The impact of the renewable distributed generations (RDGs), such as photovoltaic (PV) and wind turbine (WT) systems can be positive or negative on the system, based on the location and size of the DG. So, the correct location and size of DG in the distribution network remain an obstacle to achieving their full possible benefits. Therefore, the future distribution networks with the high penetration of DG power must be planned and operated to improve their efficiency. Thus, this paper presents a new methodology for integrated of renewable energy-based DG units with electrical distribution network. Since the main objective of the proposed methodology is to reduce the power losses and improve the voltage profile of the radial distribution system (RDS). In this regard, the optimization problem was formulated using loss sensitivity factor (LSF), simulated annealing (SA), particle swarm optimization (PSO) and a combination of loss sensitivity index (LSI) with SA & PSO (LSISA, LSIPSO) respectively. This paper contributes a new methodology SAPSO, which prevents the defects of SA & PSO. Optimal placement and sizing of renewable energy-based DG tested on 33-bus system. The results demonstrate the reliability and robustness of the proposed SAPSO algorithm to find the near-optimal position and size of the DG units to mitigate the power losses and improve the radial distribution system's voltage profile.

This paper produces a modern meta-heuristic optimization technique based on the buoyancy principle called the Archimedes optimization algorithm (AOA). The proposed algorithm is used here to determine the optimal economic operation of interconnected micro-grids (IMGs). Each micro-grids (MG) includes different types of distributed generation (DG) units such as solar photovoltaic (PV), wind turbine (WT), and micro-turbine (MT) and aims for achieving minimization of total power generation cost as the main objective function taking into consideration the power exchange between the IMGs and utility with special emphasis on technical constraints. To prove the effectiveness of the proposed AOA algorithm, it is compared with another optimization method based on the particle swarm optimization algorithm (PSO). Results obtained with the AOA algorithm show how managing energy transfer between utility and each MG. For various daily loads, it can reduce electricity consumption while lowering the cost of overall electricity generation, minimizing utility bills, and maximizing micro turbine (MT) efficiency.

The widespread use of power electronics in industrial, commercial and even residential electrical equipment like non-linear loads causes deterioration of the quality of the electric power supply with distortion of the supply voltage and in order to mitigate this quality the shunt active power filter (SAPF) is the suitable and effective solution for harmonic elimination and reactive power compensation and lead to power quality (PQ) improvement, therefor an effective and accurate current control technique is needed in order for a SAPF where control algorithm is the heart for SAPF to perform this function and its dynamic performance is mainly depends on these control strategy. This paper proposes three different current control strategies (CCS) based on instantaneous power theory and generalized fryze theory which used for the generation or extraction of the accurate reference current signals which comparing with the actual signals through hysteresis current technique (HCT) to produce suitable gating signals for SAPF and discusses the performance for these controllers when the supply bus voltage is distorted with scope on the efficient control algorithm. Matlab / Simulink simulation results are presented to validate the control strategy and demonstrate the effectiveness of SAPF to provide mitigation of power quality problems for non-linear load to reach an acceptable value comply with recommended standards.