Optimal Transmission Switching Problem Utilizing an Enhanced Multi-Phase Seeker Optimization Algorithm

2016 ◽  
Vol 24 ◽  
pp. 85-102 ◽  
Author(s):  
Ragab A. El-Sehiemy ◽  
Mohammed Badeaa Shafiq ◽  
Ahmed M. Azmy
Keyword(s):  
Transmission Lines ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Savings ◽  
Production Costs ◽  
Voltage Deviation ◽  
Multi Phase ◽  
The Cost ◽  
Seeker Optimization Algorithm

This paper proposes a procedure based on a multi-phase seeker optimization algorithm (MSOA) for optimizing the commitment of transmission system. The under consideration problem is formulated with the aid of AC-based security constrained optimal power flow (SC-OPF) considering system constraints. The target is to detect transmission lines commitment schedule that reduces system production costs and enables sufficient reserve levels from both generation and transmission. The methodology is illustrated through several computational tests on IEEE 57 and IEEE 118 bus test systems to confirm the previous objectives. It is proven that numerical results based on the use the AC model demonstrate that the calculation time is short enough and the cost savings are reasonably better than DC power flow model. In addition, all transmission lines are preserved within their permissible boundaries and the voltage deviation is maintained at the least levels.

scholarly journals An approach to solve OPF problems using a novel hybrid whale and sine cosine optimization algorithm

2021 ◽  
pp. 1-11
Author(s):  
Ramesh Devarapalli ◽  
B. Venkateswara Rao ◽  
Bishwajit Dey ◽  
K. Vinod Kumar ◽  
H. Malik ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
System Performance ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Techniques ◽  
Power Losses ◽  
Optimal Power ◽  
Generation Cost ◽  
Voltage Deviation ◽  
Power System Performance

Nowadays, improvement in power system performance is essential to obtaine economic and technical benifits. To achieve this, optimize the large number of parameters in the system based on optimal power flow(OPF). For solving OPF problem efficiently, it needs robust and fast optimization techniques. This paper proposes the application of a newly developed hybrid Whale and Sine Cosine optimization algorithm to solve the OPF. It has been implemented for optimization of the control variables. The reduction of true power generation cost, emission, true power losses, and voltage deviation are considered as different objectives. The hybrid Whale and Sine Cosine optimization is validated by solving OPF problem with various intentions using IEEE30 bus system. To varidate the proposed technique, the results obtained from this are compared with other methods in the literature. The robustness achieved with the proposed algorithm has been analyzed for the considered OPF problem using statistical analysis and whisker plots.

scholarly journals Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4310 ◽  
Author(s):  
Zelan Li ◽  
Yijia Cao ◽  
Le Van Dai ◽  
Xiaoliang Yang ◽  
Thang Trung Nguyen
Keyword(s):  
Success Rate ◽  
Transmission Lines ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transmission Power ◽  
Power Networks ◽  
Solution Exchange ◽  
Optimal Power ◽  
Conventional Methods

In the paper, a modified coyote optimization algorithm (MCOA) is proposed for finding highly effective solutions for the optimal power flow (OPF) problem. In the OPF problem, total active power losses in all transmission lines and total electric generation cost of all available thermal units are considered to be reduced as much as possible meanwhile all constraints of transmission power systems such as generation and voltage limits of generators, generation limits of capacitors, secondary voltage limits of transformers, and limit of transmission lines are required to be exactly satisfied. MCOA is an improved version of the original coyote optimization algorithm (OCOA) with two modifications in two new solution generation techniques and one modification in the solution exchange technique. As compared to OCOA, the proposed MCOA has high contributions as follows: (i) finding more promising optimal solutions with a faster manner, (ii) shortening computation steps, and (iii) reaching higher success rate. Three IEEE transmission power networks are used for comparing MCOA with OCOA and other existing conventional methods, improved versions of these conventional methods, and hybrid methods. About the constraint handling ability, the success rate of MCOA is, respectively, 100%, 96%, and 52% meanwhile those of OCOA is, respectively, 88%, 74%, and 16%. About the obtained solutions, the improvement level of MCOA over OCOA can be up to 30.21% whereas the improvement level over other existing methods is up to 43.88%. Furthermore, these two methods are also executed for determining the best location of a photovoltaic system (PVS) with rated power of 2.0 MW in an IEEE 30-bus system. As a result, MCOA can reduce fuel cost and power loss by 0.5% and 24.36%. Therefore, MCOA can be recommended to be a powerful method for optimal power flow study on transmission power networks with considering the presence of renewable energies.

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

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