A novel multi‐objective unified optimal power flow based methodology for optimal installation of VSC‐HVDC converter. (24th May 2021)

• Record Type: Journal Article
• Authors: Agrawal, Sunilkumar; Kundu, Prasanta
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-24
• Subjects: multi‐objective particle swarm optimization (MO‐PSO) -- multi‐objective unified optimal power flow (MO‐UOPF) modelling -- VSC‐HVDC; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.12956
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: This paper proposes a novel methodology for installing a converter station at optimal AC bus in a hybrid AC‐DC system using a multi‐objective unified optimal power flow (MO‐UOPF). The MO‐UOPF model is developed here for the voltage source converter‐high voltage direct current (VSC‐HVDC) network. The various techno‐economic objectives are considered for the OPF problem: minimizing total generation cost, AC/DC transmission line losses with converter station losses, enhancement of voltage stability improvement, and reduction in CO2 gas emissions. The particle swarm optimization (PSO) algorithm has proven its superiority over many meta‐heuristic algorithms for solving the multi‐objective optimization problem. Also, various improved versions of PSO have been developed since its inception. Here, the MO‐UOPF problem is solved using a modified multi‐objective particle swarm optimization (MO‐PSO) technique with a simultaneous approach. The proposed methodology is analyzed with modified 5‐bus and IEEE‐30 bus test systems with different configurations for two‐terminal point‐to‐point transmission. Abstract : This work mainly finds the optimal buses for converter installation by minimizing the multi‐objective functions. The unified optimal power flow approach is modelled here and solved with MO‐PSO algorithm with modifying cognitive and social component in particle velocity search. The above images show the optimal pareto fronts for multi‐objective case for most optimal converter location for different test system.