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. Firstly, the uncertainties of wind power and photovoltaic output forecasting errors are modeled as an ambiguity set based on the Wasserstein metric. Secondly, combined with the chance constraint, the expected probability of the inequality constraint with uncertain variables is limited to the lowest allowable confidence level to improve the reliability of the model. Thirdly, the forecast errors of wind power and photovoltaic are considered in the constraint conditions, so that the system can effectively resist the interference of uncertain output. Besides, based on the strong duality theory, the DD-DRCCO model is equivalent to a MILP problem which is easy to solve. Finally, simulations implemented on a typical MEVPP are delivered to show that our proposed model: 1) The model is data-driven, and the conservativeness is kept at a low level, and the solution time is about 7s~8s; 2) The MEVPP system can achieve a balance between economy and low-carbon, making the total operation cost reduced by 0.89% compared with no increase of electric boiler; 3) The CO2 emission during the operation of the MEVPP system was significantly reduced by about 87.33 kg.

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. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model's validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants' behavior, which is more practical in the operation of carbon trading markets.

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. The results of spatial convergence estimation support that China's provincial carbon intensity presents significant spatial absolute and conditional convergence, and after considering regional differences, the spatial convergence speed is significantly accelerated. Meanwhile, economic level, urbanization, energy consumption structure, and industrial structure have significant spatial radiation effects on carbon intensity, and carbon intensity itself also has a spatial diffusion effect, indicating that carbon emission reduction requires multi-regional coordinated actions. This paper examined the spatial distribution and convergence of China's provincial carbon intensities over 2000-2017. The empirical findings verified the spatial agglomeration and radiation effects, as well as the absolute and conditional spatial convergence of China's provincial carbon intensities, which supports the policy-making related to the carbon reduction in China.