Advanced Methodologies to Facilitate Wind Power Integration Studies Into Existing Power Networks

Abstract

The exponential rate of integrating renewable energy sources, especially wind farms, into existing networks-while environmentally beneficial-impacts the operation of power systems economically as well as technically. Reduction in system damping is one possible outcome of large scale wind power integration while the location of wind power sources could easily lead to long distance power transmission through congested lines-especially if the network load is assumed to be rapidly growing at different sites during different times-which may significantly change the generation profile along with the typical power flows; thus causing a considerable impact on small signal stability. Moreover, wind power cannot be scheduled with the same certainty as conventional power plants and it is not really dispatchable. Therefore, rethinking the methods of power system operation becomes a necessity. The group of generators which are most suitable for manipulation in order to make way for new energy sources, e.g., wind generators that do not provide similar support to the system are identified through a novel method for ranking synchronous generators in a power system according to their contribution to angular and voltage stability. The method is based on the sensitivity analysis of electromechanical modes and takes into account the location of generators, their inertia, active and reactive power outputs and control functions. FACTS devices are utilized to alleviate any power transmission congestion while gaining maximum financial benefits. Economical considerations take into account the cost of generated active and reactive power, the cost of wind power integration, the cost of allocated FACTS devices along with their maintenance cost for a range of operating conditions in each load growth profile. The Identification of congested areas as well as determining the financial benefits relies heavily on the Optimal Load Flow (OPF) while Genetic Algorithms (GA) is assigned the task of allocating the FACTS devices. The Net Present Value (NPV) is integrated into the GA as an objective function; thus providing a good financial assessment of the location at hand. Finally, dynamic benefits of the allocated FACTS devices-where the tuning was accomplished through adaptive control-are analyzed to confirm their consistency on the wide range operating points across the different load growth stages.

Date of Award	31 Dec 2011
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Jovica Milanovic (Supervisor)