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计及新能源备用的全场景可行随机机组组合模型

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新型电力系统正面临新的挑战,这是由于高比例新能源的引入,需要对系统进行更为精细的优化调度,并确保其安全稳定运行.为减轻电力系统因大规模风电并网引发的运行安全压力,提出一套适用于电力系统的备用模型,纳入风电资源的随机机组组合方案.首先,可采用无参统计方法,以获得风电功率预测误差的概率密度估计,随后基于最佳信赖水平来构建风电备用模型.其次,提出应运用基于可变不确定性集顶点场景的全场景可行随机优化技术,以有效应对风电资源的不确定性,并引入严格的非预期性约束条件,以满足电力系统的经济调度需求.这一方法有望降低风电并网对电力系统安全运行所带来的挑战.最后,在改进的IEEE-24 节点系统中,采用某地的实际数据验证了所提出随机优化模型的有效性,并与基于场景的两阶段随机优化进行对比.实验结果证明,所提出模型具有良好的经济性能,并可以有效减少计算时间.同时,引入强非预期约束,保证了所提出模型可获得全场景可行的最优策略.
All-scenario-feasible stochastic unit commitment model considering renewable energy integrated into system reserve
The new power system with renewable energy generation as the main body has brought new challenges to the system dispatching.A stochastic unit commitment model considering wind power as the operating reserve is pro-posed to alleviate the reserve pressure brought by the integration of large-scale renewable energy.Firstly,the prob-ability density estimation of wind power prediction error is obtained based on the kernel density method,and the wind power reserve model is established according to the best confidence level.Secondly,we propose an all-scenar-io-feasible dispatching model based on the vertex scenarios with variable uncertainty sets and introduce strong non-anticipative constraints to meet the nonanticipativity of economic dispatching.Finally,in the improved IEEE-24 bus system,the effectiveness of the proposed stochastic unit commitment model is verified by the actual data and is compared with the scenario-based two-stage stochastic optimization.The experimental results show that the proposed model has satisfactory performance,can overcome the defects of numerical scenarios and large computing scale in stochastic optimization,and effectively reduces calculating time.

wind powerdispatching of reserveall-scenario-feasibilitynonanticipative constraints

刘阳、滕卫军、李朝晖、张子玉、丁涛

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国网河南省电力公司电力科学研究院,河南 郑州 450052

西安交通大学电气工程学院,陕西 西安 710049

风电功率 发电侧备用优化 全场景可行 非预期约束

国家电网河南省电力公司科技项目

521702220007

2024

电工电能新技术
中国科学院电工研究所

电工电能新技术

CSTPCD北大核心
影响因子:0.716
ISSN:1003-3076
年,卷(期):2024.43(4)
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