离散闵可夫斯基和来定义的，同时考虑了单个单元的振动特性和小灵通作为一个整体的单元承诺。案例研究表明，通过部分调节调度，小灵通在保证灵活性补偿的同时，可将其对容量补偿的依赖程度降低近50%。这一政策有效地平衡了经济可行性和系统支持能力。此外，灵活性补偿为小灵通运营商在复杂的电力市场环境中提供了一种降低风险的策略。在适当的运营战略和政策激励下，在完全市场化的情况下，灵活性可以提高近30%，从而有助于系统稳定性和运营效率。

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引用次数: 0

Supervised learning-driven dead band control of occupant thermostats for energy-efficient residential HVAC 节能住宅暖通空调使用人员恒温器的监督学习驱动死区控制

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-25 DOI: 10.1016/j.segan.2025.102110

Alper Savasci , Oguzhan Ceylan , Sumit Paudyal

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in demand-side management (DSM) by shaping residential electricity consumption and enabling flexible, grid-responsive operation. Thermostats in HVAC systems regulate indoor temperature as part of a closed-loop control framework, typically incorporating a fixed temperature dead band–a range around the setpoint where no action is taken–to reduce energy use and prevent frequent cycling of the HVAC system. Although essential for efficiency and equipment longevity, fixed dead bands limit adaptability, as dynamically adjusting them under varying environmental conditions remains challenging for occupants. To address this limitation, we propose a machine learning (ML)-based dead band tuning framework that optimally adjusts thermostat settings in real time. The method integrates conventional optimization with data-driven modeling: a mixed-integer linear programming (MILP) model is first used to generate optimal dead band values under measured outdoor temperature records (diverse seasonal weather scenarios) which are then employed to train the ML-based predictor to learn a real-time discrete dead band decision policy that approximates the MILP-optimal hysteresis-aware decisions. Among the evaluated models, Random Forest demonstrates superior predictive performance, achieving a mean squared error (MSE) of 0.0399 and a coefficient of determination (

R^{2}

) of 95.75 %.

供暖、通风和空调（HVAC）系统在需求侧管理（DSM）中发挥着至关重要的作用，它塑造了住宅用电量，实现了灵活的电网响应式运行。暖通空调系统中的恒温器作为闭环控制框架的一部分调节室内温度，通常包含一个固定的温度死区-在设定值周围不采取任何行动的范围-以减少能源使用并防止暖通空调系统的频繁循环。虽然对于效率和设备寿命至关重要，但固定死区限制了适应性，因为在不同的环境条件下动态调整它们对使用者来说仍然是一个挑战。为了解决这一限制，我们提出了一个基于机器学习（ML）的死区调优框架，该框架可以实时优化调整恒温器设置。该方法将传统优化与数据驱动建模相结合：首先使用混合整数线性规划（MILP）模型在室外测量温度记录（不同季节天气情景）下生成最优死区值，然后使用该模型训练基于ml的预测器，以学习接近MILP最优迟滞感知决策的实时离散死区决策策略。在评价的模型中，随机森林模型的预测性能较好，均方误差（MSE）为0.0399，决定系数（R2）为95.75%。

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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 : 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

Comparative analysis of machine learning methods for residential net load forecasting of solar-integrated households 机器学习方法在太阳能集成家庭净负荷预测中的比较分析

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-24 DOI: 10.1016/j.segan.2025.102106

Panagiotis Herodotou , Georgios Tziolis , George Makrides , George E. Georghiou

Accurate short-term net load forecasting (STNLF) of residential buildings with increased solar photovoltaic (PV) power penetration is critical for enabling reliable operation and enhancing grid stability. This paper presents a systematic comparative analysis of twelve deep learning and machine learning (ML) models for day-ahead net load forecasting, evaluated using data from a pilot study involving 68 households in Cyprus equipped with grid-connected PV systems. The proposed approach utilized historical, weather, and temporal features derived from the dataset. A rigorous evaluation procedure was followed, including cross-validation, recursive forecasting, and multiple error metrics. Results indicate that the random forest (RF) algorithm exhibited the best performance, with normalized root mean square error of 5.71 %, normalized relative to the range of observed net load values. RF achieved this due to its robustness in capturing non-linear interactions and its ability to handle mixed feature types. In contrast, the gated recurrent unit (GRU) network presented higher adaptability to sudden weather changes, attributed to its sequential learning structure and memory capabilities. The differences in model performance were verified with Diebold-Mariano test, indicating the superiority of recurrent and ensemble models over the simpler baselines. Feature importance analysis showed that lagged net load features were important in all models, but deep learning (DL) models better captured the impact of temporal and weather variables more effectively. The systematic approach for STNLF in PV-integrated residential buildings used in this study extends to the broader field of solar-integrated residential microgrids, promoting adaptable, interpretable models for effective energy management and renewable energy integration.

随着太阳能光伏发电（PV）的普及，住宅建筑的短期净负荷准确预测（STNLF）对于实现可靠运行和提高电网稳定性至关重要。本文对用于日前净负荷预测的12种深度学习和机器学习（ML）模型进行了系统的比较分析，并使用了一项涉及塞浦路斯68个配备并网光伏系统的家庭的试点研究数据进行了评估。该方法利用了数据集中的历史、天气和时间特征。遵循严格的评估程序，包括交叉验证、递归预测和多个误差度量。结果表明，随机森林（RF）算法表现出最好的性能，相对于观测到的净负荷值范围归一化的均方根误差为5.71 %。RF实现这一目标是由于其在捕获非线性相互作用方面的鲁棒性以及处理混合特征类型的能力。相比之下，门控循环单元（GRU）网络由于其顺序学习结构和记忆能力，对突发天气变化具有更高的适应性。通过Diebold-Mariano检验验证了模型性能的差异，表明循环模型和集合模型优于简单基线。特征重要性分析表明，滞后净负荷特征在所有模型中都很重要，但深度学习（DL）模型更有效地捕获了时间和天气变量的影响。本研究中使用的光伏集成住宅建筑STNLF系统方法扩展到太阳能集成住宅微电网的更广泛领域，促进了有效能源管理和可再生能源整合的适应性、可解释模型。

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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 : 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

Development of grid-compliance metric for reliable integration of fast charging stations in power networks 电网快速充电站可靠集成的电网顺应性指标研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-20 DOI: 10.1016/j.segan.2025.102088

Salman Harasis , Irfan Khan , Ahmed Massoud

With the fast deployment of electric bus fleets in public transportation, efficient energy consumption and grid impact have become major concerns and challenges. Moreover, due to the accelerated need for fast charging, low-voltage distribution networks show insufficient hosting capacity for E-transportation systems, which encourages stations to connect to the medium voltage lines. In addition to the voltage level constraints, many factors associated with fast charging affect the grid interaction level and the maximum charging power that can be applied to charge the fleets. Although several studies have analyzed voltage deviations, harmonics, and renewable support individually, there remains a lack of a comprehensive grid-compliance evaluation methodology that can holistically quantify these impacts for large-scale charging stations. Therefore, this paper proposes a reliable grid interaction framework for E-bus fleets and develops a novel grid impact metric to ensure efficient charging power with minimal grid impact in a PV grid-connected system. The measures include voltage profile, charging power, and grid-injected harmonics. This work examines an optimal charging strategy to address fast charging challenges, featuring novel performance indices that quantify the grid impact and PV power generation. The proposed strategy is demonstrated by evaluating the charging station deployed at the IEEE 34-node network under different voltage levels. The proposed IGIM is demonstrated on the IEEE 34-node test feeder, where results show that MV connection significantly outperforms LV in terms of grid hosting capacity, which reduces voltage deviations by more than 50 %. Harmonic analysis reveals that constant-current mode charging up to 80 % SoC complies better with IEEE-519 limits than constant-voltage mode. In addition, PV-assisted charging increases self-consumption by up to 60 %.

随着电动公交车队在公共交通中的快速部署，高效的能源消耗和对电网的影响已经成为主要的问题和挑战。此外，由于对快速充电的需求加快，低压配电网络对电子交通系统的承载能力不足，这鼓励了车站连接到中压线路。除了电压水平的限制外，与快速充电相关的许多因素也会影响电网交互水平和可用于为车队充电的最大充电功率。尽管有几项研究分别分析了电压偏差、谐波和可再生能源支持，但仍然缺乏一种全面的电网合规性评估方法，可以全面量化这些对大型充电站的影响。因此，本文提出了一种可靠的电动公交车队电网交互框架，并开发了一种新的电网影响指标，以确保光伏并网系统中有效的充电功率和最小的电网影响。这些措施包括电压分布、充电功率和电网注入谐波。这项工作研究了一种解决快速充电挑战的最佳充电策略，具有量化电网影响和光伏发电的新型性能指标。通过评估不同电压水平下部署在IEEE 34节点网络中的充电站，验证了所提出的策略。提出的IGIM在IEEE 34节点测试馈线上进行了验证，结果表明，中压连接在电网承载能力方面明显优于低压连接，减少了50%以上的电压偏差 %。谐波分析表明，恒流模式充电高达80 % SoC比恒压模式更符合IEEE-519限制。此外，pv辅助充电增加了高达60% %的自我消耗。

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引用次数: 0

BESS and PV systems application for optimal microgrid operation with frequency security constraints BESS和PV系统在具有频率安全约束的微电网优化运行中的应用

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-19 DOI: 10.1016/j.segan.2025.102103

Mehrdad Bagheri-Sanjareh, Marjan Popov

Battery energy storage systems (BESSs) have been used in AC Microgrids (AMGs) for frequency control (FC) and energy management (EM). AMGs with low inertia might suffer large frequency deviations with high rates without the required reserve power for FC. This paper proposes a linear model for the optimal operation of grid-connected AMGs considering frequency security constraints. BESS and photovoltaic systems both participate in primary FC (PFC) and EM. PVSs can decrease their generation in power surplus conditions. They can release the energy of their DC-link capacitors in power shortage conditions. Through coordinated use of BESS and PVSs, the required BESS power for PFC decreases considerably, which allows the BESS to participate in EM more effectively and hence reduces the AMG operational cost. Frequency simulation studies show that PVSs can considerably assist BESS for PFC. Moreover, the optimization results show that without PVSs' support, load shedding is unavoidable which increases the AMG operation cost significantly. In this regard, deterministic and stochastic optimization show that PVSs' participation in PFC results in 24 % and 24.2 % reduction in the AMG operation cost compared to those when BESS is only used for PFC. Therefore, the PVSs' assist in PFC, even though short, has large impact on the optimal operation of the AMG.

电池储能系统（bess）已在交流微电网（amg）中用于频率控制（FC）和能量管理（EM）。低惯性的amg在没有FC所需的备用功率的情况下，可能会遭受高速率的大频率偏差。本文提出了考虑频率安全约束的并网AMGs优化运行的线性模型。BESS和光伏系统都参与初级FC （PFC）和EM。PFC可以在电力剩余条件下减少其发电量。它们可以在电力短缺的情况下释放直流链路电容器的能量。通过协调使用BESS和pss， PFC所需的BESS功率大大降低，这使得BESS能够更有效地参与EM，从而降低AMG的运行成本。频率仿真研究表明，PVSs对pfc的BESS有很大的辅助作用，优化结果表明，没有PVSs的支持，系统的减载不可避免，大大增加了AMG的运行成本。因此，确定性优化和随机优化表明，与BESS仅用于PFC相比，pfs参与PFC可使AMG运行成本降低24% %和24.2% %。因此，pfs参与PFC的时间虽短，但对AMG的优化运行影响较大。

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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 : 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

A novel bilevel model for service restoration in distribution systems integrating technical constraints and the energy market environment 考虑技术约束和能源市场环境的配电系统服务恢复新二层模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-13 DOI: 10.1016/j.segan.2025.102092

Etiane O.P. Carvalho , Wandry R. Faria , Leonardo H. Macedo , Gregorio Muñoz-Delgado , Javier Contreras , Benvindo R. Pereira Junior , João Bosco A. London Junior

This paper introduces a bilevel programming model for service restoration in distribution systems, integrating private distributed generations (DGs) and market strategies. The upper-level problem minimizes costs associated with unsupplied loads and voltage regulator parameters, while the lower-level problem maximizes the profits of DG owners. By incorporating realistic market-based pricing to incentivize privately owned DGs during contingencies, the model addresses the gap in current literature, where DG ownership and production costs are often overlooked. Validation using a 53-node test system under multiple fault scenarios demonstrates the model’s effectiveness in achieving cost-efficient restoration and providing fair compensation to DG owners. This approach ultimately enhances the resilience and reliability of distribution systems.

本文提出了一种结合私有分布式代（dg）和市场策略的配电系统服务恢复双层规划模型。上层问题最小化与未供电负载和稳压器参数相关的成本，而下层问题最大化DG所有者的利润。通过结合现实的市场定价来激励突发事件中的私有DG，该模型解决了当前文献中的空白，即DG所有权和生产成本经常被忽视。在多个故障场景下使用53节点测试系统验证了该模型在实现成本效益恢复和为DG所有者提供公平补偿方面的有效性。这种方法最终提高了配电系统的弹性和可靠性。

引用次数: 0