Hybrid cross entropy—cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads

2021 ◽  
Author(s):  
Jigar Sarda ◽  
Kartik Pandya ◽  
Kwang Y. Lee
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Cross Entropy ◽  
Optimal Power ◽  
Hybrid Cross

scholarly journals Optimal Power Flow Using Gbest-Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Gonggui Chen ◽  
Siyuan Qiu ◽  
Zhizhong Zhang ◽  
Zhi Sun ◽  
Honghua Liao
Keyword(s):  
Feedback Control ◽  
Control Strategy ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Penalty Function Method ◽  
Optimal Power ◽  
Feedback Control Strategy

The optimal power flow (OPF) is well-known as a significant optimization tool for the security and economic operation of power system, and OPF problem is a complex nonlinear, nondifferentiable programming problem. Thus this paper proposes a Gbest-guided cuckoo search algorithm with the feedback control strategy and constraint domination rule which is named as FCGCS algorithm for solving OPF problem and getting optimal solution. This FCGCS algorithm is guided by the global best solution for strengthening exploitation ability. Feedback control strategy is devised to dynamically regulate the control parameters according to actual and specific feedback value in the simulation process. And the constraint domination rule can efficiently handle inequality constraints on state variables, which is superior to traditional penalty function method. The performance of FCGCS algorithm is tested and validated on the IEEE 30-bus and IEEE 57-bus example systems, and simulation results are compared with different methods obtained from other literatures recently. The comparison results indicate that FCGCS algorithm can provide high-quality feasible solutions for different OPF problems.

Multi-Objective Optimal Power Flow Using Metaheuristic Optimization Algorithms With Unified Power Flow Controller to Enhance the Power System Performance

2018 ◽  
pp. 1-33
Author(s):  
G. V. Nagesh Kumar ◽  
B. Venkateswara Rao ◽  
D. Deepak Chowdary ◽  
Polamraju V. S. Sobhan
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Unified Power Flow Controller ◽  
Multi Objective ◽  
Optimal Power

In this chapter a multi objective optimal power flow (OPF) is obtained by using latest Metaheuristic optimization techniques BAT search algorithm (BAT), cuckoo search algorithm (CSA) and firefly algorithm (FA) with Unified power flow controller (UPFC). UPFC is a voltage source converter type Flexible Alternating Current Transmission System (FACTS) device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. To enhance the power system performance, the optimal power flow has been incorporated UPFC along with BAT algorithm, cuckoo search algorithm and firefly algorithm based multi objective function comprising of two objectives those are total real power loss and the fuel cost of total real power generation. The BAT algorithm, cuckoo search algorithm and firefly algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm, cuckoo search algorithm and firefly algorithms are compared with Differential Evaluation (DE).

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search

scholarly journals Minimization of Risks of Voltage and Frequency Violations in Islanded Microgrids using Robust Probabilistic Optimal Power Flow

2021 ◽  
Author(s):  
Elton A. Chagas ◽  
Anselmo B. Rodrigues ◽  
Maria G. Silva
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Computational Cost ◽  
Forecast Errors ◽  
Droop Control ◽  
Control Parameters ◽  
Distributed Generators ◽  
Optimal Power

The main aim of this paper is to propose a robust probabilistic optimal power flow model to determine the droop control parameters for the Distributed Generators (DG) of a islanded microgrid. The term robust is related to the droop control parameters being immune to uncertainties associated with: load forecast errors, DG outages and variability of power output in renewable DG. This optimization problem is solved by an improved gravitational search algorithm (GSA). The test results demonstrated that the proposed method can achieve significant reductions in the load curtailments due to frequency and voltage violations. In addition, a comparison between GSA and the Particle Swarm Optimization (PSO) demonstrated that GSA is more suitable for evaluating the droop control parameters than PSO in relation to the computational cost and the optimal quality of the solution.

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