Control Issues in Robust High-efficiency Energy Conversion with Application to Renewable Energy Systems

现代可再生能源系统在其发展过程中面临着两个主要挑战：(1)生产可靠和经济能源的基础科学和技术；(2)将能源可靠、优化地转化为可用形式的能源处理技术。虽然电子技术在过去的几十年里已经完善，但它必须重新设计和适应新的挑战。为此，控制、通信和计算技术，以及新的电力电子转换器和设备，预计将在现代res中变得越来越重要。其中一个特别重要的领域是间歇性电源、负载和储能设备的稳定和鲁棒控制。特别是，开关电源变换器本质上是非线性的，受到严格的输入/状态约束，并受到各种不确定性的影响。大多数现有的控制方法使用线性化稳态模型，忽略了上述约束，或者不能用于设计间歇性能源所需的鲁棒控制器。可再生能源变换器的另一个共同特点是存在一个最优功率工作点，它依赖于可用的输入功率和负载条件。为了最大限度地利用这些转换器，功率处理阶段必须以可靠和准确的方式自主跟踪最优点。此外，可再生能源系统往往具有固有的非线性特性，具有显著的参数和动态不确定性。因此，基于线性化的设计方法可能无法提供反馈系统的全部好处，因为忽略了非线性效应。

Modern renewable energy systems (RES) face two main challenges in their evolution: (1) Fundamental sciences and technologies to produce reliable and economical energy sources; and (2) Energy processing technologies for reliable and optimal conversion of energy into usable forms. Although the electronics technology has been perfected in the past decades, it has to be redesigned and adapted to match new challenges. To this end, control, communication, and computing technologies, along with new power electronics converters and devices are expected to become increasingly important in modern RES. An area of particular importance is stable and robust control of intermittent power sources, loads, and energy storage devices. In particular, switching power converters are intrinsically nonlinear, subject to strict input/state constraints, and suffer from various uncertainties. Most existing control methods utilize linearized steady state models that neglect the above constraints or cannot be used to design robust controllers required by intermittent energy sources. Another common feature of RES energy converters is the existence of an optimal power operating point, which is dependable on the available input power and load conditions. To maximally utilize such converters, the power processing stage has to autonomously track the optimal point in a reliable and accurate manner. Furthermore, renewable energy systems often have inherently nonlinear characteristics with significant parametric and dynamic uncertainties. Hence, design methods based on linearization may fail to provide the full benefits of feedback systems due to neglecting nonlinear effects.