能源部门的规划策略:将贝叶斯神经网络和不确定性量化集成到场景分析和优化中
Funda Iseri1,2, Halil Iseri3,2, Harsh Shah1,2
1Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA.
Computers & chemical engineering
|August 29, 2025
概括
本研究引入了使用贝叶斯神经网络 (BNNs) 的灵活框架,以优化太阳能,风能和天然气系统的能源投资. 它解决了成本效益的容量规划和运营决策的不确定性.
科学领域:
- 能源系统分析
- 在不确定性下的优化
- 可再生能源的整合
背景情况:
- 全球能源市场面临不断增长的需求,竞争和向可再生能源过渡的挑战.
- 有效的决策需要先进的方法来管理投资和运营的不确定性.
- 现有的方法可能无法完全捕捉能源系统的复杂未来不确定性.
研究的目的:
- 在混合能源系统 (太阳能,风能,天然气) 中制定灵活的基于场景的容量和投资规划框架.
- 整合贝叶斯神经网络 (BNNs) 进行概率,数据驱动的场景生成,解决预测的不确定性.
- 加强在复杂的能源环境中进行具有成本效益的投资和运营的决策.
主要方法:
- 贝叶斯神经网络 (BNNs) 的整合,以模拟能源生产和需求预测的不确定性.
- 一个双阶段的随机多期混合整数线性优化模型的开发.
- 从BNN后期分布生成概率,数据驱动的场景,以进行可靠的规划.
主要成果:
- 该框架为天然气,风能和太阳能发电厂提供了详细的产能扩张和投资策略.
- 通过德克萨斯州的案例研究证明了适用性,解决了不断增长的能源需求.
- 该模型有效地结合了现实世界的限制,如建筑滞后和场景依赖的需求.
结论:
- 拟议的框架增强了能源系统的灵活性,并使成本效益高,可靠的投资和运营决策成为可能.
- 通过捕捉微妙的不确定性分布,它比传统方法具有显著的优势.
- 这种方法在不断变化的现代能源环境中支持战略规划.
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