Adaptive model predictive control of renewable energy-based micro-grid.
| dc.contributor.advisor | Saha, Akshay Kumar. | |
| dc.contributor.author | Gbadega, Peter Anuoluwapo. | |
| dc.date.accessioned | 2022-06-15T07:54:18Z | |
| dc.date.available | 2022-06-15T07:54:18Z | |
| dc.date.created | 2021 | |
| dc.date.issued | 2021 | |
| dc.description | Doctoral Degree. University of KwaZulu-Natal, Durban. | en_US |
| dc.description.abstract | Energy sector is facing a shift from a fossil-fuel energy system to a modern energy system focused on renewable energy and electric transport systems. New control algorithms are required to deal with the intermittent, stochastic, and distributed nature of the generation and with the new patterns of consumption. Firstly, this study proposes an adaptive model-based receding horizon control technique to address the issues associated with the energy management system (EMS) in micro-grid operations. The essential objective of the EMS is to balance power generation and demand through energy storage for optimal operation of the renewable energy-based micro-grid. At each sampling point, the proposed control system compares the expected power produced by the renewable generators with the expected load demand and determines the scheduling of the different energy storage devices and generators for the next few hours. The control technique solves the optimization problem in order to minimize or determines the minimum running cost of the overall micro-grid operations, while satisfying the demand and taking into account technical and physical constraints. Micro-grid, as any other systems are subject to disturbances during their normal operation. Hence, the power generated by the renewable energy sources (RESs) and the demanded power are the main disturbances acting on the micro-grid. As renewable sources are used for the generation, their time-varying nature, their difficulty in predicting, and their lack of ability to manipulate make them a problem for the control system to solve. In view of this, the study investigates the impacts of considering the prediction of disturbances on the performance of the energy management system (EMS) based on the adaptive model predictive control (AMPC) algorithm in order to improve the operating costs of the micro-grid with hybrid-energy storage systems. Furthermore, adequate management of loads and electric vehicle (EV) charging can help enhance the micro-grid operation. This study also introduced the concept of demand-side management (DSM), which allows the customers to make decisions regarding their energy consumption and also help to reduce the peak load demand and to reshape the load profile so as to improve the efficiency of the system, environmental impacts, and reduction in the overall operational costs. More so, the intermittent nature of renewable energy and consumer random behavior introduces a stochastic component to the problem of control. Therefore, in order to solve this problem, this study utilizes an AMPC control technique, which provides some robustness to the control of systems with uncertainties. Lastly, the performances of the micro-grids used as a case study are evaluated through simulation modeling, implemented in MATLAB/Simulink environment, and the simulation results show the accuracy and efficiency of the proposed control technique. More so, the results also show how the AMPC can adapt to various generation scenarios, providing an optimal solution to power-sharing among the distributed energy resources (DERs) and taking into consideration both the physical and operational constraints and similarly, the optimization of the imposed operational criteria. | en_US |
| dc.identifier.uri | https://researchspace.ukzn.ac.za/handle/10413/20481 | |
| dc.language.iso | en | en_US |
| dc.subject.other | Energy management system. | en_US |
| dc.subject.other | Demand side management. | en_US |
| dc.subject.other | Fossil-fuel energy system. | en_US |
| dc.subject.other | Electric transport systems. | en_US |
| dc.subject.other | Demand response techniques. | en_US |
| dc.subject.other | Renewable energy--Microgrids. | en_US |
| dc.title | Adaptive model predictive control of renewable energy-based micro-grid. | en_US |
| dc.type | Thesis | en_US |