An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm

Maher G.M. Abdolrasol, Mahammad A. Hannan, Azah Mohamed, Ungku Anisa Ungku Amirulddin Al Amin, Izham Zainal Abidin, Mohammad Nasir Uddin

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to the IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) toward sustainable renewable energy sources integration. The VPP and MGs models are simulated and tested based on real parameters and loads data recorded in Perlis, Malaysia, employed on each bus of the system for 24 h. BBSA optimization algorithm provides the best binary fitness function, i.e., global minimum fitness for finding the best cell to generate the optimal schedule. The fitness function is generated based on real conditions such as solar irradiation and wind speed and preparation of battery charge/discharges, fuel states and demand of the specific hour. The obtained results show that the BBSA algorithm provides the best schedule to control DGs ON and OFF based on controller decision. Results obtained from the BBSA are compared with binary particle swarm optimization in terms of objective function and power saving to validate the developed controller. The developed BBSA optimization algorithm minimizes the power generation cost, reduces power losses, delivers reliable and high-quality power to the loads, and integrates priority-based sustainable MGs into the grid. Thus, VPP can enable efficient integration of DGs and MGs into the grid by balancing their variability.

Original languageEnglish
Pages (from-to)2834-2844
Number of pages11
JournalIEEE Transactions on Industry Applications
Volume54
Issue number3
DOIs
Publication statusPublished - 01 May 2018

Fingerprint

Power plants
Scheduling
Controllers
Power quality
Particle swarm optimization (PSO)
Power generation
Irradiation
Costs

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

@article{5bd11894057b4e3abbcdfb1970b50318,
title = "An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm",
abstract = "This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to the IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) toward sustainable renewable energy sources integration. The VPP and MGs models are simulated and tested based on real parameters and loads data recorded in Perlis, Malaysia, employed on each bus of the system for 24 h. BBSA optimization algorithm provides the best binary fitness function, i.e., global minimum fitness for finding the best cell to generate the optimal schedule. The fitness function is generated based on real conditions such as solar irradiation and wind speed and preparation of battery charge/discharges, fuel states and demand of the specific hour. The obtained results show that the BBSA algorithm provides the best schedule to control DGs ON and OFF based on controller decision. Results obtained from the BBSA are compared with binary particle swarm optimization in terms of objective function and power saving to validate the developed controller. The developed BBSA optimization algorithm minimizes the power generation cost, reduces power losses, delivers reliable and high-quality power to the loads, and integrates priority-based sustainable MGs into the grid. Thus, VPP can enable efficient integration of DGs and MGs into the grid by balancing their variability.",
author = "Abdolrasol, {Maher G.M.} and Hannan, {Mahammad A.} and Azah Mohamed and {Ungku Amirulddin Al Amin}, {Ungku Anisa} and {Zainal Abidin}, Izham and Uddin, {Mohammad Nasir}",
year = "2018",
month = "5",
day = "1",
doi = "10.1109/TIA.2018.2797121",
language = "English",
volume = "54",
pages = "2834--2844",
journal = "IEEE Transactions on Industry Applications",
issn = "0093-9994",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm. / Abdolrasol, Maher G.M.; Hannan, Mahammad A.; Mohamed, Azah; Ungku Amirulddin Al Amin, Ungku Anisa; Zainal Abidin, Izham; Uddin, Mohammad Nasir.

In: IEEE Transactions on Industry Applications, Vol. 54, No. 3, 01.05.2018, p. 2834-2844.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm

AU - Abdolrasol, Maher G.M.

AU - Hannan, Mahammad A.

AU - Mohamed, Azah

AU - Ungku Amirulddin Al Amin, Ungku Anisa

AU - Zainal Abidin, Izham

AU - Uddin, Mohammad Nasir

PY - 2018/5/1

Y1 - 2018/5/1

N2 - This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to the IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) toward sustainable renewable energy sources integration. The VPP and MGs models are simulated and tested based on real parameters and loads data recorded in Perlis, Malaysia, employed on each bus of the system for 24 h. BBSA optimization algorithm provides the best binary fitness function, i.e., global minimum fitness for finding the best cell to generate the optimal schedule. The fitness function is generated based on real conditions such as solar irradiation and wind speed and preparation of battery charge/discharges, fuel states and demand of the specific hour. The obtained results show that the BBSA algorithm provides the best schedule to control DGs ON and OFF based on controller decision. Results obtained from the BBSA are compared with binary particle swarm optimization in terms of objective function and power saving to validate the developed controller. The developed BBSA optimization algorithm minimizes the power generation cost, reduces power losses, delivers reliable and high-quality power to the loads, and integrates priority-based sustainable MGs into the grid. Thus, VPP can enable efficient integration of DGs and MGs into the grid by balancing their variability.

AB - This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to the IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) toward sustainable renewable energy sources integration. The VPP and MGs models are simulated and tested based on real parameters and loads data recorded in Perlis, Malaysia, employed on each bus of the system for 24 h. BBSA optimization algorithm provides the best binary fitness function, i.e., global minimum fitness for finding the best cell to generate the optimal schedule. The fitness function is generated based on real conditions such as solar irradiation and wind speed and preparation of battery charge/discharges, fuel states and demand of the specific hour. The obtained results show that the BBSA algorithm provides the best schedule to control DGs ON and OFF based on controller decision. Results obtained from the BBSA are compared with binary particle swarm optimization in terms of objective function and power saving to validate the developed controller. The developed BBSA optimization algorithm minimizes the power generation cost, reduces power losses, delivers reliable and high-quality power to the loads, and integrates priority-based sustainable MGs into the grid. Thus, VPP can enable efficient integration of DGs and MGs into the grid by balancing their variability.

UR - http://www.scopus.com/inward/record.url?scp=85040985677&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040985677&partnerID=8YFLogxK

U2 - 10.1109/TIA.2018.2797121

DO - 10.1109/TIA.2018.2797121

M3 - Article

VL - 54

SP - 2834

EP - 2844

JO - IEEE Transactions on Industry Applications

JF - IEEE Transactions on Industry Applications

SN - 0093-9994

IS - 3

ER -