摘要
储能作为一种灵活性调节资源,合理的容量配置可有效平抑风电波动,提高利用率;需求响应作为发电侧和用户侧重要的互动资源,可在降低购电费用的同时促进风电消纳。基于此,提出考虑风电商消纳需求和用户用电需求的混合储能容量配置方法。首先,从用户侧柔性负荷响应特性出发,构建可平移负荷和可削减负荷模型;其次,建立由蓄电池和超级电容组成的混合储能系统,并提出充放电策略;再次,以混合储能系统成本最低、弃风率最小、用电满意度最高为目标函数构建混合储能容量优化模型,利用多目标粒子群算法求解;最后,通过算例分析验证该文模型的有效性。结果表明,所提混合储能容量配置模型不仅可满足多方需求,还能提升系统经济性。
Energy storage,as a flexible resource,can effectively suppress wind power fluctuations and improve the utilization rate of wind power through reasonable capacity allocation.Demand response,as an interactive resource for power generation side and users,can reduce electricity purchase cost and promote wind power consumption at the same time.Based on this,a hybrid energy storage capacity configuration method is proposed,which takes into account the consumption demand of wind power suppliers and the power demand of users.Firstly,based on the flexible load response characteristics on the user side,the models of shifting load and reducing load are constructed.Secondly,a hybrid energy storage system composed of batteries and ultracapacitors is established,and the charging and discharging strategy is put forward.Thirdly,the hybrid energy storage capacity optimization model is constructed with the objective function of the lowest cost,minimum wind abandoning rate and highest electricity comfort of the hybrid energy storage system,and solved by multi-objective particle swarm optimization algorithm.Finally,the validity of the proposed model is verified by example analysis.The results show that the proposed hybrid energy storage capacity configuration model can not only meet the requirements of multiple parties,but also improve the system economy.
作者
孟贤
郭启蒙
李颖欢
刘吉成
Meng Xian;Guo Qimeng;Li Yinghuan;Liu Jicheng(School of Economics and Management,North China Electric Power University,Beijing 102206,China;State Grid Corporation of China Limited,Beijing 100031,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2023年第8期45-53,共9页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(71771085)。
关键词
风电
储能
需求侧管理
容量配置
多目标优化
wind power
energy storage
demand side management
capacity allocation
multi-objective optimization