Particle swarm optimization algorithm for dynamic synchronization of smart grid

ABSTRACT

In recent decades, traditional energy generation and consumption have undergone a transformation to cleaner energy sources. The transition to a sustainable energy system based on efficiency and renewable energies will thus require the replacement of a complex and heavily implemented energy system with one based on concepts such as microgrids. A microgrid is a controlled small-scale power system and the elements that make up it are: distributed generation systems; energy storage systems; load management techniques; monitoring systems, etc. Consequently, since ac devices are used, synchronization criteria must be satisfied to switch operation between them. The synchronization criteria consist of making the values of the phase-angle difference, slip frequency, and voltage difference as small as possible. This is very important to ensure the correct operation of the grid. In the other hand, the use of Particle Swarm Optimization (PSO) algorithm has been increasing, because of their simplicity and efficiency in engineering optimization problems. Therefore, this paper proposes an active synchronizing control scheme that allows to synchronize a generator set to the grid and a particle swarm optimization algorithm, which makes the synchronizing control as efficient as possible. With the implemented control and with the help of the synchronization algorithm, improvements in the reduction of settling time (from 9 seconds to 3 seconds) are obtained in comparison to other controls in the literature.

KEYWORDS:

• Smart grid
• microgrid
• synchronization
• genset
• Particle swarm optimization (PSO)
• genetic algorithms
• intelligent optimization algorithms

Abbreviations

DG: Distributed Generation

AC: Altern Current

DC: Direct Current

SG: Smart Grid

MG: Microgrid

DER: Distributed Energy Resources

PSO: Particle Swarm Optimization

DE: Differential Evolution

SM: Synchronous Machine

PLL: Phase-Locked Loop

Acknowledgments

The authors are grateful for the support provided by the SGIker of UPV/EHU.

Author contributions

Conceptualization, I.A. and N.R.; methodology, I.A. and N.R.; software, E.Z., I.A. and N.R.; validation, E.Z. and U.F.-G.; formal analysis, I.A., N.R and A.Z.; investigation, I.A., N.R. and E.Z.; resources, U.F.-G. and E.Z.; data curation, E.Z.; writing—original draft preparation, I.A. and N.R.; writing—review and editing, E.Z. and U.F.-G.; visualization, A.Z.; supervision, U.F.-G.; project administration, U.F.-G.; funding acquisition, E. Z. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors are thankful to the Government of the Basque Country for the ELKARTEK21/10-KK-2021/00014 (Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industriales) and ELKARTEK21/KK-2021/00087 (Control Activo y Gestión de Almacenamiento para SmartGrid mediante paralelado de Convertidores Electrónicos, grupos Electrógenos y pila de hidrógeno, SmartCGH) research programs.