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工程科学与技术:2023,55(2):128-140
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基于数据驱动分布鲁棒优化的梯级水光蓄联合优化调度
(1.国网成都供电公司,四川 成都 610041;2.四川大学 电气工程学院,四川 成都 610065)
Data-driven Distributionally Robust Optimization Based Coordinated Dispatching for Cascaded Hydro-PV-PSH Combined System
(1.State Grid Chengdu Power Supply Co., Chengdu 610041, China;2.College of Electrical Eng., Sichuan Univ., Chengdu 610065, China)
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投稿时间:2022-08-31    修订日期:2023-02-20
中文摘要: 多种可再生能源互补联合发电技术因其独特优越性正在成为“双碳”背景下电力系统优选供电方案之一,而其不确定性复杂耦合特性下的互补联合调度问题越来越受到人们关注。针对不确定性优化调度问题,本文引入能较好平衡不确定性及鲁棒性的数据驱动分布鲁棒优化理论(data-driven DRO),提出了一种新的基于数据驱动 DRO梯级水光蓄联合发电系统协同优化调度方法。首先,考虑系统互补经济调度成本建立两阶段调度模型,制定各电站日前出力调度计划;然后,引入综合范数约束限定概率置信区间,并考虑最恶劣分布下的实时运行调整成本,获取日前调度计划的最优调整方案,日调度计划和调度调整方案形成最优调度计划;最后,本方法采用MP-SP框架,引入CCG算法展开两阶段协同求解。为验证所提方法的性能,引入四川示范区实际运行数据,开展了有效性验证、性能对比分析、计算效率仿真验证等。结果表明:本调度方法的有效性在数据规模、置信度水平两个维度得到了验证;对于SO、RO及本方法鲁棒性及经济性等性能指标的对比,本方法可获得高于SO的鲁棒性及高于RO的经济性;将本调度方法与概率性时序生产模拟方法的计算耗时进行对比,该方法实现了相同计算精度的较高计算效率。基于两阶段调度模型及循环迭代求解的DRO梯级水光蓄联合优化调度方法实现了协同调度结果经济性与保守性的均衡,其高效性能得到验证,为多种可再生能源互补协同调度提供了新思路。
Abstract:Due to its unique advantages, the multiple renewable energy complementary combined power generation technology is becoming one of the preferred power supply scheme under the “carbon peaking and carbon neutrality” context. The optimal dispatching of this complementary coordinated generation system considering its complex uncertain coupling characteristics has attracted more and more attention. For uncertain optimal dispatching problems, this paper introduced the data-driven distributionally robust optimization (DRO) theory, which can better balance the uncertainty and the robustness of the problem. A new coordinated optimal dispatching method for the cascaded hydro-PV-pumped storage combined system was further proposed based on the data-driven DRO theory. This method established a two-stage DRO dispatch model to formulate the daily dispatch schedule considering the complementary economic dispatch cost of the system firstly. The comprehensive norm constraint was introduced to limit the probability confidence interval. Considering the adjustment cost of the real-time operation under the worst distribution, the optimal dispatch schedule was formed by the optimized adjustment scheme for the day-ahead dispatch schedule, the daily dispatch schedule and the adjustive dispatch scheme. The two-stage dispatch model was solved by the CCG algorithm according to the MP-SP framework finally. In order to verify the performance of the proposed method, the actual operation data of the demonstration area in Sichuan was taken to carry out the validity verification, the performance comparative analysis and the simulation verification of computational efficiency. The results show that, the validity of the proposed scheduling method is verified in the data scale and the confidence level dimensions. For the robustness and economy comparison of the SO, the RO and the proposed method, the method proposed in this paper can achieve higher robustness than SO and higher economy than RO. Comparing the calculation time of this scheduling method with the probabilistic sequential production simulation method, this method achieves a higher computational efficiency with the same computational accuracy. Based on the two-stage dispatching model and the iterative calculation, the data-driven DRO coordinated dispatching method for the cascaded hydro-PV-pumped storage combined system can achieve the balance between the economy and robustness of the dispatching results. Its efficient performance has been verified and a new way is obtained for the complementary coordinated dispatching of multi renewables.
文章编号:202200921     中图分类号:TM7    文献标志码:
基金项目:国家重点基础研究发展计划:请在下栏中列出明细(含项目号和具体课题名),国家重点研发计划项目“分布式光伏与梯级小水电互补联合发电技术研究及应用示范”(2018YFB0905200)
作者简介:第一作者:张帅(1987—),男,工程师,博士. 研究方向:可再生能源互补联合发电;主动配电网. E-mail:zhangshuaisgcc@qq.com
引用文本:
张帅,汪子涵,张蜀程,胡俊刚,罗颖,刘俊勇.基于数据驱动分布鲁棒优化的梯级水光蓄联合优化调度[J].工程科学与技术,2023,55(2):128-140.
ZHANG Shuai,WANG Zihan,ZHANG Shucheng,HU Jungang,LUO Ying,LIU Junyong.Data-driven Distributionally Robust Optimization Based Coordinated Dispatching for Cascaded Hydro-PV-PSH Combined System[J].Advanced Engineering Sciences,2023,55(2):128-140.