Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production. (1st May 2017)

• Record Type: Journal Article
• Authors: Geng, Zhaowei; Conejo, Antonio J.; Chen, Qixin; Xia, Qing; Kang, Chongqing
• Is Part Of: Applied energy. Volume 193(2017)
• Page Start: 540
• Page End: 549
• Publication Date: 2017-05-01
• Subjects: Generation schedule -- Emission -- Renewable integration -- Social welfare -- Multi-objective; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.02.051
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Power generation is scheduled with wind power uncertainty and dispatchable loads. Objectives are: max social welfare, min emission, and max renewable production. A multi-objective stochastic scheduling model is formulated and tested. Scheduling outcomes under different policies are discussed. Abstract: Some areas in China are facing pressing air pollution problems. Measures from the power sector can be taken to cope with air pollution issues, including reducing emission levels of thermal units and integrating wind and solar power. Social welfare, emission, and renewable integration are three major concerns in modern power system operations. This paper describes three stochastic scheduling models aiming at maximizing social welfare (SW), minimizing emission (EM) and maximizing renewable production (RE). A multi-objective scheduling model (MT) is also proposed that properly balances the above objectives. Wind power uncertainty and dispatchable loads are considered in the model. The outcomes of the three models are compared through an illustrative example and a 57-node case study. Results show that model EM results in 36% of the social welfare of model SW, 27% of its emissions, and 43% of its wind spillage, while model RE results in 55% of the social welfare of model SW, 56% of its emissions and 28% of its wind spillage. Additionally, we analyze how the optimal generation scheduling is affected by the weights in model MT. This work provides insight to policy makers on how to balance social welfare, emissions and renewable production.