Two-stage stochastic planning for integrated energy systems accounting for carbon trading price uncertainty

• Published in: International journal of electrical power & energy systems Vol. 143; p. 108452
• Main Authors: Wang, Lei, Shi, Zhongwang, Dai, Wei, Zhu, Liuzhu, Wang, Xuli, Cong, Hao, Shi, Tiancheng, Liu, Qian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2022
• Subjects: Carbon trading price uncertainty; Integrated energy systems; Portfolio forecasting; Segmented adaptive gray wolf optimization; Portfolio forecasting; Carbon trading price uncertainty; Integrated energy systems; Segmented adaptive gray wolf optimization
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2022.108452

•A segmented adaptive gray wolf optimization algorithm is proposed.•A multi-objective carbon trading point prediction model based on FCEEMD-SAGWO-LSSVR was constructed.•A segmented index-based carbon trading costing model is proposed.•A two-stage stochastic planning model for integrated energy systems that takes into account the uncertainty analysis of carbon trading is proposed. With the development and improvement of carbon trading mechanism and the promotion of energy internet strategy, integrated energy system (IES) as the physical carrier of energy internet has an important strategic positioning. Taking electricity-gas-heat multi-energy flow model of IES as the research object, it has important theoretical value and engineering significance to carry out the planning and design research of IES on the basis of quantitative and qualitative analysis of the operational coupling mechanism between different systems and full consideration of the uncertainty of carbon trading market. Based on this, a regional integrated energy system expansion planning model based on the uncertainty of carbon trading price is proposed in this paper. First, a combined forecasting model based on fast integrated empirical modal decomposition (FCEEMD), improved segmented adaptive gray wolf optimization (SAGWO) and least squares support vector machine (LSSVR) was used to simulate the trend of carbon price fluctuations. Second, a stochastic probability distribution model for carbon trading prices was designed using a mean regression model in combination with a sample of carbon trading forecasts. Then, a two-stage stochastic planning model taking into account the grid constraints and system operation constraints is constructed with the objective of minimizing the whole life cycle cost. Finally, the planning schemes under different carbon trading models are compared and analyzed by means of calculation examples, and then the effectiveness of the proposed method is demonstrated.