AI 中文总结
提出一种基于滚动时域优化的虚拟电厂中央控制器，在日前和实时层面分配功率与备用，以最大化收益并支持电网频率恢复，仿真验证了其在预测不确定性下的有效性。
AI 中文摘要
虚拟电厂（VPP）是提高电力系统中可再生能源（RES）渗透率的有效解决方案。此外，如果发电机和负载紧密连接并适当协调，虚拟电厂可以提供辅助服务。这一方面已在多项研究中得到探讨，但大多数研究集中在调度优化层面，实时运行方面的问题仍未解决。为弥补这一空白，本文提出了一种基于滚动时域优化的虚拟电厂中央控制器。其主要目标是在提供频率恢复服务的同时最大化收益。日前优化首先定义虚拟电厂的功率设定点以及向上和向下备用容量。然后，每分钟执行一次的滚动时域优化器在虚拟电厂各单元之间分配功率和备用参考值，以实现其紧密跟踪，同时考虑资源可用性（风速、辐照度等）、运行约束和电池退化。控制器的有效性在具有不同可再生能源发电和需求曲线的两区域互联电力系统中进行了测试。仿真在MATLAB/Simulink中进行，并包含虚拟电厂元件的详细动态模型，证明了该算法在实际系统中的适用性。日前和滚动时域优化问题使用YALMIP建模，并使用Gurobi求解。所得结果表明，即使在存在预测不确定性的情况下，虚拟电厂也能通过优化分配其资源来支持电网频率恢复。
英文摘要
Virtual power plants (VPPs) are an effective solution for increasing the penetration of renewable energy sources (RES) in power systems. Moreover, VPPs can provide ancillary services if generators and loads are closely connected and properly coordinated. This aspect has been addressed in several studies, yet they mostly focus on the dispatch optimisation level, leaving real-time operation aspects unresolved. To close this gap, a central controller for VPPs based on a rolling-horizon optimisation is proposed in this work. Its main objective is to maximise revenues while delivering frequency restoration services. A day-ahead optimisation firstly defines the power setpoint and the upward and downward reserves of the VPP. Then, the rolling-horizon optimiser, executed every minute, distributes the power and reserve references between the VPP units to achieve their close tracking while taking into account resource availability (wind speed, irradiance, etc.), operational constraints and battery degradation. The effectiveness of the controller is tested using a two-area interconnected power system under different RES generation and demand profiles. The simulations are performed in MATLAB/Simulink and include detailed dynamic models of the VPP elements, demonstrating the applicability of the algorithm to real systems. The day-ahead and the rolling-horizon optimisation problems are modelled using YALMIP and solved using Gurobi. The obtained results demonstrate the VPP supporting grid frequency restoration by optimally allocating its resources, even under the presence of forecast uncertainty.
CommentsSubmitted to a scientific journal for possible publication. 10 pages, 11 figures
