Two-stage electricity production scheduling with energy storage and dynamic emission modelling

Abstract

With increasing environmental concerns and energy crisis, a variety of renewable energy sources (RES) are being increasingly utilised worldwide. However, the integration of RES such as wind power and photovoltaics in large-scale can lead to increased load fluctuations, which can undermine the overall environmental benefits and pose risks to the secure and stable operation of the power system. To mitigate this challenge, a two-stage electricity production scheduling is developed incorporating energy storage system (ESS) and dynamic emission modelling (DEM). In the first stage, a multi-objective mixed integer programming model schedules the production of RES, increasing penetration rate and system stability. In the second stage, a data-driven dynamic emission model is developed to optimise the load allocation of thermal power unit to reduce the carbon emissions. Furthermore, a flexible operating reserve strategy is proposed to handle the uncertainty resulting from the intermittent character of RES. Experimental results demonstrate that the proposed method effectively schedules the production of RES thereby alleviating the contradiction between high RES utilisation and stable system operation. Compared to the benchmark model, the proposed method can reduce the carbon emissions and total cost of the system by 20.34% and 10.65%, respectively.

KEYWORDS:

• Renewable integration
• energy storage system
• dynamic emission
• generation schedule
• operational flexibility

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data presented in this study are available as request.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant numbers 72174124, 71871146, 71701136], the Natural Science Foundation of Guangdong Province [grant numbers 2022A1515011009, 2021A1515010987], Shenzhen Science and Technology Program [grant number JCYJ20210324093414039], and by NTUT-SZU Joint Research Program [grant number 2023005].

Notes on contributors

Bi Fan

Bi Fan, is an Associate Professor in the College of Management, Shenzhen University, Shenzhen, China. He received his Ph.D. degree in System Engineering and Engineering Management from City University of Hong Kong, in 2014. His research interests include the optimisation problems related to energy system management, intelligent manufacturing, and data-driven decisions.

Fengjie Liao

Fengjie Liao, is currently a postgraduate at College of Management, Shenzhen University, Shenzhen, China. He received the B.S degree from Shanghai Maritime University, Shanghai, China. His main research interests include power system dispatch and renewable energy planning.

Chao Yang

Chao Yang, is currently an Assistant Professor in Shenzhen University. He received the Ph.D. degree from Shenzhen University, Shenzhen, China, in 2020. His research interests include urbanisation, sustainable development, and the social-ecological effects of human activities.

Quande Qin

Quande Qin, is a professor at the College of Management, Shenzhen University, located in Shenzhen, China. He obtained his Ph.D. degree in Management Science and Engineering from South China University of Technology in 2011. His research interests encompass a wide range of topics, including energy economics, environmental economics, energy technology management, energy system modelling, and energy policy. He has published in as Environmental & Resource Economics, Ecological Economics, Energy Economics, Energy Policy, Applied Energy, Renewable and Sustainable Energy Reviews, among others.