考虑新能源极端出力事件的水风光互补长期优化调度研究

Long-term optimized scheduling of hydro-wind-PV complementary systems considering extreme new energy output events

摘要

【目的】为应对新能源持续多日极端出力下水风光互补系统面临的缺电或弃水风险，协调优化系统中期调度风险和长期发电效益，提出了一种考虑新能源极端出力的水风光互补长期优化调度方法。【方法】首先，定义了新能源极端出力场景，并从多年历史数据中提取样本，揭示了新能源极端出力特性。其次，提出面向新能源极端场景的水风光系统调度风险评估方法。最后，建立兼顾发电量与调度风险的水风光互补多目标优化模型，以提升系统对新能源极端事件的响应能力，并以北盘江清洁能源基地为实例进行验证。【结果】结果显示：以平水年为例，优化前系统汛前缺电风险最大为0.75亿kWh,汛末弃电风险最大达0.31亿kWh。均衡方案较发电最优方案调度风险降低了0.48亿kWh、发电量降低了0.17亿kWh;较最优方案调度风险增加了0.21亿kWh、发电量提升了0.66亿kWh。所提模型推荐光照水库汛前水位控制范围为692.15~694.93 m、汛末水位控制范围743.84~745.00 m。【结论】水风光系统在汛前和汛末两个关键节点灵活调节性能存在明显不足，提出的多目标优化模型能够有效协调新能源极端场景下水风光互补系统的发电效益与调度风险。Pareto前沿集对应的关键节点水位区间可作为合理的水位控制范围。均衡方案在兼顾发电效益与风险控制方面表现较为出色，能以较小的电量损失代价换取调度风险的显著下降，可为制定防御新能源极端出力的调度方案提供参考。

Abstract

[Objective] To address the risks of power shortage or water abandonment in hydro-wind-PV complementary systems under continuous extreme new energy output events, and to coordinate the mid-term scheduling risks and long-term power generation benefits of the system, a long-term optimized scheduling method for hydro-wind-PV complementarity considering extreme new energy output is proposed. [Methods] First, extreme new energy output scenarios were defined, and samples were extracted from multi-year historical data to reveal the characteristics of extreme new energy output. Second, a scheduling risk assessment method for hydro-wind-PV systems under extreme new energy scenarios was developed. Finally, a multi-objective optimization model for hydro-wind-PV complementarity was established, considering both power generation and scheduling risks to enhance the system's response capability to extreme new energy events, and it was validated using the Beipan River Clean Energy Base as a case study. [Results] The result showed that taking a normal year as an example, before optimization, the maximum power shortage risk reached 0.75×10⁸ kWh in the pre-flood stage, while the maximum power curtailment risk reached 0.31×10⁸ kWh in the post-flood stage. Compared with the power-generation-optimal solution, the balanced scheme reduced scheduling risk by 0.48×10⁸ kWh and decreased power generation by 0.17×10⁸ kWh. Compared with the risk-optimal solution, the balanced scheme increased scheduling risk by 0.21×10⁸ kWh while improving power generation by 0.66×10⁸ kWh. The proposed model recommended controlling the water level of Guangzhao Reservoir within 692.15~694.93 m in the pre-flood stage and 743.84~745.00 m in the post-flood stage. [Conclusion] The hydro-wind-PV system shows significant limitations in flexible regulation performance. The proposed multi-objective optimization model can effectively coordinate power generation benefits and scheduling risks under extreme new energy scenarios. The key-node water level intervals corresponding to the Pareto front set can serve as reasonable water level control ranges. The balanced scheme demonstrates excellent performance in balancing power generation benefits and risk control and achieves a significant reduction in scheduling risks at the expense of only a small loss in generation, thereby providing valuable guidance for formulating scheduling strategies against extreme new energy output.

关键词

水风光互补 / 新能源极端出力 / 关键节点水位控制 / 风险量化 / 优化调度 / 径流 / Pareto / 多目标优化

Key words

hydro-wind-PV complementarity / extreme new energy output / key-node water level control / risk quantification / optimized scheduling / runoff / Pareto / multi-objective optimization

基金资助

中国博士后科学基金资助项目(2024M760738)

国家自然科学基金项目(52479013)