Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power

Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this...

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Veröffentlicht in:Mathematics (Basel) Jg. 10; H. 3; S. 361
Hauptverfasser: Mohamed, Amal Amin, Kamel, Salah, Hassan, Mohamed H., Mosaad, Mohamed I., Aljohani, Mansour
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.02.2022
Schlagworte:
Algorithms
Alternative energy sources
Case studies
Compensators
Electric power
Electric power grids
Electric power systems
Electricity distribution
Emissions
FACTS devices
Generators
gradient-based optimizer and moth–flame optimization algorithm
Heuristic
Hybrid systems
hybrid technique
Industrial plant emissions
Linear programming
Mathematics
Multiple objective analysis
Network control
optimal power flow
Optimization
Optimization algorithms
Optimization techniques
Phase shifters
Power flow
Power plants
Probability density functions
Production costs
Renewable energy sources
Thermal power plants
Thyristors
transmission line
Variables
Wind power
ISSN:2227-7390, 2227-7390
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Zusammenfassung:Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this issue. This work presents the optimal location and size of some FACTS devices in a hybrid power system containing stochastic wind and traditional thermal power plants considering OPF. The FACTS devices used are thyristor-controlled series compensator (TCSC), thyristor-controlled phase shifter (TCPS), and static var compensator (SVC). This optimal location and size of FACTS devices was determined by introducing a multi-objective function containing reserve costs for overestimation and penalty costs for underestimating intermittent renewable sources besides active power losses. The uncertainty in the wind power output is predicted using Weibull probability density functions. This multi-objective function is optimized using a hybrid technique, gradient-based optimizer (GBO), and moth–flame optimization algorithm (MFO).
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ISSN:2227-7390
2227-7390
DOI:10.3390/math10030361