Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

TANG, Huiling; Jiekang, Wu; Wu, Zheng-Mao; Chen, Lingmin; Mo, Chao

doi:10.3906/elk-1602-65

Cited by 11 publications

(4 citation statements)

References 15 publications

(28 reference statements)

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“…Being different from the traditional VNE algorithm, the ES-VNE algorithm needs to consider not only the acceptance rate of VN requests and average energy consumption, but also the hibernation rate of SN resources and long-term revenue/cost ratio, to reduce energy consumption of the SN as well as to improve acceptance rate as much as possible in the long run [21,22].…”

Section: Energy-saving Virtual Network Embedding Evaluation Metricsmentioning

confidence: 99%

(:unav)

Zhu

Wang

Zang

et al. 2020

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Current networks are designed for peak loads leading to low utilization of power resources. In order to solve this problem, a heuristic energy-saving virtual network embedding algorithm based on the Katz centrality (Katz-VNE) is proposed. For solving an energy-saving virtual network embedding problem, we introduce the Katz centrality to represent the node influence. In order to minimize the energy consumption of the substrate network, the energy-saving virtual network embedding problem is formulated as an integer linear program, and the Katz-VNE is used to solve this problem. The Katz-VNE tries to embed the virtual nodes onto the substrate nodes with high Katz centrality, which is effective, and uses the shortest paths offering the best factor of bandwidths to avoid the hot nodes. The simulation results demonstrate that the long-term average energy consumption of the substrate network is reduced significantly, and the long-term revenue/cost ratio, the acceptance rate of virtual network requests, and the hibernation rate of substrate nodes as well as links are improved significantly.

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Section: Energy-saving Virtual Network Embedding Evaluation Metricsmentioning

confidence: 99%

(:unav)

Zhu

Wang

Zang

et al. 2020

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“…They include second-order cone (SOC) [7], the semi-definite programming (SDP) [8], Convex-DistFlow (CDF) [9], and the quadratic convex (QC) [10]. However, out of the abovementioned algorithms, the second-order cone programming method is widely accepted in power system applications such as; switching of shunt capacitors for minimisation of total power losses [11], voltage constraints management [12] and loss minimisation in distribution systems [13]. In [14], the authors proved that the second-order cone programming is computationally simpler than semi-definite programming, and also it fits well in the problems involving convex quadratic functions [15].…”

Section: Introductionmentioning

confidence: 99%

Coordinated Voltage Support with Reactive Power from High-power Charging Stations for EVs

Rana

Torsater

2021

2021 IEEE Madrid PowerTech

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High-power charging stations have recently drawn the attention of many researchers and electric vehicle (EV) infrastructure industries. However, the installation of fast chargers at various corridors of highways and cities can cause high peak loads and voltage deviations in distribution networks. In addition, the usage patterns can vary drastically from one fast charging station to another at any given instant. However, future highpower charging stations could be able to operate in any of the four P-Q quadrants. Thus, high-power charging stations for EVs have the capability of serving as a flexibility resource and minimising voltage deviations. This research introduces a method for mitigating voltage quality problems in distribution network by effective utilisation of the reactive power potential from fast charging stations. The suggested method introduces a secondorder cone programming approach that is validated on the IEEE 69 bus distribution system. The performance of the method is analysed in a case study with measured charging profiles from real high-power charging stations in Norway.

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“…The problem of distribution network reconfiguration becomes complex due to the influence of DG [5,6] on distribution network power flow, node voltage, network loss and many other aspects. Some scholars have studied the related problems and achieved certain results [7,8]. Rao R.S.…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Tang,

Zhang,

Wang

et al. 2021

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In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased 0

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Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

Cited by 11 publications

References 15 publications

(:unav)

(:unav)

Coordinated Voltage Support with Reactive Power from High-power Charging Stations for EVs

Archives of Electrical Engineering

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