A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units Zhifan Zhang College of Electrical Engineering, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China Ruijin Zhu College of Electrical Engineering, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China

With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system’s peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness.
03 07 2024 534 pr12030534 the National Natural Science Foundation of China http://dx.doi.org/10.13039/ 52167015 2022D-ZN-01 https://creativecommons.org/licenses/by/4.0/ 10.3390/pr12030534 https://www.mdpi.com/2227-9717/12/3/534 https://www.mdpi.com/2227-9717/12/3/534/pdf