GPU‐based parallel real‐time volt/var optimisation for distribution network considering distributed generators. Issue 20 (7th June 2018)

• Record Type: Journal Article
• Authors: Huang, Shengjun; Dinavahi, Venkata
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 20(2018)
• Page Start: 4472
• Page End: 4481
• Publication Date: 2018-06-07
• Subjects: distribution networks -- distributed power generation -- particle swarm optimisation -- graphics processing units -- parallel programming -- data structures -- load flow -- on load tap changers -- iterative methods -- public domain software -- software packages -- power engineering computing; GPU‐based parallel real‐time volt‐var optimisation -- distribution network -- distributed generators -- advanced metering infrastructure -- full AC real‐time VVO -- particle swarm optimisation framework -- PSO framework -- direct approach power flow method -- DA power flow method -- distributed generator -- on‐load tap changer transformer -- iterative DA process -- graphics processing unit -- data structure -- thread organisation pattern -- Matpower open source package -- CPU sequential program -- CPU parallel program -- GPU parallel program; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.1887
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Although the wide integration of advanced metering infrastructure on distribution network facilitates the application of volt/var optimisation (VVO) in real‐time circumstance, the contradiction between heavy computation load and low solution efficiency is still a big challenge, thus the system scales investigated in the literature are limited. In this study, the full AC real‐time VVO is formulated based on particle swarm optimisation (PSO) framework and direct approach (DA) power flow method, where all components, such as distributed generator and on‐load tap changer transformer, are formulated and integrated into the iterative DA process. Since both PSO and DA are suitable for parallel implementation, the graphics processing unit (GPU) is introduced for acceleration in order to achieve the possibility for real‐time application. All the solution process is executed by GPU with the well‐established data structure and thread organisation pattern, resulting in high efficiency by guaranteeing coalesced access within each warp. Case studies are conducted on four systems with sizes ranging from 136‐bus to 1760‐bus. Solution accuracy and convergence property are validated by the popular open source package Matpower. Based on the results from solution efficiency comparison between CPU sequential, CPU parallel, and GPU parallel programs, the promise of the proposed parallel implementation scheme for practical application is established.