Abstract
Wind energy has become the world’s fastest growing source of clean and renewable energy and
now contributes a large proportion of total power generation. This proportion will continue to
increase because of the global preference for a clean and renewable energy source. However,
wind power is difficult to integrate into traditional generation and distribution systems with current
technology because it is intermittent, unpredictable and volatile. Thus, it is difficult to match
wind generation to energy demand, and the imbalances between demand and generation can cause
adverse voltage variations. This power quality problem cannot be solved effectively only by renewable
generating technology and/or power electronics. The adverse effects of wind generators
on power quality are currently an important issue. As a whole, wind power integration challenge
the power quality, energy planning and power flow controls in the grid. This can be more severe
in weak networks, where the whole wind power source may even be disconnected from the grid
as an extreme case.
In this thesis, we apply wind power prediction, battery energy storage and concepts and ideas
from Model Predictive Control (MPC) theory to make wind power more attractive and reliable for
power utility companies. The research consists of the following steps.
We have developed a wind power prediction model that allows accurate prediction 10 minutes
ahead. This is combined with a direction-dependent power curve model, which provides significant
improvement in the accuracy of predictions. We have developed a wind power smoothing model, based on a Battery Energy Storage System (BESS), the above prediction model and MPC,
to suppress the output power fluctuations of a wind farm, thus allowing smoother operation of the
power grid and improved power quality.
The concept was further developed to include power frequency regulation, and to optimise
BESS capacity. This model supported excellent control of power quality with a reduced BESS
requirement by distributing the BESS capacity efficiently. Our control system also ensures more
careful regulation of BESS charging/discharging rates to allow longer battery life. It thus reduces
the capital cost of the BESS system and extends its operational life, reducing the total capital cost
of wind power generation.
While our models were developed for wind power and battery storage, they could be applied
equally effectively to other generation and storage technologies.