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DESCRIPTION:Abstract\n\nThe ongoing electric grid modernization efforts str
 ive to develop grid architecture concepts\, power systems tools\, and tech
 nologies to provide a clean\, resilient\, affordable\, and flexible electr
 icity infrastructure. These developments have been essential to achieving 
 critical milestones of grid decarbonization and reliability targets. Howev
 er\, they have simultaneously added several layers of complexity and uncer
 tainty to the grid infrastructure and have affected the fundamental power 
 systems planning and management models.\n\nThis dissertation investigates 
 and addresses the challenges of the electric grid’s added uncertainties an
 d complexities in planning models by exploiting power system flexibility. 
 Here\, we leverage the broader definition of flexibility as the system’s a
 bility to react to uncertainty and changes. System flexibility is vital in
  optimizing these transformations while maintaining reliability and lowest
 -cost solutions. Four flexibility paradigms are investigated: grid archite
 cture flexibility\, planning framework flexibility\, demand-side flexibili
 ty\, and network topology flexibility. We develop quantitative models for 
 each form and present case studies to validate their performance.\n\nThe d
 issertation investigates grid flexibility on the architecture level by lev
 eraging Systems Engineering (SE) tools to develop insights and manage its 
 increasing complexity. A numerical framework based on the design structure
  matrix (DSM) is developed to improve the coordination across the spatial-
 temporal scales of the grid functions and evaluate the potential benefits 
 of grid designs and the impacts of infusing emerging technologies. The met
 hod proposes a technology infusion index (TII) metric to assess the risk-b
 enefit takeoff of these upgrades on the overall grid structure.\n\nTo proa
 ctively manage long-term planning uncertainties\, the dissertation propose
 s a novel real options in-projects (ROiP) framework to embed flexibility i
 n the transmission expansion planning (TEP) optimization problem. Specific
 ally\, a decision-making framework is developed to exercise investment dec
 isions based on the change in the system’s physical features as the uncert
 ainty factors become known. The constraints are added to the TEP optimizat
 ion model and compared against the stochastic formulation with and without
  battery energy storage system (BESS). The results reveal the added value 
 of enhancing the system’s strategic flexibility\, which lowers the model’s
  total cost by getting insights from the system’s technical indicators.\n
 \nOn the demand side\, a two-step optimization approach is proposed to mit
 igate the aggregate peak demand from the increasing electric vehicle (EV) 
 loads. The approach combines advanced retail electricity tariffs with elec
 tric vehicle managed charging (EVMC) to limit the aggregate distribution f
 eeder load profile and increase the system’s flexibility in managing peak 
 loads. Additionally\, transmission and distribution (T&D) coordination is 
 analyzed to understand the cascading impacts of the distribution systems l
 oading on transmission congestion and planning operations.\n\nFinally\, a 
 machine learning (ML) model is proposed to improve the solution of the opt
 imal transmission switching (OTS) problem.\n\nThe data-driven approach pre
 sents a hybrid two-step framework that combines the ML capabilities to pre
 dict the power flow patterns and provide the output as an initial solution
  for the mixed integer linear programming (MILP) problem. We demonstrate t
 he enhanced performance of the proposed approach in improving the optimal 
 solution and reducing the solving time on multiple large-scale test system
 s compared to the state-of-the-art MILP solvers.\n
DTSTART:20241111T160000Z
DTEND:20241111T180000Z
LOCATION:Mecheng MD267\, Seminar Room\, McConnell Engineering Building\, CA
 \, QC\, Montreal\, H3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Abdelrahman Ayad – Advancing Electric Grid Architect
 ure: Flexibility-driven Approaches for Optimizing Power Systems Planning a
 nd Management
URL:https://www.mcgill.ca/ece/channels/event/phd-defence-abdelrahman-ayad-a
 dvancing-electric-grid-architecture-flexibility-driven-approaches-360901
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