设计基于人工智能的智能多层优化框架,用于网联太阳能光伏燃料电池混合能源系统
Prashant Nene1, Dolly Thankachan1
1Department of Electrical and Electronics Engineering, Oriental University, Indore (M.P.), India.
MethodsX
|August 18, 2025
概括
这种AI框架增强了太阳能燃料电池混合系统,降低了成本并改善了电池寿命. 它为智能电网提供了一个可扩展的实时解决方案.
科学领域:
- 人工智能的人工智能
- 可再生能源系统可再生能源系统
- 智能电网技术 智能电网技术
背景情况:
- 网联太阳能PV-燃料电池混合能源系统需要先进的控制以获得最佳性能.
- 现有的能源管理方法往往缺乏实时适应性和可扩展性.
- 提高效率和降低运营成本对于广泛采用混合可再生能源系统至关重要.
研究的目的:
- 提出和评估一个多层次的人工智能框架,以提高网联太阳能光伏燃料电池混合能源系统的性能.
- 为了尽量减少净当前成本 (NPC) 和能源成本 (COE),同时最大限度地提高电池寿命.
- 为未来的智能电网开发一个可扩展,实时和保护隐私的能源管理解决方案.
主要方法:
- 开发一个集成的人工智能框架,结合了强化学习驱动的进化神经网络 (RL-ENN),基于变压器的时空预测 (T-STFREP),基于联合学习的分布式优化 (FL-DEO),图形神经网络电源路由器 (GNN-HSCO) 和量子启发的生成对抗网络 (Q-GAN-ESO).
- 适应性能源调度和成本最小化的RL-ENN.
- T-STFREP用于准确的时间预测.
- FL-DEO用于分散的,保护隐私的优化.
- GNN-HSCO用于最大限度地减少传输损失.
- Q-GAN-ESO用于合成降解场景分析和储能管理.
- 用MATLAB/Simulink和Python与TensorFlow进行模拟,使用30年的气象数据.
主要成果:
- 拟议的AI框架显著降低了27.5%的净当前成本 (NPC) 和18.2%的能源成本 (COE).
- 通过优化能源管理,电池寿命延长了30.2%.
- 该模型与遗传算法 (GA) 和粒子群优化 (PSO) 等传统方法相比,表现出更高的性能.
结论:
- 集成的人工智能框架为智能电网系统提供了可扩展和实时节能解决方案.
- 先进的人工智能技术的结合在增强混合可再生能源系统方面提供了强大的性能.
- 在降低成本和延长电池寿命方面取得的改进证明了该框架在实际应用中的有效性.
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