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1.武汉理工大学自动化学院,湖北省武汉市 430070
2.武汉理工大学深圳研究院,广东省深圳市 518000
3.国网浙江省电力有限公司电力科学研究院,浙江省杭州市 310014
侯慧(1981—),女,博士,副教授,博士生导师,主要研究方向:港口多能微电网运行调度、能源互联网。E-mail:husthou@126.com
谢应彪(2000—),男,通信作者,硕士研究生,主要研究方向:港口多能微电网规划运行及其不确定性调度。E-mail:xyingbiao@126.com
甘铭(1998—),男,硕士,工程师,主要研究方向:港口多能微电网规划运行及其不确定性调度。E-mail:szganming@126.com
收稿:2023-07-21,
修回:2023-10-31,
网络出版:2024-01-12,
纸质出版:2024-03-25
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侯慧,谢应彪,甘铭等.冷能梯级利用的港口多能微网双层不确定性经济调度[J].电力系统自动化,2024,48(6):205-215.
HOU Hui,XIE Yingbiao,GAN Ming,et al.Bi-layer Uncertainty Economic Scheduling for Port Multi-energy Microgrid with Cascade Utilization of Cold Energy[J].Automation of Electric Power Systems,2024,48(6):205-215.
侯慧,谢应彪,甘铭等.冷能梯级利用的港口多能微网双层不确定性经济调度[J].电力系统自动化,2024,48(6):205-215. DOI: 10.7500/AEPS20230721001.
HOU Hui,XIE Yingbiao,GAN Ming,et al.Bi-layer Uncertainty Economic Scheduling for Port Multi-energy Microgrid with Cascade Utilization of Cold Energy[J].Automation of Electric Power Systems,2024,48(6):205-215. DOI: 10.7500/AEPS20230721001.
为有效挖掘港口液化天然气(LNG)冷能利用的低碳灵活性潜力,充分发挥多时间尺度协同优化效应,提出一种考虑LNG冷能梯级利用的港口多能微网(MEMG)鲁棒-随机双层不确定性经济调度模型。首先,考虑LNG深冷-中冷-浅冷等各个温区的低碳灵活性潜力,建立低温碳捕集-冷能发电-直接冷却的冷能梯级利用模型,并以此为基础形成捕集-存储-利用协同的碳处理流程。其次,根据等概率逆变换生成考虑预测误差时序相关性的风电场景,并基于Wasserstein距离的0-1规划模型进行场景削减。再次,针对风电预测误差随时间尺度增加而增大的特性,构建多时间尺度优化的鲁棒-随机双层不确定性经济调度模型,上层通过分布鲁棒优化保证日前预调度决策鲁棒性,下层通过随机优化保证日内滚动调度决策经济性。最后,仿真结果表明,所提考虑冷能梯级利用的鲁棒-随机双层调度模型在解决日前长时间尺度预测精度低与日内短时间尺度易陷入局部最优矛盾的同时,可赋予港口MEMG更多经济性、低碳性及供电灵活性。
To effectively exploit the low-carbon flexibility potential of liquified natural gas (LNG) cold energy utilization in ports and give full play to the synergistic optimization effects across multiple time scales
a robust-stochastic bi-layer uncertainty economic scheduling model for port multi-energy microgrid (MEMG) considering the cascade utilization of LNG cold energy is proposed. Firstly
considering the low-carbon flexibility potential of LNG cold energy utilization across each deep cooling-mid cooling-shallow cooling temperature zone
a cold energy cascade utilization model of low-temperature carbon capture
cold energy power generation
and direct cooling is established
and a collaborative carbon processing of capture-storage-utilization is formed on this basis. Secondly
the wind power scenarios considering the temporal correlation of prediction errors are generated based on the equal probability inversion
and the scenarios are reduced by using a 0-1 planning model based on the Wasserstein distance. Thirdly
concerning the characteristic of wind power prediction error increasing with the increase of time scale
a robust-stochastic bi-layer uncertainty economic scheduling model with multi-time-scale optimization is constructed. The upper layer guarantees the robustness of day-ahead pre-scheduling decisions through distributionally robust optimization
and the lower layer guarantees the economic benefits of intra-day rolling scheduling decisions through stochastic optimization. Finally
the simulation results demonstrate that the proposed robust-stochastic bi-layer scheduling model considering the cold energy cascade utilization can not only better solve the contradiction of low prediction accuracy on day-ahead long-time-scale and easy to fall into the local optimum on intra-day short-time-scale
but also provide more economy
low-carbon emissions and power supply flexibility to the port MEMG.
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