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 7 publications

(6 citation statements)

References 38 publications

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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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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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“…The cost is the product of the power received from the network and the price of energy [23]. Equations ( 2)-( 6) pertain to power flow Equations [24][25][26][27][28], respectively, denoting the active and reactive power balances in each bus, the active and reactive power flow in each line, and the voltage angle of the reference bus. PG and QG represent the active and reactive power of the upstream network injected into the medium voltage (MV) distribution network.…”

Section: Stochastic Scheduling Of Ress In Sdn 21 Power Management Of ...mentioning

confidence: 99%

Active and Reactive Power Management in the Smart Distribution Network Enriched with Wind Turbines and Photovoltaic Systems

et al. 2022

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The penetration of renewable energy sources has been intensified during the last decade to tackle the climate crisis by providing clean energy. Among various renewable energy technologies, wind turbines and photovoltaic systems have received increasing attention from investors. Generally, electronic power converters are used to control renewable generations. The present study discusses the power management of smart distribution networks enriched with wind and photovoltaic units. The model aims to minimize the expected network operating cost of the system formulated as an objective function regarding AC optimal power flow constraints. In addition, stochastic programming based on unscented transformation is adopted to model the probable behavior of loads, renewable generations, and energy market prices. The model employs a linear approximation model to burden the complexity of the problem and achieve the optimum solution. The problem is tested to a 33-bus system using the General Algebraic Modeling System (GAMS). The obtained results confirm the proposed model’s potential in reducing energy costs, power losses, and voltage deviations compared to conventional power flow studies. In the proposed scheme compared to network load distribution studies, the active and reactive power losses, network energy costs, and voltage deviations are improved by about 40.7%, 33%, 36%, and 74.7%, respectively.

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

Cited by 7 publications

References 38 publications

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

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

Active and Reactive Power Management in the Smart Distribution Network Enriched with Wind Turbines and Photovoltaic Systems

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

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