Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Optimal operation and control of remote area microgrids

Shoeb, Md AsaduzzamanORCID: 0000-0002-6653-107X (2019) Optimal operation and control of remote area microgrids. PhD thesis, Murdoch University.

PDF - Whole Thesis
Download (2MB) | Preview


Microgrid (MG) is a promising approach to proliferate distributed energy resources for electrification in remote areas. Remote area MGs usually operate as standalone systems and are supplied by a combination of conventional fossil fuel-based generators, renewable energy resources and energy storage systems. Irrespective of all considerations at the planning and design stage, such MGs are always prone to the uncertainties of their demand variation and the generation of their non-dispatchable renewable sources. Such events can cause voltage or frequency violation in the MG. This thesis has focused on developing proper operational and control techniques for such MGs. First, an effective management technique has been proposed and developed that can retain the voltage and frequency of the MGs within a predefined desired region, at least cost, using a multilayer scheme. If a violation is detected, the proposed technique will aim to define the most optimal generation level of dispatchable sources, MG’s best network configuration and engagement level of the supportive actions such as exchanging power with neighbouring MGs, utilising energy storages, demand response and renewable energy curtailment (if and when available). The technical, reliability and environmental aspects of the MG are considered within the proposed technique along with the operational cost. The determined optimal control variables will then be sent to the local controllers to apply proper arrangements in the system to retain the voltage or frequency within the desired range. On the other hand, some techniques are available in the literature that can predict the uncertainties of demand and renewable energy sources a few minutes ahead. Using such techniques, the voltage or frequency violation can also be predicted in short-horizon and prevented with the introduction of a suitable preventive controller. Hence, this thesis has then proposed and developed a look-ahead controller that uses the short-horizon prediction data of demand and renewable generation to determine any prospective voltage or frequency violation. Another alternative is temporarily coupling the adjacent MGs to support each other and form a system of coupled MGs. Thus, the thesis has then proposed and developed a suitable technique to form systems of coupled MGs while preserving the voltage and frequency of each MG and reassuring the optimal performance of all MGs. The proposed optimisation approach tries to solve the voltage or frequency problem by coupling the MGs when the local actions, such as energy storages, are inadequate or cost-ineffective. Another technique has also been proposed and developed that can readjust the dispatch of the suitable generation units, between the optimisations, to support small changes in load. To this end, the potential field concept is used by the loads to select suitable generation units to make the decision very quickly. The decision is made based on different criteria, such as cost, reliability, emission, and power loss. This process requires low computational efforts and can be done instantly. Besides, a periodic optimisation is performed by the MG’s central controller to retune the whole system and reconfirm the optimal operation. The performance of the developed techniques has been demonstrated and validated through extensive numerical analyses in MATLAB®.

Item Type: Thesis (PhD)
Murdoch Affiliation(s): Engineering and Energy
United Nations SDGs: Goal 7: Affordable and Clean Energy
Supervisor(s): Shafiullah, GM and Shahnia, Farhad
Item Control Page Item Control Page


Downloads per month over past year