No abstract

Along with the world population growth, the need for a source of electrical energy is higher, so a reliable system with higher capacities is expected. Renewable energy becomes an alternative that supports the goal of reducing the risk of disruption, thus increasing the distribution system’s reliability. A lot of industries and public settlement uses renewable sources of energy as an alternative power supply to comply their energy needs. This research uses wind turbine as a source of renewable energy in the distributed generation (DG). However, the required investment in wind-based DG is commonly considered too costly to deploy and require a proper planning on its placement method. The flower pollination algorithm (FPA) method could be a solution to achieve optimal placement of wind-based DG, thus increase the distribution system’s reliability, which is indicated by minimum energy not supplied (ENS) index.

The need for energy, especially electricity, is increasing along with the development of technology. An increase in electrical load and the location of the powerplant far causes voltage drops and causes power line losses. One solution can be chosen by adding a distributed generation (DG) to the distribution network. This study aims to enhance the voltage profile and reduce power losses according to DG-based photovoltaic (PV)'s optimal placement and capacity in the Bantul Feeder 05 distribution network. The flower pollination algorithm (FPA) method is used to determine the optimal DG placement and capacity. The study was conducted using three additional DG scenarios: scenario 1 with single DG and scenario 2 with multi-DG (2 DG and 3 DG). The results showed that the optimal placement and capacity of DG were on buses 9, 19, and 33 with DG sizes of 1.880 kW, 2.550 kW, and 2.300 kW, respectively. This placement can increase the voltage profile and reduce the active power loss from 439.8 kW to 77.5 kW. The research also considers the increase in the reliability of the distribution system in terms of the Energy not Supplied (ENS) and Cost of Energy not Supplied (CENS) index.