Pig production in Malawi and in most of the developing countries is shifting increasingly from pasture or dirt lot to total confinement with improved housing facilities. Keeping pig level temperatures within the comfort zone in hot weather is a common problem in naturally ventilated intensive pig buildings. Automatically controlled natural ventilation (ACNV) has proved to be effective in reducing the problem of heat build up in pig houses and is becoming very popular. However, a method for reducing summer temperatures at animal level in non-automated naturally ventilated intensive pig buildings has not been developed. An attempt was made to develop a natural ventilation system which would maximise the cooling effect of wind at animal level by manual control in buildings suitable for the Tropics. The research project was conducted in three stages: (1) air flow pattern studies, using 1:20 scale two-dimensional models, (2) wind tunnel testing, using three-dimensional, 1:20 scale models with simulated pigs, and (3) validation of results from the wind tunnel studies made with a 1:4 scale model, put out in the field. Air deflectors were used as a means of increasing the effect of wind on the ventilation pattern in the models. Monopitch, duopitch and offest gable models were tested in the water table, and monopitch models were tested in the wind tunnel. The use of air deflectors in monopitch and offset gable models showed a marked increase in airflow towards the animal zone area and a reduction in the difference between the surface temperature of the model pigs and the outside air temperatures. The deflectors improved the performance of the models by about 10% with the front orientation and about 20% with the rear orientation. In the duopitch model an increase in the roof overhang improved flow circulation on the leeward side. The use of air deflectors also improved flow circulation on the leeward side. The wind speed and air temperature at the experimental site for the 1:4 scale model were used to validate the results from the wind tunnel tests. The measured temperature values showed similar response curves to the predicted values. Temperature differentials increased with an increase in the angle of the deflector.

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

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

Made available in DSpace on 2014-06-12T17:40:03Z (GMT). No. of bitstreams: 2 arquivo601_1.pdf: 1267622 bytes, checksum: be155bb6cf3a42c2e6a784cfb8941167 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / O trabalho desenvolvido tem como objetivo explorar o planejamento de sistemas de informação, utilizando a abordagem BSP (Business System Planning), focando-se principalmente na etapa de priorização de sistemas de informação. O planejamento de sistemas de informação é uma fase de grande importância para o apoio à estratégia da organização, visto que os sistemas de informação apoiarão as decisões em todos os níveis da empresa, tendo grande influência sobre o desempenho global da mesma. Para a priorização de SI, é proposto um modelo que utiliza o BSP em conjunto com o método de decisão multicritério PROMETHEE V, gerando um resultado diferente na etapa anterior ao problema da mochila, comparando-os ao final. É feita uma análise sobre os resultados, comparando-os com o resultado do PROMETHEE II, visando identificar qual é o mais adequado para o problema de priorização. Este modelo é aplicado a um problema com dados simulados e a um problema com dados de estudos anteriores. No problema com dados simulados, é feita uma análise de sensibilidade nos pesos dos critérios de decisão, sobre os custos de cada alternativa e sobre a relação entre os processos e os critérios estratégicos no resultado obtido com o PROMETHEE V com a intenção de verificar como o modelo consegue lidar com variações nas preferências do decisor, além de sua sensibilidade à variação dos custos dos projetos de sistemas de informação

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.