An energy harvesting device driven by aeroelastic vibration with self-sustained pitching and heaving using an induction based power take off mechanism has been designed and tested for performance under various operating conditions. From the data collected the results show that the device achieved a maximum power output of 48.3 mW and a maximum efficiency of 2.26% at a dimensionless frequency of 0.143. For all airfoils tested the device was shown to be self-starting above 3 m/s. A qualitative description relating to the performance of the device considering dynamic stall and the flow conditions at optimal dimensionless frequency has been proposed and related to previous work. Performance for angles off the wind up to 22 degrees and was observed to have no reduction in power output due to the change in angle to the wind. The device has shown evidence of having a self-governing capability, tending to decrease its power output for heavy windpspeeds, a thorough examination of this capability is recommended for future work. / Graduate / 0548 / 0544 / opirquet@uvic.ca

As an energy-importing developing country, Turkey depends heavily on imported petroleum and natural gas. The increase in the global petroleum price has affected the Turkish economy adversely in the last decade. Renewable energy is an important alternative in reducing Turkey’s energy dependency. Turkey’s strategies are improving domestic production and diversifying energy sources for the security of supply. New investments, especially in renewables, have been chosen to achieve these objectives. As a model for Turkey, Texas is the leader in non-hydroelectric renewable energy production in the U.S. and has one of the world’s most competitive electricity markets. However, wind generation creates unique challenges for the Electric Reliability Council of Texas (ERCOT), the transmission system operator of Texas. The market environment has forced the Public Utility Commission of Texas (PUCT) to develop unique deregulated energy markets. In 2005, the Texas Legislature passed Senate Bill 20, in part to break the deadlock between transmission and wind generation development. This legislation instructed the PUCT to establish Competitive Renewable Energy Zones (CREZs) throughout the State, and to designate new transmission projects to serve these zones. In this context, first of all, the electricity market development in Turkey is introduced in terms of renewable energy, especially wind power. Next, considering wind power, the progress in the Texas electricity market is investigated. Subsequently, we examine the development of CREZs in Texas from a regulatory perspective and discuss Texas’ policy initiatives, including the designation of CREZs. Finally, we review the impact of wind power on the primary electricity market of Texas and evaluate market conditions and barriers to renewable energy use in Turkey in order to extract suggestions. This experience may be particularly instructive to Turkey, which has a similar market structure on the supply and transmission sides. This study suggests ways that Turkey might handle renewable applications in combination with existing transmission constraints. / text

The research presented in this thesis examines the use of electric vehicles and renewable energy to reduce emissions of CO₂, SO₂ and NO[subscript x], and within the state of Texas. The analysis examines the impact of increased renewable energy output and electric vehicle charging on the emissions of fossil fuel electric generators used to serve the bulk power system within Texas. The analysis then compares those impacts to alternative scenarios in which fossil fuel generation replaces some renewable energy generation, and Internal Combustion Engine (ICE) vehicles of varying efficiency are used instead of electric vehicles. This research uses temporally-resolved regression analysis combined with a unit commitment and dispatch model that incorporates several different scenarios for EV charging and fuel mixes to evaluate emissions outcomes based on a variety of conditions. Hourly historical generation and emission data for each fossil fuel generator, combined with hourly output data for non-fossil fuel units aggregated by fuel type (i.e. nuclear, wind, hydro-electric) within the Electric Reliability Council of Texas (ERCOT) footprint is regressed to assess the impact of wind generation output on fossil-fuel generation emissions. The regression analysis is used to assess potential increases in emissions resulting from the ramping of fossil-fuel Electric Generation Units (EGUs) to compensate for variability in wind generation output due to changing weather conditions. The unit commitment dispatch model is used to evaluate the impact of changes in customer demand due to increased usage and charging of electric vehicles on the ERCOT system and any resulting increase in emissions from generation used to meet this new demand. The model uses detailed cost, performance and emissions data for EGUs in the ERCOT footprint to simulate the impact of a variety of charging scenarios and fuel mixes on EGU dispatch patterns and any resulting change in system-wide emissions. The results of this model are combined with the results of the regression analysis to present a more complete analysis of the combined impacts of increase EV and renewable energy usage on the emissions of CO₂, SO₂ and NO[subscript x] within the ERCOT footprint. Based on these analyses the increases in renewable energy generation demonstrate clear benefits in terms of emission reductions when the impacts of increased emissions due to more frequent ramping of fossil-fuel units are taken into account. This analysis also finds that EV charging generally has emissions benefits across a range of charging patterns and bulk power system fuel mixes, although in certain circumstances EV charging might result in higher emissions than the use of ICE vehicles. This research finds when future ICE vehicles with reduced emissions are taken into account, approximately half of the modeled scenarios show net emissions benefits from EV charging, while half show net emissions costs when emissions impacts across pollutants are taken into account. / text

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

To combat global climate change, the reduction of carbon emissions in different industries, particularly the power industry, has been gradually moving towards a low-carbon profile to alleviate any irreversible damage to the planet and our future generations. Traditional fossil-fuel-based generation is slowly replaced by more renewable energy generation while it can be harnessed. However, renewables such as solar and wind are stochastic in nature and difficult to predict accurately. With the increasing content of renewables, there is also an increasing challenge to the planning and operation of the grid. With the rapid deployment of smart meters and advanced metering infrastructure (AMI), an emerging approach is to schedule controllable end-use devices to improve energy efficiency. Real-time pricing signals combined with this approach can potentially deliver more economic and environmental advantages compared with the existing common flat tariffs. Motivated by this, the thesis presents an automatic and optimal load scheduling framework to help balance intermittent renewables via the demand side. A bi-level consumer-utility optimization model is proposed to take marginal price signals and wind power into account. The impact of wind uncertainty is formulated in three different ways, namely deterministic value, scenario analysis, and cumulative distributions function, to provide a comprehensive modeling of unpredictable wind energy. To solve the problem in off-the-shelf optimization software, the proposed non-linear bi-level model is converted into an equivalent single-level mixed integer linear programming problem using the Karush-Kuhn-Tucker optimality conditions and linearization techniques. Numerical examples show that the proposed model is able to achieve the dual goals of minimizing the consumer payment as well as improving system conditions. The ultimate goal of this work is to provide a tool for utilities to consider the demand response model into their market-clearing procedure. As high penetration of distributed renewable energy resources are most likely applied to remote or stand-alone systems, planning such systems with uncertainties in both generation and demand sides is needed. As such, a three-level probabilistic sizing methodology is developed to obtain a practical sizing result for a stand-alone photovoltaic (PV) system. The first-level consists of three modules: 1) load demand, 2) renewable resources, and 3) system components, which comprise the fundamental elements of sizing the system. The second-level consists of various models, such as a Markov chain solar radiation model and a stochastic load simulator. The third-level combines reliability indices with an annualized cost of system to form a new objective function, which can simultaneously consider both system cost and reliability based on a chronological Monte Carlo simulation and particle swamp optimization approach. The simulation results are then tested and verified in a smart grid laboratory at the University of Hong Kong to demonstrate the feasibility of the proposed model. In summary, this thesis has developed a comprehensive framework of demand response on variable end-use consumptions with stochastic generation from renewables while optimizing both reliability and cost. Smart grid technologies, such as renewables, microgrid, storage, load signature, and demand response, have been extensively studied and interactively modeled to provide more intelligent planning and management for the smart grid. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

The photovoltaic industry provides a pathway to allow renewable energy to meet world wide consumer energy needs. Past and present research and development on silicon based solar cells have helped make them the dominant player in the photovoltaic industry accounting for over 75% in 2005 as accounted by the US Department of Energy. One of the current technological problems is to identify and eliminate sources of mechanical defects such as thermo-elastic stress and cracks leading to the loss of wafer integrity and ultimately breakage of as-grown and processed Si wafers and cells.The RUV method, developed at the University of South Florida, enables fast and accurate crack detection with simple criteria for wafer rejection from solar cell production lines. The RUV system relies on variation of modal vibration characteristics due to physical variations in the wafers caused by cracks. Ultrasonic vibrations are introduced into the wafer using a vacuum coupled transducer and received by an acoustic probe mounted along the periphery of the wafer. Cracks are detected by monitoring shifts in the resonance peak's frequency, bandwidth, and amplitude. In Cz-Si wafers it has been shown that increased crack length leads to a decrease in peak frequency and an increase in peak bandwidth and decreasing peak amplitude. Minimum crack length sensitivity is related to the uniformity of the RUV parameters from wafer to wafer within a batch as well as system characteristics. Typically the RUV system is capable of detecting sub-millimeter length cracks. The use of auto loading and unloading allows the RUV system to achieve mass production level speeds of approximately two seconds per wafer. The RUV system has been successful in detecting cracks in single crystalline and multi-crystalline silicon wafers. Further development of the RUV system would solidify its place in manufacturing plants for non-destructive crack detection in PV cells.The contributing work of the author toward the further development of the RUV crack detection method will be examined in this thesis.

Carbon reduction commitments and renewable energy targets have become legal drivers of electricity policy in the UK. Meeting those targets will require a transition in the way that electricity is generated, supplied and consumed. This thesis argues that small-­‐scale renewable electricity technologies (<5MW) could have an important role in driving that transition. The thesis evaluates the role of the feed-­‐in tariff -­‐ a policy mechanism designed to stimulate the deployment of small-­‐scale renewable electricity technologies -­‐ in electricity system transition in the UK. The research is based on empirical information generated from 37 industry interviews, observations of industry and government meetings and events, and secondary analysis of consultation responses, publications and statistics from government and the energy regulator, Ofgem. The analysis is structured with a framework that draws on transition theory and breaks down the findings into a niche (micro/developing) level, a regime (incumbent electricity system) level and a landscape (contextual) level. The thesis finds that the FIT has driven solar photovoltaic development and innovation at an unprecedented rate. The other renewable technologies supported under the FIT (wind, hydropower and anaerobic digestion) have not been as widely deployed. It is argued that additional policy support is required to overcome the non-­‐financial barriers that these technologies face. The thesis concludes that the role of the FIT in system transition has been to drive a level and pace of activity in the solar sector that has demonstrated the potential for alternative generation options. This has informed the politicised debate around electricity policy in the UK but it is argued that continued, broader, stable support is required if small-­‐scale renewable technologies are to have a positive role in electricity system transition. The research has relevance to both academic and policy circles focused on electricity policy, the decarbonisation of energy systems and socio-­‐technical system transitions.

No description available.

The objective of this thesis is to evaluate the potential for wind as an alternative energy source to replace fossil fuels and reduce global CO2 emissions. From 1995 to 2007, fossil fuels as the major energy source accounted for an addition of 89.3 Gt of carbon to the atmosphere over this period, 29 % of which was transferred to the ocean, 15 % to the global biosphere, with the balance (57 %) retained in the atmosphere. Building a low-carbon and climate-friendly energy system is becoming increasingly urgent to combat the threat of global warming. / Engineering and Applied Sciences