A data-driven approach to support voltage profiles & loss reduction in wind generator integrated active distribution network considering solid-state transformers with twofold reactive power compensation

Power loss reduction is an important problem that needs to be addressed with respect to generating electrical power. It is important to reduce power loss using locally generated power sources and/or compensations. This chapter brings a method of presents a method of maximizing energy utilization, feeder loss reduction, and voltage profile improvement for radial distribution network using the active and reactive power sources. Distributed Generation (DG) (wind and solar with backup by biomass generation) and shunt capacitor (QG) for reactive power demand are used. Integrating DG and QG at each bus might reduce the loss but it is economically unaffordable, especially for developing countries. Therefore, the utilization optimization method is required for finding an optimal size and location to feeders for placing QG and DG to minimize feeder loss.

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Optimal allocation of distributed reactive power compensation for low voltage distribution network considering pump loads’ characteristics

2018 International Conference on Power System Technology (POWERCON) ◽

10.1109/powercon.2018.8601703 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yanghua LIU ◽

Shunjiang LIN

Keyword(s):

Optimal Allocation ◽

Reactive Power ◽

Low Voltage ◽

Distribution Network ◽

Reactive Power Compensation ◽

Voltage Distribution

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Two-Stage Active and Reactive Power Coordinated Optimal Dispatch for Active Distribution Network Considering Load Flexibility

Energies ◽

10.3390/en13225922 ◽

2020 ◽

Vol 13 (22) ◽

pp. 5922

Author(s):

Yu Zhang ◽

Xiaohui Song ◽

Yong Li ◽

Zilong Zeng ◽

Chenchen Yong ◽

...

Keyword(s):

Real Time ◽

Prediction Error ◽

Reactive Power ◽

Distribution Network ◽

Constant Current ◽

Economic Losses ◽

Time Varying ◽

Two Stage ◽

Active Distribution Network ◽

Active And Reactive Power

A high proportion of renewable energy connected to the power grid has caused power quality problems. Voltage-sensitive loads are extremely susceptible to voltage fluctuations, causing power system safety issues and economic losses. Considering the uncertainty factor and the time-varying characteristic, a linearized random ZIP model (constant impedance (Z), constant current (I), and constant power (P)) with time-varying characteristics was proposed. In order to improve the voltage quality of the voltage-sensitive loads in the day-here stage in an active distribution network (ADN), a linearized two-stage active and reactive power coordinated stochastic optimization model was established. The day-ahead active and reactive power coordination optimization was to smooth the large voltage fluctuation and develop a reserve plan to eliminate the unbalanced power caused by the prediction error in the day-here optimization. In the day-here real-time redispatch, the voltage was further improved by the continuous reactive power compensation device. Finally, the simulation results on the IEEE-33 bus system showed that the control strategy could better eliminate the unbalanced power caused by the prediction error and obviously improve the voltage of sensitive loads in the real-time stage on the premise of maintaining economic optimality.

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Reactive Power Compensation of Distribution Network Based on Immune and Particle Swarm Optimization Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.229-231.1030 ◽

2012 ◽

Vol 229-231 ◽

pp. 1030-1033

Author(s):

Wei Cui ◽

Lin Chuan Li ◽

Lei Zhang ◽

Qian Sun

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Reactive Power ◽

Distribution Network ◽

Particle Swarm ◽

Reactive Power Compensation ◽

Swarm Optimization ◽

Operation Costs ◽

Sweep Algorithm

The reactive power compensation optimization in distribution network has the important meaning in maintaining system voltage stability, decreasing network loss and reducing operation costs. In order to meet factual conditions, we assume the system operates in minimum, normal and maximum three load modes and the objective function of problem includes the costs of power loss and the dynamic reactive power compensation devices allocated. In this paper we use Artificial Immune Algorithm(AIA) and Particle Swarm Optimization Algorithm(PSO) to determine compensate nodes and use the back/forward sweep algorithm calculate load flows. After applied into 28-nodes system, the result demonstrates the method is feasible and effective.

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