In the modern smart grid paradigm, hierarchical markets – the day-ahead markets (DAM), the real-time markets (RTM), and the ancillary service markets – coordinate diverse energy systems to synchronize electricity supply and demand. Participation in these markets provides electrical energy generation companies, energy-intensive industries, grid-connect storage operators, and prosumers new revenue opportunities. With the increasing penetration of renewable resources, markets are anticipated to incentivize more flexible operation of generators and prosumers to maintain electric grid reliability and resiliency. For decades, modeling and optimization have informed electricity generation-related engineering decisions spanning seconds to decades timescales. Yet these models are often highly tailored to specific analysis objectives and applied in isolation. The Department of Energy-funded Institute for the Design of Advanced Energy Systems (IDAES) seeks to establish an open-source framework that unifies these modeling paradigms and enables unique capabilities for data-informed optimization of existing and future energy conversion systems to meet the evolving needs of the electric grid.

In this talk, we present recent modeling innovations in the IDAES ecosystem. We start by reviewing prevailing modeling paradigms and analysis questions pertinent to the electric grid. In Part I, we share our recent work on optimal market participation strategies from the perspective of an individual resource owner/operator. This includes optimization capabilities to estimate revenue opportunities from various technologies using physics-based constraints using publicly available historical data. The impact of price uncertainty and equipment degradation are considered. Overall, this work shows the importance of multi-market and multi-product participation across several emerging technologies including battery energy storage, smart manufacturing systems, and concentrated solar power. In Part II, we explore a new multiscale simulation workflow to integrated decision-making from both individual resources and the market operator. Through a small case study, we show resource-grid interactions are non-trivial and the common price-taker assumptions may often be invalid. We conclude by discussing open modeling and optimization challenges for energy conversation and electricity infrastructure.

Bio:
Alexander W. Dowling is an Assistant Professor in Chemical and Biomolecular Engineering at the University of Notre Dame (Indiana, USA). His research combines chemical engineering, computational optimization, and uncertainty quantification to enable principled molecular-to-systems engineering of sustainable energy and environmental technologies. Applications areas include energy markets and infrastructure, carbon sequestration, shale gas utilization, and advanced separations (membranes, ionic liquids). Prof. Dowling was recently honored with an NSF CAREER award (2020) to develop new novel Bayesian hybrid modeling paradigms for optimization and uncertainty quantification. Ongoing collaborative projects include the Institute for Advanced Design of Energy Systems (IDAES - DOE), Carbon Capture Simulation for Industrial Impact (CCSI² - DOE) and Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR - NSF). He holds a B.S.E from the University of Michigan - Ann Arbor and a Ph.D. from Carnegie Mellon University, all in chemical engineering.