The University of Texas at Dallas
MECH 4365: Energy Analytics
Energy analytics provides an introduction to energy systems and data analytics, which include the use of data, statistical and quantitative analysis, exploratory and predictive models, and data visualization to inform power and energy systems decisions and actions. The key objective of this course is to familiarize the students with most important analytics technologies used in managing and analyzing big data in energy systems (especially in renewable energy systems such as wind and solar). This course will cover major energy-related applications of descriptive and predictive analytics, such as energy data analysis, energy resource analytics, design of experiments, response surface, load forecasting, price forecasting, renewable generation forecasting, demand response and customer analytics, and utilities outage analytics. Students will use R/Python for project design. The course builds on prerequisite knowledge in engineering mathematics, probability, and statistics.
MECH 6318: Engineering Optimization
Basics of optimization theory, numerical algorithms, and applications. The course is divided into three main parts: linear programming (simplex method, duality theory), unconstrained methods (optimality conditions, descent algorithms and convergence theorems), and constrained minimization (Lagrange multipliers, Karush-Kuhn-Tucker conditions, active set, penalty and interior point methods). Applications in engineering, operations, finance, statistics, etc. will be emphasized. Students will also use MATLAB optimization toolbox to obtain practical experience with the material.
MECH 6342: Renewable Energy and Grid Integration
As the amount of wind and solar power capacity has rapidly increased in the past few years, variable renewable energy has started to play an increasing role in power system operations and planning. This course will discuss renewable energy and energy efficiency systems modeling, design, and optimization. This course will begin with an introduction to the power grid including planning and operations for the transmission and distribution level power grid. After examining the technological specifications of the most important renewable energy sources (wind energy, photovoltaics, and solar thermal power) and energy efficiency technologies (energy storage, home and building energy, electric vehicles), grid integration of renewable energy and energy efficiency technologies will be examined in detail. From the bulk power system level, the unit commitment and economic dispatch process will be thoroughly covered, with exercises that emphasize how it can change based on new variable generation. This includes topics such as dynamic reserve levels, stochastic unit commitment, and flexibility reserves, variable generation forecasting, and demand response. Distribution planning with high penetrations will be examined. All of these concepts will be explored in great detail and reinforced through the completion of a semester long project, where the students will be solving problems of broad interest in a group setting. Students will use Matlab and R for project design. The course builds on prerequisite knowledge in engineering system design, engineering mathematics, probability and statistics, and optimization methods.
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