Multi-stage risk-averse operation of integrated electric power and natural gas systems. (March 2021)

• Record Type: Journal Article
• Authors: Sun, Guoqiang; Sun, Jinghong; Chen, Sheng; Wei, Zhinong
• Is Part Of: International journal of electrical power & energy systems. Volume 126(2021)Part A
• Publication Date: 2021-03
• Subjects: Regional integrated energy system -- Multi-stage operation model -- Conditional value at risk -- Demand response; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106614
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: We develop a multi-stage operation model of electric and natural-gas networks. Flexible wait-and-see decisions are made based on newly updated uncertainties. We propose a CVaR-based risk-averse operation model. The value of the multi-stage risk-averse operation model is demonstrated. Abstract: Regional electric power and natural gas systems have become increasingly interdependent in recent years, and have become subject to increasing supply and demand uncertainties associated with renewable energy sources. The present work addresses these issues by developing a multi-stage risk-averse operation model at the level of a regional integrated energy system that considers integrated demand response. Specifically, the developed multi-stage operation model can make flexible wait-and-see decisions, which are adaptive to newly observed uncertainties in the output of renewable energy sources, and the conditional value at risk approach is employed to achieve an economic operating cost at a given risk level. As a result, the multi-stage decision model outperforms conventional two-stage decision models in terms of operating costs. Test results indicate that the operation cost of the multi-stage operation model is reduced by 4.2% compared with a regular two-stage operation model. The impact of congestion in both the electricity and natural gas networks on system operations and energy prices is also investigated.