Planning, Management and Control in Large-Scale Systems: Enabling the Integration of Intermittent Energy Sources

智力价值：可再生能源在电力系统中的高渗透率是清洁和可持续发电愿景的主要组成部分。然而，目前可再生能源发电的主要目标是风力发电。这些能源的间歇性以及一次能源风能高可用性的位置与负荷中心的位置不一致是必须解决的主要挑战。为了成功地整合风力发电，我们必须找到一种方法，有效地平衡这种间歇性，并将电力传输到负载，而不需要对环境不友好的后备发电和输电系统的大幅扩展。在这个项目中，通过提出一种新的分布式预测控制算法来解决这些挑战，该算法用于间歇能源与存储、需求控制和备用发电的协调。拟议的预测控制可最大限度地利用现有能力，目的是最大限度地减少对后备发电的总体使用，同时减少其产出的变化，并最大限度地减少所需的需求控制。考虑到电力系统的控制在几个实体之间共享，以分布式方式进行控制。此外，问题是需要多少存储和后备发电能力以及负载灵活性，才能在给定的间歇可再生发电渗透率水平下可靠地运行电力系统，反之亦然。这是未来电力系统规划的一个基本问题。提出了一个基于随机规划的系统工具来回答这个问题，并对间歇发电穿透的可行性进行了估计。广泛的影响：由此产生的问题规模和涉及的计算量或共享的控制结构到目前为止往往阻碍了预测控制在大规模系统中的应用。该分布式预测控制具有计算量小、收敛速度快、无需参数整定等特点，适用于大系统，优于其他分布式预测控制方法。尽管分布式预测控制方法是为电力系统提出的，但其数学框架也适用于许多其他大规模问题，为预测控制打开了通往其他大系统的大门。该项目的成果将包括在现有课程的内容中，为学生提供获取研究成果的途径。

Intellectual Merit: A high penetration of renewable energy sources in the power system is a major component of the vision for a clean and sustainable electric power generation. However, the main target in terms of renewable generation is currently on wind generation. The intermittency of these sources as well as the fact that the locations of high availability of the primary energy source wind do not coincide with the locations of the load center are major challenges which have to be resolved. For a successful integration of wind generation, we must find a way to balance this intermittency effectively and to transmit the power to the loads without the need for environmentally unfriendly backup generation and a substantial extension of the transmission system. In this project, these challenges are addressed by proposing a new distributed predictive control algorithm for the coordination of the intermittent energy sources with storage, demand control and backup generation. The proposed predictive control allows for an optimal utilization of the available capacities with the objective to minimize the overall use of backup generation but also the changes in its output as well as minimizing the required demand control. The control is carried out in a distributed way taking into account that control of the power system is shared among several entities. In addition, the question is asked how much storage and backup generation capacity and load flexibility is needed for a reliable operation of the power system with a given level of intermittent renewable power generation penetration and vice versa. This is a fundamental question for the planning of the future power system. A systematic tool based on stochastic programming is proposed to answer this question and to give an estimate for the feasibility of the intended intermittent generation penetration.Broader Impacts: The resulting problem size and the involved computation effort or the shared control structure so far often prevented an application of predictive control to large-scale systems. The features of the proposed distributed predictive control are low computational effort, fast convergence and no parameter tuning which make this control algorithm applicable to large-scale systems and superior to other distributed predictive control methods. Even though the distributed predictive control method is proposed for power systems, the mathematical framework is applicable to many other large-scale problems opening the door for predictive control to other large scale systems. The results of this project will be included in the content of existing courses providing access to research results for students.