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基于双层优化模型的火-储协同调频性能研究OA

Research on the performance of fire-storage coordinated frequency modulation based on a bi-level optimization model

中文摘要英文摘要

[目的]为使火电机组更好地应对新型能源迅速发展下对原有电网结构带来的冲击,提升火电机组运行的稳定性与经济性,提出构建基于火电机组实时功率预测及储能功率模糊控制分配的火-储调频双层优化模型.[方法]该模型上层利用频率偏差分解与火电实时功率预测优化功率基准,有效克服了机组响应时延;下层引入模糊逻辑控制策略,实现功率的自适应精准分配.在此基础上,利用多目标遗传算法对储能容量配置方案进行寻优求解,并以系统频率波动程度等指标为依据,对不同控制策略下的调频性能进行量化评价.以 600 MW火电机组为研究对象,通过算法求得最优储能配置为:飞轮储能功率8.5 MW、容量 1.3 MW·h,锂电池储能功率 3.6 MW、容量 14.6 MW·h.该配置对应的总投资成本为 2.027 7×105万元,可在400 s调频周期内实际收益 850.95元.[结果]通过MATLAB/Simulink软件进行仿真验证后,得出在阶跃扰动下,火-储协同双层优化策略使系统频率波动程度降至4.826×10-² Hz,较火电机组独立调频下降 38.53%,功率波动平均绝对偏差降至4.224 MW,较独立运行降幅达 32.57%;而在连续扰动下系统频率波动程度减小19.31%,功率波动平均绝对偏差下降 78.71%,实际贡献电量增加 0.527 MW·h.[结论]火-储协同双层优化控制策略既有效平抑了系统频率与功率波动,缓解了火电机组调频压力,又提升了储能系统利用效率与调频经济效益,为火电灵活性改造提供了新的技术升级方向.

[Objective]In order to better cope with the impact of the rapid development of new energy on the existing power grid structure and improve the stability and economy of thermal power unit operation,this paper proposes to construct a dual-layer optimization model of fire storage frequency regulation based on real-time power prediction of thermal power units and fuzzy control allocation of energy storage power.[Methods]The upper layer of the model utilizes frequency deviation decomposition and real-time power prediction of thermal power to optimize the power benchmark,effectively overcoming the response delay of the unit.The lower layer introduces a fuzzy logic control strategy to achieve adaptive and precise power allocation between the thermal unit and the energy storage system.On this basis,multi-objective genetic algorithm is used to optimize the energy storage capacity configuration scheme,and the frequency modulation performance under different control strategies is quantitatively evaluated based on indicators such as system frequency fluctuation.Taking a 600 MW thermal power unit as the research object,the optimal energy storage configuration was obtained through algorithm as follows:flywheel energy storage power of 8.5 MW and capacity of 1.3 MW·h,and lithium battery energy storage power of 3.6 MW and capacity of 14.6 MW·h.The total investment cost corresponding to this configuration is 2.027 7×109 yuan,and the actual income during the 400 s frequency modulation cycle is 850.95 yuan.[Results]After simulation verification using MATLAB/Simulink,it was found that under step disturbance,the dual layer optimization strategy of fire storage coordination reduces the frequency fluctuation of the system to 4.826×10-2 Hz,which is 38.53%lower than the independent frequency regulation of the fire power unit.The average absolute deviation of power fluctuation is reduced to 4.224 MW,which is 32.57%lower than the independent operation.Under continuous disturbance,the frequency fluctuation of the system decreased by 19.31%,the average absolute deviation of power fluctuation decreased by 78.71%,and the actual contribution of electricity increased by 0.527 MW·h.The results show that the thermal-storage coordinated dual layer optimization control strategy presented in this paper effectively mitigates system frequency and power fluctuations,thereby alleviating the frequency regulation pressure on thermal power units.Concurrently,it enhances the utilization efficiency of the energy storage system and improves the economic viability of frequency regulation services.[Conclusion]This research thus provides a novel technical direction for the flexible transformation of thermal power plants,enabling them to play a more supportive and complementary role in future power systems dominated by renewable energy sources.

韩旭;仲宣宇;刘仲稳

华北电力大学河北省低碳高效发电技术重点实验室,河北 保定 071003华北电力大学河北省低碳高效发电技术重点实验室,河北 保定 071003中电华创电力技术研究有限公司,江苏 苏州 215009

预测控制多元复合储能双层优化控制火电一次调频评价指标

predictive controlmultivariate composite energy storagedual-layer optimization controlthermal power primary frequency regulationevaluation index

《热力发电》 2026 (2)

75-85,11

河北省高等学校科学研究项目青年拔尖项目(BJ2025053)河北省自然科学基金(E2023502025) Hebei Province Higher Education Science Research Project Youth Elite Project(BJ2025053)Hebei Natural Science Foundation(E2023502025)

10.19666/j.rlfd.202511015

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