Multi-Objective Optimisation of Photovoltaic Systems and Unified Power Quality Conditioners with Simultaneous Distribution Network Reconfiguration

Nowadays, renewable energy sources are being increasingly integrated to power systems. This research analyses various cases of distribution network reconfiguration (DNR) as well as the placement and sizing of photovoltaic (PV) systems and unified power quality conditioners (UPQC). Non- dominated Sor...

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Vydané v:2020 3rd International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM) s. 191 - 197
Hlavní autori: Ramsami, Pamela, Ah King, Robert T. F.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 25.11.2020
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Shrnutí:Nowadays, renewable energy sources are being increasingly integrated to power systems. This research analyses various cases of distribution network reconfiguration (DNR) as well as the placement and sizing of photovoltaic (PV) systems and unified power quality conditioners (UPQC). Non- dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm-2 (SPEA2) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) were applied to minimise the active power loss, voltage deviation and total cost subject to constraints. The efficacy of the three algorithms was analysed according to their simulation time and performance metrics. The simulation results revealed that the integration of PV systems and UPQC with the simultaneous reconfiguration of the network produces acceptable compromises among the objective functions. NSGA-II outperformed the other algorithms in terms of simulation time at the expense of a minor deterioration in the quality of the solutions in some cases. With equal importance of each objective, smaller PV systems are required to achieve the optimal solution when UPQCs are present in the distribution system. The integration of UPQC with simultaneous DNR further reduces the size of PV systems required.
DOI:10.1109/ELECOM49001.2020.9297033