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dc.contributor.authorNarayan, Apurva 13:40:57 (GMT) 13:40:57 (GMT)
dc.description.abstractMicrogrids are local energy providers that can potentially reduce energy expenses and emissions by utilizing distributed energy resources (DERs) and are alternatives to existing centralized systems. This thesis investigates the optimal design and planning of such microgrids using a multidisciplinary design optimization approach based framework. Among a variety of DERs it is widely accepted that renewable resources of energy play an important role in providing a sustainable energy supply infrastructure, as they are both inexhaustible and nonpolluting. However the intermittent nature and the uncertainties associated with renewable technologies pose sufficient technological and economical challenges for system planners. Design of complex engineering systems has evolved into a multidisciplinary field of study. We develop a framework for design and planning of complex engineering systems under uncertainty using an approach of multidisciplinary design optimization under uncertainty (MDOUU). The framework has been designed to be general enough to be applicable to a large variety of complex engineering systems while it is simple to apply. MDOUU framework is a three stage planning strategy which allows the system planners to consider all aspects ranging from uncertainty in resources, technological feasibility, economics, and life cycle impacts of the system and choose an optimal design suited to their localized conditions. Motivation behind using MDOUU lies not only in the optimization of the individual systems or disciplines but also their interactions between each other. Following the modeling of the resources, a deterministic optimization model for planning microgirds is developed and results are evaluated using Monte Carlo simulations. Given the obvious limitations of the deterministic model in not being able to handle uncertainty efficiently and resulting in an expensive design we extended the model to a two stage stochastic programming model which provides a unified approach in determining the sizing of microgrids by considering uncertainty implicitly by means of scenarios. Probabilistic scenarios are developed using C-vine copulas that model nonlinear dependence. We evaluate the significance of the stochastic programming model using standardized metrics evaluating benefits of using the stochastic model. As any product or service needs to be evaluated for its environmental impacts, MDOUU provisions an LCA module that evaluates the environmental impacts and energy demands of the components of the system based on extensive literature and databases using openLCA as a tool. The overall system selection involves multiple criteria and interests of different stakeholders. This requires a multi-attribute decision system and a comprehensive ranking approach providing a list of possible configuration based on their relative importance as denoted by the stakeholders. We use Analytical Hierarchical Process (AHP) combined with compromise programming to rank a list of configurations based on economic and environmental attributes such as GHG emissions saved, cost of energy, annual energy production, net present value (NPV) etc. It allows the planners to make decisions considering the interests of a majority of stakeholders. The MDOUU framework proposed in this thesis with specific application to the microgrid planning problem contributes in helping the planners handle uncertainty of renewable resources of energy and environmental impacts in a systematic way. As such there is no method available in the literature which considers planning of microgrid using such holistic and multidisciplinary framework. The MDOUU framework is a generic tool and is useful for planning problems in a variety of complex systems.en
dc.publisherUniversity of Waterlooen
dc.subjectStochastic Optimizationen
dc.subjectRenewable Energyen
dc.subjectLife Cycle Analysisen
dc.subjectMultidisciplinary Design Optimizationen
dc.titleA Framework For Microgrid Planning Using Multidisciplinary Design Optimizationen
dc.typeDoctoral Thesisen Design Engineeringen Design Engineeringen of Waterlooen
uws-etd.degreeDoctor of Philosophyen
uws.contributor.advisorPonnambalam, Kumaraswamy
uws.contributor.affiliation1Faculty of Engineeringen

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