Low-Carbon Economic Bi-Level Optimal Dispatching of an Integrated Power and Natural Gas Energy System Considering Carbon Trading

• Main Authors: Hong Li, Yazhong Ye, Lanxin Lin
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Applied Sciences
• Subjects: integrated power and natural gas energy system; carbon trading; reward and punishment ladder-type; optimal carbon price; stochastic optimal; bi-level model
• Online Access: https://www.mdpi.com/2076-3417/11/15/6968

The integrated power and natural gas energy system (IPGES) is of great significance to promote the coordination and complementarity of multi-energy flow, and it is an important carrier to increase the proportion of wind power accommodation and achieve the goal of carbon emission reduction. In this paper, firstly, the reward and punishment ladder-type carbon trading model is constructed, and the impact of the carbon trading mechanisms on the carbon emission sources in the power system is comparatively analyzed. Secondly, in order to achieve a reasonable allocation of carbon resources in IPGES, a bi-level optimization model is established while taking into account the economics of dispatching and the requirements of carbon emission reduction. Among them, the outer layer is the optimal carbon price solution model considering carbon trading; in the inner layer, considering the power system constraints, natural gas system constraints, and coupling element operation constraints, a stochastic optimal dispatching model of IPGES based on scenario analysis is established. Scenario generation and reduction methods are used to deal with the uncertainty of wind power, and the inner model is processed as a mixed integer linear programming problem. In the MATLAB environment, program the dichotomy and call the Gurobi optimization solver to complete the interactive solution of the inner and outer models. Finally, case studies that use an integrated IEEE 39-bus power system and Belgian 20-node gas system demonstrate the effectiveness and scalability of the proposed model and optimization method.