Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- A methodology for prediction interval adjustment for short term load forecasting(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12) Zúñiga García, Miguel Ángel; Batres Prieto, Rafael; hermlugo; Santamaría Bonfil, Guillermo (Co-advisor); Noguel Monroy, Juana Julieta; Ceballos Cancino, Héctor Gibrán; School of Engineering and Sciences; Campus Estado de México; Arroyo Figueroa, GustavoElectricity load forecasting is an essential tool for the effective power grid operation and for energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabilities in the power grid or cause load cuts. Hence, probabilistic load forecasting methods have become more relevant since these allow to understand, not only load point forecasts but also the uncertainty associated with it. In this thesis, a framework to generate prediction models that generate prediction intervals is proposed. This framework is designed to create a probabilistic STLF model by completing a series of tasks. First, prediction models will be generated using a prediction method and a segmented time series dataset. Next, prediction models will be used produce point forecast estimations and errors will be registered for each subset. At the same time, an association rules analysis will be performed in the same segmented time series dataset to model cycling patterns. Then, with the registered errors and the information obtained by the association rules analysis, the prediction intervals are created. Finally, the performance of the prediction intervals is measured by using specific error metrics. This methodology is tested in two datasets: Mexico and Electric Reliability Council of Texas (ERCOT). Best results for Mexico dataset are a Prediction Interval Coverage Probability (PICP) of 96.49% and Prediction Interval Normalized Average Width 12.86, and for the ERCOT dataset a PICP of 94.93% and a PINAW of 3.6. These results were measured after a reduction of 14.75% and 5.25% in the prediction intervals normalized average width of the Mexico and ERCOT dataset respectively. Reduction of the prediction interval is important because it can helps in reducing the amount of electricity purchase, and reducing the electricity purchase even in 1% represents a large amount of money. The main contributions of this work are: a framework that can convert any point forecast model in a probabilistic model, the Max Lift rule method for selection of high quality rules, and the metrics probabilistic Mean Absolute Error and Root Mean Squared Error.
- An integral approach for the synthesis of optimum operating procedures of thermal power plants towards better operational flexibility(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-11) Rosado Tamariz, Erik; GANEM CORVERA, RICARDO; 60681; Batres Prieto, Rafael; emipsanchez; Ganem Corvera, Ricardo; Genco, Filippo; School of engineering and sciences; Campus Ciudad de MéxicoTo deal with the challenge of a balance between the large-scale introduction of variable renewable energies and intermittent energy demand scenarios in the current electrical systems, operational flexibility plays a key role. The electrical system operational flexibility can be addressed from different areas such as power generation, transmission and distribution systems, energy storage (both electrical and thermal), demand management, and coupling sectors. Regarding power generation, specifically at the power plant level, operational flexibility can be managed through the cyclic operation of conventional power plants which involve load fluctuations, modifications in ramp rates, and frequents startup and shutdowns. Since conventional power plants were not designed to operate under cyclic operating schemes with involve fast response times, must develop these capabilities through the design of operating procedures that minimize the time needed to take the power plant from an initial state to the goal state without compromising the structural integrity of critical plant components. This thesis proposes a dynamic optimization methodology to the synthesis of optimum operating procedures of thermal power plants which determine the optimal control valves sequences that minimize its operating times based on techniques of dynamic simulation, metaheuristic optimization, and surrogate modeling. Based on such an approach, the power plants must be increasing its operational flexibility to address a large-scale introduction of variable renewable energies and intermittent energy demand scenarios. This thesis proposes a dynamic optimization framework based on the implementation of a metaheuristic optimization algorithm coupled with a dynamic simulation model, using the modeling and simulation environment OpenModelica and a surrogate model to estimate in a computationally efficient way the structural integrity constraint of the dynamic optimization problem. Two case studies are used to evaluate the proposed framework by comparing their results against information published in the literature. The first case study focuses on managing the thermal power plant's flexible operation based on the synthesis of the startup operating procedure of a drum boiler. The second case study addresses the synthesis of an optimum operating strategy of a combined heat and power system to improve the electric power system’s operational flexibility.