Syftet med rapporten är att beskriva hur fastighetsbolaget, Varbergs Fastighets AB och energibolaget, Halmstad Energi och Miljö hanterar de risk-, och osäkerhetsfaktorer som förekommer vid solcellsinvesteringar. För att energisamhället skall nå en hållbar framtid krävs att elproduktionen kommer ifrån förnybara energikällor. Sedan år 2008 har installationstakten för solceller i Sverige ökat. År 2012 installerades 8,3 MW solceller och år 2013 mer än fördubblades den installerade effekten till 19 MW (Lindahl, 2014). Trots utvecklingen står solenergin för 0,03 procent av Sveriges totala elproduktion, vilket kan jämföras med Tyskland där solenergin står för 5,3 procent av den totala elproduktionen år 2014 (IEA International energy agency, 2014). En investerare vill från investeringen nå en så hög avkastning som möjligt och samtidigt reducera och undvika de uppkomma riskerna. Det multinationella brittiskt-amerikanska revisor och konsultföretaget Pricewaterhousecoopers definierar risk som en osäker framtida händelse som kan påverka företagets möjlighet att uppnå de framtida strategiska, operationella och finansiella mål (Pricewaterhousecoopers, 1999). Den här studien har sin utgångspunkt i den samhällsvetenskapliga hermeneutiken. Studien antar att verkligheten är subjektiv och att observationer av den kräver tolkning. Det resultat som presenteras kommer därmed vara en tolkning av verkligheten och inte resultera i några generella teorier (Eriksson & Wiedersheim-Paul, 2014). Vår kvalitativa fallstudie grundar sig i en induktiv ansats där vi samlat in empirisk data genom att intervjua två företag och en solcellsexpert. Vi har intervjuat Varbergs Fastighets ABs processledare och hållbarhetsansvarig och Halmstad Energi och Miljös strategichef. Vi har även intervjuat branschorganisationen Svensk solenergis ordförande. Våra respondenter har stärkt vårt arbete med konstruktiv data för att vidare studera risk och osäkerhet i solcellsinvesteringar. Studien tyder på att de risker som föreligger vid solcellsinvesteringar är förhållandevis lika mellan aktörerna men det som skiljer är företagens riskhanteringsstrategier för att behandla dem. De allmänna riskerna som belyses av solcellsexperten är i enlighet med de risker som företagen påvisar men att den genomgående risken är en allmän låg kunskap kring solceller. / This study attempts to describe how the real estate company, Varbergs Fastighets AB and the energy-company, Halmstad Energi och Miljö copes with factors of risks and uncertainties that may occur while investing in photovoltaic. To enable the energy society to attain a sustainable future, the production of energy must be provided by renewable sources. Since 2008, the rate of the installation of photovoltaic in Sweden has increased. In 2012, 8.3 MW of solar cells were installed and by 2013, the installed effect was more than doubled (Lindahl, 2014). Despite this bright development, the solar energy in Sweden only provides 0.03 percent of the overall energy production in the year of 2014 (IEA International energy agency, 2014). An investor wants from the investment reach as high a return as possible while reducing and avoiding the risks incurred. The multinational British-American accountant and consulting firm PricewaterhouseCoopers defines risk as an uncertain future event that may affect the company's ability to achieve future strategic, operational and financial targets (PricewaterhouseCoopers, 1999). This study takes its point of departure in the hermeneutics of the social science. The study assumes that the reality is subjective, and thus demands an interpretation of the observations from it. The result presented in this report will therefore be an interpretation of the reality and consequently not answer in any general theories (Eriksson & Wiedersheim-Paul, 2014). Our qualitative case study has its base in an inductive approach, where empirical data has been gathered by interviewing two companies and one expert of photovoltaic. We have interviewed the process manager and the manager of sustainability of Varbergs Fastighet AB and the strategy executive of Halmstad Energy och Miljö. We have also interviewed the chairman of Swedish solar energy. Our respondents have strengthened our work with constructive data, to further study the risks and uncertainties associated with investments in photovoltaic. This study indicates that the risks presented by investments in photovoltaic are relatively similar between the operators, but the distinguishing factor is the treatment of the risks by the companies. The common risks illuminated by the experts of photovoltaic runs accordingly with the risks demonstrated by the companies, though it is clear that the pervading risk is general poor knowledge of photovoltaic.

This work integrates perennial feedstock yield modeling using climate model data from current and future climate scenarios, land use datasets, transportation network data sets, Geographic Information Systems (GIS) tools, and Mixed integer linear programming (MILP) optimization models to examine biorefinery network designs in the southeastern United States from an overall systems perspective. Both deterministic and stochastic cases are modeled. Findings indicate that the high transportation costs incurred by biorefinery networks resulting from the need to transport harvested biomass from harvest location to processing facilities can be mitigated by performing initial processing steps in small scale mobile units at the cost of increased unit production costs associated with operating at smaller scales. Indeed, it can be financially advantageous to move the processing units instead of the harvested biomass, particularly when considering a 10-year planning period (typical switchgrass stand life). In this case, the mobile processing supply chain configuration provides added flexibility to respond to year-to-year variation in the geographic distribution of switchgrass yields. In order to capture the effects of variation in switchgrass yields and incorporate it in optimization models, yield modeling was conducted for both current and future climate scenarios. (In general profits are lower in future climate scenarios). Thus, both the effects of annual variation in weather patterns and varying climate scenarios on optimization model decisions can be observed.

Photovoltaic (PV) DGs can be optimized to provide reactive power support to the grid, although this feature is currently rarely utilized as most DG systems are designed to operate with unity power factor and supply real power only to the grid. In this work, the voltage stability of a power system embedded with PV DG is examined in the context of the high reactive power requirement after a voltage sag or fault. A real-time dynamic multi-function power controller that enables renewable source PV DGs to provide the reactive power support necessary to maintain the voltage stability of the microgrid, and consequently, the wider power system is proposed. The loadability limit necessary to maintain the voltage stability of an interconnected microgrid is determined by using bifurcation analysis to test for the singularity of the network Jacobian and load differential equations with and without the contribution of the DG. The maximum and minimum real and reactive power support permissible from the DG is obtained from the loadability limit and used as the limiting factors in controlling the real and reactive power contribution from the PV source. The designed controller regulates the voltage output based on instantaneous power theory at the point-of-common coupling (PCC) while the reactive power supply is controlled by means of the power factor and reactive current droop method. The control method is implemented in a modified IEEE 13-bus test feeder system using PSCAD® power system analysis software and is applied to the model of a Tampa Electric® PV installation at Lowry Park Zoo in Tampa, FL. This dissertation accomplishes the systematic analysis of the voltage impact of a PV DGembedded power distribution system. The method employed in this work bases the contribution of the PV resource on the voltage stability margins of the microgrid rather than the commonly used loss-of-load probability (LOLP) and effective load-carrying capability (ELCC) measures. The results of the proposed method show good improvement in the before-, during-, and post-start voltage levels at the motor terminals. The voltage stability margin approach provides the utility a more useful measure in sizing and locating PV resources to support the overall power system stability in an emerging smart grid.

Significant progress has been made in the development of wave energy converters (WECs) during recent years, with prototypes and farms of WECs being installed in different parts of the world. With increasing sizes of individual WECs and farms, it becomes necessary to consider the impacts of connecting these to the electricity network and to investigate means by which these impacts may be mitigated. The time-varying and the unpredictable nature of the power generated from wave power farms supplemented by the weak networks to which most of these farms will be connected to, makes the question of integrating a large quantity of wave power to the network more challenging. The work reported here focuses on the fluctuations in the rms-voltage introduced by the connection of wave power farms. Two means to reduce these rms-voltage fluctuations are proposed. In the first method, the physical placement of the WECs within a farm is selected prior to the development of the farm to reduce the fluctuations in the net real power generated. It is shown that spacing the WECs or the line of WECs within a farm at a distance greater than half the peak wavelength and orienting the farm at 90◦ to the dominant wave direction produces a much smoother power output. The appropriateness of the following conclusions has been tested and proven for a wave power farm developed off the Outer Hebrides, using real wave field and network data. The second method uses intelligent reactive power control algorithms, which have already been tested with wind and hydro power systems, to reduce voltage fluctuations. The application of these intelligent control methods to a 6 MW wave power farm connected to a realistic UK distribution network verified that these approaches improve the voltage profile of the distribution network and help the connection of larger farms to the network, without any need for network management or upgrades. Using these control methods ensured the connection of the wave power farm to the network for longer than when the conventional control methods are used, which is economically beneficial for the wave power farm developer. The use of such intelligent voltage - reactive power (volt/VAr) control methods with the wave power farm significantly affects the operation of other onshore voltage control devices found prior to the connection of the farm. Thus, it is essential that the control of the farm and the onshore control devices are coordinated. A voltage estimation method, which uses a one-step-ahead demand predictor, is used to sense the voltage downstream of the substation at the bus where the farm is connected. The estimator uses only measurements made at the substation and historical demand data. The estimation method is applied to identify the operating mode of a wave power farm connected to a generic 11 kV distribution network in the UK from the upstream substation. The developed method introduced an additional level of control and can be used at rural substations to optimise the operation of the network, without any new addition of measuring devices or communication means.

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

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.