Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

被引:49
作者
Hassan, Mohamed H. [1 ]
Kamel, Salah [1 ]
El-Dabah, Mahmoud A. [2 ]
Khurshaid, Tahir [3 ]
Dominguez-Garcia, Jose Luis [4 ]
机构
[1] Aswan Univ, Fac Engn, Dept Elect Engn, Aswan 81542, Egypt
[2] Al Azhar Univ, Fac Engn, Elect Engn Dept, Cairo 11651, Egypt
[3] Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea
[4] Catalonia Inst Energy Res IREC, St Adria de Besos 08930, Spain
关键词
Optimization; Uncertainty; Reactive power; Wind speed; Renewable energy sources; Probability density function; Generators; Renewable energy; uncertainty; time-varying demand; optimal reactive power dispatch (ORPD); RAO algorithm; backward reduction algorithm;
D O I
10.1109/ACCESS.2021.3056423
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem.
引用
收藏
页码:23264 / 23283
页数:20
相关论文
共 78 条
[1]  
Abdel-Fatah S, 2019, PROC INT MID EAST P, P594, DOI [10.1109/MEPCON47431.2019.9008183, 10.1109/mepcon47431.2019.9008183]
[2]  
Abdel-Fatah S, 2019, 2019 IEEE CONFERENCE ON POWER ELECTRONICS AND RENEWABLE ENERGY (IEEE CPERE), P118, DOI [10.1109/cpere45374.2019.8980056, 10.1109/CPERE45374.2019.8980056]
[3]  
Abdel-Fatah S, 2019, PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN COMPUTER ENGINEERING (ITCE 2019), P510, DOI [10.1109/ITCE.2019.8646460, 10.1109/itce.2019.8646460]
[4]   Differential evolution algorithm for optimal reactive power dispatch [J].
Abou El Ela, A. A. ;
Abido, M. A. ;
Spea, S. R. .
ELECTRIC POWER SYSTEMS RESEARCH, 2011, 81 (02) :458-464
[5]   Optimal reactive power dispatch using ant colony optimization algorithm [J].
Abou El-Ela, A. A. ;
Kinawy, A. M. ;
El-Sehiemy, R. A. ;
Mouwafi, M. T. .
ELECTRICAL ENGINEERING, 2011, 93 (02) :103-116
[6]   On possibilistic and probabilistic uncertainty assessment of power flow problem: A review and a new approach [J].
Aien, Morteza ;
Rashidinejad, Masoud ;
Fotuhi-Firuzabad, Mahmud .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 37 :883-895
[7]   AEFA: Artificial electric field algorithm for global optimization [J].
Anita ;
Yadav, Anupam .
SWARM AND EVOLUTIONARY COMPUTATION, 2019, 48 :93-108
[8]  
[Anonymous], 2020, POWER SYSTEMS TEST C
[9]   Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization [J].
Atwa, Y. M. ;
El-Saadany, E. F. ;
Salama, M. M. A. ;
Seethapathy, R. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2010, 25 (01) :360-370
[10]   Optimal power flow solutions incorporating stochastic wind and solar power [J].
Biswas, Partha P. ;
Suganthan, P. N. ;
Amaratunga, Gehan A. J. .
ENERGY CONVERSION AND MANAGEMENT, 2017, 148 :1194-1207