Showing papers by "Mohamed Abdel-Nasser published in 2017"

Efficient SPF approach based on regression and correction models for active distribution systems

Abstract: This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.

Accurate economic dispatch solution using hybrid whale-wolf optimization method

Abstract: The aim of economic dispatch is to allocate the generated power to minimize the total fuel costs while satisfying the overall constraints. In this paper, we propose a hybrid whale-wolf optimization method to accurately solve the economic dispatch problem. The proposed method efficiently integrates the mechanisms of whale optimization algorithm and gray wolf optimization with crossover and mutation operators. To demonstrate the effectiveness of the proposed method, it is compared with six optimization methods: gray wolf optimization, whale optimization, particle swarm optimization, artificial bee colony algorithm, ant lion algorithm, and dragonfly algorithm. Two different test systems (6 and 10 generating units) are used to evaluate the performance of the proposed method. The experimental results show that the hybrid whale-wolf optimization method shows better performance to find the optimal solution of the economic dispatch problem compared to the other methods.

Performance evaluation of metaheuristic optimization methods with mutation operators for combined economic and emission dispatch

Abstract: This paper solves the combined economic and emission dispatch (CEED) problem which aims to achieve minimum generating costs with emission reduction using different optimization methods. Six methods are discussed: moth-flame optimization (MFO), moth swarm algorithm (MSA), grey wolf optimization (GWO), antlion optimization (ALO), sine cosine algorithm (SCA), and multi-verse optimization (MVO). Different mutation operators are integrated to these methods to improve their performance. Two test systems are simulated, and the results are compared to see the effectiveness of applying mutation operators to the optimization methods.