EN基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制
Stochastic model predictive control for hybrid energy storage assisted thermal power unit in AGC based on adaptive Markov power prediction
- 热力发电 2026年55卷第2期 页码：147-157
- 作者机构：
- 作者简介：
- 基金信息：国网浙江省电力有限公司科技项目（B311DS240011）~~
DOI：10.19666/j.rlfd.202511048
中图分类号：-
纸质出版：2026
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王天宇, 张江丰, 尹昊蕊, 等. 基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制[J]. 热力发电, 2026,55(2):147-157.
王天宇, 张江丰, 尹昊蕊, et al. Stochastic model predictive control for hybrid energy storage assisted thermal power unit in AGC based on adaptive Markov power prediction[J]. Thermal Power Generation, 2026, 55(2): 147-157.
王天宇, 张江丰, 尹昊蕊, 等. 基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制[J]. 热力发电, 2026,55(2):147-157. DOI： 10.19666/j.rlfd.202511048.
王天宇, 张江丰, 尹昊蕊, et al. Stochastic model predictive control for hybrid energy storage assisted thermal power unit in AGC based on adaptive Markov power prediction[J]. Thermal Power Generation, 2026, 55(2): 147-157. DOI： 10.19666/j.rlfd.202511048.
摘要
【目的】为提升混合储能系统（hybrid energy storage system
HESS）辅助火电机组响应自动发电控制（automatic generation control
AGC）指令时的调节性能，提出一种基于随机模型预测控制（stochastic model predictive control
SMPC）的火储联合功率分配策略。【方法】首先，针对包括功率型储能钛酸锂电池与能量型储能磷酸铁锂电池构成的HESS系统，提出基于马尔科夫概率矩阵构建未来时段火电机组响应AGC指令的HESS功率需求模型，并引入自适应机制实时动态修正状态转移概率，以提升AGC指令波动下的预测精度；其次，提出一种基于概率阈值与分层抽样相结合的场景树生成方法用于将自适应马尔科夫模型输出的概率分布转化为可用于优化的有限场景集合，描述多场景下功率需求预测的不确定性；最后，在上述框架基础上构建随机预测控制器，实现火电机组和HESS的功率最优分配。【结果】仿真实验表明，所提策略在调节性能上优于不考虑功率预测的传统联合调频策略以及未引入动态修正的由静态转移概率矩阵构建的SMPC策略，其性能指标K_p分别提升14.1%和7.5%。【结论】该策略有效提升了火电机组与HESS的协同调节性能，具有较强的应用潜力。未来可以进一步优化模型，提升其在实际应用中的鲁棒性和适应性，推动该技术的实际落地。
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