水力发电
发电
光伏系统
风力发电
调度(生产过程)
计算机科学
可靠性工程
地铁列车时刻表
电
工程类
数学优化
功率(物理)
运营管理
电气工程
数学
物理
操作系统
量子力学
作者
Yi Guo,Bo Ming,Qiang Huang,Yimin Wang,Xudong Zheng,Wei Zhang
出处
期刊:Applied Energy
[Elsevier]
日期:2022-03-01
卷期号:309: 118467-118467
被引量:35
标识
DOI:10.1016/j.apenergy.2021.118467
摘要
Optimizing day-ahead generation schedules of hydro–wind–photovoltaic (PV) complementary systems (HWPCSs) can help to promote the accommodation of wind and solar energies. However, it is challenging to formulate appropriate generation schedules for the large HWPCS that contains cascade hydropower plants, in particular, a steady requirement of power delivery is considered in the optimization model. To further improve complementary performance of the large HWPCS, we propose a risk-averse day-ahead generation scheduling approach that considers the steady requirement of power delivery. First, a representative scenario set is used to characterize forecast uncertainties of the wind and PV power. Then, a multi-objective optimal generation scheduling model with consideration of the operational risks of electricity curtailment and power shortage is proposed. Finally, a two-layer nested optimization framework is designed to derive the system’s generation schedule. The clean energy base in the upper Yellow River basin, China was selected as a case study. The results show that: (1) forecast uncertainties of wind and PV power are more likely to induce power shortage risk in summer and autumn, but to induce electricity curtailment risk in spring and winter; (2) without using extra constraint handling strategies, the proposed approach could directly yield a stair-shaped power delivery curve, which is good for long-distance power transmission applications; and (3) compared with a traditional method without considering the operational risks, the proposed generation scheduling approach could significantly reduce the comprehensive risk rate by 65% on average, while the cascade hydropower production and peak shaving performance are satisfactory. Therefore, the proposed approach is effective in guiding the day-ahead generation scheduling of the HWPCSs that contain cascade hydropower plants.
科研通智能强力驱动
Strongly Powered by AbleSci AI