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Phase Identification and Balancing in LV Distribution Networks Using Smart Meters Data

Heidari Akhijahani, Adel | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52562 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Safdarian, Amir
  7. Abstract:
  8. With the rapid proliferation of residential rooftop photovoltaic (PV) systems, current and voltage unbalance issues have become a matter of great concern in low voltage (LV) distribution feeders. This, however, necessitates identification of the hosting phase of the connected single-phase customers and PV panels. Both of the challenges have been addressed in this thesis. In the first place, this thesis proposes a mixed integer linear programming (MILP) model for phase identification problem. The model considers potential error in the input data provided by smart meters. To mitigate negative impacts of the error, the data associated with several consecutive time intervals are taken into account by the model. In the model, objective is to identify hosting phase of customers and PV panels such that the mean absolute error between estimated and measured parameters is minimized. Furthermore, Benders decomposition algorithm has been applied to the model. To demonstrate performance of the model, IEEE 13-node and 34-node test feeders with smart meter data is studied thoroughly. Second, this paper proposes a model to optimally rephase customers and PVs among the three phases via static transfer switches (STSs). The optimal STS placement is also considered in the model to achieve a cost effective solution with optimum number and location of STSs. The objective is to minimize total energy losses, minimize the number of STSs, and keep voltage unbalance along the feeder within the acceptable range. The model is solved via a non-dominated sorting genetic algorithm-II (NSGA-II) which provides a Pareto front. A fuzzy decision making approach is then applied to choose the final solution among the Pareto front points. The proposed model is simulated on the IEEE 123-Node Test Feeder. The simulations are conducted in MATLAB where the COM interface capability is used to call OpenDSS to evaluate NSGA-II populations
  9. Keywords:
  10. Benders Decomposition ; Fuzzy Decision Making ; Mixed Integer Linear Programming ; Voltage Unbalance ; Non-Dominate Sorting Genetic Algorithm (NSGAII) Method ; Phase Balancing ; Low Voltage Distribution Network

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