Abdullah M. Shaheen

Bio: Abdullah M. Shaheen is an academic researcher from Suez University. The author has contributed to research in topics: Computer science & AC power. The author has an hindex of 16, co-authored 57 publications receiving 747 citations.

Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm

Abstract: This study proposes a multi-objective differential evolution algorithm (MO-DEA) based on forced initialisation to solve the optimal power flow (OPF) problem. The OPF problem is formulated as a non-linear MO optimisation problem. The considered objective functions are fuel cost minimisation, power losses minimisation, voltage profile improvement, and voltage stability enhancement. For solving the MO-OPF, the proposed approach combines a new variant of DE (DE/best/1) with the ɛ-constraint approach. This combination guarantees high convergence speed and good diversity of Pareto solutions without computational burden of Pareto ranking and updating or additional efforts to preserve the diversity of the non-dominated solutions. The proposed approach has the ability to generate Pareto-optimal solutions in a single simulation run through adaptive variation of the ɛ-value. In addition, the best compromise solution is extracted based on fuzzy set theory. The effectiveness of the proposed MO-DEA is tested on the IEEE 30-bus and IEEE 57-bus standard systems. The numerical results obtained by the proposed MO-DEA are compared with other evolutionary methods reported in this literature to prove the potential and capability of the proposed MO-DEA for solving the MO-OPF at acceptable economical and technical levels.

MOPF solution methodology

Abstract: This study investigates a novel multi-objective differential evolution (MDE) solution methodology for multi-objective optimal power flow (MOPF) problem. The MOPF problem is modelled with various technical and economical objective functions. These objectives are handled as mono, bi, tri, and quad-objective MOPF problems. For solving these MOPF formulations, a novel MDE algorithm is proposed. The novel MDE algorithm modifies the DE variant (DE/best/1) with Pareto ranking in the selection operator and develops a fuzzy-based best compromise solution for each generation to feed the mutation operator. This modification guarantees high convergence speed and enhances the search capability via exploring the neighbourhood of the best compromise solution in successive generations. The standard IEEE 57-bus power system is emulated to prove the effectiveness and competence solutions of the mono, bi, tri, and quad-objective MOPF at acceptable techno-economic benefits compared with other evolutionary methods. Similarly, the standard IEEE 118-bus test system is used to show the effectiveness of the proposed algorithm for solving the OPF problem in a large-scale power system.

A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models

Abstract: The accurate parameter extraction of photovoltaic (PV) module is pivotal for determining and optimizing the energy output of PV systems into electric power networks. Consequently, a Photovoltaic Single-Diode Model (PVSDM), Double Diode Model (PVDDM), and Triple- Diode Model (PVTDM) is demonstrated to consider the PV losses. This article introduces a new application of the Forensic-Based Investigation Algorithm (FBIA), which is a new meta-heuristic optimization technique, to accurately extract the electrical parameters of different PV models. The FBIA is inspired by the suspect investigation, location, and pursuit processes that are used by police officers. The FBIA has two phases, which are the investigation phase applying by the investigators team, and the pursuit phase employing by the police agents team. The validity of the FBIA for PVSDM, PVDDM, and PVTDM is commonly considered by the numerical analysis executing under diverse values of solar irradiations and temperatures. The optimal five, seven, and nine parameters of PVSDM, PVDDM, and PVTDM, respectively, are accomplished using the FBIA and compared with those manifested by various optimization techniques. The numerical results are compared for the marketable Photowatt-PWP 201 polycrystalline and Kyocera KC200GT modules. The efficacy of the FBIA for the three models is properly carried out checking its standard deviation error with that obtained from various recently proposed optimization techniques in 2020 which are Jellyfish search (JFS) optimizer, Manta Ray Foraging optimizer (MRFO), Marine Predators Algorithm(MPA), Equilibrium Optimizer (EO), Heap Based Optimizer (HBO). The standard deviations of the fitness values over 30 runs are developed to be less than $1 \times 10^{-6}$ for the three models, which make the FBIA results are extremely consistent. Therefore, FBIA is foreseen to be a competitive technique for PV module parameter extraction.

Optimal Placement and Sizing of Distributed Generation and Capacitor Banks in Distribution Systems Using Water Cycle Algorithm

Abstract: Integration of distributed generation units (DGs) and capacitor banks (CBs) in distribution systems aim to enhance the system performance. This paper proposes water cycle algorithm (WCA) for optimal placement and sizing of DGs and CBs. The proposed method aims to achieve technical, economic, and environmental benefits. Different objective functions: minimizing power losses, voltage deviation, total electrical energy cost, total emissions produced by generation sources and improving the voltage stability index are considered. WCA emulates the water flow cycle from streams to rivers and from rivers to sea. Five different operational cases are considered to assess the performance of the proposed methodology. Simulations are carried out on three distribution systems, namely IEEE 33-bus, 69-bus test systems, and East Delta network, as a real part of Egyptian system. The simulated results demonstrate the effectiveness of the proposed method compared with other optimization algorithms. Also, the results demonstrate that the proposed WCA gives superior performance for the system and give distinguished improvements in both economic and environmental benefits. Moreover, the results give the flexible operation with controllable power factor DGs that is better than those using DGs at fixed power factor.

A review of optimal power flow studies applied to smart grids and microgrids

Abstract: The term smart grid refers to a modernization of the electrical network consisting in the integration of various technologies such as dispersed generation, dispatchable loads, communication systems and storage devices which operates in grid-connected and islanded modes. As a result, traditional optimization techniques in new power systems have been seriously influenced during the last decade. One of the most important technical and economical tools in this regard is the Optimal Power Flow (OPF). As a fundamental optimization tool in the operation and planning fields, OPF has an undeniable role in the power system. This paper reviews and compares the OPF approaches mainly related to smart distribution grids. In this work, the main OPF approaches are compared in terms of their objective functions, constraints, and methodologies. Furthermore, computational performances, case study networks and the publication date of these methods are reported. Finally, some basic challenges arising from the new OPF methodologies in smart grids are addressed.