Distributed Control of HVDC Transmission Grids

Priority access of renewable resources such as offshore wind recommended by European energy directives, new market models and trading the electric energy among countries lead to new requirements on the operation and expansion of transmission grids. Since AC grid expansions are limited by legislative issues and long distance transmission capacity, there is a considerable attention drawn to application of HVDC transmission grids on top of, or in complement to, existing AC power systems. Potential benefits of HVDC transmission grids includes the possibility to access remote energy sources thereby increasing renewable penetration, improving grid security and decreasing congestion in the system. However, the secure operation of HVDC grids requires a hierarchical control system to manage different functions such as voltage or power flow control. In HVDC grids, the primary control action to deal with power or DC voltage deviations is communication-free and local which can be carried out by different control schemes such as DC voltage droop control. In addition to primary local actions, the higher supervisory control actions are needed to guarantee the optimal operation of HVDC grid. This thesis presents distributed control of an HVDC grid. To this end, three functions are investigated to be deployed in HVDC supervisory system; coordination of power injection set-points in the presence of large wind farms, DC slack bus selection and two-stage network topology identification. However, the implementation of supervisory control functions is linked to the arrangement of system operators; i.e. an individual HVDC operator (central structure) or sharing tasks among AC system operators (distributed structure). In this thesis, all three functions are first investigated for the central structure. As main contribution, this thesis presents the distributed solutions for the determined supervisory control applications. Furthermore, to study all aspects of proposed algorithms, a co-simulation platform is introduced. In this thesis, two different distributed algorithms based on Alternating Direction Method of Multipliers (ADMM) and Auxiliary Problem Principle (APP) are used to solve coordination of power injection. However, for distributed implementation of DC slack bus, the choice of parameters for quantitative ranking of converters is important. These parameters should be calculated based on local measurements if distributed decision making is desired. To this end, the short circuit capacity of connected AC grid and power margin of converters are considered for the evaluation of converters to work as slack bus. To estimate the short circuit capacity as one of the required parameters for selection of DC slack bus, the result of this thesis shows that the recursive least square algorithm can be very efficiently used. Besides, it is possible to intelligently use a naturally occurring droop response in HVDC grids as a local measurement for this estimation algorithm. Regarding the network topology, a two-stage distributed algorithm is introduced to use the abstract information about the neighbouring substation topology to determine the grid connectivity.