Multi-objective Optimization in Power Systems Including UPFC Controllerwith NSGA III

Merah, Ahmed; Adjabi, Mohamed

doi:10.7763/ijmo.2018.v8.672

Cited by 1 publication

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References 10 publications

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“…The flowchart of the NSGA-III algorithm is visualized in Fig. 1 [46]. The flow chart of proposed NSGA-III…”

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confidence: 99%

Probabilistic Planning of Renewable Energy Source with Different Load Models

saleh

2024

Preprint

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this paper presents an optimization algorithm, named Non dominated Sorting Genetic Algorithm III (NSGA-III) method to identify the optimal allocation of hybrid distributed energy resources (DERs) including photovoltaic system (PV), micro-turbine (MT), and fuel cell (FC) in distribution system considering four different load models such as industrial, residential, commercial and constant load model. With increasing the integration of renewable energy resources, the uncertainties resulting from these sources posed a serious challenge to the operation of the network. To be closer to the reality, the network performance is analyzed considering the influence of the uncertainties produced from RES. Point Estimate Method (PEM) is used to model the uncertainties generated from renewable energy resources. The main aim of this paper is to maximize technical and economic benefits of DERs by minimizing various objective functions such as the total power loss, cost, and voltage deviation subject to different power system constraints. Multi-objective planning framework is appraised using two standards IEEE networks with various scenarios. Comparative analyses are conducted on standard distribution systems under different load model and mix of different types of DERs. Level diagram is implemented to analysis and compares the influence of different combinations of load models on the system performance. The obtained results show that, the system performance is greatly influenced by uncertainties accompanied with DER and power system itself. The suggested multi-objective planning frameworks shows high accuracy compared to other techniques applied in previous researches.

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“…The flowchart of the NSGA-III algorithm is visualized in Fig. 1 [46]. The flow chart of proposed NSGA-III…”

Section: 𝐻 =mentioning

confidence: 99%