Reactive Power Compensation in AC Power Systems

Load switches in power systems may cause oscillations in active and reactive power flow. Such oscillations can be damped by synthetic inertia provided by smart inverters providing power from DC sources such as photovoltaic or battery storage. However, AC current provided by inverters is inherently non-sinusoidal, making measurements of active and reactive power subject to harmonic distortion. As a result, transient effects due to load switching can be obscured by harmonic distortion. An RLC circuit serves as a reference load. The oscillation caused by switching in the load presents as a dual-sideband suppressed-carrier signal. The carrier frequency is available via voltage data but the phase is not. Given a group of candidate signals formed from phase voltages, an algorithm based on Costas Loop that can quickly quantify the phase difference between each candidate and carrier (thus identifying the best signal for demodulation) is presented. Algorithm functionality is demonstrated in the presence of inverter-induced distortion.

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Reactive Power Compensation Using Plugged-In Electric Vehicles for an AC Power Grid

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon.2018.8591249 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mohammadshayan Latifi ◽

Reza Sabzehgar ◽

Mohammad Rasouli

Keyword(s):

Electric Vehicles ◽

Reactive Power ◽

Power Grid ◽

Reactive Power Compensation ◽

Ac Power

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Fast Heuristic AC Power Flow Analysis with Data-Driven Enhanced Linearized Model

Energies ◽

10.3390/en13133308 ◽

2020 ◽

Vol 13 (13) ◽

pp. 3308

Author(s):

Xingpeng Li

Keyword(s):

Power Systems ◽

Power System ◽

Power Flow ◽

Flow Model ◽

Reactive Power ◽

Unit Commitment ◽

Flow Analysis ◽

Data Driven ◽

Dc Power ◽

Ac Power

Though the full AC power flow model can accurately represent the physical power system, the use of this model is limited in practice due to the computational complexity associated with its non-linear and non-convexity characteristics. For instance, the AC power flow model is not incorporated in the unit commitment model for practical power systems. Instead, an alternative linearized DC power flow model is widely used in today’s power system operational and planning tools. However, DC power flow model will be useless when reactive power and voltage magnitude are of concern. Therefore, a linearized AC (LAC) power flow model is needed to address this issue. This paper first introduces a traditional LAC model and then proposes an enhanced data-driven linearized AC (DLAC) model using the regression analysis technique. Numerical simulations conducted on the Tennessee Valley Authority (TVA) system demonstrate the performance and effectiveness of the proposed DLAC model.

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A Novel Methodology for Optimal SVC Location Considering N-1 Contingencies and Reactive Power Flows Reconfiguration

Energies ◽

10.3390/en14206652 ◽

2021 ◽

Vol 14 (20) ◽

pp. 6652

Author(s):

Diego Carrión ◽

Edwin García ◽

Manuel Jaramillo ◽

Jorge W. González

Keyword(s):

Power Systems ◽

Large Scale ◽

Reactive Power ◽

Voltage Profile ◽

Expansion Planning ◽

Transmission Expansion Planning ◽

Transmission Expansion ◽

Ac Power ◽

The Stability ◽

Line Switching

In this research, an alternative methodology is proposed for the location of Static VAR Compensators (SVC) in power systems, considering the reconfiguration of reactive power flows through the optimal switching of the transmission stage, which resembles the contingency restriction N-1 usually considered in transmission expansion planning. Based on this methodology, the contingency index was determined, which made it possible to determine which is the contingency that generates the greatest voltage degradation in the system. For the quantification of reactive flows, optimal AC power flows were used, which minimize the operating costs of the power system subject to transmission line switching restrictions, line charge-ability, voltages and node angles. To determine the node in which the compensation should be placed, the contingency index criterion was used, verifying the voltage profile in the nodes. The proposed methodology was tested in the IEEE test systems of 9, 14 nodes and large-scale systems of 200, 500 and 2000 bus-bars; to verify that the proposed methodology is adequate, the stability of the EPS was verified. Finally, the model allows satisfactorily to determine the node in which the SVC is implemented and its compensation value.

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Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

Applied Sciences ◽

10.3390/app12010027 ◽

2021 ◽

Vol 12 (1) ◽

pp. 27

Author(s):

Hassan Shokouhandeh ◽

Sohaib Latif ◽

Sadaf Irshad ◽

Mehrdad Ahmadi Kamarposhti ◽

Ilhami Colak ◽

...

Keyword(s):

Power Systems ◽

Power System ◽

Artificial Bee Colony ◽

Reactive Power ◽

Optimization Problems ◽

Pso Algorithm ◽

Reactive Power Compensation ◽

Voltage Profile ◽

Bee Colony ◽

Control Devices

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

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