Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems

Energy ◽  
2016 ◽  
Vol 101 ◽  
pp. 506-518 ◽  
Author(s):  
A.Y. Abdelaziz ◽  
E.S. Ali ◽  
S.M. Abd Elazim
Keyword(s):  
Power Systems ◽  
Flower Pollination Algorithm ◽  
Economic Load Dispatch ◽  
Flower Pollination ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

scholarly journals Hybrid Flower Pollination Algorithm with Sequential Quadratic Programming Technique for Solving Dynamic Combined Economic Emission Dispatch Problem

2021 ◽  
Vol 40 (2) ◽  
pp. 371-382
Author(s):  
Rabia Noreen Gul ◽  
Aftab Ahmad ◽  
Saqib Fayyaz ◽  
Muhammad Kashif Sattar ◽  
Syed Saddam ul Haq
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
System Optimization ◽  
Test System ◽  
Flower Pollination Algorithm ◽  
Time Interval ◽  
Flower Pollination ◽  
Conflicting Objectives ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

This paper presents the solution of highly complex, non-linear, multi-objective Dynamic Combined Economic Emission Dispatch (DCEED) problem. DCEED is a power system optimization problem with conflicting objectives of fuel cost and emission. DCEED includes constraints like valve point loading effect, Transmission Losses and Ramp Rate limits. Solution of DCEED problem is given by a novel Hybridized Flower Pollination Algorithm (FPA) with Sequential Quadratic Programming (SQP). FPA is a nature inspired population based meta-heuristic optimization technique that models its search on the flower pollination process. The non-convex nature of generation because of numerous operational, physical and dynamic constraints, makes search space highly multi model and complex. This makes DCEED a challenging as well as an attractive problem for research. The effectiveness of FPA-SQP is tested and validated by applying it on IEEE Standard 5-unit and 10-unit non-convex test system in MATLAB environment for the time interval of 24 hours. The results achieved by this algorithm show significant reduction in cost and emission as compared to other available techniques in the literature.

scholarly journals Optimal Voltage Stability Improvement under Contingencies using Flower Pollination Algorithm and Thyristor Controlled Series Capacitor

2018 ◽  
Vol 12 (2) ◽  
pp. 497
Author(s):  
Zulkiffli Abdul Hamid ◽  
Ismail Musirin ◽  
Muhammad Amirul Adli Nan ◽  
Zulkifli Othman
Keyword(s):  
Power Systems ◽  
Voltage Stability ◽  
Reactive Power ◽  
Computation Time ◽  
Flower Pollination Algorithm ◽  
Stability Problems ◽  
Total Transmission ◽  
Flower Pollination ◽  
Thyristor Controlled Series Capacitor ◽  
Series Capacitor

Recent power systems necessitate for maintaining a safe voltage stability as the number of problems such as contingencies and reactive power insufficiency are increasing. In this paper, installation and sizing of Flexible Alternating Current Transmission System (FACTS) devices have been introduced for solving the voltage stability problems under contingencies. The FACTS device to be used is Thyristor Controlled Series Capacitor (TCSC). Besides improving the voltage magnitude at all buses to a desired level, installation of TCSC at proper locations can minimize total transmission losses of the system. To conduct the sizing task, the newly developed Flower Pollination Algorithm (FPA) has been implemented as the engine for optimization. Through experimentation, the results proved that the proposed placement and sizing technique has successfully mitigated the voltage stability problems. In addition, the computation time for FPA’s convergence was tolerable with optimum results.

Quantum-Inspired Computational Intelligence for Economic Emission Dispatch Problem

2020 ◽  
pp. 634-657
Author(s):  
Fahad Parvez Mahdi ◽  
Pandian Vasant ◽  
Vish Kallimani ◽  
M. Abdullah-Al-Wadud ◽  
Junzo Watada
Keyword(s):  
Global Warming ◽  
Power Systems ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Teaching And Learning ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Emission Dispatch ◽  
Further Development ◽  
Economic Emission Dispatch

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limited reserves of fossil fuel and global warming make this topic into the center of discussion and research. In this chapter, we will discuss the use and scope of different quantum inspired computational intelligence (QCI) methods for solving EED problems. We will evaluate each previously used QCI methods for EED problem and discuss their superiority and credibility against other methods. We will also discuss the potentiality of using other quantum inspired CI methods like quantum bat algorithm (QBA), quantum cuckoo search (QCS), and quantum teaching and learning based optimization (QTLBO) technique for further development in this area.

scholarly journals Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sahil Lalljith ◽  
Ismail Fleming ◽  
Umeshan Pillay ◽  
Kiveshen Naicker ◽  
Zachary Naidoo ◽  
...  
Keyword(s):  
Power Systems ◽  
Electrical Power ◽  
Flower Pollination Algorithm ◽  
Electrical Power Systems ◽  
Flower Pollination

Quantum-Inspired Computational Intelligence for Economic Emission Dispatch Problem

2017 ◽  
pp. 445-468 ◽  
Author(s):  
Fahad Parvez Mahdi ◽  
Pandian Vasant ◽  
Vish Kallimani ◽  
M. Abdullah-Al-Wadud ◽  
Junzo Watada
Keyword(s):  
Global Warming ◽  
Power Systems ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Teaching And Learning ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Emission Dispatch ◽  
Further Development ◽  
Economic Emission Dispatch

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limited reserves of fossil fuel and global warming make this topic into the center of discussion and research. In this chapter, we will discuss the use and scope of different quantum inspired computational intelligence (QCI) methods for solving EED problems. We will evaluate each previously used QCI methods for EED problem and discuss their superiority and credibility against other methods. We will also discuss the potentiality of using other quantum inspired CI methods like quantum bat algorithm (QBA), quantum cuckoo search (QCS), and quantum teaching and learning based optimization (QTLBO) technique for further development in this area.

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