Multi-objective voltage/VAR regulation in high PV-penetrated active distribution networks

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Copyright: Xu, Ruipeng
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Abstract
Distributed energy resources (DERs) are widely applied in modern power systems, especially in active distribution networks, and they bring both significant technical and economic benefits. DERs include distributed generation (DG) units, energy storage systems and flexible loads. However, high-penetrated renewable DG also brings technical challenges such as high uncertainty and intermittency to the systems leading to unexpected voltage instability and low network operation efficiency. Thus, this thesis proposes new methods to deal with the voltage instability and the low efficiency. This thesis firstly proposes an inverter-based multi-objective optimization model for volt-VAR regulation (VVR) with stochastic photovoltaic (PV) power generation in active distribution networks. The multi-objective model aims to improve the power quality and reduce the power loss in the distribution networks. This thesis applies Taguchi’s orthogonal array testing (TOAT) method to select a small set of representative scenarios to approximate the stochastic and uncertain PV generation. With these scenarios, the PV-associated inverters are dispatched to support VAR compensation, which can reduce the voltage deviations. Compared with the existing methods, the proposed method can accomplish a robust voltage level and lower power loss under the PV power generation uncertainty. Secondly, this thesis proposes a VVR model which coordinates capacitor banks (CBs), on-load tap changers (OLTCs) and PV-associated inverters to enhance voltage stability and reduce power loss. On the other hand, conservation voltage reduction (CVR) is adopted to reduce load demand. This VVR method with the CVR forms a multi-objective optimization problem which minimizes (i) voltage collapse proximity indicator (VCPI) which is used to assess the network voltage stability (ii) load demand and (iii) power loss. Besides, uncertain PV power generation significantly impairs the VVR results. To deal with the uncertainty issue, this thesis applies a rolling-horizon framework to determine VVR and applies the TOAT method to generate scenarios, achieving a rolling-horizon based multi-objective robust VVR method. The proposed approaches have been successfully presented and compared with existing works. Simulation results have verified their efficiency and superiority over the compared methods.
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Author(s)
Xu, Ruipeng
Supervisor(s)
Dong, Zhaoyang
Xu, Yan
Zhang, Cuo
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Publication Year
2019
Resource Type
Thesis
Degree Type
Masters Thesis
UNSW Faculty
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