(717e) Optimal Scheduling and Control of Hybrid Energy Systems in Multiscale Electricity Markets

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 [1,2,3]. 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. The Institute for the Design of Advanced Energy Systems (IDAES) seeks to establish open-source frameworks for data-informed optimization of existing and future energy conversion systems to meet the evolving needs of the electric grid.

As part of the IDAES ecosystem, we are developing an economic assessment framework for energy systems that elucidate the complex relationship between system dynamics and market dispatches. In our previous study, we prototype this framework to analyze a generation company with 6 thermal units [4]. An autoregressive price forecaster based on Gaussian Processes (GP) is developed. Using the probabilistic forecasts, we benchmark self-schedule and bidding with stochastic programming. We show that self-schedule is sensitive to price forecast errors, whereas bidding requires forecasts covering extreme events.

In this presentation, we extend the IDAES framework to consider hybrid energy systems, i.e. co-located generators and energy storage systems. Because of high operational flexibility, hybrid energy systems are projected to have potential advantages in the future energy markets with high renewable penetration [5]. Due to their unique physical and operational characteristics, e.g. the state-of-charge (SOC) and the cooperation between generator and storage, market participation and clearing are undetermined under the current market structure. In this study, we develop an economic assessment framework for hybrid energy systems which enables market uncertainty forecasting, market participation under uncertainty, market clearing, and market signal tracking. With the framework, we compare the revenue opportunities of different hybrid systems with day-ahead market prices from CAISO and show the cooperation between generator and storage system is beneficial, ultimately giving insightful guidelines for hybrid energy system design. We also discuss how to go beyond the price-taker assumption by linking our market participation optimization framework with a rigorous Production Cost Model. These new multiscale linkages capture how new hybrid energy systems influence market prices and dispatch throughout the grid.

Reference:

[1] D. J. Chmielewski, “Smart grid the basics-what? why? who? how?,” Chemical Engineering Progress, vol. 110, no. 8, pp. 28–33, 2014.

[2] Dowling, A. W., & Zavala, V. M. (2018). Economic opportunities for industrial systems from frequency regulation markets. Computers & Chemical Engineering, 114, 254-264.

[3] Dowling, A. W., Kumar, R., & Zavala, V. M. (2017). A multi-scale optimization framework for electricity market participation. Applied Energy, 190, 147-164.

[4] Gao, X., & Dowling, A.W. (2020). Making money in energy markets: Probabilistic

forecasting and stochastic programming paradigms. Proceedings of the 2020 American Control

Conference, Accepted.

[5] Gorman, W., Mills, A., Bolinger, M., Wiser, R., Singhal, N. G., Ela, E., & O’Shaughnessy, E. (2020). Motivations and options for deploying hybrid generator-plus-battery projects within the bulk power system. The Electricity Journal, 33(5), 106739.