The Blue Nile Grid of Sudan, which supplies Electrical Power to the central region including the capital, Khartoum, has experienced a history of problems, of which the most important are the instability of the system and the generation shortages, which become particularly acute during certain months of the year. These problems have been complicated by a lack of real understanding of the system's behaviour, especially as it grows in size and complexity, and as the demand increases.

Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model.

Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model.

This dissertation proposes a new transmission planning method for electric power systems with large planned additions of uncertain intermittent renewable resources. The major contribution of this dissertation is applying stochastic programming that represents two uncertain parameters, wind and load, to transmission planning. We apply an ad hoc partition method to approximate the bivariate random variables of load and wind. A two-stage stochastic transmission planning problem is repeatedly solved by replacing continuous random variables with approximations that have a more refined partition at each iteration. A candidate solution is provided when improvement is not observed at an optimal value, even with more refined approximations. Numerical results show the efficiency of the method. However, if the number of samples is not sufficient to represent the original random variable's characteristics, the solution may be poor. Therefore, we employ a sampling method using Gaussian copula in order to generate as many random samples as necessary. The problem is replicated and solved using a fixed number of samples generated by Gaussian copula. In order to asses solution quality, a 95\%-confidence interval on the optimality gap is formed. A candidate stochastic solution for transmission investment is used to simulate the operation of a utility-scale storage system. A mixed integer program (MIP) is applied to this formulation. As a case study, the Electric Reliability Council of Texas (ERCOT) wind and load data is employed, along with a simplified model of the transmission system. Energy storage is also considered. The storage operation shifts wind power from off-peak hours to on-peak hours, and its wind power generation shows a close character to that of a base load generator. / text

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Judicious Power System Planning ensures the adequacy of infrastructure to support continuous reliability and economy of power system operations. Planning processes have a long and rather successful history in the United States, but the recent infl‚ux of unpredictable, nondispatchable generation such as Wind Energy Conversion Systems (WECS) necessitates the re-evaluation of the merit of planning methodologies in the changing power system context. Traditionally, planning has followed a logical progression through generation, transmission, reactive power, and finally auxiliary system planning using expertise and ranking schemes. However, it is challenging to incorporate all of the inherent dependencies between expansion candidates' system impacts using these schemes. Simulation based optimization provides a systematic way to explore acceptable expansion plans and choose one or several "best" plans while considering those complex dependencies. Using optimization to solve the minimum-cost, reliability-constrained Generation and Transmission Expansion Problem (GTEP) is not a new concept, but the technology is not mature. This work inspects: load uncertainty modeling; sequential (GEP then TEP) versus unified (GTEP) models; and analyzes the impact on the methodologies achieved near-optimal plan. A sensitivity simulation on the original system and final, upgraded system is performed. / Master of Science

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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.