Voltage Stability Constrained Optimal Power Flow for Unbalanced Distribution System Based on Semidefinite Programming

Lin, Yanling; Zhang, Xiaohu; Wang, Jianhui; Shi, Dongquan; Bian, Desong

doi:10.35833/mpce.2021.000220

Cited by 10 publications

(5 citation statements)

References 48 publications

(76 reference statements)

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“…Furthermore, second-order cone programming (SOCP) facilitates effective solutions for problems involving linear and quadratic constraints [30]. New studies, like [31], look into semidefinite programming (SDP), and the work in [32] uses convexification techniques. Likewise, exponential programming (EXP), which models NOPT objectives and constraints through exponential functions [33].…”

Section: Related Workmentioning

confidence: 99%

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A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

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Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as non-convex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Besides, it uses physics principles to build the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, RPINN’s robustness against noisy input measurements makes it particularly valuable in environments with fluctuating information. Additionally, RPINN’s ANN-based foundation offers significant flexibility and scalability.

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Section: Related Workmentioning

confidence: 99%

“…Yet, these classical methods face challenges such as scalability, computation time, convergence, and practical precision, underscoring their inherent complexity and limitations. Furthermore, the use of relaxations or approximations affects the optimization accuracy [35].…”

Section: Related Workmentioning

confidence: 99%

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

View full text Add to dashboard Cite

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“…O NE of the distinctive features of modern power systems is the growing penetration of renewable energy sources into power systems, which has the notable impacts on the secure and stable operation of power systems [1]. It has been reported that power systems tend to operate near the stability boundary due to the increasing load demand and the intense uncertainty driven by the volatility and randomness of renewable energy sources [2], [3]. Coupled with that comes from loads, the uncertainties from both renewable energy sources and loads increase the complexities of the analysis and control of power systems.…”

Section: Introductionmentioning

confidence: 99%

Day-ahead Voltage-stability-constrained Network Topology Optimization with Uncertainties

Guo,

Wang,

Jiao

et al. 2024

Journal of Modern Power Systems and Clean Energy

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A day-ahead voltage-stability-constrained network topology optimization (DVNTO) problem is proposed to find the day-ahead topology schemes with the minimum number of operations (including line switching and bus-bar splitting) while ensuring the sufficient hourly voltage stability margin and the engineering operation requirement of power systems. The AC continuation power flow and the uncertainty from both renewable energy sources and loads are incorporated into the formulation. The proposed DVNTO problem is a stochastic, largescale, nonlinear integer programming problem. To solve it tractably, a tailored three-stage solution methodology, including a scenario generation and reduction stage, a dynamic period partition stage, and a topology identification stage, is presented. First, to address the challenges posed by uncertainties, a novel problem-specified scenario reduction process is proposed to obtain the representative scenarios. Then, to obtain the minimum number of necessary operations to alter the network topologies for the next 24-hour horizon, a dynamic period partition strategy is presented to partition the hours into several periods according to the hourly voltage information based on the voltage stability problem. Finally, a topology identification stage is performed to identify the final network topology scheme. The effectiveness and robustness of the proposed three-stage solution methodology under different loading conditions and the effectiveness of the proposed partition strategy are evaluated on the IEEE 118-bus and 3120-bus power systems.

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“…Her iki durumda da, güç sistemlerinde gerilim kararlılığı problemi ortaya çıkabilmektedir. Güç sistemlerinde gerilim kararlılığı, bara gerilim değerlerinin tüm koşullar altında (herhangi bir bozucu etki olması durumunda bile) belirlenen sınır değerleri arasında kalabilmesi olarak ifade edilmektedir [2].…”

Section: Giriş (Introduction)unclassified

Güç sistemlerinde FACTS cihazları ve dağıtık üretim kaynakları kullanılarak gerilim kararlılığının ve yükleme kapasitesinin iyileştirilmesi

Uzun,

Pamuk,

Taskın

2024

GUMMFD

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Gerilim kararlılığı, güç sistemlerinde incelenmesi gereken önemli bir konudur. Sürekli artan elektrik enerjisi talebi, artan talebi karşılamak için yeni elektrik üretim santrallerinin kurulumu ve güç sistem arızaları gerilim kararlılığını olumsuz yönde etkilemektedir. Günümüzde gerilim kararlılığını iyileştirmek için güç sistemlerinde Esnek Alternatif Akım İletim Sistemleri (FACTS cihazları) kullanılmakta olup, ayrıca enerji talebini karşılamak ve gerilim kararlılığını iyileştirmek için güç sistemlerine Dağıtık Üretim Kaynakları (DGs) entegre edilmektedir. Bu çalışmada, IEEE 30 baralı güç sistemi ile Kuzeybatı Anadolu (KBA) güç sistemi, gerilim kararlılığı bakımından temel (nominal), aşırı yüklenme ve hat kopması durumlarında incelenmiştir. İncelenen güç sistemlerine FACTS cihazlarından Statik Var Kompanzatör (SVC) ve Tristör Kontrollü Seri Kapasitör (TCSC), DGs’lerden ise Yakıt Hücresi (YH), Güneş Enerji Santrali (GES) ve Rüzgâr Enerji Santrali (RES) ayrı ayrı ve birlikte bağlanmıştır. Güç sistemlerinin bara gerilim değerlerinde ve yüklenme parametresinde meydana gelen değişimler analiz edilmiştir.

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Voltage Stability Constrained Optimal Power Flow for Unbalanced Distribution System Based on Semidefinite Programming

Cited by 10 publications

References 48 publications

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Day-ahead Voltage-stability-constrained Network Topology Optimization with Uncertainties

Güç sistemlerinde FACTS cihazları ve dağıtık üretim kaynakları kullanılarak gerilim kararlılığının ve yükleme kapasitesinin iyileştirilmesi

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