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使用IGDT方法为考虑P2G-CCS和云储能系统的公园集成能源系统的最佳调度模型

Lixin Wang¹, Jing Cheng², Xu Luo¹

¹Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Connection Control, Xinjiang University, Urumqi, 830017, China.

Scientific reports

|July 30, 2024

概括

这项研究优化了公园集成能源系统 (PIES),使用云储能 (CES) 的双层模型. 该方法在不确定性条件下平衡能源效率和经济效益,实现低碳运营.

科学领域:

能源系统工程能源系统工程
优化理论优化理论
环境科学环境科学

背景情况:

园区综合能源系统 (PIES) 面临能源效率和经济可行性方面的挑战,原因是源负载的不确定性.
集成云储能 (CES) 提供了潜在的解决方案,但需要复杂的管理策略.
现有的模型往往难以同时解决多个不确定性和不同的运营商风险偏好.

研究的目的:

为PIES和CES开发一个双层优化模型,同时考虑源负载的不确定性.
提高PIES的能源效率和财务业绩.
为综合能源系统的低碳经济运行提供框架.

主要方法:

为PIES建立一个编程框架,将精炼电气 (P2G),碳捕获和储存 (CCS) 和CES集成在一起.
在梯级式碳价格机制 (LCPMRP) 下,与PIES和CES运营商建立一个双级优化模型.
应用权适应性信息差距决策理论 (EAIGDT) 方法来处理不确定性,并制定风险回避 (RA) 和风险寻求 (RS) 策略.

主要成果:

拟议的双层优化模型有效地整合了P2G,CCS和CES,以加强PIES操作.
该EAIGDT方法成功地解决了多个源负载的不确定性,没有主观因素.
最佳的调度方法证明了能够平衡各种利益相关者的利益,从而实现低碳经济运作.

结论:

开发的双级优化模型和EAIGDT方法为优化PIES与CES提供了有效的方法.
该战略成功地平衡了经济和环境目标,实现了低碳和成本效益的能源系统运行.
使用现实世界的数据验证了这些发现,证实了拟议方法的实际适用性.

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