In view of the issues that the current comprehensive energy supply reliability evaluation mainly focuses on the distributed energy system and rarely involves the mutual coupling of energy supply networks and the coordination of energy stations in the regional energy Internet, an evaluation method of energy supply reliability considering electricity–gas coupling and coordination between energy stations was proposed. First, the framework of regional energy Internet (REI) is established, the energy flow relationship among the main parts in the REI and the influence of electricity–gas coupling on reliability are analysed; second, considering building virtual energy storage and demand response, the rigid and flexible reliability evaluation indexes are proposed from the energy and time levels; third, the operation strategies of normal, failure and repair periods of each energy station are put forward, and the optimal load reduction model of REI aiming at the minimum total rigid energy shortage is established. Moreover, the method and process of energy supply reliability evaluation combining fault mode effect analysis (FMEA) and Monte Carlo simulation are proposed. Finally, through a practical example, the impact of demand response, multiple energy storage resources, electricity–gas coupling and inter‐station coordination on reliability is analysed.

In this article, a comprehensive analysis is conducted on the influencing factors of carbon emissions in the transportation sector during the evolution of electrification in Beijing. We also considered the impact of indirect carbon emissions caused by carbon emissions from public and private cars and electricity consumption on overall carbon emissions. Based on the LMDI decomposition theory, nine influencing factors are separated, including development level, energy intensity, vehicle structure, number of private cars, and private transportation energy consumption. The analysis results show that, from 2010 to 2021, the carbon emissions from transportation in Beijing increased from 922.98 × 10⁴t to 1490.6 × 10⁴t. The private road carbon emission accounts for about 77.97%, and has become a decisive factor affecting road carbon emissions. In the public domain the contribution values of the development level and energy intensity are 25.20% and −38.71%, respectively. And they are the two most critical factors affecting carbon emissions on public road carbon emissions. In the private domain, the contribution values of the number of private cars, energy consumption of private transportation, and vehicle structure are 60.17%, 47.86%, and −12.99%, respectively. They are the three key factors affecting the private road carbon emissions. There is a significant difference in the proportion of indirect carbon emissions from electricity in the public and private domain. Indirect carbon emissions from electricity account for about 13.6% of road carbon emissions in the public sector, and about 0.9% in the private sector. The results in this paper provide useful references for decision-making in the adjustment of transportation energy structure and the promotion of electrified transportation in Beijing and other cities.

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy.

It is effective to reduce haze in winter by changing the distributed heat supply system. Thus, the studies on comprehensive index system and scientific evaluation method of distributed heat supply project are essential. Firstly, research the influence factors of heating modes, and an index system with multiple dimension including economic, environmental, risk and flexibility was built and all indexes were quantified. Secondly, a comprehensive evaluation method based on AHP was put forward to analyze the proposed multiple and comprehensive index system. Lastly, the case study suggested that supplying heat with electricity has great advantage and promotional value. The comprehensive index system of distributed heating supply project and evaluation method in this paper can evaluate distributed heat supply project effectively and provide scientific support for choosing the distributed heating project.