基于机会限制的多能源虚拟发电厂的两阶段最佳调度和基于数据的分布强大的优化,考虑到碳交易
Huiru Zhao1, Xuejie Wang2, Zhuoya Siqin1
1School of Economics and Management, North China Electric Power University, Beijing, 102206, China.
概括
一个新的数据驱动模型通过解决可再生能源的不确定性来优化多能源虚拟发电厂 (MEVPP) 的运行. 这种方法平衡了经济效率和低碳目标,降低了成本和二氧化碳排放.
科学领域:
- 能源系统工程 能源系统工程
- 优化理论 优化理论
- 整合可再生能源的整合
背景情况:
- 多能源虚拟发电厂 (MEVPP) 对可再生能源消耗和碳减排至关重要.
- 运营挑战来自于多种能源的合和可再生能源的变化.
- 现有的模型可能无法充分解决MEVPP预测的不确定性.
研究的目的:
- 为MEVPP调度提出一个数据驱动的分布性稳健的机会约束优化模型 (DD-DRCCO).
- 提高MEVPP操作的可靠性和稳定性,防止预测错误.
- 在MEVPP中实现经济目标和低碳排放目标之间的平衡.
主要方法:
- 风能和光伏预测错误的建模,使用基于瓦斯斯坦度数的模糊性集.
- 实施机会约束,以限制违反不平等约束的可能性.
- 将DD-DRCCO模型转化为可通过强二元化解决的混合整数线性编程 (MILP) 问题.
主要成果:
- 数据驱动模型以低保守性和快速解决时间 (7-8秒) 运行.
- 与没有电气炉增长的基线相比,实现了总运营成本的0.89%降低.
- 在MEVPP系统运行期间,二氧化碳排放量减少了约87.33公斤.
结论:
- 该DD-DRCCO模型有效地管理MEVPP中的不确定性.
- 提出的方法为优化MEVPP运营提供了一种实用方法,既能带来经济效益,也能带来环境效益.
- 这项研究有助于可靠和高效地将可再生能源集成到电网中.
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