Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network

Abstract

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.

Optimizing the hybrid PVDG and DSTATCOM integration in electrical distribution systems based on a modified homonuclear molecules optimization algorithm

“…In order to evaluate the performance of the competitive HMO and mHMO algorithms, several standard test systems were considered, including the IEEE 33-bus and 69-bus EDS which are shown in Figure 1 [32,33]. These test systems are commonly used in the field of electrical power systems for benchmarking and comparison of optimization algorithms.…”

Integration and Control of Distributed Renewable Energy Resources

“…The investigation into optimized allocation of distributed energy resources (DERs) in distribution systems with the goal of minimizing the system loss was pursued in [18]. A hybrid optimization approach, composed of the tunicate swarm algorithm (TSA) and the sine-cosine algorithm (SCA), was introduced to identify the best size and location of DERs in the system.…”

Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

“…Finding the best DG size and location is the most important aspect of DG deployment. Many studies focus solely on reducing power loss as a primary goal for addressing the DG deployment problem, utilizing a variety of analytic and computational intelligence methods [9][10][11][12][13][14]. Metaheuristic techniques are ideally adequate for the DG placement challenge.…”

“…The MO rendering indicator is determined as a weighted sum of several technical aspects such as the reduction of actual power loss, minimization of voltage deviation, and operational cost reduction [15][16][17][18]. Many studies have given reviews as well as polls on the DG layout issue in recent years [9][10][11][12][13][14][15][16][17][18], which are based on a DG short caption, objective functions (OFs), restrictions, and distinctive strategies.…”