考虑源荷随机时序波动的电力系统概率化供需平衡分析方法

doi:10.19595/j.cnki.1000-6753.tces.252005

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摘要

随着高比例新能源并网以及柔性负荷规模不断扩大,调节能力不足已成为制约新能源消纳和电力保供的关键瓶颈。为应对源荷双侧不确定性对系统供需平衡的影响,本文从功率曲线整体视角出发,采用联合概率模型刻画源荷功率的随机时序波动,以净负荷时序随机向量统一表征系统调节需求,并结合火电与储能运行可行域给出系统总体调节能力模型。在此基础上,引入给定置信度水平下的概率化供需平衡判据,基于“典型场景优化+海量场景校验”迭代优化框架,提出数据驱动的概率化供需平衡分析方法。算例结果表明,所提方法在运行成本小幅增加的前提下,显著增强了调度方案对尾部风险场景的适应性,为新型电力系统的供需平衡分析提供了有效工具。

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关键词 ：新能源, 供需平衡, 随机时序波动, 调节能力, 不确定性

Abstract：

With the increasing penetration of renewable energy and the continuous expansion of flexible loads, the uncertainty on both supply and demand sides pose growing challenges to maintaining power balance in power systems. Insufficient regulation capability has become a critical bottleneck for renewable energy accommodation and secure power supply. To effectively address the impact of uncertainties in renewable generation and load demand, this paper proposed a data-driven probabilistic supply-demand balance analysis method from the perspective of the overall power curve. Instead of characterizing uncertainty only at a single time slice or by local ramping indices between adjacent periods, the method treats the full scheduling horizon as an integrated trajectory.
Firstly, a time-series probabilistic modeling framework is developed for both regulation demand and regulation capacity over the entire scheduling horizon. Specifically, renewable generation and load demand across the horizon are represented through a joint probability distribution that captures stochastic chronological fluctuations, including temporal and spatial correlations. Based on this joint distribution, the net load time-series random vector is introduced as a unified representation of the power system's regulation demand, enabling the analysis to preserve both randomness and intertemporal structure of the power curve. Furthermore, the paper emphasizes that the regulation capacity of power systems is fundamentally constrained by the time-coupling characteristics of regulation resources, rather than by isolated per-period adjustable intervals. By defining the full-cycle feasible operating region of regulation resources, such as thermal units and energy storage, time-coupling constraints and system-level supply-demand balancing constraints are introduced to comprehensively characterize the overall regulation capacity of the power system over the entire scheduling horizon.
Secondly, a probabilistic supply-demand balance criterion for chronological power balance at a given confidence level is proposed. The system is considered adequate if the probability of maintaining feasibility (i.e., the existence of a valid resource trajectory satisfying all constraints) meets the required confidence. On this basis, a data-driven probabilistic supply-demand balance analysis method is developed within an iterative optimization framework that alternates between representative-scenario optimization and massive-scenario verification. A stochastic security-constrained unit commitment (SCUC) problem is solved on a reduced set of representative scenarios to obtain feasible and economical day-ahead commitment decisions. Then, a massive generated scenario set is used to assess loss-of-load risk due to supply-demand imbalances. To improve coverage of these extreme but impactful scenarios, a scenario update mechanism based on loss-of-load distribution quantiles is introduced to achieve adaptive expansion of the representative scenario set. Through iterative optimization, the method accurately captures tail-risk scenarios and enhances the regulation capability of the power system.
Finally, case studies validate the proposed method on a modified six-bus system with wind, photovoltaic generation, and battery storage. Results show that, compared with a deterministic expected-value model and a conventional stochastic SCUC that considers only a few typical scenarios, the proposed approach substantially improves adaptability to small-probability tail-risk net load trajectories with only a modest increase in operating cost. Notably, the estimated supply-demand balance probability is increased to 0.955, demonstrating the method’s effectiveness as a practical tool for probabilistic supply-demand balance analysis in power systems.

Key words： Renewable energy supply-demand balance stochastic chronological fluctuation regulation capacity uncertainty

收稿日期: 2025-11-24

PACS:

TM732

基金资助:

国家自然科学基金联合资助项目（U24B2077）

通讯作者: 孙英云男,1975年生,教授,博士生导师,研究方向为新型电力系统规划、调度和运行,人工智能在能源互联网中的应用。E-mail：sunyy@ncepu.edu.cn

作者简介: 赵鹏飞男,1993年生,博士研究生,研究方向为不确定环境下的电力系统规划。E-mail：pf.zhao@hotmail.com

引用本文:

赵鹏飞, 毛志航, 刘栋, 郭国栋, 孙英云. 考虑源荷随机时序波动的电力系统概率化供需平衡分析方法[J]. 电工技术学报, 0, (): 19-. Zhao Pengfei, Mao Zhihang, Liu Dong, Guo Guodong, Sun Yingyun. Probabilistic Supply-Demand Balance Analysis for Power Systems Considering Stochastic Chronological Fluctuations in Generation and Load. Transactions of China Electrotechnical Society, 0, (): 19-.

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