Bibliographic Details
| Title: |
Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method |
| Authors: |
Khaled Nusair, Feras Alasali |
| Source: |
Energies, Vol 13, Iss 14, p 3671 (2020) |
| Publisher Information: |
MDPI AG, 2020. |
| Publication Year: |
2020 |
| Collection: |
LCC:Technology |
| Subject Terms: |
power loss, fuel cost, emission index, optimal power flow, golden ratio optimization method, renewable energy, Technology |
| More Details: |
An optimal operation system is a potential solution to increase the energy efficiency of a power network equipped with stochastic Renewable Energy Sources (RES). In this article, an Optimal Power Flow (OPF) problem has been formulated as a single and multi-objective problems for a conventional power generation and renewable sources connected to a power network. The objective functions reflect the minimization of fuel cost, gas emission, power loss, voltage deviation and improving the system stability. Considering the volatile renewable generation behaviour and uncertainty in the power prediction of wind and solar power output as a nonlinear optimization problem, this paper uses a Weibull and lognormal probability distribution functions to estimate the power output of renewable generation. Then, a new Golden Ratio Optimization Method (GROM) algorithm has been developed to solve the OPF problem for a power network incorporating with stochastic RES. The proposed GROM algorithm aims to improve the reliability, environmental and energy performance of the power network system (IEEE 30-bus system). Three different scenarios, using different RES locations, are presented and the results of the proposed GROM algorithm is compared to six heuristic search methods from the literature. The comparisons indicate that the GROM algorithm successfully reduce fuel costs, gas emission and improve the voltage stability and outperforms each of the presented six heuristic search methods. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1996-1073 |
| Relation: |
https://www.mdpi.com/1996-1073/13/14/3671; https://doaj.org/toc/1996-1073 |
| DOI: |
10.3390/en13143671 |
| Access URL: |
https://doaj.org/article/eab6308ff3eb40ac92929573c423016d |
| Accession Number: |
edsdoj.b6308ff3eb40ac92929573c423016d |
| Database: |
Directory of Open Access Journals |
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