2021-04-09 10:00:00 2021-04-09 11:00:00 America/New_York Distributed optimization algorithms for inter-regional coordination of electricity markets Verónica Bósquez Fóti, Ph.D. Candidate https://purdue-edu.zoom.us/j/96452842362
Distributed optimization algorithms for inter-regional coordination of electricity markets
|Event Date:||April 9, 2021|
|Sponsor:||Dr. Andrew Liu|
|Time:||9:00 am EDT
|School or Program:||Industrial Engineering
In the US, seven regional transmission organizations (RTOs) operate wholesale electricity markets within three largely independent transmission systems. The Eastern interconnect, the largest of the three, includes five RTO regions and many vertically integrated utilities.
Several studies have concluded that improving inter-regional transmission is a requirement for transitioning to clean electric power generation, not only because renewable resources are geographically congregated, but also because resource diversification is essential to reliably operate a system with many intermittent generators. This requires both strengthening the physical system and implementing mechanisms that allow for its efficient utilization.
RTO market clearing processes include a day-ahead market, where the operator optimally schedules generation resources based on bids and offers for the next day, and a real-time market, where adjustments to the schedules are made based on actual operating conditions. Both markets involve a unit commitment calculation, a mixed integer program that determines which generators will be online, and an economic dispatch calculation, which determines the output of each online generator for every interval and calculates locational marginal prices (LMPs).
Through this work, we aim to extend the efficiencies and price transparency brought about by LMP-markets to the coordination across RTOs. Existing schemes to improve inter-regional coordination are limited to incremental changes in the real-time market. By solving a multi-regional unit-commitment problem, we allow for coordination during the day-ahead commitment stage. This is achieved by applying a distributed optimization approach to find a system-wide optimal commitment and dispatch while allowing each region to largely maintain their own rules, to only model their internal transmission up to the boundary, and to keep offer information confidential. A heuristic algorithm based on an extension of the alternating directions method of multipliers (ADMM) for nonconvex problems is applied to the unit commitment. The proposed market clearing algorithm takes advantage of the existing structure, where the nonconvex unit commitment optimization is followed by the, usually convex, dispatch optimization.
The proposed coordinated solution was simulated and compared to the ideal single-market scenario and to a representation of the current uncoordinated solution, achieving at least 58% of the maximum potential savings. Based on test cases, the total savings could add up to nearly $7 billion per year in terms of the annual cost of electric generation in the US. In addition to the coordinated day-ahead solution, we develop a distributed solution for financial transmission rights (FTR) auctions with minimal information sharing across RTOs that constitutes the first known work to provide a viable option for market participants to seamlessly hedge price variability exposure on cross-border transactions.