Recent Ph.D. Projects

Mohammed Abttew
Design of Energy Portfolio For Sustainable Developments

Susanna Duecker Augilar
Electric Vehicle Storage Supply Chain Risk and Energy market: A Micro and Macro Economic Risk ISK Management Approach

Ghazale Haddadian
Power Grid Operation Risk Management: V2G Deployment for Sustainable Development

The proposed optimization modeling in this dissertation utilizes the application of Mixed-Integer Linear Programing (MILP) to large-scale temporal systems, and uses the electric power system as an example to solve the hourly Security-Constrained Unit Commitment (SCUC) – an optimal scheduling concept in the economic operation of electric power systems. The study in this dissertation utilizes the scenario-based Monte Carlo Simulation (MCS) approach to evaluate potential scenarios concerning uncertainties in large-scale systems and applies the MILP method to the modeling of the hourly operation of electric power systems.  Further, in order to expedite the real-time solution of the proposed large-scale system optimization algorithm, the study in this dissertation considers a two-stage model using the Benders Decomposition (BD) and applies the BD method to the hourly SCUC solution of electric power systems with significant uncertainties.
  
Access to energy is a foundation to establish a positive impact on multiple aspects of human development.  These aspects include achieving sustainable development and poverty reduction efforts in developing nations. However, if left unchecked, energy generation and use become a health hazard and eventually consume energy sources much faster than can be generated. Consequently, this situation threatens the sustainability of the underlying energy base, endangers peace, and agitates the environment in terms of pollution, waste, and degradation. to develop an improved mixed-integer programming energy-source optimization model for sustainable energization strategies and to show the model’s relevance and economic, social, and environmental viability for developing countries. This model overcomes the limitations of current models by ensuring diversification in the energy mix, including off-grid power sources, considering energy footprints, enforcing energizing requirements, and incorporating risk management provisions in the model.
As a cost effective storage technology for renewable energy sources, Electric Vehicles can be integrated into energy grids. Integration must be optimized to ascertain that renewable energy is available through storage when demand exists so that cost of electricity is minimized. Optimization models can address economic risks associated with the EV supply chain- particularly the volatility in availability and cost of critical materials used in the manufacturing of EV motors and batteries. Supply chain risk can reflect itself in a shortage of storage, which can increase the price of electricity. We propose a micro-and macroeconomic framework for managing supply chain risk through utilization of a cost optimization model in combination with risk management strategies at the microeconomic and macroeconomic level. The study demonstrates how risk from the EVs vehicle critical material supply chain affects manufacturers, smart grid performance, and energy markets qualitatively and quantitatively. Our results illustrate how risk in the EV supply chain affects EV availability and the cost of ancillary services, and how EV critical material supply chain risk can be mitigated through managerial strategies and policy.