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Agent-based dynamic optimization of local controller configurations in converter dominated electricity grids using decoder functions

机译：使用解码器功能在基于转换器的电网中基于代理的动态优化本地控制器配置

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With an increasing share of distributed energy resources (DER) in the electrical energy system it is becoming more and more important that DER not only take part in active power provision but are also involved in the provision of ancillary services like frequency control or voltage regulation. Due to the large number of DER connected to the lower voltage levels via power-electronic converters the distribution grid evolves from a formerly mostly passive to a highly active system with a high number of actuating variables distributed over multiple stakeholders. The coordination and optimization of this kind of distribution grid requires new control and optimization approaches, not only with regard to the distribution grid itself, but also with regard to the coordination with the overlying transmission grid. This abstract presents first ideas of a PhD-project that aims to use machine learning surrogate models and decoder functions for agent-based dynamic optimization of local controller configurations particularly with regard to voltage regulation. Decoder functions derived from machine learning surrogate models abstract optimization problems from technical system specifications and allow for constraint-free optimization with standard optimization heuristics such as evolutionary optimization methods.
机译：随着电力系统中分布式能源（DER）份额的增加，DER不仅参与有功功率的提供而且还参与诸如频率控制或电压调节之类的辅助服务的变得越来越重要。由于大量的DER通过电力电子转换器连接到较低的电压水平，因此配电网从以前的大部分为无源系统演变为具有许多调节变量分布在多个利益相关者上的高度活跃的系统。这种配电网的协调和优化要求新的控制和优化方法，不仅涉及配电网本身，而且还涉及与上层输电网的协调。本摘要介绍了一个博士项目的第一个想法，该项目旨在使用机器学习代理模型和解码器功能对基于代理的本地控制器配置进行动态优化，尤其是在电压调节方面。从机器学习代理模型派生的解码器功能从技术系统规范中抽象出优化问题，并允许使用诸如进化优化方法之类的标准优化试探法进行无约束优化。

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