Analysis of the joint trading of local green power certificates, carbon emissions rights, and electricity considering demand flexibility
Jifeng Li, Xingtang He, Wei Niu, Xiangning Liu
Abstract
Establishing synergy among distinct yet associated markets through market orientation is crucial because it can facilitate a clean and low-carbon energy transition. However, researchers have not yet developed a quantitative analytical dynamic model for studying market participants in the context of the joint electricity-carbon-green power certificates market. Moreover, the solution algorithm merits further exploration. This article proposes an analytical method for evaluating the joint trading of local green power certificates, carbon emission rights, and electricity, while taking into account the demand flexibility. First, a framework for the joint trading of local green power certificates, carbon emission rights, and electricity is designed. Second, market participants, such as traditional thermal power plants, renewable energy power plants, and load aggregators, are modelled via system dynamics theory. On this basis, a deep distributed reinforcement learning algorithm is applied to solve the problem. Finally, the effectiveness of the proposed theoretical approach is verified through real-life simulations, and the sensitivity of the key factors influencing joint trading is also analysed.