Large distributed generation (DG) penetration into the power system needs to be accompanied by proper planning to maximize the benefits and minimize the negative effects that may arise on the system. Determining the location and size of DGs in power systems is a complex issue because it involves hundreds or even thousands of buses and lines distribution. Various studies have been conducted to overcome these problems, including by developing existing methods or even discovering new methods. This study deals with location optimization and sizing DG in the radial distribution system to minimize power loss and voltage deviation. The location of the DG is identified using a loss reduction sensitivity factor (LRSF) while the size of the DG is determined using the improved method of symbiotic organisms search (SOS) called New Enhanced SOS (NeSOS). There are two methods developed in the NeSOS, namely random weighted inverse vector (RWIV) and dual-phase parasitism (DPP). DPP consists of classic parasitism (CP) and random weight parasitism (RWP). The NeSOS is programmed under MATLAB software and validated using 26 mathematical benchmark functions. NeSOS also tested on IEEE 33 and IEEE 69 bus test system and compared with other methods. The simulation results show that the convergence rate of NeSOS is 30% faster than SOS. NeSOS also provides an average power loss of 1.53% lower than other methods.

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This paper presents performance analysis of Unified Power Quality Conditioner-Battery Energy Storage (UPQC-BES) system supplied by Photovoltaic (PV)-Wind Hybrid connected to three phase three wire (3P3W) of 380 volt (L-L) and 50 hertz distribution system. The performance of supply system is compared with two renewable energy (RE) sources i.e. PV and Wind, respectively. Fuzzy Logic Controller (FLC) is implemented to maintain DC voltage across the capacitor under disturbance scenarios of source and load as well as to compare the results with Proportional Intergral (PI) controller. There are six scenarios of disturbance i.e. (1) non-linear load (NL), (2) unbalance and nonlinear load (Unba-NL), (3) distortion supply and non-linear load (Dis-NL), (4) sag and non-linear load (Sag-NL), (5) swell and non-linear load (Swell-NL), and (6) interruption and non-linear load (Inter-NL). In disturbance scenario 1 to 5, implementation of FLC on UPQC-BES system supplied by three RE sources is able to obtain average THD of load voltage/source current slightly better than PI. Furthermore under scenario 6, FLC applied on UPQC-BES system supplied by three RE sources gives significantly better result of average THD of load voltage/source current than PI. This research is simulated using Matlab/Simulink.

This paper presents enhancement of load active power transfer using Unified Power Quality Conditioner-Photovoltaic-Battery Energy Storage (UPQC-PV-BES) system. This system is connected to a three phase three wire (3P3W) system with a voltage of 380 V (line to line) and 50 hertz. The proposed model is also compared with UPQC and UPQC-PV respectively. The parameters investigated are load voltage, load current, load active power, and efficiency. BES functions to save excess energy generated by PV, distribute it to the load, avoid interruption voltage, and regulate the charging process and energy utilization. The fuzzy logic controller (FLC) is proposed and compared with proportional integral (PI) method to control DC voltage variable and input DC reference voltage, to produce a reference current source on hysteresis current controller on shunt active filter in 12 disturbance scenarios (scns). In Scenario (Scn) 1 to 5, the 3P3W system uses three combinations of UPQC with PI controller and FLC, still keeps load voltage and load current above 300 V and 8 A. Whereas in Scn 6, only the UPQC-PV-BES with FLC is able to maintain load voltage and load current higher compared to UPQC and UPQC-PV combinations as 304.1 V and 8.421 A, respectively. In Scn 1 to 5, the 3P3W system uses three combinations of UPQC with PI controller and FLC, capable of producing load active power above 3600 W. Whereas in Scn 6, only a combination of UPQC-PV-BES with PI controller and FLC is able to produce a load voltage of 3720 W and 3700 W, respectively. In Scn 1 to 6, UPQC-PV-BES results in lower efficiency compared to using UPQC and UPQC-PV. However, in Scn 6, UPQC-PV-BES with FLC is able to produce load voltage, load current, and load active power higher than UPQC-PV and UPQC. Thus, the UPQC-PV-BES model using FLC is able to compensate load voltage and load current, as well as to enhance load active power, especially for an interruption on source bus. This research is simulated using Matlab/Simulink.

The Keywords: adaptive real coded GA, benefit cost, expenditure cost, microgridsCopyright © 2018 Universitas Ahmad Dahlan. All rights reserved. IntroductionThe intense competition encourages electric energy producers to do all the effort to provide cheap electrical energy with good quality. Conventional generating systems typically serve loads with centralized power generation. Electrical energy is sent across long transmission lines to the load centers. Recent developments show that this fashion is becoming obsolete. Electrical energy producers began to use small to medium-scale generators that were placed directly in the load center known as distributed generation (DG) [1]. DG is considered as the answer of various limitations on conventional systems, so it is not surprising that DG has reached about 20%-30% of total energy production [2][3][4].Basically, DG is used to improve network reliability, security and power quality to customers [5]. However, with large-scale multi-type DG penetration and improper planning, the network system will face some serious problems [6][7][8] [15]. One of the most discussed elements in almost all DG evaluations is power quality. However, for a comprehensive assessment, various aspects of DG performance need to be examined including economic issues.The economical issue is an essential element to determine whether DG should be installed or not. Techno-economic analysis of PV and wind turbine WT is performed in [16] using HOMER software. This study focuses on the effort to determine the most economical combination of power plants, but the network costs are not taken into account. Optimal planning of renewable energy based DGs has been performed in [17] to maximize the worth of installing DGs. The optimization is conducted using mixed integer programming. The worth of DG installing is determined based on deferral of upgrade investments, cost of energy losses and interruption cost. Optimal planning of DGs with the aim of profit maximization is presented in [18]. Optimal location of DGs is determined using local marginal price (LMP) and Consumer Paymen (CP) index. Benefit cost is determined by LMP index, while expenditure cost is