Open AccessStochastic scheduling ensuring air quality through wind power and storage coordination
Author(s) -
Geng Zhaowei,
Conejo Antonio J.,
Kang Chongqing,
Chen Qixin
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1619
Subject(s) - wind power , scheduling (production processes) , air quality index , environmental science , production (economics) , energy storage , reliability engineering , pollution , air pollution , electric power system , computer science , automotive engineering , stochastic programming , environmental economics , engineering , power (physics) , meteorology , operations management , electrical engineering , mathematical optimization , ecology , physics , chemistry , mathematics , organic chemistry , quantum mechanics , biology , economics , macroeconomics
In highly polluted regions, it might be necessary to operate power plants enforcing appropriate emission limits to help ensuring air quality. If the air quality is low and pollutants are difficult to diffuse, few additional emissions should be allowed to avoid further deteriorating the air quality. Wind power helps reducing pollution levels as it substitutes polluting thermal production. However, wind and air pollution are generally anti‐correlated. Storage can be used to shift wind power production from hours when higher pollution levels are allowed to hours when lower pollution levels are allowed to help meeting emission constraints at critical hours. Storage availability also achieves a comparatively lower production cost for the system as a whole. To represent the stochastic nature of wind power production and weather‐dependent emission limits, the authors propose a two‐stage stochastic programming model to analyse the trade‐off emission versus cost in an electric energy system with storage facilities. The first stage represents the day‐ahead scheduling, while the second one represents the real‐time operation under different scenarios. The authors analyse the impact of emission limits and/or storage on generation scheduling by comparing different models using an illustrative example and a case study based on the IEEE 118‐node system.