Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Baziar, Aliasghar; Bo, Rui; Ghotbabadi, Misagh Dehghani; Veisi, Mehdi; Rehman, Waqas ur

doi:10.1109/access.2021.3108763

Cited by 6 publications

(4 citation statements)

References 38 publications

(109 reference statements)

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“…In recent years, due to high renewable energy penetration, the placement of FACTS devices with distributed energy has gain more consideration [63]. A model for optimal placement of FACTS controllers to curtail wind power cost in a highly wind power penetrated market environment was presented by Zhang et al [64].…”

Section: Optimal Sizing and Location/placement Of Facts Controllersmentioning

confidence: 99%

Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook

2022

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With the current transitioning and increasing complexity of power systems owing to the continuous integration of distributed generators (DGs) and Flexible AC Transmission Systems (FACTS), power system quality and security studies have extended to incorporate the impacts of these technologies. This paper presents a review of the operation and reliability impacts of FACTS technologies in improving power quality and security in modern Cyber-Physical Power Systems (CPPS). While introducing DG to the power system helps to decentralize the network for easy accessibility and enhances clean energy system, it creates new challenges such as harmonics, voltage instability, and frequency distortion. These challenges can be tackled with FACTS devices which are flexible and dynamic smart electronic controllers used to stabilize power system parameters to improve power quality and reliability. This paper examines the current state-of-the-art optimization techniques and artificial intelligence and/or computational techniques for optimal placement and operation of FACTS devices. This review highlights the generational advancement of FACTS technologies and the different objectives of optimal placement and operation of these devices. Moreover, the concept of CPPS is discussed with the potential utilization of distribution-FACTS (D-FACTS) devices for network security. Furthermore, a bibliometric analysis was carried out to show research trend of FACTS utilization. The result presents future trajectories for power utility industries and researchers interested in power system optimization and the application of FACTS technologies in smart power system networks. Some of the significant findings leads to proposed demand-side management for placement of DGs and FACTS technologies as a more strategic optimal system sizing to minimize cost. It was also concluded that future design of FACTS/D-FACTS devices must consider and appreciate interactions with the automated systems of CPPS to enhance effective integration. To this end, design modification of the operational configuration of these devices with sensors for real-time synchronized control and interaction with other CPPS technologies is an area that requires more research attention in the future.

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Section: Optimal Sizing and Location/placement Of Facts Controllersmentioning

confidence: 99%

Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook

2022

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“…The lower-level problem model is described in Equations ( 6)- (14). Equation ( 6) represents the objective function of the problem, which minimizes the annual operating cost (fuel cost) of conventional units [47][48]. We presume that several conventional units are connected to the transmission network, which are managed by the network operator who implements the lower-level problem with the aim of minimizing the operating cost of generating units.…”

Section: Bi-level Formulationmentioning

confidence: 99%

Optimal Planning and Bidding Strategy for Wind Farms in Joint Balancing and Day‐Ahead Energy Markets

Toolabi

Sarani

Rezaie

et al. 2022

IET Renewable Power Gen

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Due to the fact that wind farms have a meagre operating cost, it is expected that their optimum utilization in the transmission network would improve the economic and technical condition of the network. Henceforth, this work aims to allocate wind farms' locations in the transmission network while persuading the farm owners to participate in the balancing and day‐ahead energy markets. The proposed scheme is formulated in the form of a bi‐level optimization model. The upper‐level problem aims to maximize wind farms' profit in joint day‐ahead and balancing markets bounded by the location and operational constraints. On the other hand, the lower‐level problem provides the operating model of the transmission network. Herein, the network operator is in authority to minimize the operational cost of non‐renewable resources, considering the linearized AC power flow and technical constraints of resources. Then, the Karush—Kuhn–Tucker is adopted to extract the proposed scheme single‐level model. One of the eminent novelties of this work is to propose a linear model for wind farms and to introduce an incentive plan for farm owners in the energy market by considering bidding errors. In the end, by implementing this scheme on IEEE 6‐bus and 24‐bus transmission networks, the numerical outcomes indicate the proposed scheme's potential to improve the economic and operational status of wind farms and the transmission network. With linear programming based on optimum utilization of wind farms, the utilization of traditional units, energy prices, energy plans, and the maximum voltage drop are reduced by 35%, 15%, 20%, and 21%, respectively, compared to the cases without wind farms penetrations.

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“…In Baziar et al, 8 shunt FACTS and renewable energy system optimal sizing and placement are addressed in both IEEE 6‐bus and IEEE 118‐bus systems to solve the AC security‐constrained unit commitment problem, employing a hybridized algorithm of teaching learning‐based optimization and grey wolf optimizer and comparing the results obtained by other optimization algorithms. In Baziar et al, 8 the optimal allocation of SVC and TCSC was investigated in IEEE 30‐bus and IEEE 57‐bus systems to solve optimal reactive power dispatch problem, employing a modified algorithm, known as modified lightning attachment procedure, comparing its results with other algorithms. Also, in Khan et al, 9 multiobjective multiverse optimization algorithm was developed to explore the optimal allocation of SVC and TCSC in an IEEE 57‐bus system for voltage profile enhancement and power losses minimization.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

Awad,

Kamel,

Hassan

et al. 2023

Energy Science & Engineering

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The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm “LWaOA,” making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA.

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Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Cited by 6 publications

References 38 publications

Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook

Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook

Optimal Planning and Bidding Strategy for Wind Farms in Joint Balancing and Day‐Ahead Energy Markets

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

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