考虑极限场景的电力系统分布鲁棒低碳经济调度  

Distributed Robust Low-Carbon Economic Dispatch of Power Systems Considering Extreme Scenarios

作　　者：胡恒 秦建茹 李海波 刘捷丰 HU Heng;QIN Jianru;LI Haibo;LIU Jiefeng(College of Electrical Engineering,Guangxi University,Nanning 530004,China;Sichuan Energy Internet Research Institute-Tsinghua University,Chengdu 610213,China)

机构地区：[1]广西大学电气工程学院,南宁市530004 [2]清华四川能源互联网研究院,成都市610213

出　　处：《电力建设》2025年第4期160-172,共13页Electric Power Construction

基　　金：国家自然科学基金联合基金项目(U23B20111)。

摘　　要：【目的】在国家"双碳"目标深入推进的能源转型背景下,随着新能源渗透率持续攀升,其出力的强随机性与波动性不仅推高了系统旋转备用需求,更导致保障供电可靠性的调峰调频成本陡增,加剧了系统经济运行代价与供电可靠性两者间的本质性冲突。尤其是极端天气等小概率高影响事件引发的尾部风险,使得基于典型场景假设的传统调度模型不再适用。【方法】针对此问题,首先采用拉丁超立方抽样与改进k-means聚类生成风光出力典型场景集,并校验其备用可行性,进而通过场景聚类将备用不可行场景重构为极限场景集。在此基础上,构建以日前调度阶段运行成本与日内调整阶段碳交易成本、火电再调度成本及风险成本联合最小化为目标的两阶段分布鲁棒调度模型,并通过引入综合范数约束构建计及极限场景修正的离散概率模糊集,从而更精确刻画新能源出力不确定性。最后,采用列与约束生成(column and constraint generation,C&CG)算法进行求解,并在改进IEEE 39节点系统进行仿真验证。【结果】结果表明,与传统确定性模型及基于典型场景的分布鲁棒模型相比,所提模型虽然调度成本增加了7.11%和14.37%,但平均弃电率降低8.28%和34.65%,平均切负荷率减少8.19%和33.32%。【结论】所提模型在控制经济运行成本的同时可显著提升系统鲁棒性,有效解决了传统方法风险应对能力不足,无法兼顾系统运行经济性、可靠性和低碳性的问题,为新型电力系统的安全稳定运行提供了有效解决方案。[Objective]Under the advancing energy transition driven by China's national"dual-carbon"strategy,the escalating penetration of renewable energy(RE)sources has not only heightened power system spinning reserve requirements due to their inherent stochasticity and volatility in generation patterns,but has also precipitated a marked surge in peak-shaving and frequency regulation expenditures necessary for maintaining power supply reliability.This dynamic further exacerbates the fundamental multi-objective conflict between economic operation costs and reliability assurance in modern power systems.Particularly,tail risks triggered by extreme weather and the dynamic mismatches between stochastic RE fluctuations and conventional unit regulation rates invalidate conventional deterministic scheduling models reliant on typical scenarios.[Methods]To address this,this paper first constructs RE generation scenarios using Latin hypercube sampling(LHS)and modified k-means clustering,verifying their reserve feasibility,while transforming reserve-infeasible scenarios into extreme scenario sets.A two-stage distributionally robust optimization(DRO)model is proposed,minimizing day-ahead operational costs and intraday costs including carbon trading,rescheduling expenses,and risk penalties.A discrete probability ambiguity set with comprehensive norm constraints is established to rigorously characterize RE uncertainty by incorporating extreme scenarios.[Results]Case studies on an improved IEEE 39-node system using the column-and-constraint generation(C&CG)algorithm demonstrate that,compared with traditional deterministic and DRO models based on typical scenarios,the proposed approach increases scheduling costs by 7.11%and 14.37%respectively,but reduces renewable curtailment rates by 8.28%and 34.65%,and load shedding rates by 8.19%and 33.32%.[Conclusions]This methodology effectively resolves the limitations of conventional approaches in coordinating economic efficiency,reliability,and low-carbon requirements while ensuring system robustn

关 键 词：极限场景 分布鲁棒优化 不确定性 碳交易 

分 类 号：TM732[电气工程—电力系统及自动化]