A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network

Abstract

Intra-day control and scheduling of energy systems require high-speed computation and strong robustness. Conventional mathematical driven approaches usually require high computation resources and have difficulty handling system uncertainties. This paper proposes two data-driven scheduling approaches for hydrogen penetrated energy system (HPES) operational scheduling. The two data-driven approaches learn the historical optimization results calculated out using the mixed integer linear programing (MILP) and conditional value at risk (CVaR), respectively. The intra-day rolling optimization mechanism is introduced to evaluate the proposed data-driven scheduling approaches, MILP data-driven approach and CVaR data-driven approach, along with the forecasted renewable generation and load demands. Results show that the two data-driven approaches have lower intra-day operational costs compared with the MILP based method by 1.17% and 0.93%. In addition, the combined cooling and heating plant (CCHP) has a lower frequency of changing the operational states and power output when using the MILP data-driven approach compared with the mathematical driven approaches.

Boosting operational optimization of multi-energy systems by artificial neural nets

“…Similarly, Alabi et al (2022b) combine deep learning for prediction and optimization methods for optimal scheduling of multi-energy systems. Zhou et al (2019) use a LSTM model trained on pre-solved MILP solutions to predict the operation of a hydrogen-penetrated energy system. However, the computation time of the operational optimization is not examined, and the optimization problem is relatively small (11 variables over 24 h).…”