scholarly journals A Hybrid Differential Evolution and Harmony Search for Optimal Power Flow With FACTS Devices

2020 ◽  
Vol 11 (3) ◽  
pp. 39-65
Author(s):  
Luong Dinh Le ◽  
Dieu Ngoc Vo ◽  
Sy T. Huynh ◽  
Tuan Minh Nguyen-Hoang ◽  
Pandian Vasant
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Phase Shifter ◽  
Harmony Search ◽  
Stability Index ◽  
Solution Quality ◽  
Facts Devices ◽  
Optimal Power

This paper proposes a hybrid differential evolution (DE) and harmony search (HS) for solving optimal power flow (OPF) problem with FACTS devices including static Var compensator (SVC), thyristor-controlled series compensation (TCSA), and thyristor-controlled phase shifter (TCPS). The proposed hybrid DE-HS is to utilize the advantages of the DE and HS methods to enhance its search ability for dealing with large-scale and complex problems. The proposed method has been tested on the IEEE 30 bus system with the variety of objective functions including quadratic fuel cost, power loss, voltage deviation, and voltage stability index and the obtained results from the proposed hybrid DE-HS have been compared to those from other algorithms. The result comparison has indicated that the proposed hybrid DE-HS algorithm can obtain better solution quality than many other methods. Therefore, the proposed hybrid DE-HS method can be an efficient method for solving the OPF problem incorporating FACTS devices.

Optimal Power Flow with TCSC and TCPS Modeling using Craziness and Turbulent Crazy Particle Swarm Optimization

2010 ◽  
Vol 1 (3) ◽  
pp. 34-50 ◽  
Author(s):  
P. K. Roy ◽  
S. P. Ghoshal ◽  
S. S. Thakur
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Facts Devices ◽  
Computation Efficiency ◽  
Optimal Power ◽  
Real Coded Genetic Algorithm

This paper presents two new Particle swarm optimization methods to solve optimal power flow (OPF) in power system incorporating flexible AC transmission systems (FACTS). Two types of FACTS devices, thyristor-controlled series capacitor (TCSC) and thyristor controlled phase shifting (TCPS), are considered. In this paper, the problems of OPF with FACTS are solved by using particle swarm optimization with the inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), craziness based particle swarm optimization (CRPSO), and turbulent crazy particle swarm optimization (TRPSO). The proposed methods are implemented on modified IEEE 30-bus system for four different cases. The simulation results show better solution quality and computation efficiency of TRPSO and CRPSO algorithms over PSOIWA and RGA. The study also shows that FACTS devices are capable of providing an economically attractive solution to OPF problems.

scholarly journals A Fuzzy Based Evolutionary Algorithm for Solving Multiobjective Optimal Power Flow with FACTS Devices

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
R. Vanitha ◽  
J. Baskaran
Keyword(s):  
Power System ◽  
Differential Evolution ◽  
Goal Programming ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Technique ◽  
Fuzzy Goal Programming ◽  
Facts Devices ◽  
Optimal Power ◽  
Fuzzy Goal

A new Fuzzy Differential Evolution (FDE) algorithm is proposed for solving multiobjective optimal power flow with FACTS devices. This new optimization technique combines the advantages of Weighted Additive Fuzzy Goal Programming (WAFGP) and Differential Evolution (DE) in enhancing the capacity, stability, and security of the power system. As the weights used in WAFGP would have a significant impact on the operational and economical enhancements achieved in the optimization, they are optimized using evolutionary DE algorithm. This provides a way for exploring a balanced solution for a multiobjective problem without sacrificing any individual objective’s uniqueness and priority. The multiple objectives considered are maximizing the loadability condition of the power system with minimum system real power loss and minimum installation cost of the FACTS devices. Indian utility Neyveli Thermal Power Station (NTPS) 23 bus system is used to test the proposed algorithm using multiple FACTS devices. The results compared with that of DE based fuzzy goal programming (FGP) demonstrates that DE based WAFGP algorithm not only provides a balanced optimal solution for all objectives but also provides the best economical solution.

Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm

2017 ◽  
Vol 11 (1) ◽  
pp. 177-192
Author(s):  
Gonggui Chen ◽  
Zhengmei Lu ◽  
Zhizhong Zhang ◽  
Zhi Sun
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Power Losses ◽  
De Algorithm ◽  
Optimal Power ◽  
Evolution Algorithm

Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

Optimal Power Flow with TCSC and TCPS Modeling using Craziness and Turbulent Crazy Particle Swarm Optimization

2012 ◽  
pp. 148-164
Author(s):  
P. K. Roy ◽  
S. P. Ghoshal ◽  
S. S. Thakur
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Flexible Ac Transmission ◽  
Facts Devices ◽  
Optimal Power

This paper presents two new Particle swarm optimization methods to solve optimal power flow (OPF) in power system incorporating flexible AC transmission systems (FACTS). Two types of FACTS devices, thyristor-controlled series capacitor (TCSC) and thyristor controlled phase shifting (TCPS), are considered. In this paper, the problems of OPF with FACTS are solved by using particle swarm optimization with the inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), craziness based particle swarm optimization (CRPSO), and turbulent crazy particle swarm optimization (TRPSO). The proposed methods are implemented on modified IEEE 30-bus system for four different cases. The simulation results show better solution quality and computation efficiency of TRPSO and CRPSO algorithms over PSOIWA and RGA. The study also shows that FACTS devices are capable of providing an economically attractive solution to OPF problems.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

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