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.

No description available.

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

No description available.

abstract: This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable resources. The primary purpose of this research is to identify soft spots of the contemporary industry practices and propose innovative algorithms, methodologies, and tools to improve economics and reliability in power systems. First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies. The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

No description available.

<p><p>In the operation, planning and design of the transmission system it is of greatest concern to quantify the reliability security margin to unwanted conditions. The deterministic N-1 criterion has traditionally provided this security margin to reduce the consequences of severe conditions such as widespread blackouts. However, a deterministic criterion does not include the likelihood of different outage events. Moreover, experience from blackouts shows, e.g. in Sweden-Denmark September 2003, that the outages were not captured by the N-1 criterion. The question addressed in this thesis is how this system security margin can be quantified with probabilistic methods. A quantitative measure provides one valuable input to the decision-making process of selecting e.g. system expansions alternatives and maintenance actions in the planning and design phases. It is also beneficial for the operators in the control room to assess the associated security margin of existing and future network conditions.</p><p>This thesis presents a method that assesses each component's risk to an insufficient transfer capability in the transmission system. This shows on each component's importance to the system security margin. It provides a systematic analysis and ranking of outage events' risk of overloading critical transfer sections (CTS) in the system. The severity of each critical event is quantified in a risk index based on the likelihood of the event and the consequence of the section's transmission capacity. This enables a comparison of the risk of a frequent outage event with small CTS consequences, with a rare event with large consequences.</p><p>The developed approach has been applied for the generally known Roy Billinton Test System (RBTS). The result shows that the ranking of the components is highly dependent on the substation modelling and the studied system load level.</p><p>With the restriction of only evaluating the risks to the transfer capability in a few CTSs, the method provides a quantitative ranking of the potential risks to the system security margin at different load levels. Consequently, the developed reliability based approach provides information which could improve the deterministic criterion for transmission system planning.</p></p>

Este trabalho analisou os leilões de comercialização de energia elétrica no ambiente de contratação regulada no Brasil, realizados entre 2005 e 2011, com o objetivo de propor aperfeiçoamentos em sua metodologia. Para tanto, foram estudadas três linhas de pesquisa: teoria de leilões, internalização (adicionais) de custos não privados (externalidades) e organização de mercados de capacidade. Após a análise dos 21 leilões de novos empreendimentos realizados no período, conclui-se que o desenho do leilão com fase discriminatória final é adequado aos objetivos de modicidade tarifária, mas que também permite melhoras. As fraquezas da sistemática atual identificadas foram: metodologia de contratação termelétrica por disponibilidade, com viés das fontes de maior custo variável unitário, adoção de preço-teto nem sempre adequado, dificuldade em mitigar o exercício de poder de mercado da Eletrobras nos leilões de energia existente e licitação pelo custo econômico privado. De forma a aprimorar os leilões, as seguintes ações foram sugeridas: realização de uma etapa adicional e prévia ao desenho de leilão híbrido atual visando contornar a problemática de estabelecimento de preço-teto adequado; utilização de adicionais ao lance do leilão para internalizar os custos de transmissão não recolhidos pelo gerador; substituição do mecanismo de contratação termelétrica pelo modelo Colombiano de opções; condução de leilões de energia nova e existente em conjunto, e segmentação de produtos no leilão pela ótica da demanda com possibilidade de lances em pacotes. Com a adoção destas propostas entende-se que o valor negociado nos leilões de comercialização de energia elétrica refletirão melhor o custo social dos projetos, aumentando a eficiência dos certames. / This study analyzes the regulated electricity procurement auctions conducted between 2005 and 2011 in Brazil, in order to propose improvements in its methodology. Thus, it was reviewed three research areas: auctions design, internalization of externalities, and capacity markets. After analyzing the 21 new energy auctions that period, it is concluded the auction design with a second discriminatory bid is appropriate to the aims at achieving as low as possible prices, however there is room for improvement. Identified weaknesses are: (1) thermal availability capacity hiring method biased in favor of energy sources with higher variable costs; (2) inadequate price cap, unable to mitigate Eletrobras market power in existing power auctions, (3) and bidding by private economic cost. So, it is suggested the following actions to improve the efficiency of energy auctions: (a) carrying out an additional step prior to the current hybrid auction design in order to solve the problem of establishing appropriate ceiling price; (b) use of additional to internalize transmission costs not paid by generator; (c) replacement of the bid mechanism used for thermal power plants to Colombian options model; (d) driving new and existing energy auctions together, and (e) segment auctions products by the demand side with the possibility of bidding in packages. In adopting these proposals it is expected the value traded in the electricity procurement auction conducted in Brazil will better reflect the social cost of projects and so improving its efficiency.