发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对住宅能源枢纽中热能储存短期运行价值下降问题，提出数据驱动季节性高速公路框架，通过动态终端集连接季节与日常优化，实现最优容量配置与运行，兼顾成本、电池寿命和热舒适性。
AI 中文摘要
具有多种能源载体（电力、热能、移动性）的住宅能源枢纽的运行因其在时间常数、往返效率和自放电率方面的储能差异而构成重大挑战。通常，热能储存在年度规划优化或长期情景中表现出灵活性。然而，随着优化时域缩短（1-48小时），由于较低的往返效率，其提供的价值也随之降低。为避免运行期间这种早期耗尽，本文提出一种数据驱动的高速公路，将短期日常控制引导向长期最优性。所提出的方法还展示了如何最优地配置热能储存规模并避免年度模拟，以及这一切如何与日常运行中的非线性相关联。所提出的框架通过动态终端集和值函数将季节性与日常优化联系起来。具有季节性意识的非线性经济模型预测控制器实现了最均衡的性能，在所有MPC中平均电网成本排名第二，为-€209。它还在电池退化控制方面优于其线性对应物（提升26-34%），并在非线性基准中实现了最佳热舒适性。然而，数据驱动的季节性高速公路限制了控制电池退化的能力，并略微增加了计算时间。
英文摘要
The operation of residential energy hubs with multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to the energy storage differences in time-constants, round-trip efficiencies and self-discharge rates. Usually, thermal storage exhibits flexibility in yearly planning optimizations or long-term scenarios. However, as optimization horizons shrink (1-48hs) so does their supplied value due to the lower round-trip efficiencies. To avoid this early depletion during operation this paper proposes a data-driven highway to steer the short-term daily control towards long-term optimality. The proposed methodology also presents how to optimally size the thermal storage and avoid yearly simulations and how all of this is related to nonlinearities in the daily operation. The presented framework links seasonal and daily optimizations through dynamic terminal sets and value functions. The seasonally-aware nonlinear economic model predictive controller achieves the most balanced performance, with the second best mean grid cost of all MPCs at -\texteuro 209. It also achieves better battery degradation control than its linear counterparts (between 26-34%) and the best thermal comfort of the nonlinear benchmarks. Nevertheless, the data-driven seasonal highway restrains the ability to control battery degradation and slightly increases computational time.
