The Integrated Energy System (IES) that coordinates multiple energy sources can effectively improve energy utilization and is of great significance to achieving energy conservation and emission reduction goals. In this context, a low-carbon and economic dispatch model for IES is proposed. Firstly, a hydrogen energy-based IES (H2-IES) is constructed to refine the utilization process of hydrogen energy. Secondly, the carbon emissions of different energy chains throughout their life cycle are analyzed using the life cycle assessment method (LCA), and the carbon emissions of the entire energy supply and demand chain are considered. Finally, a staged carbon trading mechanism is adopted to promote energy conservation and emission reduction. Based on this, an IES low-carbon and economic dispatch model is constructed with the optimization goal of minimizing the sum of carbon trading costs, energy procurement costs, and hydrogen sales revenue, while considering network constraints and constraints on key equipment. By analyzing the model under different scenarios, the introduction of life cycle assessment, staged carbon trading, and hydrogen energy utilization is shown to promote low-carbon and economic development of the comprehensive energy system.

The uncertainty caused by the growing use of renewable energy sources, such as wind and solar energy, makes it difficult to forecast the operation costs of micro-energy systems, particularly those in remote rural areas. Motivated by this point, this paper analyzes the possible operational risks and then introduces Condition Value at Risk (CVaR) to quantify the cost of the operational risk. On this basis, stochastic programming based on a multi-energy microgrid planning model that minimizes the investment cost, the operating cost, and the cost of operational risk, while considering the physical limitations of the multi-energy microgrid, is presented. Especially, scenarios of wind and solar energy output are generated using the Latin hypercube sampling method and reduced using the crowding measure-based scenario reduction method. After piecewise linearization and second-order cone relaxation, the model proposed in this paper is processed as a mixed integer linear model and solved by CPLEX. According to the achieved typical scenarios processed by the reduction method, the simulation shows that the presented configuration model can balance the investment cost and the cost of the operational risk, which effectively enhances the system’s ability to cope with uncertainties and fluctuations. Moreover, by adjusting the risk preference coefficient, the conservativeness of the planning scheme can be correspondingly adjusted.