Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving

Hannan M A, M. G.M. Abdolrasol, M. Faisal, P. J. Ker, Rawshan Ara Begum, A. Hussain

Research output: Contribution to journalArticle

Abstract

This paper introduces a novel optimal schedule controller to manage renewable energy resources (RESs) in virtual power plant (VPP) using binary particle swarm optimization (BPSO) algorithm. It is crucial to minimize the costs giving priority for sustainable resources use instead of purchasing from the national grid. The effectiveness of the proposed approach is examined by the IEEE 14 bus system containing microgrids (MGs) integrated with RESs in the form of VPP. Real load demand recorded is used to model and simulate the test case studies of the system for 24 h in Perlis, Malaysia. Moreover, weather data collected from the Malaysian Meteorological Department such as wind, solar, fuel, and battery status data are used in the BPSO to find the best ON and OFF schedules. The results found that the developed BPSO algorithm is robust in reducing energy consumption and emissions of the VPP. This study contributes to the development of an optimization algorithm for an optimal scheduling controller of MG integrated VPP in order to reduce carbon emissions and manage sustainable energy. Finally, a comparative analysis of the optimal algorithms over conventional justifies the use of RESs integration and validates the developed BPSO for sustainable energy management and emissions reduction.

Original languageEnglish
Article number8787762
Pages (from-to)107937-107951
Number of pages15
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Particle swarm optimization (PSO)
Energy conservation
Power plants
Scheduling
Renewable energy resources
Controllers
Solar wind
Energy management
Purchasing
Carbon
Energy utilization
Costs

Keywords

  • carbon reduction
  • energy management
  • microgrid
  • optimization
  • scheduling controller
  • Virtual power plant

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

M A, H., Abdolrasol, M. G. M., Faisal, M., Ker, P. J., Begum, R. A., & Hussain, A. (2019). Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving. IEEE Access, 7, 107937-107951. [8787762]. https://doi.org/10.1109/ACCESS.2019.2933010

Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving. / M A, Hannan; Abdolrasol, M. G.M.; Faisal, M.; Ker, P. J.; Begum, Rawshan Ara; Hussain, A.

In: IEEE Access, Vol. 7, 8787762, 01.01.2019, p. 107937-107951.

Research output: Contribution to journalArticle

M A, H, Abdolrasol, MGM, Faisal, M, Ker, PJ, Begum, RA & Hussain, A 2019, 'Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving', IEEE Access, vol. 7, 8787762, pp. 107937-107951. https://doi.org/10.1109/ACCESS.2019.2933010
M A, Hannan ; Abdolrasol, M. G.M. ; Faisal, M. ; Ker, P. J. ; Begum, Rawshan Ara ; Hussain, A. / Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving. In: IEEE Access. 2019 ; Vol. 7. pp. 107937-107951.
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