|dc.date.accessioned||2015-11-10 13:40:57 (GMT)||
|dc.date.available||2015-11-10 13:40:57 (GMT)||
|dc.description.abstract||Microgrids 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.publisher||University of Waterloo||en
|dc.subject||Life Cycle Analysis||en
|dc.subject||Multidisciplinary Design Optimization||en
|dc.title||A Framework For Microgrid Planning Using Multidisciplinary Design Optimization||en
|uws-etd.degree.department||Systems Design Engineering||en
|uws-etd.degree.discipline||System Design Engineering||en
|uws-etd.degree.grantor||University of Waterloo||en
|uws-etd.degree||Doctor of Philosophy||en
|uws.contributor.affiliation1||Faculty of Engineering||en