A multi-objective optimization dispatching and adaptability analysis model for wind-PV-thermal-coordinated operations considering comprehensive forecasting error distribution. (20th May 2020)

• Record Type: Journal Article
• Authors: Tan, Qinliang; Mei, Shufan; Dai, Mei; Zhou, Lijing; Wei, Yongmei; Ju, Liwei
• Is Part Of: Journal of cleaner production. Volume 256(2020)
• Publication Date: 2020-05-20
• Subjects: Renewable energy -- Forecasting error -- Spinning reserve -- Dispatching model; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.120407
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Developing clean energy power generation is one of the main strategies to promote a sustainable economy. With the development of clean coal power technology, it is necessary to make full use of existing power generation resources instead of blindly building renewable power plants. For this reason, a dispatching model based on renewable energy forecasting errors is proposed in this paper to analyze the operation of the existing power supply. The contributions of this paper include a comprehensive forecasting error model, constructed to introduce wind and photovoltaic (PV) power forecasting errors into the dispatching system, as well as a dynamic spinning reserve (SR) model, constructed based on conditional value at risk, which analyses the SR model at different risk levels. An adaptive analysis model is also introduced to verify stability when additional errors occur in both the renewable energy output and the load demand. The optimization model was applied to the auxiliary power dispatching system of the Tianzhong ±800 kV ultra-high voltage direct current transmission channel in Xinjiang. The results show that (1) renewable energy forecasting errors have a significant effect on the SR. By separately constraining the SR, the total SR is reduced by 6.59%; (2) through the optimization of the SR, the output ranges of the thermal power unit are expanded, and the utilization rate goes up to 73.55%; (3) it is important to set an appropriate risk level when dispatching decision making. Setting low-risk levels will make the power system unable to deal with sudden failures, whereas setting high-risk levels will limit the effective use of renewable energy. Highlights: A convolution formula was used to determine the comprehensive forecasting error distribution. A dynamic spinning reserve model was constructed based on conditional value at risk. Spinning reserve was divided into two parts and separately constrained. An adaptability analysis model was used to verify the applicability of the optimization dispatching model.