Optimal Operation of Integrated Energy Systems Under Uncertainties: Distributionally Robust and Stochastic Models discusses new solutions to the rapidly emerging concerns surrounding energy usage and environmental deterioration. Integrated energy systems (IESs) are acknowledged to be a promising approach to increasing the efficiency of energy utilization by exploiting complementary (alternative) energy sources and storages. IESs show favorable performance for improving the penetration of renewable energy sources (RESs) and accelerating low-carbon transition. However, as more renewables penetrate the energy system, their highly uncertain characteristics challenge the system, with significant impacts on safety and economic issues.

To this end, this book provides systematic methods to address the aggravating uncertainties in IESs from two aspects: distributionally robust optimization and online operation.

Bo Yang received the Ph.D. degree in electrical engineering from the City University of Hong Kong, Hong Kong, in 2009. He is currently a Full Professor with Shanghai Jiao Tong University, Shanghai, China. Prior to joining Shanghai Jiao Tong University in 2010, he was a Post-Doctoral Researcher with the KTH Royal Institute of Technology, Stockholm, Sweden, from 2009 to 2010, and a Visiting Scholar with the Polytechnic Institute of New York University in 2007. His research interests include optimization for energy networks and internet of things. He has been the Principal Investigator in several research projects, including the NSFC Key Project. He was a recipient of the Ministry of Education Natural Science Award 2016, the Shanghai Technological Invention Award 2017, the Shanghai Rising- Star Program 2015, and the SMC-Excellent Young Faculty Award by Shanghai Jiao Tong University

1. Introduction
2. Day-ahead energy management of IES with distributionally robust approach
3. Distributionally robust heat-and-electricity pricing for IES with decision dependent uncertainties
4. Multi-level coordinated energy management for IES in hybrid markets
5. Energy management based on multi-agent deep reinforcement learning for IES
6. Stochastic multi-energy management schemes with deferrable loads
7. Energy trading for multiple IESs