考虑风电出力波动性的混合储能双层优化配置  

Hybrid Energy Storage Double-layer Optimal Configuration Considering Wind Power Output Volatility

作　　者：武晓朦 孙安磊 李晨晨 张钦凯 李飞 WU Xiao-meng;SUN An-lei;LI Chen-chen;ZHANG Qin-kai;LI Fei(Key Laboratory of Shaanxi Province for Gas-oil Logging Technology,School of Electronic Engineering,Xi’an Shiyou University,Xi’an 710065,China)

机构地区：[1]西安石油大学电子工程学院,陕西省油气井测控技术重点实验室,西安710065

出　　处：《科学技术与工程》2024年第24期10313-10320,共8页Science Technology and Engineering

基　　金：国家自然科学基金企业创新发展联合基金重点项目(U20B2029);陕西省科技计划基础研究项目(2021JM-404);陕西省教育厅科研计划项目(21JK0843);西安石油大学研究生创新与实践能力培养项目(YCS22214241)。

摘　　要：在新型能源与配电网协调发展的背景下,针对分布式储能的优化配置问题,提出了一种混合储能双层优化配置模型。通过上层优化确定储能接入位置和容量,并通过傅里叶变换对功率进行分频,利用超级电容器和蓄电池分别承担不同频率部分的功率。下层优化以低储高放效益为目标函数,通过结合Pareto档案的粒子群算法进行优化。通过IEEE33节点的仿真实验,验证了该模型的可行性。结果表明:该模型能够实现多目标综合优化,包括降低网络损耗、优化电能指标和降低储能设备投资成本,为配电网接入分布式储能的优化配置提供了有效的解决方案。In the context of the coordinated development of new energy sources and distribution networks,a hybrid energy storage twolayer optimal configuration model was proposed for the optimal configuration of distributed energy storage connected to distribution networks.The upper layer optimization determined the energy storage access location and capacity,and divided the power by Fourier transform,using super capacitor and battery to bear the power of different frequency parts respectively.The lower layer optimization was designed with the objective function of maximizing the benefits from low storage and high discharge operations.It was optimized using a combination of the particle swarm algorithm and the Pareto file.The models rationality and effectiveness were confirmed through simulation experiments conducted on the IEEE33 nodes network.The results show that the model can achieve multi-objective comprehensive optimization,including reducing network losses,optimizing power index and reducing investment costs of energy storage equipment,which provides an effective solution for the optimal configuration of distributed energy storage connected to distribution networks.

关 键 词：混合储能 双层优化 傅里叶变换 Pareto档案 粒子群算法 

分 类 号：TM715[电气工程—电力系统及自动化]