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