Two-stage optimal dispatching of multi-energy virtual power plants based on chance constraints and data-driven distributionally robust optimization considering carbon trading.

Multi-energy virtual power plant (MEVPP) has attracted more and more attention due to its advantages in renewable energy consumption and carbon emission reduction. However, the characteristics of multi-energy coupling and the access of renewable energy may lead to some challenges in the operation of MEVPP. In this paper, a data-driven distributionally robust chance constraints optimization model (DD-DRCCO) is proposed for the dispatching of MEVPP.

Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China.

Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models' universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model.

Spatial distribution and convergence of provincial carbon intensity in China and its influencing factors: a spatial panel analysis from 2000 to 2017.

Unbalanced and inadequate development in China has resulted in significant temporal and spatial differences in carbon intensity, impeding the achievement of carbon reduction targets. This paper explores the spatial distribution and convergence of China's provincial carbon intensity during 2000-2017 and its influencing factors employing spatial panel techniques. The spatial distribution analysis supports the existence of significant spatial agglomeration and radiation effects in China's provincial carbon intensity, and several provinces play key roles in the spatial distribution of carbon intensity, which are an important focus of carbon emission reduction policies.