The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems.<p> A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities.<p> There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator.

Distribution system design and planning is facing a major change in paradigm because of deregulation of the power industry and with rapid penetration of distributed generation (DG) sources. Distribution system design and planning are key features for determining the best expansion strategies to provide reliable and economic services to the customer. In classical planning, the load growth is typically met by adding a new substation or upgrading the existing substation capacity along with their feeders. Today, rapid advances in DG technology and their numerous benefits have made them an attractive option to the distribution companies, power system planners and operators, energy policy makers and regulators, as well as developers. This thesis first presents a comprehensive planning framework for the distribution system from the distribution company perspective. It incorporates DG units as an option for local distribution companies (LDCs) and determines the sizing, placement and upgrade plans for feeders and substations. Thereafter, a new heuristic approach to multi-year distribution system planning is proposed which is based on a back-propagation algorithm starting from the terminal year and arriving at the first year. It is based on cost-benefit analysis, which incorporates various energy supply options for LDCs such as DG, substations and feeders and determines the size, placement and upgrade plan. The proposed heuristic approach combines a bi-level procedure in which Level-1 selects the optimal size and location of distribution system component upgrades and Level-2 determines the optimal period of commissioning for the selected upgrades in Level-1. The proposed heuristic is applied to a 32-bus radial distribution system. The first level of the distribution system planning framework is formulated as a mixed integer linear programming (MILP) problem while the second level is a linear programming (LP) model. The results demonstrate that the proposed approach can achieve better performance than a full optimization for the same distribution system.

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A recém-adquirida competitividade pela energia eólica frente a outras fontes alternativas e, até mesmo, convencionais tem sido comemorada pelo setor de energia elétrica. No entanto, a base para essa comparação leva em conta apenas os parâmetros dos investimentos do parque viabilizado, não sendo considerados outros fatores inerentes à inserção da fonte na matriz, como, por exemplo, o aumento do risco ao sistema, investimento necessário à ampliação da Rede Básica para escoamento da energia para o centro de carga e os custos com redespacho de geração, reserva girante e controles do sistema. O maior potencial está concentrado no Nordeste e o maior centro de carga no Sudeste, assim, em caso de exploração intensiva do potencial da fonte, será necessário passar por grandes investimentos em reforços e ampliações da rede básica. A partir desse panorama, busca-se realizar as primeiras análises de sensibilidade sobre a eficiência econômica dos parques eólicos instalados no Nordeste e, ao mesmo tempo, analisar as condições de atendimento da ponta. / The newfound competitiveness in wind power compared to other alternative and even conventional energy sources has been celebrated by the electric power sector. However, the basis for this comparison takes into account only the parameters of the Capex, not considering other factors inherent in the insertion of the source in the array, for example, increase the risk to the system, expansion of the investment necessary for Basic network for transport of energy to the load center and the cost of redispatch generation, spinning reserve, and system controls. The greatest potential is concentrated in the Northeast and the largest load hub in the Southeast, so, in case of intensive exploitation of the potential of the source, large investments in reinforcements and expansions of the grid will be necessary. From this overview, we attempt the firsts sensitivity analysis on the economic efficiency of wind farms in the Northeast and the load peak supply conditions.

This research investigates the role of laboratory information systems on business outcomes in medical pathology in Australia. Pathology information systems are inherently large-scale systems handling large numbers of data daily to service not only the pathology laboratory itself, but also referring medical practitioners. Patient results are often required in a

A paradigm shift in distribution system design and planning is being led by the deregulation of the power industry and the increasing adoption of distributed generation (DG). Technology advances have made DG investments feasible by both local distribution companies (LDCs) and small power producers (SPPs). LDCs are interested in finding optimal long term plans that best serve their customers at the lowest cost. SPPs, as private entities, are concerned about maximizing their rates of return. Also keenly interested in distribution design and planning is the government, which, through an electricity regulator, strives to meet DG penetration and emissions reduction goals through policy implementations. This thesis first examines the distribution system planning problem from the LDC's perspective. An innovative hierarchical dynamic optimization model is proposed for the planning of distribution systems and the energy scheduling of units that is also capable of reconciling uncoordinated SPP investments in DG. The first stage of the two-stage framework consists of a siting-cum-period planning model that sets element sizing and commissioning dates. The second stage consists of a capacity-cum-production planning model that finalizes element capacities and energy import/export and production schedules. The proposed framework is demonstrated on a 32-bus radial distribution system. Four case studies encompassing different policy sets are also conducted, demonstrating that this model's usefulness also extends to predicting the impact of different energy policies on distribution system operation and economics. The analysis of different policy sets is further expanded upon through the proposal of a new mathematical model that approaches the distribution design problem from the regulator's perspective. Various case studies examining policies that may be used by the regulator to meet DG penetration and emissions goals, through DG investment, are constructed. A combination of feed-in-tariffs, CO$_2$ tax, and cap-and-trade mechanisms are among the policies studied. The results, in the context of Ontario, Canada and its Standard Offer Program, are discussed, with respect to achieving objectives in DG investment, participation by SPPs, consumer costs, and uncertainty in carbon market prices. In jurisdictions such as Ontario, the LDC cannot invest in its own DG capacity but must accommodate those of SPPs. With the successful implementation of DG investment incentives by the regulator, there is a potential for significant investments in DG by SPPs, which may exceed that of the LDCs ability to absorb. This thesis proposes a novel method that can be used by the regulator or LDC to fairly assess, coordinate, and approve multiple competing investments proposals while maintaining operational feasibility of the distribution system. This method uses a feedback between the LDC and SPPs to achieve maximum investor participation while adhering to the technical operational limits of the distribution system. The proposed scheme is successfully demonstrated on a 32-bus radial distribution system, where it is shown to increase SPP-DG investments and production, improve the system's voltage profile, and reduce losses.

A paradigm shift in distribution system design and planning is being led by the deregulation of the power industry and the increasing adoption of distributed generation (DG). Technology advances have made DG investments feasible by both local distribution companies (LDCs) and small power producers (SPPs). LDCs are interested in finding optimal long term plans that best serve their customers at the lowest cost. SPPs, as private entities, are concerned about maximizing their rates of return. Also keenly interested in distribution design and planning is the government, which, through an electricity regulator, strives to meet DG penetration and emissions reduction goals through policy implementations. This thesis first examines the distribution system planning problem from the LDC's perspective. An innovative hierarchical dynamic optimization model is proposed for the planning of distribution systems and the energy scheduling of units that is also capable of reconciling uncoordinated SPP investments in DG. The first stage of the two-stage framework consists of a siting-cum-period planning model that sets element sizing and commissioning dates. The second stage consists of a capacity-cum-production planning model that finalizes element capacities and energy import/export and production schedules. The proposed framework is demonstrated on a 32-bus radial distribution system. Four case studies encompassing different policy sets are also conducted, demonstrating that this model's usefulness also extends to predicting the impact of different energy policies on distribution system operation and economics. The analysis of different policy sets is further expanded upon through the proposal of a new mathematical model that approaches the distribution design problem from the regulator's perspective. Various case studies examining policies that may be used by the regulator to meet DG penetration and emissions goals, through DG investment, are constructed. A combination of feed-in-tariffs, CO$_2$ tax, and cap-and-trade mechanisms are among the policies studied. The results, in the context of Ontario, Canada and its Standard Offer Program, are discussed, with respect to achieving objectives in DG investment, participation by SPPs, consumer costs, and uncertainty in carbon market prices. In jurisdictions such as Ontario, the LDC cannot invest in its own DG capacity but must accommodate those of SPPs. With the successful implementation of DG investment incentives by the regulator, there is a potential for significant investments in DG by SPPs, which may exceed that of the LDCs ability to absorb. This thesis proposes a novel method that can be used by the regulator or LDC to fairly assess, coordinate, and approve multiple competing investments proposals while maintaining operational feasibility of the distribution system. This method uses a feedback between the LDC and SPPs to achieve maximum investor participation while adhering to the technical operational limits of the distribution system. The proposed scheme is successfully demonstrated on a 32-bus radial distribution system, where it is shown to increase SPP-DG investments and production, improve the system's voltage profile, and reduce losses.

In the pursuit of a cleaner and sustainable environment, solar photovoltaic (PV) power has been established as the fastest growing alternative energy source in the world. This extremely fast growth is brought about, mainly, by government policies and support mechanisms world-wide. Solar PV technology that was once limited to specialized applications and considered very expensive, with low efficiency, is becoming more efficient and affordable. Solar PV promises to be a major contributor of the future global energy mix due to its minimal running costs, zero emissions and steadily declining module and inverter costs. With the expanding practice of managing decentralized power systems around the world, the role of private investors is increasing. Thus, the perspective of all stakeholders in the power system, including private investors, has to be considered in the optimal planning of the grid. An abundance of literature is available to address the central planning authority’s perspective; however, optimal planning from an investor’s perspective is not widely available. Therefore, this thesis focuses on private investors’ perspective. An optimization model and techniques to facilitate a prospective investor to arrive at an optimal investment plan in large-scale solar PV generation projects are proposed and discussed in this thesis. The optimal set of decisions includes the location, sizing and time of investment that yields the highest profit. The mathematical model considers various relevant issues associated with PV projects such as location-specific solar radiation levels, detailed investment costs representation, and an approximate representation of the transmission system. A detailed case study considering the investment in large-scale solar PV projects in Ontario, Canada, is presented and discussed, demonstrating the practical application and usefulness of the proposed methodology and tools.

Generation and transmission investment planning in deregulated markets faces new challenges particularly as deregulation has introduced more uncertainty to the planning problem. Tradi- tional planning techniques and processes cannot be applied to the deregulated planning problem as generation investments are profit driven and competitive. Transmission investments must facilitate generation access rather than servicing generation choices. The new investment plan- ning environment requires the development of new planning techniques and processes that can remain flexible as uncertainty within the system is revealed. The optimisation technique of Stochastic Dual Dynamic Programming (SDDP) has been success- fully used to optimise continuous stochastic dynamic planning problems such as hydrothermal scheduling. SDDP is extended in this thesis to optimise the stochastic, dynamic, mixed integer power system investment planning problem. The extensions to SDDP allow for optimisation of large integer variables that represent generation and transmission investment options while still utilising the computational benefits of SDDP. The thesis also details the development of a math- ematical representation of a general power system investment planning problem and applies it to a case study involving investment in New Zealand’s HVDC link. The HVDC link optimisation problem is successfully solved using the extended SDDP algorithm and the output data of the optimisation can be used to better understand risk associated with capital investment in power systems. The extended SDDP algorithm offers a new planning and optimisation technique for deregulated power systems that provides a flexible optimal solution and informs the planner about investment risk associated with uncertainty in the power system.

In the pursuit of a cleaner and sustainable environment, solar photovoltaic (PV) power has been established as the fastest growing alternative energy source in the world. This extremely fast growth is brought about, mainly, by government policies and support mechanisms world-wide. Solar PV technology that was once limited to specialized applications and considered very expensive, with low efficiency, is becoming more efficient and affordable. Solar PV promises to be a major contributor of the future global energy mix due to its minimal running costs, zero emissions and steadily declining module and inverter costs. With the expanding practice of managing decentralized power systems around the world, the role of private investors is increasing. Thus, the perspective of all stakeholders in the power system, including private investors, has to be considered in the optimal planning of the grid. An abundance of literature is available to address the central planning authority’s perspective; however, optimal planning from an investor’s perspective is not widely available. Therefore, this thesis focuses on private investors’ perspective. An optimization model and techniques to facilitate a prospective investor to arrive at an optimal investment plan in large-scale solar PV generation projects are proposed and discussed in this thesis. The optimal set of decisions includes the location, sizing and time of investment that yields the highest profit. The mathematical model considers various relevant issues associated with PV projects such as location-specific solar radiation levels, detailed investment costs representation, and an approximate representation of the transmission system. A detailed case study considering the investment in large-scale solar PV projects in Ontario, Canada, is presented and discussed, demonstrating the practical application and usefulness of the proposed methodology and tools.