Energizing the Oak Ridges Moraine? Analyzing the Policy Implications of Three Wind Energy Developments on the Oak Ridges Moraine and their Potential Impact on the Coordinated Land Use Planning Review through the Involvement of First Nations and Environmental Non-governmental Organizations

This research explores the policy implications of the approval of three wind energy projects on the Oak Ridges Moraine, and their impact on the Coordinated Land Use Planning Review process. Specifically, it focuses on the involvement of First Nations and environmental non-governmental organizations (ENGOs). This research was conducted through analyzing submissions to the Coordinated Land Use Planning Review, related legislation and policy, Environmental Review Tribunal hearing documents, and interviews with key informants. This research culminates in a number of recommendations to the Coordinated Review informed by the analysis.

Federal Wind Energy Program : program summary, January 1978.

Includes index.

Federal wind energy program : summary report /

"UC-60."

Uncertainty in determining thermal performance of liquid-heating flat-plate solar collectors /

"Issued April 1981."

Survey of historical and current site selection techniques for the placement of small wind energy conversion systems /

"BNWL-WIND-9. UC-60."

A report on wind energy /

Presents an overview of the wind energy industry and discusses potential future wind energy development in Illinois.

The possible effects of wind energy on Illinois birds and bats.

"200/2007"

Slowing down a pine invasion despite uncertainty in demography and dispersal

1. Management decisions regarding invasive plants often have to be made quickly and in the face of fragmentary knowledge of their population dynamics. However, recommendations are commonly made on the basis of only a restricted set of parameters. Without addressing uncertainty and variability in model parameters we risk ineffective management, resulting in wasted resources and an escalating problem if early chances to control spread are missed. 2. Using available data for Pinus nigra in ungrazed and grazed grassland and shrubland in New Zealand, we parameterized a stage-structured spread model to calculate invasion wave speed, population growth rate and their sensitivities and elasticities to population parameters. Uncertainty distributions of parameters were used with the model to generate confidence intervals (CI) about the model predictions. 3. Ungrazed grassland environments were most vulnerable to invasion and the highest elasticities and sensitivities of invasion speed were to long-distance dispersal parameters. However, there was overlap between the elasticity and sensitivity CI on juvenile survival, seedling establishment and long-distance dispersal parameters, indicating overlap in their effects on invasion speed. 4. While elasticity of invasion speed to long-distance dispersal was highest in shrubland environments, there was overlap with the CI of elasticity to juvenile survival. In shrubland invasion speed was most sensitive to the probability of establishment, especially when establishment was low. In the grazed environment elasticity and sensitivity of invasion speed to the severity of grazing were consistently highest. Management recommendations based on elasticities and sensitivities depend on the vulnerability of the habitat. 5. Synthesis and applications. Despite considerable uncertainty in demography and dispersal, robust management recommendations emerged from the model. Proportional or absolute reductions in long-distance dispersal, juvenile survival and seedling establishment parameters have the potential to reduce wave speed substantially. Plantations of wind-dispersed invasive conifers should not be sited on exposed sites vulnerable to long-distance dispersal events, and trees in these sites should be removed. Invasion speed can also be reduced by removing seedlings, establishing competitive shrubs and grazing. Incorporating uncertainty into the modelling process increases our confidence in the wide applicability of the management strategies recommended here.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

Risk and uncertainty in supply markets and sustainability-related innovation

tentially valuable innovations. In energy policy, much attention is given to analysing and incentivising customer demand, but new technologies also need new supply markets, to provide products and services to build, operate and maintain the innovative technology. This paper addresses the impact of supply constraints on the long-term viability of sustainability related innovations, using the case of energy from waste (EfW). Uncertainties in the pricing and availability of feedstock (i.e. waste) deter potential investors in EfW projects. We draw on prior supply management research to conceptualise the problem, and identify what steps might be taken to address it. Based on this analysis, we propose a research agenda aimed at purchasing and supply scholars and centred on the need to understand better how markets evolve and how stakeholders can (legitimately) influence the evolution of supply markets to support the adoption of sustainability related innovation. Within this broad case, specific themes are recommended for further investigation.

Variation and Uncertainty in Evaporation from a Subtropical Estuary: Florida Bay

Variation and uncertainty in estimated evaporation was determined over time and between two locations in Florida Bay, a subtropical estuary. Meteorological data were collected from September 2001 to August 2002 at Rabbit Key and Butternut Key within the Bay. Evaporation was estimated using both vapor flux and energy budget methods. The results were placed into a long-term context using 33 years of temperature and rainfall data collected in south Florida. Evaporation also was estimated from this long-term data using an empirical formula relating evaporation to clear sky solar radiation and air temperature. Evaporation estimates for the 12-mo period ranged from 144 to 175 cm yr21, depending on location and method, with an average of 163 cm yr21 (6 9%). Monthly values ranged from 9.2 to 18.5 cm, with the highest value observed in May, corresponding with the maximum in measured net radiation. Uncertainty estimates derived from measurement errors in the data were as much as 10%, and were large enough to obscure differences in evaporation between the two sites. Differences among all estimates for any month indicate the overall uncertainty in monthly evaporation, and ranged from 9% to 26%. Over a 33-yr period (1970–2002), estimated annual evaporation from Florida Bay ranged from 148 to 181 cm yr21, with an average of 166 cm yr21. Rainfall was consistently lower in Florida Bay than evaporation, with a long-term average of 106 cm yr21. Rainfall considered alone was uncorrelated with evaporation at both monthly and annual time scales; when the seasonal variation in clear sky radiation was also taken into account both net radiation and evaporation were significantly suppressed in months with high rainfall.

An Empirically Based Stochastic Turbulence Simulator with Temporal Coherence for Wind Energy Applications

In this dissertation, we develop a novel methodology for characterizing and simulating nonstationary, full-field, stochastic turbulent wind fields.

In this new method, nonstationarity is characterized and modeled via temporal coherence, which is quantified in the discrete frequency domain by probability distributions of the differences in phase between adjacent Fourier components.

The empirical distributions of the phase differences can also be extracted from measured data, and the resulting temporal coherence parameters can quantify the occurrence of nonstationarity in empirical wind data.

This dissertation (1) implements temporal coherence in a desktop turbulence simulator, (2) calibrates empirical temporal coherence models for four wind datasets, and (3) quantifies the increase in lifetime wind turbine loads caused by temporal coherence.

The four wind datasets were intentionally chosen from locations around the world so that they had significantly different ambient atmospheric conditions.

The prevalence of temporal coherence and its relationship to other standard wind parameters was modeled through empirical joint distributions (EJDs), which involved fitting marginal distributions and calculating correlations.

EJDs have the added benefit of being able to generate samples of wind parameters that reflect the characteristics of a particular site.

Lastly, to characterize the effect of temporal coherence on design loads, we created four models in the open-source wind turbine simulator FAST based on the \windpact turbines, fit response surfaces to them, and used the response surfaces to calculate lifetime turbine responses to wind fields simulated with and without temporal coherence.

The training data for the response surfaces was generated from exhaustive FAST simulations that were run on the high-performance computing (HPC) facilities at the National Renewable Energy Laboratory.

This process was repeated for wind field parameters drawn from the empirical distributions and for wind samples drawn using the recommended procedure in the wind turbine design standard \iec.

The effect of temporal coherence was calculated as a percent increase in the lifetime load over the base value with no temporal coherence.

Capacity Investment in Renewable and Conventional Energy Sources

This dissertation studies capacity investments in energy sources, with a focus on renewable technologies, such as solar and wind energy. We develop analytical models to provide insights for policymakers and use real data from the state of Texas to corroborate our findings.

We first take a strategic perspective and focus on electricity pricing policies. Specifically, we investigate the capacity investments of a utility firm in renewable and conventional energy sources under flat and peak pricing policies. We consider generation patterns and intermittency of solar and wind energy in relation to the electricity demand throughout a day. We find that flat pricing leads to a higher investment level for solar energy and it can still lead to more investments in wind energy if considerable amount of wind energy is generated throughout the day.

In the second essay, we complement the first one by focusing on the problem of matching supply with demand in every operating period (e.g., every five minutes) from the perspective of a utility firm. We study the interaction between renewable and conventional sources with different levels of operational flexibility, i.e., the possibility

of quickly ramping energy output up or down. We show that operational flexibility determines these interactions: renewable and inflexible sources (e.g., nuclear energy) are substitutes, whereas renewable and flexible sources (e.g., natural gas) are complements.

In the final essay, rather than the capacity investments of the utility firms, we focus on the capacity investments of households in rooftop solar panels. We investigate whether or not these investments may cause a utility death spiral effect, which is a vicious circle of increased solar adoption and higher electricity prices. We observe that the current rate-of-return regulation may lead to a death spiral for utility firms. We show that one way to reverse the spiral effect is to allow the utility firms to maximize their profits by determining electricity prices.

Energizing the Oak Ridges Moraine? Analyzing the Policy Implications of Three Wind Energy Developments on the Oak Ridges Moraine and their Potential Impact on the Coordinated Land Use Planning Review through the Involvement of First Nations and Environmental Non-governmental Organizations

This research explores the policy implications of the approval of three wind energy projects on the Oak Ridges Moraine, and their impact on the Coordinated Land Use Planning Review process. Specifically, it focuses on the involvement of First Nations and environmental non-governmental organizations (ENGOs). This research was conducted through analyzing submissions to the Coordinated Land Use Planning Review, related legislation and policy, Environmental Review Tribunal hearing documents, and interviews with key informants. This research culminates in a number of recommendations to the Coordinated Review informed by the analysis.

Estimating Bat and Bird Mortality Occurring at Wind Energy Turbines from Covariates and Carcass Searches Using Mixture Models

Environmental impacts of wind energy facilities increasingly cause concern, a central issue being bats and birds killed by rotor blades. Two approaches have been employed to assess collision rates: carcass searches and surveys of animals prone to collisions. Carcass searches can provide an estimate for the actual number of animals being killed but they offer little information on the relation between collision rates and, for example, weather parameters due to the time of death not being precisely known. In contrast, a density index of animals exposed to collision is sufficient to analyse the parameters influencing the collision rate. However, quantification of the collision rate from animal density indices (e.g. acoustic bat activity or bird migration traffic rates) remains difficult. We combine carcass search data with animal density indices in a mixture model to investigate collision rates. In a simulation study we show that the collision rates estimated by our model were at least as precise as conventional estimates based solely on carcass search data. Furthermore, if certain conditions are met, the model can be used to predict the collision rate from density indices alone, without data from carcass searches. This can reduce the time and effort required to estimate collision rates. We applied the model to bat carcass search data obtained at 30 wind turbines in 15 wind facilities in Germany. We used acoustic bat activity and wind speed as predictors for the collision rate. The model estimates correlated well with conventional estimators. Our model can be used to predict the average collision rate. It enables an analysis of the effect of parameters such as rotor diameter or turbine type on the collision rate. The model can also be used in turbine-specific curtailment algorithms that predict the collision rate and reduce this rate with a minimal loss of energy production.