摘要
针对智能电网中可再生能源整合面临的不确定性挑战,提出了一种基于隐马尔可夫模型的优化方法。该方法结合了需求响应策略、实时电价和储能系统的优化调度。通过在修改的IEEE 30节点系统上进行仿真,结果表明马尔可夫决策过程的预测准确率显著提高。应用需求响应策略后,系统的发电成本和排放量大幅降低,同时有效减少了峰值需求。研究证明,所提出的集成能源管理系统能够有效降低运营成本、减少排放,并提高电网可靠性,为智能电网中可再生能源的高效整合提供了可行解决方案。
This paper proposes an optimization method based on hidden Markov models.It addresses the uncertainties in integrating renewable energy into smart grids.The method combines demand response strategies,real-time pricing,and optimal scheduling of storage systems.Simulations were conducted on a modified IEEE 30-bus system.The Markov decision process was found to significantly improve prediction accuracy.After applying demand response strategies,generation costs and emissions were substantially reduced.Peak demand was also effectively decreased.The proposed integrated energy management system was proven to lower operational costs and emissions.Grid reliability was improved as well.This study provides a viable solution for efficient integration of renewable energy in smart grids.
作者
李萌
沈力
陆佳鑫
奚梦婷
查俊杰
LI Meng;SHEN Li;LU Jia-xin;XI Meng-ting;ZHA Jun-jie(State Grid Jiangsu Elecric Power Co.,Ltd Information and telecommunication branch,Nanjing 210000)
出处
《环境技术》
2024年第8期162-168,共7页
Environmental Technology
基金
国家自然科学基金项目,项目编号:61501224。
关键词
需求响应
动态编程
能源管理
可再生能源发电
储能系统
demand response
dynamic programming
energy management
renewable energy generation
energy storage systems
