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Distributed photovoltaic integration as complementary energy: consideration of solutions for power loss and load demand growth problems

Manjang Salama, Rahman Yuli Asmi

Przegląd Elektrotechniczny

2020

R. 96, nr 9

56--61

The purpose of this study is to optimize the location and capacity of PV in the feeder distribution system 20 kV of Central Sulawesi, Indonesia. The proposed method uses the optimization method of development from the genetic algorithm, namely NSGA-II. Optimization is carried out in three scenarios by considering the value of the total active PV power capacity which produces the minimum active power loss and voltage deviation. The simulation result shows that the integration of PV-DG can improve drop voltage of distribution system performance due to load growth effect.

Celem tego badania jest optymalizacja lokalizacji i wydajności PV w systemie dystrybucji zasilania 20 kV w środkowym Sulawesi w Indonezji. Proponowana metoda wykorzystuje optymalizację opartą na algorytmie genetycznym, mianowicie NSGA-II. Optymalizację przeprowadza się w trzech scenariuszach, biorąc pod uwagę wartość całkowitej mocy czynnej PV, która powoduje minimalne straty mocy czynnej i odchylenie napięcia. Wynik symulacji pokazuje, że integracja PV-DG może poprawić wydajność systemu dystrybucji ze względu na efekt wzrostu obciążenia.

Distributed generation’s integration planning involving growth load models by means of genetic algorithm

Rahman Y. A., Manjang S., Yusran A., Ilham A.

Archives of Electrical Engineering

2018

Vol. 67, nr 3

667-–682

The growth in the system load accompanied by an increase of power loss in the distribution system. Distributed generation (DG) is an important identity in the electric power sector that substantially overcomes power loss and voltage drop problems when it is coordinated with a location and size properly. In this study, the DG integration into the network is optimally distributed by considering the load conditions in different load models used to surmount the impact of load growth. There are five load models tested namely constant, residential, industrial, commercial and mixed loads. The growth of the electrical load is modeled for the base year up to the fifth year as a short-term plan. Minimization of system power loss is taken as the main objective function considering voltage limits. Determination of the location and size of DG is optimally done by using the breeder genetic algorithm (BGA). The proposed studies were applied to the IEEE 30 radial distribution system with single and multiple placement DG scenarios. The results indicated that installing an optimal location and size DG could have a strong potential to reduce power loss and to secure future energy demand of load models. Also, commercial load requires the largest DG active injection power to maintain the voltage value within tolerable limits up to five years.