Efficient joint dispatch method for energy and frequency regulation markets incorporating deep peak shaving of thermal units

ZHENG Yawen; XIANG Mingxu; YANG Zhifang

doi:10.19783/j.cnki.pspc.260074

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Efficient joint dispatch method for energy and frequency regulation markets incorporating deep peak shaving of thermal units

更新时间：2026-07-19

- Efficient joint dispatch method for energy and frequency regulation markets incorporating deep peak shaving of thermal units
- Power System Protection and Control Vol. 54, Issue 14, (2026)
- 作者机构：
  
  输变电装备技术全国重点实验室(重庆大学),重庆,400044
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.260074
  CLC：
- Published：2026
- 稿件说明：
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ZHENG Yawen, XIANG Mingxu, YANG Zhifang. Efficient joint dispatch method for energy and frequency regulation markets incorporating deep peak shaving of thermal units[J]. Power System Protection and Control, 2026, 54(14).
DOI：

ZHENG Yawen, XIANG Mingxu, YANG Zhifang. Efficient joint dispatch method for energy and frequency regulation markets incorporating deep peak shaving of thermal units[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.260074.

摘要

调频市场以及融合调峰的电能量市场通过合理安排火电机组日前运行计划以应对新能源随机波动。然而，当前能量-调频联合出清机制未计及火电深度调峰-调频互斥特性，且未考虑系统预测随机性的影响，易导致机组运行方式安排不合理。对此，构建了计及调峰容量需求的能量-调频市场联合出清决策模型。基于条件约束建立了火电深度调峰-调频容量互斥约束。同时，引入系统调峰容量需求，构建了火电深度调峰容量-调频容量互斥约束，保障未提供调频的机组具备足量深度调峰能力以应对净负荷预测误差。然后，针对融合火电深度调峰引入的大规模整数变量，提出了基于深度调峰特征辨识的简化模型构造与伴随引导无损加速方法。构建了保留深度调峰特征的小规模伴随模型，并以伴随模型求解结果引导原模型热启动无损加速。最后，以我国某省网实际数据开展算例仿真。结果表明，所提方法能够提高出清决策方案的合理性，所提加速方法在不同场景下均能取得超1.7倍的加速效果。

Abstract

The frequency regulation market and the electricity energy market incorporating peak shaving cope with the random fluctuations of new energy by coordinating the day-ahead scheduling of thermal power units. However

existing joint clearing mechanisms of energy and frequency regulation do not consider the mutual exclusion characteristics of deep peak shaving and frequency regulation

nor do they account for the impact of system forecasting uncertainty

which can lead to suboptimal unit dispatch decisions. In response to this problem

this paper constructs a joint clearing decision model for the energy-frequency regulation market considering the peak shaving capacity demands. Based on conditional constraints

a mutual exclusion constraint between deep peak shaving capacity and frequency regulation capacity of thermal power units is established. Meanwhile

system peak shaving capacity demands are introduced to construct a mutual exclusion constraint between the deep peak shaving capacity and frequency regulation capacity of thermal power units

ensuring that the units not providing frequency regulation retain sufficient deep peak shaving capacity to accommodate net load forecasting errors. To address the large-scale integer variables introduced by deep peak shaving

a simplified model construction and adjoint-guided lossless acceleration method based on deep peak shaving feature identification is proposed. A small-scale adjoint model retaining deep peak shaving features is constructed

and its solution is used to guide the hot-start lossless acceleration of the original model. Finally

case studies are carried out using actual data from a provincial power grid in China. The results show that the proposed method improves the rationality of the clearing decision scheme

and the acceleration method achieves an acceleration effect of more than 1.7 times under different scenarios.

关键词

Keywords

references

HE Q, LIN Z, CHEN H, et al. Bi-level optimization based two-stage market clearing model considering guaranteed accommodation of renewable energy generation[J]. Protection and Control of Modern Power Systems, 2022, 7(3): 1-13.

QIU Y, LI Q, AI Y, et al. Two-stage distributionally robust optimization-based coordinated scheduling of integrated energy system with electricity-hydrogen hybrid energy storage[J]. Protection and Control of Modern Power Systems, 2023, 8(2): 1-14.

XIANG M, YANG Z, YU J, et al. Real-time dispatch with secondary frequency regulation: a pathway to consider intra-interval fluctuations[J]. IEEE Systems Journal, 2022, 16(4): 5556-5567.

LIU Y, LI X, LI D, et al. Multi-resource joint power dispatch with coordinated consideration of deep peak and frequency regulation requirements[C] // 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, Hangzhou, China, December 15-18, 2023, 4747-4753.

YIN Y, HE C, LIU T, et al. Risk-averse stochastic midterm scheduling of thermal-hydro-wind system: a network- constrained clustered unit commitment approach[J]. IEEE Transactions on Sustainable Energy, 2022, 13(3): 1293-1304.

PINEDA S, MORALES J M. Chronological time-period clustering for optimal capacity expansion planning with storage[J]. IEEE Transactions on Power Systems, 2018, 33(6): 7162-7170.

LI X, ZHAI Q, ZHOU J, et al. A variable reduction method for large-scale unit commitment[J]. IEEE Transactions on Power Systems, 2020, 35(1): 261-272.

QU S, YANG Z. Optimality guaranteed UC acceleration via interactive utilization of adjoint model[J]. IEEE Transactions on Power Systems, 2024, 39(3): 5191-5203.

LIU J, ZHANG X, WANG H. Optimal peak shaving capacity allocation considering prediction errors of renewable energy and load[J]. IEEE Transactions on Power Systems, 2020, 35(4): 2876-2885.

XIANG M, YANG Z, YU J, et al. Determination and cost allocation for regulation reserve with renewables: a data-driven assisted approach[J]. IEEE Transactions on Sustainable Energy, 2023, 14(2): 813-825.

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