Main Research Objectives

1. Decision Making in Smart-Grids Supporting OPF & Renewable Energy Standards: A Bi-Level Multi-Period Formulation Ahmad F. Taha1 and Jitesh H. Panchal2 1 Ph.D. Student, School of Electrical and Computer Engineering, Purdue University 2Assistant Professor, School of Mechanical Engineering, Purdue University Main Research Objectives Smart Grids Optimization Problem Preliminaries and Objectives ISO’s Decision Problem • Modernized electrical grid, uses communications to gather & act on data such as: • Consumers' & suppliers' behaviors and preference • Market competition, pricing, etc… • Goal: improve efficiency, reliability, economics, and sustainability of electricity production & distribution • Key entities: GENCOs (BEPs or GEPs), ISOs, Utilities, & Consumers • ISO maximizes an overall system welfare (OSW) function • OSW function is a function of: consumer surplus, renewable energy production levels, environmental constraints, and policy costs • Creating framework for integrated policy & engineering design • Considering multiple objectives & time-varying constraints for policy designers & stakeholders • Establishing domain-independent multi-level design techniques through: • MPECs • Stability Theories • Cooperative and Non-Cooperative Games • Applying this framework to different engineering areas: smart grid, transportation, telecom, manufacturing. • Time-period: i • GENCO index: j • Consumer index: u • GEPs rewarded with a time-varying subsidy price • Optimization problem objectives: • Solve for optimal generation and consumption quantities • Maximizing consumers’ and GENCOs’ payoff functions • Maximizing ISO’s welfare function GENCO’s Decision Problem • GENCOs are profit maximizing firms. Their optimization problem can be formulated as: Research Structure Problem Solution • Challenging problem to solve due to non-convexity in the feasible space & nonlinearity in the problem • Global minimizer hard to find, stuck in local minima • Best solver: NEOS’s KNITRO (AMPL) Smart Grids: Past, Present, and Future • Key Point: The "smartening" of the electricity system is an evolutionary process, not a one-time event • Facts: • 5% lowering of demand would have resulted in 50% price reduction during California electricity crisis • 1% shift in peak demand would result in $3.9 billion savings • 10% reduction would result in systems savings of between $8 to $28 billion • GENCOs' OP modeled as a non-cooperative game • KKT optimality conditions derived • Appended to ISO's decision problem as constraints Simulation Results Consumer’s Decision Problem • Objective: a better demand response during peak hours • Consumers decide on their optimal backlogged consumption quantity • Decision problem modeled as an optimal control problem Integrated Policy Design: Interacting Entities • Problem Formulation of each entity • We first study the interaction between two entities at a time • Followed by the interaction between the interacting blocks Future Work • Simulate the overall system • Study overall system performance, modeling uncertainty in the grid , convergence & stability analysis • Model the lower-level problem as a cooperative game • Apply this framework to different engineering areas