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

M. G.M. Abdolrasol, Hannan M A, Azah Mohamed, U. A.U. Amiruldin, I. Z. Abidin, Mohammad Uddin

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) towards 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 hours. 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 (BPSO) 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
JournalIEEE Transactions on Industry Applications
DOIs
Publication statusAccepted/In press - 22 Jan 2018

Fingerprint

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

Keywords

  • Binary Backtracking Search Algorithm
  • Fuels
  • Generators
  • Microgrid
  • Microgrids
  • Optimal scheduling
  • Power generation
  • Reliability
  • Scheduling controller
  • Virtual power plant
  • Wind speed

ASJC Scopus subject areas

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

Cite this

An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration using Binary Backtracking Search Algorithm. / Abdolrasol, M. G.M.; M A, Hannan; Mohamed, Azah; Amiruldin, U. A.U.; Abidin, I. Z.; Uddin, Mohammad.

In: IEEE Transactions on Industry Applications, 22.01.2018.

Research output: Contribution to journalArticle

Abdolrasol, M. G.M. ; M A, Hannan ; Mohamed, Azah ; Amiruldin, U. A.U. ; Abidin, I. Z. ; Uddin, Mohammad. / An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration using Binary Backtracking Search Algorithm. In: IEEE Transactions on Industry Applications. 2018.
@article{ea7189286bdf49efaf0545964ba11019,
title = "An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration using Binary Backtracking Search Algorithm",
abstract = "This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) towards 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 hours. 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 (BPSO) 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.",
keywords = "Binary Backtracking Search Algorithm, Fuels, Generators, Microgrid, Microgrids, Optimal scheduling, Power generation, Reliability, Scheduling controller, Virtual power plant, Wind speed",
author = "Abdolrasol, {M. G.M.} and {M A}, Hannan and Azah Mohamed and Amiruldin, {U. A.U.} and Abidin, {I. Z.} and Mohammad Uddin",
year = "2018",
month = "1",
day = "22",
doi = "10.1109/TIA.2018.2797121",
language = "English",
journal = "IEEE Transactions on Industry Applications",
issn = "0093-9994",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

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

AU - Abdolrasol, M. G.M.

AU - M A, Hannan

AU - Mohamed, Azah

AU - Amiruldin, U. A.U.

AU - Abidin, I. Z.

AU - Uddin, Mohammad

PY - 2018/1/22

Y1 - 2018/1/22

N2 - This paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) towards 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 hours. 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 (BPSO) 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 IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) towards 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 hours. 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 (BPSO) 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.

KW - Binary Backtracking Search Algorithm

KW - Fuels

KW - Generators

KW - Microgrid

KW - Microgrids

KW - Optimal scheduling

KW - Power generation

KW - Reliability

KW - Scheduling controller

KW - Virtual power plant

KW - Wind speed

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

AN - SCOPUS:85040985677

JO - IEEE Transactions on Industry Applications

JF - IEEE Transactions on Industry Applications

SN - 0093-9994

ER -