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dc.contributor.authorArriaga Marin, Mariano 18:03:19 (GMT) 18:03:19 (GMT)
dc.description.abstractElectricity is widely seen as a flexible energy source that can potentially improve access to services and economic development in remote locations. Worldwide, there are 1.3 billion people without electricity access, out of which 950 million are not likely to be connected to the main grid in the foreseeable future. Furthermore, there is a population sector which solely relies on diesel-fuel for electricity generation; these communities have usually limited installed capacity, lack of operation flexibility, significantly high operating costs, and different operation characteristics involving multiple stakeholders. Incorporation of adequate Renewable Energy (RE) technologies can potentially reduce the energy deficit, addressing some of the aforementioned issues, such as requirement of increased installed capacity and reducing fuel consumption. In this thesis, the Long-Term Renewable Generation Planning (LTRGP) problem in Remote Communities (RCs) is tackled to address some of energy-access issues, based on a mathematical model that results in economic and technically-feasible RE deployment plans that consider current operating conditions, bringing benefits to the community. Proper understanding of the energy situation in remote locations is an essential requirement for proposing RE deployments in Northern and Remote Communities (N&RCs). Hence, this thesis first presents the results of a Canada-wide survey regarding N&RCs. The resulting database is then used to shape the structure of the LTRGP model, as well as giving a reliable input baseline for the presented research. In addition to energy-related information, the database contains detailed time-series data for solar and wind-related resources, which are used as inputs to the proposed planning problem. The first proposed approach to solving the LTRGP problem is based on understanding the current electricity generation structure in N&RCs, and adapt available RE planning tools accordingly. This work involves understanding the challenges of such RE projects by analyzing the current economic structure, capital costs, available natural resources, deployment, and operation and maintenance issues. Based on this analysis, the thesis presents a planning model in HOMER, a currently available RE microgrid planning tool. The model is applied and demonstrated in a case study considering the northern Ontario community of Kasabonika Lake First Nation (KLFN), with which the University of Waterloo has had a strong collaboration for several years. The results show that RE technologies are close to breaking even under certain deployment conditions; however, low economic returns are obtained. The second approach in this RE planning research is the development of an appropriate LTRGP model considering the characteristics of RCs which cover their electricity demand using mainly fuel-based generators. From a non-technical viewpoint, the model considers the different RE operating frameworks, the current electricity customer types, and the involved stakeholders in remote locations. From a technical perspective, a mathematical model of a multiple-year RE planning model is proposed considering the technical and economic constraints related to such locations, some of which are not present in the grid-connected context. The resulting model is applied to the KLFN case and the results show that RE projects can be feasible for some funding alternatives. The results demonstrate that realistic RE community plans can be obtained with the proposed model, considering wind and solar energy generation equipment that is adequate for such remote locations and the current operating and tariff structure among the parties involved.en
dc.publisherUniversity of Waterlooen
dc.subjectEnergy Capacity Planningen
dc.subjectRemote Communitiesen
dc.subjectRenewable Energyen
dc.subjectSolar Energyen
dc.subjectWind Energyen
dc.subjectOff-grid systemsen
dc.titleLong-Term Renewable Energy Generation Planning for Off-grid Remote Communitiesen
dc.typeDoctoral Thesisen
dc.subject.programElectrical and Computer Engineeringen and Computer Engineeringen
uws-etd.degreeDoctor of Philosophyen

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