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Abstract
Realizing the renewable revolution must motivate all stakeholders’ participation. How to incentivize them to actively join in distributed renewable energy trades and to self-consciously upgrade their electricity generation systems in order to improve their electricity-providing stability is of importance. However, limited studies touched on this issue. To address this issue, a series of theoretical analyses are implemented to argument which trade model can achieve the above goal, in which the corresponding trade structure, trade process, trade criteria, and trade price and penalty fee strategies are analyzed in order. In the model, due to the introduction of corresponding trade structure, trade criteria as well as the design of trade price scheme, the miscellaneous distributed renewable energies coming from different prosumers could be classified into six types and be matched to different trade-layer and relevant trade models; the prosumers of providing the electricity with different stability have enough motivation to conduct self-upgrading.
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No potential conflict of interest was reported by the author(s).
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Notes on contributors
Shi Lefeng
Shi Lefeng is a professor at National Center for Applied Mathematics in Chongqing and becomes the team head of the Center for Intelligent Energy Management and Applications since 2018. Before working at National Center for Applied Mathematics in Chongqing, he worked as a postdoctoral researcher in China’ National State Grid from 2013 to 2016 and then as a teacher in Economics and Management School of Chongqing Normal University from 2016 to 2021. Prof. Shi excels in analyzing issues using game theory and optimization theory, especially in the field of power system and transportation. Now, he are focusing on developing a theory to characterize the series changes from a CyberPhysical-Social System perspective and to reveal the inner mechanism.
Chen Guanhong
Chen Guanhong is a master degree candidate at the School of Economics and Management, Chongqing Normal University (P.R. China). His main research interests include active operation of devices and machine learning methods.
Lv Shengnan
Lv Shengnan is a doctoral student from the Business School of Sichuan University (P.R. China). Her main research interests include smart transportation, electric vehicle infrastructure, fuzzy decision.