A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

• Published in: CSEE Journal of Power and Energy Systems 2022-05, Vol.8 (3), p.789-800
• Main Authors: Hongjun Gao, Zhenyu Liu, Youbo Liu, Lingfeng Wang, Junyong Liu
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative energy sources; Boilers; Constraint modelling; Economics; Energy conversion; Energy sources; Gas turbines; Integrated energy systems; Probability distribution; Real time; Renewable energy; Renewable resources; Robustness; System effectiveness; Uncertainty; Wind power
• ISSN: 2096-0042; 2096-0042
• DOI: 10.17775/CSEEJPES.2019.03240

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system (UIES). Meanwhile, the uncertainties of renewable energy resources (e.g., wind energy) also bring increased challenges to the operation of UIES. In this study, a typical two-stage data-driven distributionally robust operation (DDRO) model based on finite scenarios is proposed for UIES including power, gas and heat networks to obtain a salient strategy from both an economic and robustness perspective. In the first stage, the forecasted information for wind power is especially included to improve the economic aspect of robust decisions. The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units (such as gas turbine, power to gas equipment, electric boiler and gas boiler). Moreover, norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind power. The whole two-stage model is solved by the column-and-constraint generation (CCG) algorithm. Finally, case studies are conducted to show the performance of the proposed model and various approaches.