ELECTRICITY GENERATION MODELING AND PHOTOVOLTAIC FORECASTS IN CHINA

With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979-2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.

ELECTROCATALYTIC PROCESSING OF RENEWABLE BIOMASS-DERIVED COMPOUNDS FOR PRODUCTION OF CHEMICALS, FUELS AND ELECTRICITY

The dual problems of sustaining the fast growth of human society and preserving the environment for future generations urge us to shift our focus from exploiting fossil oils to researching and developing more affordable, reliable and clean energy sources. Human beings had a long history that depended on meeting our energy demands with plant biomass, and the modern biorefinery technologies realize the effective conversion of biomass to production of transportation fuels, bulk and fine chemicals so to alleviate our reliance on fossil fuel resources of declining supply. With the aim of replacing as much non-renewable carbon from fossil oils with renewable carbon from biomass as possible, innovative R&D activities must strive to enhance the current biorefinery process and secure our energy future. Much of my Ph.D. research effort is centered on the study of electrocatalytic conversion of biomass-derived compounds to produce value-added chemicals, biofuels and electrical energy on model electrocatalysts in AEM/PEM-based continuous flow electrolysis cell and fuel cell reactors. High electricity generation performance was obtained when glycerol or crude glycerol was employed as fuels in AEMFCs. The study on selective electrocatalytic oxidation of glycerol shows an electrode potential-regulated product distribution where tartronate and mesoxalate can be selectively produced with electrode potential switch. This finding then led to the development of AEMFCs with selective production of valuable tartronate or mesoxalate with high selectivity and yield and cogeneration of electricity. Reaction mechanisms of electrocatalytic oxidation of ethylene glycol and 1,2-propanediol were further elucidated by means of an on-line sample collection technique and DFT modeling. Besides electro-oxidation of biorenewable alcohols to chemicals and electricity, electrocatalytic reduction of keto acids (e.g. levulinic acid) was also studied for upgrading biomass-based feedstock to biofuels while achieving renewable electricity storage. Meanwhile, ORR that is often coupled in AEMFCs on the cathode was investigated on non-PGM electrocatalyst with comparable activity to commercial Pt/C. The electro-biorefinery process could be coupled with traditional biorefinery operation and will play a significant role in our energy and chemical landscape.

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.

Processing and mechanical properties of cast aluminum containing scandium, zirconium, and ytterbium

A series of aluminum alloys containing additions of scandium, zirconium, and ytterbium were cast to evaluate the effect of partial ytterbium substitution for scandium on tensile behavior. Due to the high price of scandium, a crucible-melt interaction study was performed to ensure no scandium was lost in graphite, alumina, magnesia, or zirconia crucibles after holding a liquid Al-Sc master alloy for 8 hours at 900 °C in an argon atmosphere. The alloys were subjected to an isochronal aging treatment and tested for conductivity and Vickers microhardness after each increment. For scandium-containing alloys, peak hardnesses of 520-790 MPa, and peak tensile stresses of 138-234 MPa were observed after aging from 150-350 °C for 3 hours in increments of 50 °C, and for alloys without scandium, peak hardnesses of 217-335 MPa and peak tensile stresses of 45-63 MPa were observed after a 3 hour, 150 °C aging treatment. The hardness and tensile strength of the ytterbium containing alloy was found to be lower than in the alloy with no ytterbium substitution.

LIFE CYCLE ASSESSMENT OF BIOFUEL PRODUCED FROM ALGAE

Algae are considered a promising source of biofuels in the future. However, the environmental impact of algae-based fuel has high variability in previous LCA studies due to lack of accurate data from researchers and industry. The National Alliance for Advanced Biofuels and Bioproducts (NAABB) project was designed to produce and evaluate new technologies that can be implemented by the algal biofuel industry and establish the overall process sustainability. The MTU research group within NAABB worked on the environmental sustainability part of the consortium with UOP-Honeywell and with the University of Arizona (Dr. Paul Blowers). Several life cycle analysis (LCA) models were developed within the GREET Model and SimaPro 7.3 software to quantitatively assess the environment viability and sustainability of algal fuel processes. The baseline GREET Harmonized algae life cycle was expanded and replicated in SimaPro software, important differences in emission factors between GREET/E-Grid database and SimaPro/Ecoinvent database were compared, and adjustments were made to the SimaPro analyses. The results indicated that in most cases SimaPro has a higher emission penalty for inputs of electricity, chemicals, and other materials to the algae biofuels life cycle. A system-wide model of algae life cycle was made starting with preliminary data from the literature, and then progressed to detailed analyses based on inputs from all NAABB research areas, and finally several important scenarios in the algae life cycle were investigated as variations to the baseline scenario. Scenarios include conversion to jet fuel instead of biodiesel or renewable diesel, impacts of infrastructure for algae cultivation, co-product allocation methodology, and different usage of lipid-extracted algae (LEA). The infrastructure impact of algae cultivation is minimal compared to the overall life cycle. However, in the scenarios investigating LEA usage for animal feed instead of internal recycling for energy use and nutrient recovery the results reflect the high potential variability in LCA results. Calculated life cycle GHG values for biofuel production scenarios where LEA is used as animal feed ranged from a 55% reduction to 127% increase compared to the GREET baseline scenario depending on the choice of feed meal. Different allocation methods also affect LCA results significantly. Four novel harvesting technologies and two extraction technologies provided by the NAABB internal report have been analysis using SimaPro LCA software. The results indicated that a combination of acoustic extraction and acoustic harvesting technologies show the most promising result of all combinations to optimize the extraction of algae oil from algae. These scenario evaluations provide important insights for consideration when planning for the future of an algae-based biofuel industry.

Numerical investigation of effects of addition of ethanol to gasoline on Laminar Flame Speed (LFS), autoignition, and wall quenching

Since the advent of automobiles, alcohol has been considered a possible engine fuel1,2. With the recent increased concern about the high price of crude oil due to fluctuating supply and demand and environmental issues, interest in alcohol based fuels has increased2,3. However, using pure alcohols or blends with conventional fuels in high percentages requires changes to the engine and fuel system design2. This leads to the need for a simple and accurate conventional fuels-alcohol blends combustion models that can be used in developing parametric burn rate and knock combustion models for designing more efficient Spark Ignited (SI) engines. To contribute to this understanding, numerical simulations were performed to obtain detailed characteristics of Gasoline-Ethanol blends with respect to Laminar Flame Speed (LFS), autoignition and Flame-Wall interactions. The one-dimensional premixed flame code CHEMKIN® was applied to simulate the burning velocity and autoignition characteristics using the freely propagating model and closed homogeneous reactor model respectively. Computational Fluid Dynamics (CFD) was used to obtain detailed flow, temperature, and species fields for Flame-wall interactions. A semi-detailed validated chemical kinetic model for a gasoline surrogate fuel developed by Andrae and Head4 was used for the study of LFS and Autoignition. For the quenching study, a skeletal chemical kinetic mechanism of gasoline surrogate, having 50 species and 174 reactions was used. The surrogate fuel was defined as a mixture of pure n-heptane, isooctane, and toluene. For LFS study, the ethanol volume fraction was varied from 0 to 85%, initial pressure from 4 to 8 bar, initial temperature from 300 to 900K, and dilution from 0 to 32%. Whereas for Autoignition study, the ethanol volume fraction was varied between 0 to 85%, initial pressure was varied between 20 to 60 bar, initial temperature was varied between 800 to 1200K, and the dilution was varied between 0 to 32% at equivalence ratios of 0.5, 1.0 and 1.5 to represent the in-cylinder conditions of a SI engine. For quenching study three Ethanol blends, namely E0, E25 and E85 are described in detail at an initial pressure of 8 atm and 17 atm. Initial wall temperature was taken to be 400 K. Quenching thicknesses and heat fluxes to the wall were computed. The laminar flame speed was found to increase with ethanol concentration and temperature but decrease with pressure and dilution. The autoignition time was found to increase with ethanol concentration at lower temperatures but was found to decrease marginally at higher temperatures. The autoignition time was also found to decrease with pressure and equivalence ratio but increase with dilution. The average quenching thickness was found to decrease with an increase in Ethanol concentration in the blend. Heat flux to the wall increased with increase in ethanol percentage in the blend and at higher initial pressures. Whereas the wall heat flux decreased with an increase in dilution. Unburned Hydrocarbon (UHC) and CO % was also found to decrease with ethanol concentration in the blend.

UNDERSTANDING PEOPLE'S ADOPTION OF INDIVIDUAL-USE RENEWABLE ENERGY TECHNOLOGY

This thesis attempts to understand why people adopt or reject individual-use renewable energy technologies (IURET). I used factors from Everett Rogers' Diffusion of Innovation Theory to understand how people's perceptions towards the characteristics of a given IURET (such as price, compatibility, complexity, etc.), the characteristics of the individual adopter (such as innovativeness and environmental awareness), and the communication network (inter-personal communications and mass media) can influence adoption. An online questionnaire was sent to 101randomly selected Michigan households (using random digit dialing) to ask people whether or not they had adopted at least one IURET and to assess the above-mentioned factors from Rogers' theory. Data analysis was then conducted in SPSS using Chi-squared and binary logistic regression to determine the relationship between adoption behaviors (the dependent variable) and the factors from Rogers' theory (the independent variables) while controlling for education. The results show that Rogers' factors of price and observability and the control variable of education were all significant in explaining adoption but the other factors of Rogers' theory were not. For example, if individuals perceive the price of IURET to be reasonable or if they observe their neighbors using these technologies, then they are more likely to adopt. These results indicate that, if we want to promote greater adoption of IURET, we should focus our efforts on making the price of IURET more affordable through incentives and other mechanisms. Adopters should also be given some form of reward if they provide free demonstrations of their IURET in use to their neighbors to take advantage of the observability effects.

A COST-BENEFIT ANALYSIS OF A 25% RPS IN MICHIGAN

Michigan depends heavily on fossil fuels to generate electricity. Compared with fossil fuels, electricity generation from renewable energy produces less pollutants emissions. A Renewable Portfolio Standard (RPS) is a mandate that requires electric utilities to generate a certain amount of electricity from renewable energy sources. This thesis applies the Cost-Benefits Analysis (CBA) method to investigate the impacts of implementing a 25% in Michigan by 2025. It is found that a 25% RPS will create about $20.12 billion in net benefits to the State. Moreover, if current tax credit policies will not change until 2025, its net present value will increase to about $26.59 billion. Based on the results of this CBA, a 25% RPS should be approved. The result of future studies on the same issue can be improved if more state specific data become available.