Abstract
| Original language | English |
|---|---|
| Pages | 2834-2844 |
| Number of pages | 2549 |
| DOIs | |
| Publication status | Published - 01 May 2018 |
| Event | IEEE Transactions on Industry Applications - Duration: 01 Jul 2018 → … |
Conference
| Conference | IEEE Transactions on Industry Applications |
|---|---|
| Period | 01/07/18 → … |
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All Science Journal Classification (ASJC) codes
- Building and Construction
- Energy(all)
- Mechanical Engineering
- Management, Monitoring, Policy and Law
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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; Amiruldin, Ungku Anisa Ungku; Abidin, Izham Bin Zainal; Uddin, Mohammad Nasir.
2018. 2834-2844 Paper presented at IEEE Transactions on Industry Applications, .Research output: Contribution to conference › Paper
TY - CONF
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 - Amiruldin, Ungku Anisa Ungku
AU - Abidin, Izham Bin Zainal
AU - Uddin, Mohammad Nasir
PY - 2018/5/1
Y1 - 2018/5/1
N2 - © 1972-2012 IEEE. 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 - © 1972-2012 IEEE. 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.
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U2 - 10.1109/TIA.2018.2797121
DO - 10.1109/TIA.2018.2797121
M3 - Paper
SP - 2834
EP - 2844
ER -