DING Qili, ZHANG Xinggan, ZHANG Na, et al. A Joint Planning and Configuration Method for Power System Inertia and Primary Frequency Regulation Reserve Considering Extreme Events[J]. 2025, (22): 8779-8795.
DING Qili, ZHANG Xinggan, ZHANG Na, et al. A Joint Planning and Configuration Method for Power System Inertia and Primary Frequency Regulation Reserve Considering Extreme Events[J]. 2025, (22): 8779-8795. DOI: 10.13334/j.0258-8013.pcsee.241013.
传统频率响应备用规划将惯量隐含在一次调频资源中,新形势下无法满足系统惯量需求,亦未实现惯量与一次调频备用的最优协调规划;同时由于未考虑决策者风险偏好,可能导致小概率极端事件被同质化而埋下安全隐患。该文提出一种考虑极端事件的电力系统惯量与一次调频备用联合规划配置方法。建立含源-储-荷多类调节资源的系统频率响应模型,基于序贯蒙特卡洛随机生产模拟获取系统失负荷集合;构建惩罚模型以衡量频率变化率(rate of change of frequency,RoCoF)越限损失,提出频率安全概率指标以刻画频率越限的概率分布与损失期望;建立计及风险偏好的备用成本收益模型对多元灵活性资源进行协同规划配置,并对规划方案进行可行性检验。基于改进的IEEE-RTS79系统的算例分析表明:所提方法可依据决策者风险偏好灵活制定不同风险范围下的最优联合规划配置方案,为提升新型电力系统极端事件应对能力提供更有针对性的决策依据。
Abstract
The traditional frequency response reserve planning involves the inertia in primary frequency regulation (PFR) resources
which fails both to meet the system inertia demand under the new situation and to realize the joint planning of inertia and PFR reserve. Meanwhile
neglecting the decision maker's risk preference may lead to homogenized patterns of extreme eventsand lay potential security threats. A joint planning method of inertia and PFR reserve considering extreme events is proposed. The system frequency response model with multiple types of source-storage-load regulation resources is established. The system disturbance set is obtained by using sequential Monte Carlo probabilistic production simulation. The penalty model is constructed to measure the loss of rate of change of frequency (RoCoF) overrun. The frequency stability probability index is proposed to characterize the probability distribution and loss expectation of frequency limit violation. The cost benefit model of reserve with risk preference is developed for collaborative planning and allocation of multiple flexibility resources
and the feasibility of planning solutions is verified. Based on the improved IEEE-RTS79 case studies
it is demonstrated that the proposed method can flexibly formulate the optimal planning under different risk ranges according to the decision maker's risk preference
providing a more targeted decision-making basis for improving the new power system's response capability to extreme events.