Advanced control strategies for stability enhancement of future hybrid AC/DC networks

Musa, Aysar A Aydan; Monti, Antonello (Thesis advisor); Moser, Albert (Thesis advisor)

1. Auflage. - Aachen : E.ON Energy Research Center, RWTH Aachen University (2019)
Book, Dissertation / PhD Thesis

In: E.ON Energy Research Center : ACS, Automation of complex power systems 72
Page(s)/Article-Nr.: 1 Online-Ressource (ix, 175 Seiten) : Diagramme

Dissertation, RWTH Aachen University, 2019

Abstract

The continuous integration and deployment of renewable energy systems, alongside the increasing interest in international power exchange have brought a great interest into HVDC systems to play as the energy hub for bulk power transfer in future hybrid interconnected ac/dc networks. This constitutes a key step in the development of future energy infrastructure towards sustainable and affordable energy. However, the resulting ac/dc complexsystems pose critical challenges to system operation, control and dynamic performance. This is due to inherent system nonlinearities, likely external disturbances and low-inertia system operation. In this regard, advanced and robust control strategies are of importance to tackle such system challenges. The goal is to achieve reliable system operation, enhanced system stability and dynamic performance, robust and consistent control performance against external disturbances and system parameter changes, and adaptive participation of HVDC-connected ac grids in providing frequency support to the disturbed ac grid. In this work, a comprehensive model of hybrid ac/dc network is developed based on three main subsystems: onshore ac grids, multi-terminal HVDC (MTDC) grid, and offshore wind farms. The aim is to develop a sophisticated model as a base for a precise study and validation of proposed control strategies. These control strategies are classified according to their role and application. For MTDC grid, the Predictive Sliding Mode Control (PSMC) and Improved Synergetic Control (ISC) are proposed. The voltage-power droop mode is introduced in the control of grid-tied HVDC converters for the purpose of wind power sharing among the onshore ac grids. To achieve optimal control performance, the Particle Swarm Optimization method is used to search for the optimal control parameters. The proposed PSMC and ISC fulfill the objectives of damped and enhanced transient performance, adequate wind power sharing, and control robustness against external disturbances and system parameter changes. According to the European network codes on HVDC connection, HVDC systems are expected to participate in system frequency support. This can be done by redirecting a fixed amount of active power from the HVDC-connected ac grids to support the affected (disturbed) grid. However, the role of HVDC systems with respect to the participation mechanism is not explicitly defined for future low-inertia (weak) ac grids, for which every ac grid will likely have different power reserve, demand, technical characteristics and constraints. In this regard, this dissertation proposes innovative frequency control strategies for grid-tied HVDC converters, named Multi-Agent-based Intelligent Frequency Control (MA-IFC) and Linear Swing Dynamic-based Virtual Synchronous Generator (LSD-VSG). The aim is to enable the weak and stiff ac grids to provide intelligent and adaptive frequency support without compromising their local frequency stability. This results in a systematic enhancement in frequency stability, particularly in the disturbed and weak ac grids. Also, to define new role and behavior for HVDC systems in supporting and strengthening of ac grids, based on LSD concept. Comprehensive test scenarios are conducted on hybrid ac/dc network to validate the proposed control strategies. The simulation results proved the effectiveness, superior robustness, and enhanced performance of the proposed control strategies in comparison with classical schemes.

Institutions

• E.ON Energy Research Center [080052]
• Institute for Automation of Complex Power Systems [616310]