The built environment is responsible for a large portion of total energy use and emissions. A large portion comes from the buildings themselves, but also the transportation system to move people around. As global populations grow, and more people migrate to cities, it is critically important that new city growth is done in the most sustainable manner possible. The typical North American pattern of urban growth is urban sprawl, characterized by single use type zoning, low density, transportation system dominated by personal vehicles, and poor public transit. Urban sprawl has numerous downsides, including poorer energy efficiency in buildings and infrastructure, more congestion and higher emission from vehicles, as well as many negative health effects. This thesis presents the concept of a Mothership, a large, high-density mixed-use building designed to combat urban sprawl and minimize energy use and emissions of the built environment. A mothership is designed to provide all the amenities and housing of a typical suburb for 10,000 people. The analysis in this thesis employ building simulation tools to model various mothership designs and analyse the operational and embodied energy and carbon emissions for each design, and compare it to base cases of more traditional building use types such as single detached homes, and different types of apartment buildings. The effect of high-performance building envelopes and other building materials on operational and embodied energy and emissions are analysed. A multi objective optimization analysis is performed to determine which technologies and combinations of technologies provide the lowest cost solution to meet the mothership’s energy demands while also minimizing emissions. The mothership’s effect on transportation emissions is also investigated. The building’s mixed-use nature allows trips to be satisfied within walking distance in the building. The high concentration of people makes for a good anchor load for public transportation, so the emissions reductions of implementing a bus rapid transit system from the mothership to the central business district is estimated. To reduce transportation emissions further, the effect of an electric car share fleet for mothership residents use is also quantified. The energy system of a mothership is optimized, along with base cases of single detached homes, under numerous scenarios. These scenarios are designed to explore how the energy system changes in an attempt to answer a series of research questions. Some of the measures explored are a high carbon tax, net metering, and emissions limits of net zero, and negative emissions with two different electrical grid carbon intensities. Results showed that a highly insulated, timber framed mothership can achieve very high reductions in energy use and emissions. Overall it showed reductions of 71%, 73%, and 74% in operational energy, embodied energy and embodied carbon respectively, over a baseline case of single detached homes. It was estimated that transportation emissions could be reduced by 58% through the mixed-use development reducing the number of trips and electrically powered transportation vehicles and bus rapid transit. This gives a combined total emissions reduction of 61%. Energy system optimization showed that the mothership design in achieved far lower costs and emissions (4 and 8.7 times lower respectively) than the base case of single detached homes. Of the mothership cases examined, the most expensive case was the one which had a carbon tax, with an annualized cost of $4.3 million. The case with the lowest annualized cost was one with, among other factors, a net zero carbon emissions restriction (annualized cost of $3.08 million. Many of the cases had negative operating costs due to the sale of renewable energy or carbon credits. This illustrates that the integration of renewable energy technologies is not only beneficial for reducing emissions but can also act as an income pathway for energy systems. / Graduate

This thesis reports findings from a number of modern machine learning techniques applied to electricity market price forecasting. The techniques evaluated were Support Vector Machines, Boosting, Bayesian networks, neural networks and a weekly average method. All techniques were evaluated on seven day into the future forecasting of the Regional Reference (pool) Prices (RRP) for the New South Wales (NSW) region of the Australian National Electricity Market (NEM). Due to highly volatile and non-repetitive nature of the NSW RRP, all complex machine learning methods provided inferior accuracy forecasts compared to a weekly average method. The weekly average method was computationally less expensive and more transparent to the user than any of the machine learning techniques. The Support Vector Machine (SVM) was chosen for its novel application to electricity price forecasting because it is considered to be the next generation to neural networks. The structured SVM training algorithm proved more consistent and reliable than the neural network algorithm. Bayesian networks offer the adaptability of a neural network with the advantage of providing a price forecast with confidence intervals for each half-hour determined from the actual data. The SVM and Bayesian techniques were found to provide acceptable forecasts for NSW demand. An investigation of international electricity markets found that each market was unique with different market structures, regulations, network topologies and ownership regimes. Price forecasting techniques and results cannot be universally applied without careful consideration of local conditions. For instance, price data for the Spanish and Californian electricity markets were investigated and found to have significantly lower price volatility than the NSW region of the NEM. An extensive examination of the NSW RRP showed that the price exhibited no consistent long-term trend. A stationary data set could not be extracted from the price data. Thus, making forecasting unsuited to techniques using large historical data sets. The strongest pattern found for NSW prices was the weekly cycle, so a weekly average method was developed to utilise this weekly cycle. Over 25 weeks of NSW RRP from February to July 2002, the seven day into the future price forecast mean absolute error (MAE) for the SVM technique was 27.8%. The weekly average method was more accurate with an MAE of 20.6% and with a simple linear price adjustment for demand, the error was reduced to 18.1%. The price spikes and uneven distribution of prices were unsuitable for the Boosting or Bayesian network techniques.

For several years now it has been indicated by the scientific research that human activity has a definite impact on the temperature of the Earth. There are different ways of reducing anthropogenic climate change, to consume less energy for instance, but also to use renewable sources of energy. Since the wind power market is growing rapidly lately, it seemed interesting to compare how different countries have developed wind power energy and how they intend to do it in the future. Sweden has developed wind power energy for a long time and since Québec, a Canadian province, is similar to the Scandinavian country in many aspects; it was interesting to determine if the province of Québec in Canada can learn from Sweden in the field of wind power energy. When looking to stimulate wind power on their respective territories, it seems that Sweden and Québec have very different approaches to the development of that energy. In Québec, the governmental policy is criticised because it gives too much latitude to private companies when it is almost the opposite in Sweden, where the government is charged to have introduced too many restrictions and environmental policies that hinder the development of wind power. The conclusion is that Québec can learn from Sweden and from Sweden’s mistakes in the wind energy sector, but also from more successful countries like Denmark and Germany. First, Québec could benefit from having a more decentralised development strategy i.e. giving more power to local authorities and local populations. Second, Québec needs stronger economic incentives providing a stable market for developers.

As a cost-effective test method, a vehicle-based test rig can be utilized in small wind turbine experimental work to facilitate turbine performance tests under a range of controlled wind speeds, as well as to validate turbulent flow models. The instrumentation of a custom trailer-based mobile wind turbine test rig has been modified to provide a platform for full rotor speed control. A control system coupled to an electric vehicle controller with regenerative braking technology was developed in five steps, namely: system modeling in Simulink, system identification, control system design and analysis, control system implementation in LabVIEW, and Proportional-Integral-Derivative (PID) controller tuning in real-time. A custom Graphical User Interface (GUI) was also developed. Furthermore, a Computational Fluid Dynamics (CFD) analysis was conducted to assess the potential impact of towing vehicle’s disturbance on the free stream available to the rotor disc. This trailer rig will allow up to a 1kW wind turbine. It can be towed behind a vehicle to conduct steady state tests or it can be parked in an open area to collect unsteady field data. It has been tested in a towed scenario and the Blade Element Momentum (BEM) predictions were compared with the obtained aggregate performance curve. / Graduate / 0548 / 0791 / 0544 / khorsand@uvic.ca

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This thesis reports findings from a number of modern machine learning techniques applied to electricity market price forecasting. The techniques evaluated were Support Vector Machines, Boosting, Bayesian networks, neural networks and a weekly average method. All techniques were evaluated on seven day into the future forecasting of the Regional Reference (pool) Prices (RRP) for the New South Wales (NSW) region of the Australian National Electricity Market (NEM). Due to highly volatile and non-repetitive nature of the NSW RRP, all complex machine learning methods provided inferior accuracy forecasts compared to a weekly average method. The weekly average method was computationally less expensive and more transparent to the user than any of the machine learning techniques. The Support Vector Machine (SVM) was chosen for its novel application to electricity price forecasting because it is considered to be the next generation to neural networks. The structured SVM training algorithm proved more consistent and reliable than the neural network algorithm. Bayesian networks offer the adaptability of a neural network with the advantage of providing a price forecast with confidence intervals for each half-hour determined from the actual data. The SVM and Bayesian techniques were found to provide acceptable forecasts for NSW demand. An investigation of international electricity markets found that each market was unique with different market structures, regulations, network topologies and ownership regimes. Price forecasting techniques and results cannot be universally applied without careful consideration of local conditions. For instance, price data for the Spanish and Californian electricity markets were investigated and found to have significantly lower price volatility than the NSW region of the NEM. An extensive examination of the NSW RRP showed that the price exhibited no consistent long-term trend. A stationary data set could not be extracted from the price data. Thus, making forecasting unsuited to techniques using large historical data sets. The strongest pattern found for NSW prices was the weekly cycle, so a weekly average method was developed to utilise this weekly cycle. Over 25 weeks of NSW RRP from February to July 2002, the seven day into the future price forecast mean absolute error (MAE) for the SVM technique was 27.8%. The weekly average method was more accurate with an MAE of 20.6% and with a simple linear price adjustment for demand, the error was reduced to 18.1%. The price spikes and uneven distribution of prices were unsuitable for the Boosting or Bayesian network techniques.

The deregulation of the electricity industry has introduced many opportunities as well as challenges to the once monopolised industry. This recent reform towards a competitive electricity industry advocates a need for charging energy losses to market participants through a more satisfactory and transparent mechanism. Market participants, whether they are generators or consumers, would want a loss allocation scheme that is able to reflect each market participants' contribution of generation or usage in the network. However, as electricity is an indistinguishable entity, there is no accurate method to trace the flow of electricity thus far. Hence, the issue of power loss allocation within the deregulated market still remains an unresolved setback to progress to a fully competitive electricity market. Many loss allocation methods have been introduced, however, none have been universally accepted. This thesis investigates existing power flow tracing and loss allocation methods in order to critically analyse the advantages and disadvantages of each method. They include loss allocation methods currently employed in Australia’s National Electricity Market (NEM) and Great Britain Market, as well as a selection of better known loss allocation methods that are introduced in the academic research field. Understanding of these methods makes it easier to choose a method that is more suitable for each electricity market. Many researchers believe that a resolution is through a fair and equitable allocation of losses. However, the definition of “fair and equitable” varies from one literature to another. In general, a fair and equitable loss allocation method should meet electrical laws as well as economical laws. This is because market driven transactions have become the new independent decision variables that define the behaviour of electric power systems. This definition is then used as the basis to assess the results obtained from the implementation of each existing method analysed. It was found that a key limitation of existing methods is the lack of a method that is able to trace the usage allocation of each generator to each load in an electrically justifiable manner. Any improvement to existing loss allocation methods should address this limitation. Thus, the main objective of this thesis is to present two transaction based methods that have been developed and tested by the author of this thesis. Fundamentally, both methods hold the capability to analyse losses involved in the transfer of power from one point of the network to another point. The first investigated method is based on the network reduction method, where a system is reduced to the nodes of interest. The second method is based on the loop frame of reference. Instead of representing the network flows through the commonly accepted nodal frame of reference, power flows within the network are instead expressed as the sum of power flows around loops that links loads to active sources. This provides the loop-based method with an advantage in which it allows the power requirements of a load to be viewed as emanating from an active source and also the advantage of assessing the viability of contract agreements within a hybrid market model. The final objective of this thesis is to analytically compare selected existing loss allocation schemes with the proposed loop-based method. As there are no standard means of judging the accuracy of any loss allocation methods, the author of this thesis proposed a different way to distinguish different loss allocation methods. That is, through the type of competition that each method promotes. A wide range of results is obtained in which the loss allocations of some methods are dependent only on the real power injection at each bus. On the other hand, the loss allocations of other methods such as the loop-based method are dependent on network operation efficiency. The comprehension of the different type of competitions each method promotes aims to assist market regulators in recognising the feasibility of employing each loss allocation method.