Data-Driven Predictive Control for Ensuring Grid Security with High Penetration of Wind Energy

数据驱动的预测控制确保风能高渗透电网安全

• 批准号: 2767369
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2767369
• 关键词: Data; Driven; Predictive; Control; Ensuring

In order to achieve net-zero carbon emissions in the UK, electricity generation must complete the transition from fossil fuel-based production to Renewable Energy Sources (RES). The resulting increase in penetration of RES makes operation of the power grid more challenging. Reliance on the weather for wind and solar generation introduces uncertainties in supply capability. In traditional power plants, the inertia of rotating turbines helps maintain grid stability. However, RES use power electronics known as inverters to provide the required voltage and frequency. Inverters do not provide inertia to the power grid which makes it more susceptible to instability. The planned research is focused on addressing the challenges of grid stability and of matching supply to consumer demand through the development of novel control approaches and network architectures.A new paradigm known as direct data-driven control has begun to gain traction in the research community. System inputs are found directly from gathered data rather than by estimating an approximate model using data and then determining the optimal inputs using model predictions of system behaviour. This results in theoretical improvements in performance.To date, only a small number of direct data-driven control strategies applied to power system scenarios have been simulated to the author's knowledge. Potential applications for this approach range from controlling signals in power converters to managing a wind farm. The proposed research aims to assess the suitability of direct data-driven methods for controlling power networks and to ensuring grid stability with a high penetration of RES. The research plan involves carrying out simulations of power networks managed by a data-driven controller. Multiple network scenarios will be simulated to assess the suitability of the approach and the optimal controller design. The research aims to improve understanding of which methods may best cope with uncertainties and nonlinearities in the system as this is currently an open question. A further goal is to test the direct data-driven control strategy on a small-scale microgrid within a laboratory setting.One marker of the net-zero transition is the increasing prevalence of distributed electricity generation through local, small-scale production methods such as roof-mounted solar cells. This offers greater operational flexibility compared to the traditional centralized approach but requires a significant improvement in control and communications technology to take full advantage of its benefits.A concept known as the holonic approach has emerged that may deliver such an improvement. A holonic network is composed of holons forming a holarchy; it is analogous to a hierarchy. A holon is both part of a system and a system in itself; a smart home manages power demands within a home whilst existing as an element of the district power grid. The holarchy is able to adapt during operation to balance competing demands and manage network faults. Limited research has been published concerning such an implementation for power networks and almost no research has been published to the author's knowledge regarding the ability of holons to automatically adapt to changes in the network.The proposed research aims to address these gaps and develop the approach towards an implementable architecture for managing power networks. A further aim is to develop theory outlining the fundamental behaviour of a holarchy that could be used in designing a holarchy for specific use cases. The direct data-driven control and holonic approaches could be combined by using operational data to optimize the network structure and determine appropriate control inputs.Both direct data-driven control and the holonic approach show potential to support the development of today's power networks to the networks of the future that will deliver a net-zero power grid; a crucial part of the transition to a sustainable global e

为了在英国实现净零碳排放，发电必须完成从化石燃料生产到可再生能源（RES）的过渡。可再生能源渗透率的增加使电网的运营更具挑战性。风能和太阳能发电对天气的依赖给供应能力带来了不确定性。在传统发电厂中，旋转涡轮机的惯性有助于保持电网稳定性。然而，RES使用称为逆变器的电力电子设备来提供所需的电压和频率。逆变器不向电网提供惯性，这使得电网更容易不稳定。计划中的研究重点是通过开发新的控制方法和网络架构来解决电网稳定性和使供应与消费者需求相匹配的挑战。一种称为直接数据驱动控制的新范式已经开始在研究界获得关注。系统输入直接从收集的数据中找到，而不是通过使用数据估计近似模型，然后使用系统行为的模型预测来确定最佳输入。迄今为止，据作者所知，只有少量的直接数据驱动的控制策略应用于电力系统的情况下进行了模拟。这种方法的潜在应用范围从控制功率转换器中的信号到管理风电场。拟议的研究旨在评估直接数据驱动的方法控制电力网络的适用性，并确保电网的稳定性与高渗透率的RES。研究计划涉及进行模拟由数据驱动的控制器管理的电力网络。将模拟多个网络场景，以评估该方法的适用性和最佳控制器设计。该研究旨在提高对哪些方法可以最好地科普系统中的不确定性和非线性的理解，因为这是目前一个悬而未决的问题。另一个目标是在实验室环境中测试小规模微电网上的直接数据驱动控制策略。净零过渡的一个标志是通过屋顶安装太阳能电池等本地小规模生产方法进行分布式发电的日益普及。与传统的集中式方法相比，这提供了更大的操作灵活性，但需要在控制和通信技术方面进行重大改进，以充分利用其优势。合弄网络是由形成合弄结构的合弄组成的;它类似于层次结构。霍隆既是系统的一部分，又是系统本身;智能家居管理家庭内的电力需求，同时作为地区电网的一个元素存在。全息结构能够在运行期间进行调整，以平衡相互竞争的需求并管理网络故障。有限的研究已经发表了关于这样的实施电力网络和几乎没有研究已经发表了作者的知识，关于合弄的能力，自动适应网络中的变化，拟议的研究旨在解决这些差距，并制定了一个可实施的架构管理电力网络的方法。另一个目标是开发理论，概述全息结构的基本行为，可用于为特定用例设计全息结构。直接数据驱动控制和子整体方法可以通过使用运行数据来优化网络结构并确定适当的控制输入。直接数据驱动控制和子整体方法都显示出支持今天的电力网络向未来网络发展的潜力，未来网络将提供净零电网，这是向可持续的全球电力网络过渡的关键部分。