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https://ptsldigital.ukm.my/jspui/handle/123456789/487205Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ramizi Mohamed, Dr. | |
| dc.contributor.author | Maher G.M. Abdolrasol (P65758) | |
| dc.date.accessioned | 2023-10-11T02:30:21Z | - |
| dc.date.available | 2023-10-11T02:30:21Z | - |
| dc.date.issued | 2021-04-21 | |
| dc.identifier.other | ukmvital:124819 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/487205 | - |
| dc.description | Virtual power plant (VPPs) and micro-grid (MG) systems bring advantages to end-users and to the smart grid environment. Therefore, the generation and integration of MG system-based renewable energy sources (RESs) have been increased dramatically. However, an adequate management system for the more efficient operation of these systems toward lower environmental impact, cost reduction, and power-saving not sufficiently covered yet. This thesis introduces a novel binary backtracking search algorithm (BBSA) for intelligent and optimal scheduling management controller. This optimal scheduling controller applied in an IEEE 14 bus system for controlling MGs in the form of VPP towards sustainable renewable energy sources integration. The models of VPP and MGs are simulated and validated on the basis of actual parameters and load data reported in Perlis, Malaysia, which is used for 24 hours on each system bus. BBSA optimization algorithm offers the best binary fitness function, i.e. global minimum fitness to find the best cell in order to generate the optimum scheduling. The fitness function is obtained according to real conditions such as wind speed, solar radiation, fuel conditions, battery charging / discharging preparation, and demand of particular hour. The main concern of optimization proposed is to reducing the costs and emission via giving priority for sustainable resources use instead of purchasing from national grid. To achieve this, the BBSA have to control the distribution generators (DGs) on and off status based on controller decision. Furthermore, to compare the new BBSA best schedule controller, the binary particle swarm optimization (BPSO) is developed. The same system that used in case of BBSA including VPP, MGs, DGs, and real load data are used for BPSO to get the best scheduling via determining the optimal on/off operational status of the DGs. For predicting optimal schedules of the VPP system, an artificial neural network (ANN) hybrid BBSA (ANN-BBSA) and ANN-based BPSO (ANN-BPSO) have been developed and compared. The results of the proposed BBSA schedule controller can significantly save the power, minimize the cost, and reduce the. However, the BPSO achieved power good saving. From the comparison, the results showed that the BBSA is better than the BPSO; however the difference is quite small. The results for predicting optimal on/off status of the 25 DGs showed that the hybrid ANN-BBSA gives a mean absolute error (MAE) minimum value, whereas the hybrid ANN-BPSO gives an MAE was higher in compare. Regarding the regression coefficient (R), the ANN-BPSO was less in the case of ANN-BBSA which is better than ANN-BPSO. Overall, the regression coefficient results are in close agreement to unity; hence validate the accuracy of the ANN-BBSA and ANN-BPSO. A comparison of results shows that the hybrid ANN-BBSA is better than the hybrid ANN-BPSO in terms of scheduling DGs and MGs in the form of VPP. Finally, the validation between the predicted and actual power has been carried out to validate the obtained results. In conclusion, the developed optimization algorithms reduce the power generation cost, lower 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 toward gird decarbonization, smart grid, and green electricity.,Ph.D. | |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
| dc.rights | UKM | |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | |
| dc.subject | Dissertations, Academic -- Malaysia | |
| dc.subject | Virtual power plant | |
| dc.subject | Binary backtracing | |
| dc.subject | Algorithm | |
| dc.subject | Artificial neural network | |
| dc.title | Optimal scheduling controller for management of virtual power plants using binary backtracing search algorithm and artificial neural network | |
| dc.type | Theses | |
| dc.format.pages | 263 | |
| dc.identifier.barcode | 005830(2021)(PL2) | |
| Appears in Collections: | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_124819+SOURCE1+SOURCE1.0.PDF Restricted Access | 1.25 MB | Adobe PDF |  View/Open |
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