Modified Grey Wolf Optimization Algorithm for Transmission Network Expansion Planning Problem

2017 ◽  
Vol 43 (6) ◽  
pp. 2899-2908 ◽  
Author(s):  
Ashish Khandelwal ◽  
Annapurna Bhargava ◽  
Ajay Sharma ◽  
Harish Sharma
Keyword(s):  
Optimization Algorithm ◽  
Planning Problem ◽  
Transmission Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Network Expansion ◽  
Expansion Planning

Security constrained transmission network expansion planning using grey wolf optimization algorithm

2019 ◽  
Vol 22 (7) ◽  
pp. 1239-1249 ◽  
Author(s):  
Ashish Khandelwal ◽  
Annapurna Bhargava ◽  
Ajay Sharma ◽  
Nirmala Sharma
Keyword(s):  
Optimization Algorithm ◽  
Transmission Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Network Expansion ◽  
Expansion Planning

Transmission Network Expansion Planning with Security Constraints and Uncertainty in Load Specifications

2008 ◽  
Vol 9 (4) ◽  
Author(s):  
Ashu Verma ◽  
Pradeep R. Bijwe ◽  
Bijaya Ketan Panigrahi
Keyword(s):  
Power Flow ◽  
Planning Problem ◽  
Transmission Network ◽  
Network Expansion ◽  
Security Issues ◽  
Expansion Planning ◽  
Dc Power ◽  
Sample Test ◽  
Binary Genetic Algorithm ◽  
Long Range Planning

Transmission network expansion planning is a very critical problem due to not only the huge investment cost involved, but also the associated security issues. Any long range planning problem is confronted with the challenge of non-statistical uncertainty in the data. Although large number of papers have been published in this area, the efforts to tackle the above mentioned security and uncertainty issues have been relatively very few, due to the formidable complexity involved. This paper tries to bridge this gap by proposing a technique to tackle these problems. Boundary DC power flow is used to ascertain the worst power flows on the lines. A simple basic binary Genetic algorithm is used to solve the optimization problem as an illustration. Results for two sample test systems have been obtained to demonstrate the potential of the proposed method.

Transmission Network Expansion Planning Considering Optimal Allocation of Series Capacitive Compensation and Active Power Losses

2021 ◽  
Vol 12 (1) ◽  
pp. 388
Author(s):  
Dany H. Huanca ◽  
Luis A. Gallego ◽  
Jesús M. López-Lezama
Keyword(s):  
Genetic Algorithm ◽  
Test System ◽  
Active Power ◽  
Mixed Integer ◽  
Planning Problem ◽  
Transmission Network ◽  
Power Losses ◽  
Network Expansion ◽  
Expansion Planning ◽  
Active Power Losses

This paper presents a modeling and solution approach to the static and multistage transmission network expansion planning problem considering series capacitive compensation and active power losses. The transmission network expansion planning is formulated as a mixed integer nonlinear programming problem and solved through a highly efficient genetic algorithm. Furthermore, the Villasana Garver’s constructive heuristic algorithm is implemented to render the configurations of the genetic algorithm feasible. The installation of series capacitive compensation devices is carried out with the aim of modifying the reactance of the original circuit. The linearization of active power losses is done through piecewise linear functions. The proposed model was implemented in C++ language programming. To show the applicability and effectiveness of the proposed methodology several tests are performed on the 6-bus Garver system, the IEEE 24-bus test system, and the South Brazilian 46-bus test system, presenting costs reductions in their multi-stage expansion planning of 7.4%, 4.65% and 1.74%, respectively.

scholarly journals Transmission Network Expansion Planning Considering Phase-Shifter Transformers

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Celso T. Miasaki ◽  
Edgar M. C. Franco ◽  
Ruben A. Romero
Keyword(s):  
Mathematical Model ◽  
Transmission Lines ◽  
Phase Shifter ◽  
Planning Process ◽  
Main Idea ◽  
Mixed Integer ◽  
Planning Problem ◽  
Transmission Network ◽  
Network Expansion ◽  
Expansion Planning

This paper presents a novel mathematical model for the transmission network expansion planning problem. Main idea is to consider phase-shifter (PS) transformers as a new element of the transmission system expansion together with other traditional components such as transmission lines and conventional transformers. In this way, PS are added in order to redistribute active power flows in the system and, consequently, to diminish the total investment costs due to new transmission lines. Proposed mathematical model presents the structure of a mixed-integer nonlinear programming (MINLP) problem and is based on the standard DC model. In this paper, there is also applied a specialized genetic algorithm aimed at optimizing the allocation of candidate components in the network. Results obtained from computational simulations carried out with IEEE-24 bus system show an outstanding performance of the proposed methodology and model, indicating the technical viability of using these nonconventional devices during the planning process.

scholarly journals Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

2021 ◽  
Vol 11 (5) ◽  
pp. 2155
Author(s):  
Mohamed M. Refaat ◽  
Shady H. E. Abdel Aleem ◽  
Yousry Atia ◽  
Ziad M. Ali ◽  
Mahmoud M. Sayed
Keyword(s):  
Fuzzy Inference ◽  
Optimization Problems ◽  
Planning Problem ◽  
Transmission Network ◽  
Inference System ◽  
Network Expansion ◽  
Expansion Planning ◽  
Covariance Matrix Adaptation ◽  
Transmission Expansion ◽  
Multi Stage

This paper introduces a multi-stage dynamic transmission network expansion planning (MSDTNEP) model considering the N-1 reliability constraint. The integrated planning problem of N-1 security and transmission expansion planning is essential because a single line outage could be a triggering event to rolling blackouts. Two suggested scenarios were developed to obtain the optimal configuration of the Egyptian West Delta Network’s realistic transmission (WDN) to meet the demand of the potential load growth and ensure the system reliability up to the year 2040. The size of a blackout, based on the amount of expected energy not supplied, was calculated to evaluate both scenarios. The load forecasting (up to 2040) was obtained based on an adaptive neuro-fuzzy inference system because it gives excellent results compared to conventional methods. The linear population size reduction—Success-History-based Differential Evolution with semi-parameter adaptation (LSHADE-SPA) hybrid—covariance matrix adaptation evolution strategy (CMA-ES) algorithm (LSHADE-SPACMA)—is proposed to solve the problem. The semi-adaptive nature of LSHADE-SPACMA and the hybridization between LSHADE and CMA-ES are able to solve complex optimization problems. The performance of LSHADE-SPACMA in solving the problem is compared to other well-established methods using three testing systems to validate its superiority. Then, the MSDTNEP of the Egyptian West Delta Network is presented, and the numerical results of the two scenarios are compared to obtain an economic plan and avoid a partial or total blackout.

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