用混合解决方案解决可再生能源来源不确定性的多能源微电网的最佳能源管理
M Siva Ramkumar1, Jaganathan Subramani2, M Sivaramkrishnan3
1Department Electronics and Communication Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India.
Scientific reports
|March 5, 2025
概括
本研究介绍了一种混合算法,用于最佳的微电网管理,将可再生能源的不确定性降低15%,并提高效率20%. 该系统优化了可再生能源的工业电网的能源调度.
科学领域:
- 电气工程 电气工程
- 能源系统 能源系统
- 人工智能的人工智能
背景情况:
- 工业电网能源管理面临着不断增长的需求,成本和可再生能源整合带来的挑战.
- 有效的管理对于降低运营成本,提高电网稳定性和优化资源配置至关重要.
- 需要先进的技术来平衡供应,需求和储存在动态的工业环境.
研究的目的:
- 为网络化微电网 (MG) 系统制定低成本,最佳的管理策略.
- 通过调度优化微型源来降低电力生产成本.
- 在可再生能源 (RES) 不确定性下,制定多能源微电网 (MEMG) 的最佳调度策略.
主要方法:
- 提出了一个混合算法,结合了二次插值和新本地搜索灰色优化 (QI-NLS-G2O) 和高斯半径区域感知器网络 (GRZPNet) 的混合算法.
- 使用QI-NLS-G2O进行训练的GRZPNet使用风力轮机 (WT),光伏阵列 (PV) 和电池存储的输入预测负载需求.
- 能源转换设备的设计外性能模型被纳入了调度过程.
- 根据预测的负载需求,QI-NLS-G2O优化了MG配置.
- 混合技术是使用MATLAB/Simulink.实现和验证的.
主要成果:
- 拟议的模型显著提高了调度准确性.
- 再生能源不确定性的影响减少了大约15%.
- 在模拟中,MEMG性能效率提高了高达20%.
- 与替代方法相比,混合方法显示出更高的性能.
结论:
- 开发的混合能源管理方案有效地解决了MEMG中的可再生能源不确定性.
- 拟议的QI-NLS-G2O和GRZPNet技术为最佳的调度策略提供了一个强大的解决方案.
- 该研究强调了工业能源管理中大幅降低成本和提高效率的潜力.
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