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Impact of climate change on electricity production of rooftop photovoltaic system for powering laboratory data 气候变化对屋顶光伏发电系统发电量影响的实验数据
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-17 DOI: 10.1016/j.ijepes.2026.111589
Zoltan Čorba, Bane Popadić, Dragan Milićević, Boris Dumnić, Vladan Krsman
The aim of the study is to analyse the impact of climate change on the operation of the rooftop photovoltaic (PV) power plant with the rated power of 8.0 kW. To analyse the impact of climate change on the operation of a photovoltaic power plant, the most important factors affecting the power plant’s production were investigated; the solar radiation and air temperature, the power degradation and some anomalies of PV modules. The mean annual sum of solar irradiation was 1519.1 kWh/m2 with a growth tendency of 13.3 kWh/m2 per year. The mean value of the temperature is 15.4 °C with an annual growth tendency of 0.15 °C. In order to assess the degradation of PV module power, measurements of the parameters of all PV modules in the power plant were carried out during the summer of 2024 to determine the current state after 13 years of operation. The average annual degradation of power is 0.76 %. The average annual electricity production in the observed period is 11582.7 kWh with an annual growth tendency of 6 kWh/year. Before the construction of the power plant the production was estimated at 11,265 kWh/year using the PVSyst software. Since 2012 when the power plant production has been monitored this software has been improved and the meteorological databases have been updated. Therefore, the power plant production was estimated with newer versions of the software. In all cases the PVGIS meteorological database was used. The input horizontal radiation data show an increasing trend of 1347 kWh/m2 (2012), 1367 kWh/m2 (2019) and 1402 kWh/m2 (2023). Therefore, the annual production estimate increases to 11,531 kWh (2019) and to 11,824 kWh in the simulation in 2023. Despite the degradation of the power of the PV panels, the expected drop in production during long-term operation is not observed. This phenomenon contributes to the further reduction of CO2 emissions.
研究的目的是分析气候变化对额定功率为8.0 kW的屋顶光伏电站运行的影响。为了分析气候变化对光伏电站运行的影响,调查了影响电站生产的最重要因素;太阳辐射和气温,光伏组件的功率退化和一些异常。年平均太阳辐照量为1519.1 kWh/m2,年增长趋势为13.3 kWh/m2。气温平均值为15.4℃,年增长趋势为0.15℃。为了评估光伏组件功率的退化情况,在2024年夏季对电站中所有光伏组件的参数进行了测量,以确定运行13年后的当前状态。年平均功率退化率为0.76%。观测期内年均发电量11582.7 kWh,年增长趋势为6kwh /年。在电厂建设之前,使用PVSyst软件估计产量为11,265千瓦时/年。自2012年开始监测电厂生产以来,该软件得到了改进,气象数据库也得到了更新。因此,发电厂的产量是用较新版本的软件估计的。在所有情况下都使用了PVGIS气象数据库。输入水平辐射数据呈增加趋势,分别为1347 kWh/m2(2012年)、1367 kWh/m2(2019年)和1402 kWh/m2(2023年)。因此,在2023年的模拟中,年产量估计增加到11,531千瓦时(2019年)和11,824千瓦时。尽管光伏板的功率下降,但在长期运行期间没有观察到预期的产量下降。这一现象有助于进一步减少二氧化碳的排放。
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引用次数: 0
A practical method for short-term voltage stability assessment under multiple and large uncertainties in load models and renewable generation 负荷模型和可再生能源发电中多重大不确定因素下短期电压稳定性评估的实用方法
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-17 DOI: 10.1016/j.ijepes.2026.111579
Jae-Kyeong Kim
This paper presents a practical method to assess the impact of multiple uncertainties in dynamic load models and renewable generation on short-term voltage stability (STVS), with seamless integration into diverse analysis environments and common industry workflows. The proposed method targets deterministic worst-case analysis and does not rely on assumed probability distributions; probabilistic uncertainty quantification is considered complementary but is outside the scope of this paper. Building on trajectory sensitivity analysis, the proposed approach extends conventional sensitivity-based indices by introducing a new metric that accounts for parameter variability. The resulting index quantifies the influence of uncertain parameters, and additional descriptive measures are incorporated to support systematic interpretation of uncertainty impacts. Using the index information, the most influential parameter sets associated with worst-impact conditions are identified and evaluated via targeted nonlinear simulations. This workflow mitigates the limitations of sensitivity-based approximations and enables the assessment of extreme scenarios under substantial uncertainties by capturing nonlinear system behavior. Case studies on the IEEE 39-bus system and a large-scale real-world Korean power system demonstrate that the proposed index-based approach supports reliable uncertainty-aware STVS analysis and improves the practicality of stability studies.
本文提出了一种实用的方法来评估动态负荷模型和可再生能源发电中多种不确定性对短期电压稳定性(STVS)的影响,并与各种分析环境和通用行业工作流程无缝集成。该方法以确定性最坏情况分析为目标,不依赖于假设的概率分布;概率不确定性量化被认为是互补的,但超出了本文的范围。在轨迹灵敏度分析的基础上,提出的方法通过引入一个考虑参数可变性的新度量来扩展传统的基于灵敏度的指标。由此产生的指数量化了不确定参数的影响,并纳入了额外的描述性措施,以支持对不确定性影响的系统解释。利用指数信息,通过有针对性的非线性模拟,识别和评估与最坏影响条件相关的最具影响力的参数集。该工作流程减轻了基于灵敏度近似的局限性,并通过捕获非线性系统行为,能够在大量不确定的情况下评估极端情况。对IEEE 39总线系统和大规模韩国电力系统的案例研究表明,所提出的基于索引的方法支持可靠的不确定性感知STVS分析,并提高了稳定性研究的实用性。
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引用次数: 0
Federated reinforcement learning based dual-level voltage regulation for PV-rich distribution grids 基于联邦强化学习的富光伏配电网双电平电压调节
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-17 DOI: 10.1016/j.ijepes.2025.111492
Xiao Liu, Youbo Liu, Yongdong Chen, Zhiyuan Tang, Hongjun Gao, Zhengbo Li
In order to eliminate medium voltage (MV) voltage violations and low voltage (LV) three-phase voltage unbalance in PV-rich dual-level distribution networks simultaneously, a novel federated reinforcement learning (FRL)-based voltage regulation method is proposed. First, voltage regulation is formulated as a Markov Game, and each LV station is constructed as an agent. The rewards of MV-LV control goals are decomposed to hierarchically train agents, enabling simultaneous mitigation of MV voltage violations and LV three-phase voltage unbalance. The federated learning framework is employed on agent training for learning MV-LV voltage regulation policies by interacting with partial real data and policy rewards to achieve better privacy preservation and scalability. Moreover, to enhance robustness against imperfect communication environments, we implement weighted data filling for imputing missing data. Simulation results on MV-LV distribution systems demonstrate the effectiveness and advantages of the proposed method.
为了同时消除富pv双电平配电网中压电压违规和低压三相电压不平衡,提出了一种基于联邦强化学习(FRL)的新型电压调节方法。首先,将电压调节表述为马尔可夫博弈,将每个低压站构建为一个agent。将中低压控制目标的奖励分解为分层训练代理,从而同时缓解中压电压违规和低压三相电压不平衡。将联邦学习框架用于智能体训练,通过与部分真实数据和策略奖励交互,学习中低压电压调节策略,以达到更好的隐私保护和可扩展性。此外,为了增强对不完美通信环境的鲁棒性,我们实现了加权数据填充来输入缺失数据。中低压配电系统的仿真结果验证了该方法的有效性和优越性。
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引用次数: 0
Bionic active power control for multi-area microgrids: A large-scale multiagent deep meta reinforcement learning approach 多区域微电网的仿生有源功率控制:大规模多智能体深度元强化学习方法
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-17 DOI: 10.1016/j.ijepes.2026.111591
Dan Liu , Xia Chen , Jiawen Li
Traditional centralized load frequency control (LFC) is vulnerable to power fluctuations in tie-line power due to the conflicting objectives of multiple area controllers and distributors in an isolated multi-area microgrid. To address these problems, a cuttlefish-like cooperative load frequency control (CC-LFC) method is proposed. This AI method imitates the distributed neural network structure of cuttlefish in that it equates the controllers and power distributors of each area as agents in a multi-area microgrid. In online applications, a joint global optimization decision can be obtained from the grid areas without engaging in extensive intercommunication. In addition, this paper proposes a large-scale counterfactual multiagent deep meta-policy gradient (LSCMA-DMPG), which combines centralized training with decentralized execution in a large-scale learning framework. It employs meta-reinforcement learning to realize multitask collaborative learning, which improves the robustness and quality of the obtained CC-LFC policies. The real-time experiments and simulations for a four-area LFC model of Sansha Island in the China Southern Grid (CSG) demonstrate the superior qualities of the proposed method.
在一个孤立的多区域微电网中,由于多区域控制器和分配器的目标相互冲突,传统的集中式负荷频率控制(LFC)容易受到联络线功率波动的影响。针对这些问题,提出了一种类似墨鱼的协同负荷频率控制(CC-LFC)方法。这种人工智能方法模仿了墨鱼的分布式神经网络结构,将每个区域的控制器和电力分配器等同于多区域微电网中的代理。在在线应用中,无需大量的相互通信,即可从网格区域中获得联合全局优化决策。此外,本文还提出了一种大规模反事实多智能体深度元策略梯度(LSCMA-DMPG),在大规模学习框架中将集中训练与分散执行相结合。采用元强化学习实现多任务协同学习,提高了得到的CC-LFC策略的鲁棒性和质量。通过对中国南方电网三沙岛四区LFC模型的实时实验和仿真,验证了该方法的优越性。
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引用次数: 0
Coordinated scheduling for transmission-distribution-microgrids via chordal-based semidefinite programming 基于弦线半确定规划的输配微电网协调调度
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-16 DOI: 10.1016/j.ijepes.2026.111577
Zhe Chen , Da Lin , Xiaohui Ge , Chouwei Ni , Yuhan Ma , Jiakai Qin
With the accelerating integration of distributed energy resources (DERs) into grids, enhanced bidirectional interactions between distribution and transmission systems have significantly complicated grid coordination. The optimization challenges in coordinated transmission-distribution-microgrid systems are structurally more complex than those in standalone distribution-microgrid or transmission-distribution systems. Numerous non-convex alternating current optimal power flow (ACOPF) constraints make the design of distributed algorithms more challenging. To address this issue, this paper introduces a hierarchical scheduling method for transmission-distribution-microgrids based on chordal sparsity for non-convex OPF optimization problems. First, a coordinated scheduling model for transmission-distribution-microgrids based on ACOPF is established. Subsequently, the chordal sparsity semidefinite relaxation method technique accelerates the solution process, while the proposed polynomial semidefinite programming cuts and chordal sparsity relaxation (PCCSR) model effectively alleviates the difficulties associated with large-scale problems typical of traditional semidefinite program relaxation, enabling rapid solutions. Finally, a nested alternating direction method of multipliers algorithm is applied to achieve a distributed solution for transmission-distribution-microgrids. Case studies demonstrate the PCCSR method not only simplifies the problem-solving process but also typically results in a minimal relaxation gap. Furthermore, it strikes an effective balance between computational time and efficiency, making it particularly beneficial for large-scale grids optimization.
随着分布式能源并网进程的加快,配电网和输电网之间双向交互作用的增强,使电网协调复杂化。输配协调微网系统的优化问题在结构上比单机配微网或输配系统的优化问题更为复杂。大量的非凸交流最优潮流(ACOPF)约束使得分布式算法的设计更具挑战性。针对这一问题,提出了一种基于弦稀疏度的输配微电网分层调度方法,用于求解非凸OPF优化问题。首先,建立了基于ACOPF的输配微电网协调调度模型。随后,弦稀疏半定松弛方法技术加速了求解过程,而所提出的多项式半定规划切割和弦稀疏松弛(PCCSR)模型有效地缓解了传统半定规划松弛典型的大规模问题所带来的困难,实现了快速求解。最后,应用乘法器算法的嵌套交替方向法实现了输配微电网的分布式解。案例研究表明,PCCSR方法不仅简化了解决问题的过程,而且通常会产生最小的松弛间隙。此外,它在计算时间和效率之间取得了有效的平衡,使其特别有利于大规模网格优化。
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引用次数: 0
Multi-stage day-ahead and intra-day resource scheduling and market bidding strategy for integrated PV-ESS-EV station under multiple uncertainties 多不确定因素下PV-ESS-EV综合站多阶段日前日内资源调度及市场竞价策略
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-16 DOI: 10.1016/j.ijepes.2025.111514
Ximu Liu , Yujian Ye , Hongru Wang , Cun Zhang , Hengyu Liu , Zhi Zhang , Xi Zhang , Dezhi Xu , Goran Strbac
Integrated PV-ESS-EV Stations (IPEES) are emerging as significant market players that integrate variable photovoltaic (PV) generation with time-coupled energy storage system (ESS) and the flexible demand of electric vehicles (EVs), participating in day-ahead and intra-day energy and ancillary services markets. However, current studies on charging station operations and bidding often employ short-term or two-stage models, which overlook the temporal correlations in intra-day bidding. In addition, the intricate interactions among various physical energy systems and the diverse energy market bidding options are not comprehensively captured. To fill the above research gaps, this paper proposes a multi-stage joint energy–ancillary market bidding and on-site scheduling model for IPEES, accounting for correlated heterogeneous uncertainties. Firstly, a non-homogeneous Markov process is proposed, combined with a Density-Based Spatial clustering discretization strategy, to uncover the fluctuation patterns of multiple stochastic series, such as PV generation, prices, and aggregated charging flexibilities. Then, a market bidding and operation scheduling optimization model is developed, integrating day-ahead and intra-day markets, as well as energy and ancillary services, while considering all dynamic security constraints of ESS and EV operations. To address the challenging multi-stage mixed-integer stochastic optimization problem, a Markov chain stochastic dual dynamic integer programming algorithm (MC-SDDiP) is employed, which constructs cuts from Monte Carlo resampled Markov trajectories to ensure scalability and applicability. Case studies on a PJM-calibrated IPEES demonstrate high accuracy (0.102% gap) and significant economic benefits from joint participation: downward reserve activation reduces procurement costs — often through opportunistic ESS charging — shifting outcomes from near break-even to positive returns, while extensive upward activation increases intra-day purchases and diminishes profits. The findings provide a practical approach for IPEES to convert PV/ESS/EV flexibility into multi-market revenue under realistic settlement frictions and correlated, time-evolving uncertainties.
综合PV-ESS- ev电站(IPEES)是将可变光伏(PV)发电与时间耦合储能系统(ESS)和电动汽车(ev)的灵活需求相结合的重要市场参与者,参与了日前和日内能源和辅助服务市场。然而,目前对充电站运行和竞价的研究往往采用短期或两阶段模型,忽略了日内竞价的时间相关性。此外,各种物理能源系统之间错综复杂的相互作用和各种能源市场的投标选择没有被全面捕获。为了填补上述研究空白,本文提出了考虑相关异构不确定性的综合电站多阶段联合能源辅助市场竞价和现场调度模型。首先,提出了一种非齐次马尔可夫过程,结合基于密度的空间聚类离散化策略,揭示了光伏发电、电价和聚合充电灵活性等多个随机序列的波动模式;然后,在考虑ESS和电动汽车运行的所有动态安全约束的情况下,建立了整合日前和日内市场、能源和辅助服务的市场竞价和运行调度优化模型。为了解决具有挑战性的多阶段混合整数随机优化问题,采用马尔可夫链随机对偶动态整数规划算法(MC-SDDiP),该算法从蒙特卡罗重采样的马尔可夫轨迹中构造切点,以确保可扩展性和适用性。对pjm校准的IPEES的案例研究表明,联合参与具有很高的准确性(0.102%的差距)和显著的经济效益:向下激活储备可以降低采购成本(通常是通过机会性ESS收费),将结果从接近收支平衡转变为正回报,而广泛的向上激活增加了当天的采购并减少了利润。研究结果为IPEES在现实的结算摩擦和相关的、随时间变化的不确定性下将PV/ESS/EV灵活性转化为多市场收益提供了实用方法。
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引用次数: 0
Energy flow optimization for a hybrid DC-microgrid integrating hydrogen production via a PEM electrolyzer and fuel cell backup 通过PEM电解槽和备用燃料电池集成制氢的混合直流微电网的能量流优化
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1016/j.ijepes.2026.111568
Karim El Mezdi , Abdelmounime El Magri , Ilyass El Myasse , Fouad Giri , Pankaj Kumar
This paper proposes an advanced nonlinear control strategy coupled with energy flow optimization (EFO) for a hybrid DC-microgrid integrating a photovoltaic (PV) generator, a lithium-ion battery energy storage system (BESS), a proton exchange membrane (PEM) electrolyzer for hydrogen production, and a PEM fuel cell for backup power. All subsystems are interconnected, via power electronic converters, to a common DC-bus supplying diverse loads. The proposed control strategy ensures five key objectives: tight DC-bus voltage regulation, optimal power extraction from PV (MPPT/APPT), intelligent battery operation in constant current/voltage (CC/CV) modes, and specified hydrogen production tracking, and secure fuel cell activation under power deficit. An integrated energy-management algorithm dynamically manages power sharing among sources and storage based on renewable availability and battery state-of-charge (SoC). Nonlinear backstepping controllers are designed for all converters (PV-side DC/DC boost, bidirectional BESS DC/DC buck-boost, electrolyzer DC/DC buck, and fuel-cell DC/DC buck) to guarantee stability and fast dynamics. Simulation results across multiple operating scenarios show smooth mode transitions, reduced battery charge/discharge cycling, accurate hydrogen-production tracking, tight DC-bus regulation, and reliable continuity of supply, confirming the effectiveness and robustness of the proposed control and EFO framework.
针对光伏发电系统、锂离子电池储能系统(BESS)、制氢质子交换膜(PEM)电解槽和备用质子交换膜燃料电池组成的混合直流微电网,提出了一种结合能量流优化(EFO)的先进非线性控制策略。所有子系统都通过电力电子转换器连接到提供不同负载的公共直流总线。所提出的控制策略确保了五个关键目标:严格的直流母线电压调节、PV的最佳功率提取(MPPT/APPT)、恒流/电压(CC/CV)模式下的智能电池运行、指定的产氢跟踪,以及功率不足时燃料电池的安全激活。一种基于可再生能源可用性和电池荷电状态(SoC)的集成能源管理算法动态管理电源和存储之间的电力共享。所有变换器(pv侧DC/DC升压、双向BESS DC/DC降压、电解槽DC/DC降压和燃料电池DC/DC降压)均采用非线性反步控制器,以保证稳定性和快速动态。多个运行场景的仿真结果表明,模式转换平稳,电池充放电循环减少,产氢跟踪准确,直流母线调节严密,供电连续性可靠,证实了所提出的控制和EFO框架的有效性和鲁棒性。
{"title":"Energy flow optimization for a hybrid DC-microgrid integrating hydrogen production via a PEM electrolyzer and fuel cell backup","authors":"Karim El Mezdi ,&nbsp;Abdelmounime El Magri ,&nbsp;Ilyass El Myasse ,&nbsp;Fouad Giri ,&nbsp;Pankaj Kumar","doi":"10.1016/j.ijepes.2026.111568","DOIUrl":"10.1016/j.ijepes.2026.111568","url":null,"abstract":"<div><div>This paper proposes an advanced nonlinear control strategy coupled with energy flow optimization (<span><math><mrow><mi>E</mi><mi>F</mi><mi>O</mi></mrow></math></span>) for a hybrid <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-microgrid integrating a photovoltaic (<span><math><mrow><mi>P</mi><mi>V</mi></mrow></math></span>) generator, a lithium-ion battery energy storage system (<span><math><mrow><mi>B</mi><mi>E</mi><mi>S</mi><mi>S</mi></mrow></math></span>), a proton exchange membrane (<span><math><mrow><mi>P</mi><mi>E</mi><mi>M</mi></mrow></math></span>) electrolyzer for hydrogen production, and a <span><math><mrow><mi>P</mi><mi>E</mi><mi>M</mi></mrow></math></span> fuel cell for backup power. All subsystems are interconnected, via power electronic converters, to a common <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-bus supplying diverse loads. The proposed control strategy ensures five key objectives: tight <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-bus voltage regulation, optimal power extraction from <span><math><mrow><mi>P</mi><mi>V</mi></mrow></math></span> (<span><math><mrow><mi>M</mi><mi>P</mi><mi>P</mi><mi>T</mi></mrow></math></span>/<span><math><mrow><mi>A</mi><mi>P</mi><mi>P</mi><mi>T</mi></mrow></math></span>), intelligent battery operation in constant current/voltage (<span><math><mrow><mi>C</mi><mi>C</mi><mo>/</mo><mi>C</mi><mi>V</mi></mrow></math></span>) modes, and specified hydrogen production tracking, and secure fuel cell activation under power deficit. An integrated energy-management algorithm dynamically manages power sharing among sources and storage based on renewable availability and battery state-of-charge (<span><math><mrow><mi>S</mi><mi>o</mi><mi>C</mi></mrow></math></span>). Nonlinear backstepping controllers are designed for all converters (<span><math><mrow><mi>P</mi><mi>V</mi></mrow></math></span>-side DC/DC boost, bidirectional <span><math><mrow><mi>B</mi><mi>E</mi><mi>S</mi><mi>S</mi></mrow></math></span> <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>/<span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span> buck-boost, electrolyzer <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>/<span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span> buck, and fuel-cell <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>/<span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span> buck) to guarantee stability and fast dynamics. Simulation results across multiple operating scenarios show smooth mode transitions, reduced battery charge/discharge cycling, accurate hydrogen-production tracking, tight <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-bus regulation, and reliable continuity of supply, confirming the effectiveness and robustness of the proposed control and <span><math><mrow><mi>E</mi><mi>F</mi><mi>O</mi></mrow></math></span> framework.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111568"},"PeriodicalIF":5.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Software-defined networking-enabled rapid recovery for concurrent cyber–physical faults in cyber–physical power systems 软件定义网络支持的网络物理电力系统并发网络物理故障快速恢复
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1016/j.ijepes.2026.111562
Huibin Jia , Longyue Su , Shuaikang Wang , Shaoyan Li , Jiahe Li
As power systems evolve into deeply integrated cyber–physical power systems (CPPS), isolated system failures are increasingly transforming into becoming coupled faults across both the cyber and physical domains. During the restoration process, focusing solely on a single system, thus neglecting the CPPS’s various interdependencies, can lead to longer recovery times, secondary outages, or frequency violations. In this paper, we proposed a rapid recovery method for concurrent cyber–physical faults based on software-defined networking (SDN). First, the coupling mechanism between the physical and information systems for the grid in question was analyzed to investigate how communication failures constrain the restoration times of the generators and loads. A temporal coordination model for CPPS recovery was then established under the SDN architecture. A staged and iterative recovery strategy comprising “SDN re-routing, manual repair, and dynamic updates was proposed to coordinate the concurrent restoration of both the communication and power systems, thereby reducing any additional outage times during manual operations. Finally, to minimize outage losses, a coordinated optimization model was developed for the power and communication networks, in order to determine the best sequence in which to restore power delivery to the various loads. A commercially available software application was then used to find the most efficient solution. Virtual simulation results of the approach, based on the IEEE 30-bus system, demonstrated that our proposed method reduced outage losses by 9.23% and shortened the average recovery time by 8.6% vs traditional approaches, validating its efficacy for this application.
随着电力系统向网络-物理深度集成电力系统(CPPS)发展,孤立的系统故障越来越多地转变为跨网络和物理域的耦合故障。在恢复过程中,只关注单个系统,从而忽略了CPPS的各种相互依赖性,可能导致更长的恢复时间、二次中断或频率违规。本文提出了一种基于软件定义网络(SDN)的并发网络物理故障快速恢复方法。首先,分析了电网物理系统和信息系统之间的耦合机制,探讨了通信故障如何约束发电机和负载的恢复时间。建立了SDN架构下的CPPS恢复时间协调模型。提出了一种分阶段和迭代的恢复策略,包括“SDN重路由、人工修复和动态更新”,以协调通信和电力系统的并发恢复,从而减少人工操作期间的任何额外停机时间。最后,为了最大限度地减少停电损失,建立了电力和通信网络的协调优化模型,以确定向各种负载恢复电力输送的最佳顺序。然后使用商业上可用的软件应用程序来找到最有效的解决方案。基于IEEE 30总线系统的虚拟仿真结果表明,与传统方法相比,该方法减少了9.23%的停电损失,缩短了8.6%的平均恢复时间,验证了该方法在该应用中的有效性。
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引用次数: 0
Real-time identification and prevention of voltage instability using synchronized phasor measurements 使用同步相量测量实时识别和防止电压不稳定
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1016/j.ijepes.2026.111588
Valéria M. de Souza, Hugo R. de Brito, Kjetil O. Uhlen
This paper presents a novel measurement-based voltage stability indicator suited for real-time monitoring at the transmission level. Building upon a previously defined approach that uses variations in apparent power and impedance, the proposed indicator includes significant modifications; in particular, an adaptive estimation algorithm designed to improve robustness to random variations, measurement noise and short-term dynamics. A mathematical framework is established as basis for the definition of voltage stability regions of operation and their respective thresholds. Moreover, this paper introduces an emergency control scheme based on load shedding as a means to counteract imminent voltage collapse. Dynamic simulations conducted in the IEEE Nordic Test System evaluate the effectiveness of the combined actions of the proposed indicator and emergency control scheme in identifying and preventing voltage instability. Further validation of the novel indicator is carried out through analysis of synchrophasor data pertaining to a real voltage collapse event in the Nordic Grid. The overall results demonstrate that the developed approach constitutes a promising asset for system operators to ensure sufficient voltage stability margins in transmission networks.
本文提出了一种适用于输电级实时监测的基于测量的电压稳定指示器。在先前定义的使用视在功率和阻抗变化的方法的基础上,拟议的指标包括重大修改;特别提出了一种自适应估计算法,以提高对随机变化、测量噪声和短期动态的鲁棒性。建立了一个数学框架,作为确定运行电压稳定区域及其阈值的基础。此外,本文还介绍了一种基于减载的应急控制方案,以抵消即将发生的电压崩溃。在IEEE北欧测试系统中进行的动态模拟评估了所提出的指示器和应急控制方案在识别和防止电压不稳定方面的联合作用的有效性。通过分析与北欧电网实际电压崩溃事件相关的同步量数据,进一步验证了该新指标。总体结果表明,所开发的方法对系统运营商来说是一项有前途的资产,可以确保输电网中有足够的电压稳定裕度。
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引用次数: 0
Cost-estimation-based restoration of active distribution networks considering differentiated customer reliability demands 考虑差异化用户可靠性需求的有源配电网成本估算修复
IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1016/j.ijepes.2026.111570
Han Yan , Jianhua Wang , Chenyu Zhang , Ruihuang Liu , Xiaodong Yuan , Jianfeng Zhao
During the power restoration process in distribution networks, costs and economics are often used as guiding factors for the restoration outcomes. However, the diversity and reliability demands of different customers are also crucial considerations. This paper proposes a cost-estimation-based restoration model with considering the differentiated reliability demands of power customers. The precise estimation of interruption costs across various industrial segments of power customers, as well as the calculation of grid interruption costs and power restoration costs form the cost-estimation-based objective together. Furthermore, an improved radial topology constraint is proposed to achieve the dynamic optimization of micro-grid island numbers in the reconfiguration process. Specially, the reliability demand constraints are proposed in this paper to satisfy the differentiated reliability requirements of power customers, based on the demand analysis of loads according to the sensitivity to reliability indices. The effectiveness and superiority of the proposed model are verified by numerical results of case studies on a modified IEEE-33 node distribution system.
在配电网的电力恢复过程中,成本和经济性往往是影响恢复结果的指导因素。然而,不同客户的多样性和可靠性需求也是至关重要的考虑因素。考虑电力用户的差异化可靠性需求,提出了一种基于成本估算的恢复模型。准确估计电力客户各个工业环节的中断成本,以及电网中断成本和电力恢复成本的计算,共同构成了基于成本估计的目标。在此基础上,提出了一种改进的径向拓扑约束,实现了重构过程中微网岛数的动态优化。在对负荷进行需求分析的基础上,根据可靠性指标的敏感性,提出了满足电力用户差异化可靠性需求的可靠性需求约束。通过对一个改进的IEEE-33节点配电系统的算例分析,验证了该模型的有效性和优越性。
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International Journal of Electrical Power & Energy Systems
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