Energy Systems Engineering Option

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The ESysE option focuses on computational and data-analytic methods applied to the design and analysis of energy systems and infrastructures.  The additional courses in this option provide critical foundations in optimization, simulation, statistical analysis, and systems-based approaches.  The option will provide a consistent and rigorous track for graduate students whose research focuses on methodological innovations at the interface of energy science/engineering, energy economics, operations research methods, and statistical and data-analytic methods.  

Possible examples of research projects include game-theoretic models of electricity markets that present the physical constraints of electricity flow, uncertainty in system parameters, and require innovative mathematical programming solution techniques; machine learning algorithms for characterizing toxicological risks; designs for integrating renewable generation technologies into the electric power system that account for the time and weather dependent performance of the renewable technologies, explicitly consider uncertainties in system properties, and represent the operations and constraints of the other generation and demand resources in the grid to balance cost-effectiveness, reliability, and resilience to range of potential system shocks.

In adddition to the EME Core Courses,  the Energy Systems Engineering option requires a minimum of 8 credits (three courses) from the following list:

Covers formulation and solution methods for game-theoretic economic equilibrium models specific to energy market problems.  Topics include electricity markets, natural gas markets, mathematical programs with equilibrium constraints (MPEC), equilibrium problems with equilibrium constraints (EPEC), variational inequalities (VI), and inclusion of physical engineering constraints unique to electricity and gas infrastructures.

           Primary Instructor: Seth Blumsack

            Next Offered: Fall 2018 (every year after)

Covers the formulation or and solution methods for a full range of economic-engineering investment and operations problems for electric power systems.   Application problems include economic dispatch, unit commitment, optimal power flow, generation capacity expansion, transmission expansion, and introduction to modeling of competitive electricity markets.  Solution methods include linear programming, mixed integer programming, decomposition methods for stochastic programming (e.g., Lagrangian Relaxation, Benders Decomposition), and mixed complementarity problems, with an emphasis on numerical implementation.

            Primary Instructor: Mort Webster

            Next Offered: Spring 2019 (every other year after)

This course covers the theory and implementation of computational methods for stochastic simulation and stochastic optimization, with an emphasis on algorithms and implementation. The course emphasizes the quantitative analysis or numerical modeling of complex systems in fields such as civil, environmental, environmental, energy, mechanical and industrial engineering or energy, environmental, and natural resource economics. Topics include Monte Carlo simulation, quasi-random and pseudo-random sampling methods, Markov Chains, Dynamic Programming, Approximate Dynamic Programming, and Stochastic Programming decomposition techniques.

            Primary Instructor: Mort Webster

            Next Offered: Spring 2020 (every other year after)

This course provides an overview of the application of machine learning algorithms to engineering problems, their strengths and weaknesses, appropriate model testing and validation techniques, and the unique structure of energy, mineral, and environmental applications.   An emphasis of this course is for the graduate students to apply these methods to specific research problems of interest.

           Primary Instructor: Jeremy Gernand

            Next Offered: Fall 2018 (every other year after)

The course will discuss persistent questions in uncertainty for solar asset portfolio management with contemporary changes energy systems, and will describe the need for cumulative skills as a solar professional and analyst. Students will be introduced to core factors motivating stakeholders to opt for development, operation, and maintenance of solar power solutions in residential, commercial, industrial, and utility sectors; as well as exploring the core factors of risk and loss aversion that motivate or deter stakeholders. Resource system management and resource unit allocation will be explored from market/government/ or common pool resource approaches. Group agency will be introduced, in the framework of non-cooperative and cooperative game theory. Finally students will apply dynamic systems modeling of techno-economic performance for solar power systems (e.g. photovoltaics, battery, and smart power management systems).

           Primary Instructor: Jeffrey Brownson

            Next Offered: Spring 2019 (every year after)

This course emphasizes spatial random function models, inference and modeling of spatial statistics, spatial estimation, spatial simulation and data assimilation in spatial models, with special consideration of the structure of energy and environmental system models.

           Primary Instructor: Sanjay Srinivasan

            Next Offered: Fall 2019 (every year after)

An accelerated treatment of the main theorems of linear programming and duality structures plus introduction to numerical and computational aspects of solving large-scale problems.   Offered by the Department of Industrial and Manufacturing Engineering.

            Primary Instructor: IE Faculty

            Next Offered: Fall 2018 (every year after)

This course covers the mathematical fundamentals and tools for analyzing stochastic systems evolving over time, including concepts and techniques related to Poisson Processes, renewal processes, and discrete and continuous time Markov chains. Students will also learn to build probabilistic intuition and insights when thinking about random processes. Additionally, students will learn to apply the essential techniques of stochastic processes to real world problems in the supply chain and information systems area. Offered by the Department of Industrial and Manufacturing Engineering.

            Primary Instructor: IE Faculty

            Next Offered: Spring 2019 (every semester after)