Paper Info
Title
Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving
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.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2933010
IEEE ACCESS
Keywords
DocType
Volume
Virtual power plant,microgrid,energy management,carbon reduction,scheduling controller,optimization
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Mahammad A. Hannan12611.46
M. G. M. Abdolrasol200.34
Mohammad Faisal311.37
Pin Jern Ker465.55
R. A. Begum500.34
Aini Hussain613032.24