Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid


Autoria(s): Soares, João; Sousa, Tiago; Morais, H.; Vale, Zita; Canizes, Bruno; Silva, António S.
Data(s)

21/01/2014

21/01/2014

2013

Resumo

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Identificador

http://dx.doi.org/10.1016/j.asoc.2013.07.003

1568-4946

http://hdl.handle.net/10400.22/3389

Idioma(s)

eng

Publicador

Elsevier

Relação

Applied Soft Computing; Vol. 13, Issue 11

http://www.sciencedirect.com/science/article/pii/S1568494613002299

Direitos

openAccess

Palavras-Chave #Application specific algorithm #Hard combinatorial scheduling #Particle swarm optimization #Vehicle-to-grid scheduling
Tipo

article