Estimating Wind Farm Transformers Rating through Lifetime Characterization Based on Stochastic Modeling of Wind Power

This paper deals with the problem of the optimal rating of mineral-oil-immersed transformers in large wind farms. The optimal rating is derived based on the probabilistic analyses of wind power generation through the Ornstein–Uhlenbeck stochastic process and on thermal model of the transformer through the integration of stochastic differential equations. These analyses allow the stochastic characterization of lifetime reduction of the transformer and then its optimal rating through a simple closed form. The numerical application highlights the effectiveness and easy applicability of the proposed methodology. The proposed methodology allows deriving the rating of transformers which better fits the specific peculiarities of wind power generation. Compared to the conventional approaches, the proposed method can better adapt the transformer size to the intermittence and variability of the power generated by wind farms, thus overcoming the often-recognized reduced lifetime

Towards realistic statistical models of the grid frequency

Increased share of renewable sources of energy in a power grid leads to larger deviations in grid frequency from the nominal value resulting in more challenging control and its modelling. In this paper we focus on the grid frequency for the power system of Great Britain because the large share of renewables makes it a template for other power grids in the future and because it exhibits peculiar statistical properties, such as long-term correlations in fluctuations, periodicity, bi-modality,and heavy tails in the distribution of the grid frequency. By modifications of the swing equation and the underlying noise statistics, which we justify qualitatively and quantitatively, we reproduce these peculiar statistical properties. We apply our model to realistic frequency response services and show our predictions outperform a standard swing equation model.Comment: accepted to TPWR

Future frequency response requirements in low inertia grids

Electricity grids around the world are undergoing tremendous transitions due to the pressing need to decarbonise. Great strides have been made in Great Britain in recent years, wind and solar penetration doubled between 2014 and 2019, but there are concerns about the effect this transition will have on grid stability. Ancillary services are the tools that electricity system operators use to help maintain grid stability. One of these, frequency response, is a service with the goal of maintaining a stable grid frequency. Grid inertia is another important property of the grid, and the level of inertia is inversely proportional to the rate of change of frequency. Traditionally, frequency response and inertia have mostly been provided by large fossil-fuel power stations. Given the situation outlined above, the aim of this thesis is to understand the frequency response requirements in low inertia grids, with a focus on the situation in Great Britain. From an analysis of historic grid data, I find that frequency volatility has increased in Great Britain in recent years and the underlying causes are likely high rates of change of demand and settlement period boundaries. The increasing penetration of renewables may have also contributed. Using the swing equation, I find that to secure the GB grid in the future, for all potential infeed losses, demand, and inertia scenarios, 1600 MW of frequency response capacity at a delay/ramp time of 0.5 s/0.5 s is needed. I find that in normal day-to-day operation, the frequency volatility does not drastically deteriorate until an inertia level around 20% of current levels (inertia from nuclear and demand only). At this low level, a significant portion of the frequency response capacity needs to be fast acting for successful mitigation. Low inertia has a much greater effect on frequency response requirements in a large infeed loss situation compared to normal day-to-day operation