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.

Value quantification of multiple energy storage to low-carbon combined heat and power system.

As the proportion of renewable energy gradually increases, it brings challenges to the stable operation of the combined heat and power (CHP) system. As an important flexible resource, energy storage (ES) has attracted more and more attention. However, the profit of energy storage can't make up for the investment and operation cost, and there is a lack of measurement system for multiple values, which seriously hinders the development of energy storage industry. Based on this, this paper makes a quantitative analysis on the system value of multiple energy storage in CHP. Firstly, the uncertain output of renewable energy is characterized by Kullback-Leibler (KL) divergence, and a two-level dispatching model is constructed based on the distributionally robust optimization method, so as to study the optimal operation strategy of the ES-CHP system. Secondly, based on the system value theory, this paper analyzes the system value of multiple energy storage, including internal value and external value, and constructs the value quantitative model, respectively. Finally, in a typical ES-CHP system, the system value of multiple energy storage is quantified. The effectiveness of the two-level model constructed in this paper can be seen from the simulation results, and the influence of different electricity prices on the system value of multiple energy storage is further analyzed.

Carbon dioxide emissions, urbanization level, and industrial structure: empirical evidence from North China.

This paper aims to examine the nexus among carbon dioxide (CO2) emissions, urbanization level and industrial structure in North China over the period 2004-2019, according to an expanded Cobb-Douglas production function. The panel econometric techniques are employed to complete the empirical analysis, including cross-sectional correlation test, panel unit root test, panel co-integration test, and panel Granger causality test. The empirical results support the long-term equilibrium relationship among CO2 emissions, urbanization level and industrial structure in North China, and the urbanization level contributes most to CO2 emissions, followed by fossil energy consumption. Furthermore, the bidirectional causality between CO2 emissions and urbanization level and unidirectional causality from industrial structure to CO2 emissions are found in North China, indicating that urbanization level and industrial structure have significant impacts on CO2 emissions. Finally, according to the empirical findings, several policy suggestions are proposed for the purpose of reducing CO2 emissions in North China.