Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop

After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this p...

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Main Authors: Xiaodong Yang, Xiaotong Hua, Lun Cheng, Tao Wang, Yujing Su
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Energies
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Online Access:https://www.mdpi.com/1996-1073/18/15/3926
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author Xiaodong Yang
Xiaotong Hua
Lun Cheng
Tao Wang
Yujing Su
author_facet Xiaodong Yang
Xiaotong Hua
Lun Cheng
Tao Wang
Yujing Su
author_sort Xiaodong Yang
collection DOAJ
description After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems.
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spelling doaj-art-c42eeefae9674073868deaeeb7d4f8142025-08-20T03:04:42ZengMDPI AGEnergies1996-10732025-07-011815392610.3390/en18153926Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency DropXiaodong Yang0Xiaotong Hua1Lun Cheng2Tao Wang3Yujing Su4State Grid Hebei Electric Co., Ltd., Shijiazhuang 050000, ChinaDepartment of Electric Power Engineering, North China Electric Power University, Baoding 071003, ChinaState Grid Hebei Electric Co., Ltd., Shijiazhuang 050000, ChinaDepartment of Electric Power Engineering, North China Electric Power University, Baoding 071003, ChinaState Grid Hebei Electric Co., Ltd., Shijiazhuang 050000, ChinaAfter primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems.https://www.mdpi.com/1996-1073/18/15/3926power dip phenomenonsecondary frequency dropmulti-region frequency safetyday-ahead risk schedulingrobust optimization
spellingShingle Xiaodong Yang
Xiaotong Hua
Lun Cheng
Tao Wang
Yujing Su
Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop
Energies
power dip phenomenon
secondary frequency drop
multi-region frequency safety
day-ahead risk scheduling
robust optimization
title Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop
title_full Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop
title_fullStr Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop
title_full_unstemmed Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop
title_short Optimization Scheduling of Multi-Regional Systems Considering Secondary Frequency Drop
title_sort optimization scheduling of multi regional systems considering secondary frequency drop
topic power dip phenomenon
secondary frequency drop
multi-region frequency safety
day-ahead risk scheduling
robust optimization
url https://www.mdpi.com/1996-1073/18/15/3926
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