High penetration of photovoltaic and wind turbine-based distributed generators (DGs) can help reduce carbon emissions which is an important goal for the whole world. DG can be used to improve the voltage stability, present generation reserve/emergency, and consequently, the system power quality can be improved. However, it is very important to select the right size and location of a DG so that the power system can increase the gained benefits of such an installation to the maximum. In this paper, a hybrid optimization technique is proposed to determine the optimal allocation of DG in the standard IEEE 33-bus radial distribution system in order to improve the voltage stability and minimize the total power loss. The proposed hyprid technique is based on the gray wolf optimizer algorithm with loss sensitivity factor. The performance of the system is analyzed without DG installation, then it is compared with the performance of the system when DGs are installed with the predefined optimal sizes and locations. The study is performed by MATLAB M-Files and NEPLAN software.

Global environmental problems associated with traditional energy generation have led to a rapid increase in the use of renewable energy sources (RES) in power systems. The integration of renewable energy technologies is commercially available nowadays, and the most common of such RES technology is photovoltaic (PV). This paper proposes an application of hybrid teaching-learning and artificial bee colony (TLABC) technique for determining the optimal allocation of PV based distributed generation (DG) and battery energy storage (BES) units in the distribution system (DS) with the aim of minimizing the total power losses. Besides, some potential nodes identified by the Power Loss Sensitivity Factor (PLSF). Thereupon TLABC is applied to determine the location of the DG and its size from the candidate nodes. The Beta probability distribution function (PDF) is employed to characterize the randomness of solar radiation. High penetration of RES can lead to a high level of risk in DS stability. To maintain system stability, BES is considered to smooth out the fluctuations and improve supply continuity. The benefits of using BES is mainly dependent on operational strategies related to PV and storage in DS. The performance of the developed approach is tested on the 69 node and 118 node DSs and compared with the Differential Evolution (DE) algorithm, Genetic Algorithm (GA), for a fair comparison. Besides, the developed approach compared with other methods in literature which are solved the same problem. The results show how practical is the developed approach compared with other techniques.

Distributed generation (DG) is becoming a prominent key spot for research in recent years because it can be utilized in emergency/reserve plans for power systems and power quality improvement issues, besides its drastic impact on the environment as a greenhouse gas (GHG) reducer. For maximizing the benefits from such technology, it is crucial to identify the best size and location for DG that achieves the required goal of installing it. This paper presents an investigation of the optimized allocation of DG in different modes using a proposed hybrid technique, the tunicate swarm algorithm/sine-cosine algorithm (TSA/SCA). This investigation is performed on an IEEE-69 Radial Distribution System (RDS), where the impact of such allocation on the system is evaluated by NEPLAN software.

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