E-Blog | ANALYTECON

Wind Generation and Demand

Wed, 22 Feb 2017 08:38:01 +1100

The subject for the next few blogs will be prices and wind generation in South Australia. South Australia may foreshadow issues for all NEM regions that are planning to significantly increase wind generation capacity. At the same time, it is important not to just focus on the intermittency of wind generation in South Australia, wind needs to be place into the context of the electricity system.

The relationship between the volatility of wind generation and wholesale electricity prices is hypothesised to revolve about three key factors. These are the level of wind generation capacity relative to:

· Electricity demand

· Local fast start generation capacity, and

· Constraints on imports and exports of power between South Australia and Victoria.

The first point is taken up later in this blog. However, it is the difference between electricity demand and wind generation, referred to as residual demand, that needs to be managed, for the most part, through the normal dispatch system. Fast start generation provides the dispatch system the flexibility to respond changes in and generation and load within a short time frame. Open cycle generally has the shortest start up times and fastest ramp rates (the rate at which output can be adjusted). South Australia’s open cycle gas turbine capacity is currently about 870MW, representing a bit less than 60 per cent of average demand and 30 per cent of peak demand.

Regional Electricity Price Update

Thu, 09 Feb 2017 16:08:42 +1100

The next instalment of the blog is still in progress but we thought it would be good to update the changing electricity price distributions for South Australia, New South Wales and Victoria.

Percentiles of the distribution are shown in each figure from August 2014, after the repeal of the carbon tax, through to February 8^th 2017. The percentiles were estimated using local quantile regression. The beginnings of the upper percentiles (the 95^th and 99^th) are influenced to some extent by the carbon tax and the endpoints should be regarded with some caution.

The escalation in the upper tail of the distribution since the start of 2016 is clear in each of the NEM regions. The fact that the shoulders of the distributions are moving away from the median as well as the tails indicates that price volatility has had a substantial impact on average prices.

The escalation of the tails in New South Wales is of the same order of magnitude as South Australia. In Victoria, it is less pronounced but there is a clear upturn that starting in late 2016

Wind Generation

Mon, 23 Jan 2017 14:23:37 +1100

We start by looking at some general characteristics of wind generation in New South Wales, South Australia and Victoria. The AEMO data are actual generation for semi-scheduled wind farms in each state at five minute intervals from September 1 2015 to 16 January 2017. Data for the 28^th through the 30^th of September 2016 were removed due to the South Australian blackout.

The data are then averaged over half hourly intervals and the standard deviation in wind generation was calculated for each interval. The standard deviation over six five minute intervals is being used as a measure of variability that occurred over half an hour, as opposed to a statistical measure of the expected dispersion of possible outcomes within that half hour. The data were then smoothed to show how wind generation evolves over time.

The smooth of wind generation is shown in Figure 14. The time span over which AEMO wind generation is available online is limited, so the smooth is over the full data range. Consequently, the beginning and the end of the smooths should be regarded with caution. Nevertheless, the similarity of the seasonal pattern in each state is very clear. The differences in wind generation in each region are due, in large part, to installed capacity. Further, one additional generator came on line in South Australia in late June 2016, adding 102 MW of capacity which is also evident in the figure.

Is SA Causing Wider Spread Price Volitility

Mon, 09 Jan 2017 13:05:21 +1100

The increase in prices and price volatility observed in NSW and Victoria raise the question whether the source of this change is attributable, to a substantial extent, to South Australia. From a statistical perspective, causality is a difficult issue to address in a complex system. To start, there is need for a supporting physical explanation for the hypotheses to be tested, which cannot be offed here. However, statistical evidence of causality can be a necessary if not sufficient condition in establishing causality.

To consider causality, we need to depart from the visualisation approach that has been adopted so far. Granger causality is a relatively easy to understand statistical test of causality, which is based on temporal correlation (see https://en.wikipedia.org/wiki/Granger_causality). The idea is that what is happening, say in NSW, can be predicted by what has happened recently in that region. If what has happened recently in South Australia adds significantly to the quality of that prediction, then there is evidence of cause and effect.

Price Volatility in Other NEM Regions

Mon, 02 Jan 2017 13:03:45 +1100

While an increase in the variability of demand in South Australia may be a very unlikely source of the observed increase in the variability and consequent increase in prices in South Australia, there are other candidates. These include:

· the natural variability in dispatched wind generation in South Australia;

· transmission constraints in and out of South Australia; and

· variability in market conditions in other NEM regions that also affect South Australia.

Before looking look directly at the variability of dispatched wind generation in South Australia it is interesting to look at what has been happening in other states for a few reasons.

First we can see whether the problem is unique to or predominantly occurs within South Australia, or whether it is symptomatic of a wider problem across the Eastern Seaboard. This approach goes beyond addressing the third possible source. Wind generation accounts for a much larger proportion of dispatch in South Australia than it does in the other regions, so if the variability of wind generation is a driver of price variability, it should predominantly manifest in South Australia. Second, if price volatility is unique to South Australia, then it would be reasonably clear that whatever its source, the increased price variability it is not being passes through to the other regions. Third, if there is a similar pattern of prices and prices variability in other regions, this raises a critical question whether the problem affects the Eastern Seaboard as a whole, or whether what is happening in South Australia is impacting on other states.

South Australian Electricity Demand

Wed, 21 Dec 2016 19:53:52 +1100

In our previous blog we looked at surging electricity prices, particularly in South Australia. One possible reason is an increase in the volatility of operational demand; i.e. electricity drawn from the grid by consumers, but excluding electricity generated from unmeasured source such rooftop solar systems and other small-scale generating sources.

Volatility in the output from these unmeasured sources, which has been increasing in absolute and relative terms, would lead to greater demand volatility, which may in turn impact on energy prices in the spot market.

We can look at the trend in the level and volatility of operational demand in the same way we looked at prices using an optimised smooth. The trend in South Australian electricity demand is shown in Figure 6. The figure shows a seasonal component, but also a steady decline in trend, which, at least in part, would be due to an increase in the deployment of rooftop solar systems.

Figure 6. The trend in electricity demand in South Australia: 15 October 2012 through 15 October 2016

Prices and Price Volatility

Sat, 17 Dec 2016 07:24:25 +1100

We start by smoothing out the variability in prices to obtain a trend in average prices in South Australia over the last four years which is shown in Figure 1. The smooth is an estimate of a conditional mean, or the average price at particular point in time. As with all estimates it is subject to error and the 95 per cent confidence bounds for the estimate are also shown in the figure. As the beginning and end of the smooth are not strongly support by the data, two months of data or approximately 2,900 observations are removed from the beginning and the end. So the data used in the smooth runs from mid-August 2012 to through mid-December 2016 but is shown in the graphs for a shorter period. Nevertheless, the beginning and the end of the smooths shown in the figures should still be viewed as being dependent on the period shown.

The idea behind the smooth is to show the underlying trend in prices at a meaningful time scale while retaining a reasonable degree of fidelity with the underlying variation in the prices over time. The impact of the carbon and tax and its repeal, a strong seasonal component and the surge in prices in 2016 are all quite evident. At the same time, hourly, daily and weekly variability is not.