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Two-stage stochastic programming-based capacity optimization for a high-temperature electrolysis system considering dynamic operation strategies

机译:考虑动态操作策略的高温电解系统的两阶段随机编程的容量优化

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摘要

High-temperature electrolysis (HTE) systems are expected to operate with renewable sources as energy storage devices due to their high efficiency, reversibility and eco-friendliness. However, the high capital costs of stacks and heat management devices make capacity optimization of HTE systems necessary in the design phase. The current literature formulates the problem merely under constant loading conditions, which ignores the fact that dynamic operation is essential for renewable energy storage. Under this background, we propose a novel model to incorporate the capacity optimization of HTE systems under volatile loading conditions. Specially, this model is formulated in the form of two-stage stochastic programming in which operation optimization is treated as a subproblem of capacity optimization. The advantages include its ability to analyze the influence of different operating strategies on device capacity and its "min-max"transformation, which is convenient to solve by commercial code. In a case study, we discuss the influences of power source volatility and operating strategy on the optimal capacities. Different changing trends of the optimal device capacities with power source volatility are observed around one particular hydrogen price of approximately 4.2 $/kg, above which the capacities increase with power input volatility; at prices below 4.2 $/kg, the opposite effect occurs due to renewable spillage. In addition, considering the operating strategy, we find that an optimal stack inlet temperature of 1200 K obtains economic results comparable to those in a variant temperature operation and provides useful guidance on the controller design. Compared to previous capacity optimization studies under constant operation, this proposed method shows better system economy; e.g., the net revenue increases by approximately 29.54% and 71.52% for the HTE system under hydro and wind power inputs respectively.
机译:由于其高效率,可逆性和生态友好,预计高温电解(HTE)系统预计将与可再生源作为储能设备运行。然而,堆栈和热管理设备的高资本成本使得在设计阶段中所需的HTE系统的能力优化。目前的文献仅在恒定的负载条件下制定了该问题,这忽略了动态操作对于可再生能量存储至关重要的事实。在此背景下,我们提出了一种新型模型,可以在挥发性负载条件下掺入HTE系统的容量优化。特别是,该模型以两级随机编程的形式配制,其中操作优化被视为容量优化的子标数。优势包括其分析不同操作策略对器件容量的影响及其“最大值”转换的能力,这方便通过商业代码解决。在一个案例研究中,我们讨论了电源波动性和操作策略对最佳能力的影响。在大约4.2 $ / kg的一个特定氢气价格上观察到具有电源波动的最佳装置容量的不同变化趋势,高于电力输入波动的容量增加;价格低于4.2美元/千克,由于可再生溢出,发生了相反的效果。此外,考虑到操作策略,我们发现1200 k的最佳堆叠入口温度获得与变体温度操作中的经济结果相当,并提供对控制器设计的有用指导。与以前的产能优化研究相比,在恒定操作下,该方法显示了更好的系统经济;例如,净收入分别在水电和风电输入下的HTE系统增加约29.54%和71.52%。

著录项

  • 来源
    《Journal of Energy Storage》 |2021年第8期|102733.1-102733.13|共13页
  • 作者单位

    Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China;

    Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China;

    Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China|Tsinghua Sichuan Energy Internet Res Inst Chengdu 610213 Peoples R China;

    Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China|Univ Macau Dept Elect & Comp Engn Macau Peoples R China;

    Tsinghua Sichuan Energy Internet Res Inst Chengdu 610213 Peoples R China;

    Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China;

    Tsinghua Sichuan Energy Internet Res Inst Chengdu 610213 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    High-temperature electrolysis; Capacity optimization; Operation strategy; Power source volatility;

    机译:高温电解;容量优化;操作策略;电源波动;

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