Optimal allocation of distributed generation with the presence of photovoltaic and battery energy storage system using improved barnacles mating optimizer

Abstract

This paper proposes an improved version of Barnacles mating optimizer (BMO) for solving the optimal allocation problem of distribution generator (DGs) in radial distribution systems (RDSs). BMO is a recent bioinspired optimization algorithm that mimics the intelligence behavior of Barnacles' mating. However, like with any metaheuristic optimization approach, it may face issues such as local optima trapping and low convergence rate. Hence, an improved BMO is adopted based on the quasi oppositional (QOBMO) and the chaos maps theories (CQOBMO). The two improvement methods are applied to increase the convergence performance of the conventional BMO. To prove the efficiency of the improved QOBMO and CQOBMO algorithms, 23 benchmark functions are used, and the accomplished results are compared with the conventional BMO. Then, the improved algorithm is applied to minimize the total power and energy losses in the distribution systems considering the uncertainty of DG power generation and time-varying load demand. The uncertainty of DG is represented using photovoltaic-based DG (PVDG). The improved method is employed to find the optimal power scheduling of PVDG and battery energy storage (BES) during 24 h. Two standard IEEE RDS (IEEE 33-bus and IEEE 69-bus) are used to simulate the case studies. Finally, the obtained results show that significant loss reductions (LRs) are achieved using the improved BMO where LRs reach 65.26%, and 68.86% in IEEE 33-bus and 69-bus, respectively, in the case of PVDG integration. However, using PVDG and BES the energy loss reductions reach 64% and 67.80% in IEEE 33-bus and 69-bus, respectively, which prove the efficiency of the improved BMO algorithm in finding the optimal solutions obtained so far.

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

“…To assess the efficiency of the mPDO, it is compared with the original PDO, bat algorithm (BA) 59 , PSO 59 , improved golden jackal optimization (IGJO) 60 , novel heuristic approach (NHA), novel stochastic fractal search algorithm (SFSA), quasi-oppositional-chaotic symbiotic organisms search algorithm (QOCSOS) 29 , and chaotic quasi-oppositional barnacles mating optimizer (CQOBMO-7) 30 . It is observed from Table 3 that the mPDO consistently beats the basic PDO and the state-of-the-art optimizers, exhibiting the lowest power loss when incorporating single and multiple PVs into the test system.…”

“…It is well-established that improving the performance of optimization algorithms leads to significant benefits and more optimal solutions, as discussed in Refs. 26 , 27 , 29 , 30 . Therefore, this study focuses specifically on the effects of individually and concurrently assigning various WTs and PVs on the operation of the distribution system.…”

Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

“…From the equation, lb and ub denote the lower and upper bounds of i th parameter. Here, every barnacle in the early population is calculated by objective function and later sorting method can take place for setting the optimal solution at top of solution matrix [24].…”

Operation and Control of the Distributed Energy Resources to Improve the Power Quality in Electrical Distribution System Using Hbmo -Ann - A Novel Control Algorithm

“…First, the THD as RMS percentage of harmonic frequency components among the base frequency component for voltage and current as represented in (9) . (10) https://www.indjst.org/…”