POWER GRID OPERATION RISK MANAGEMENT: V2G DEPLOYMENT FOR SUSTAINABLE DEVELOPMENT

The production, transmission, and delivery of cost–efficient energy to supply ever-increasing peak loads/demands along with a quest for developing a low-carbon economy require significant evolutions in the power grid operations. Lower prices of vast natural gas resources in the United States, Fukushima nuclear disaster, higher and more intense energy consumptions in China and India, issues related to energy security, and recent Middle East conflicts, have urged decisions makers throughout the world to look into other means of generating electricity locally. As the world look to combat climate changes, a shift from carbon-based fuels to non-carbon based fuels is inevitable. It is possible to knock a lot of carbon out of the electric power system through large-scale integrations of renewable sources. However, the variability of distributed generation assets (such as wind and solar) in the electricity grid has introduced major reliability challenges/risks for power grid operators. While spearheading sustainable and reliable power grid operations, this dissertation develops a multi-stakeholder approach to power grid operation design; aiming to address economic, security, and environmental challenges of the constrained electricity generation. It investigates the role of Electric Vehicle (EV) fleets integration, as distributed and mobile storage assets to support high penetrations of variable and renewable energy sources, in the power grid. The vehicle-to-grid (V2G) concept is considered to demonstrate the bidirectional role of EV fleets both as a provider and consumer of energy in securing a sustainable power grid operation. The V2G concept is regarded as a novel, low-cost, low-emission and sustainable strategy that can address xv challenges involve with using renewable energy sources, which require means of storing large quantities of energy. The proposed optimization modeling is the application of Mixed-Integer Linear Programing (MILP) to large-scale systems to solve the hourly security-constrained unit commitment (SCUC) – an optimal scheduling concept in the economic operation of electric power systems. The Monte Carlo scenario-based approach is utilized to evaluate different scenarios concerning the uncertainties in the operation of power grid system. Further, in order to expedite the real-time solution of the proposed approach for large-scale power systems, 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. The numerical simulation demonstrate that the utilization of smart EV fleets in power grid systems would ensure a sustainable grid operation with lower carbon footprints, smoother integration of renewable sources, higher security, and lower power grid operation costs. Further, simulation results indicate that intelligent-controlled mode, in which electric power system operators control the EV fleets charge/discharge decisions based on the system operation requirements, is more effective compare to the rule-based mode, in which consumers control charging/discharging decisions. The numerical simulations, additionally, illustrate the effectiveness of the proposed MILP approach and its potentials as an optimization tool for sustainable operation of large scale electric power systems.PH.D in Management Science, May 201