Strategies to save energy costs

Electricity Pool Pass-through and Demand Response Results for Q1 2016

Electricity Pool Pass-through and Demand Response Results for Q1 2016

In my previous articles on electricity pool pass-through and Demand Response (see articles for SA,QLDNSW and VIC) I presented historical pool price data over the last two years and compared that with indicative retail pricing to indicate the size of the cost saving opportunity.

In this article I present how a pool pass-through plus demand response strategy would have performed for different load profiles in the first quarter of 2016 for the mainland States. I have excluded Tasmania due to the interconnector failure but that case does highlight that sometimes the worst-case scenarios can manifest themselves.

Different load profiles will result in substantially different electricity costs for different businesses. A business that runs predominantly during peak periods will obviously incur a higher average unit cost than one that runs through both peak and off peak periods. A business that can run exclusively through off peak periods will be able to achieve significantly lower unit costs.

For most businesses labour costs trump electricity costs when determining what hours to run the major loads. Some businesses also have licence conditions that restrict operating hours to particular times. Most of the clients that I deal with have one of four main load profiles:

  1. Profile A – 24 hour per day, 7 days per week
  2. Profile B – Mon – Fri, 24 hours per day
  3. Profile C – Mon – Fri, 16 hours per day
  4. Profile D – Mon-Fri, 8 hours per day

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The first quarter of 2016 was interesting in terms of pool price outcomes. Queensland continued to experience a significant number of price spikes that have been the pattern for the last two summer periods. South Australia continued to have the same price outcomes as it has experienced over the last 18-months despite the retailers setting Q1 pricing at post Northern Power Station closure pricing. NSW and Victoria both experienced an increase in average pricing for the Q1 period as would be expected.

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When analysing the opportunities for savings with a pool pass-through and demand response strategy, the opportunity is dependent on the best fixed retail pricing that the business can negotiate. For this analysis I have aggregated some actual retail pricing that some customers are experiencing to construct an indicative retail price for each region. As prices vary considerably between customers this retail pricing should be considered as indicative for illustrative purposes only.

If a business were exposed to pool pricing from 1 January this year it would have achieved the following outcomes by region and load profile.

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All of the above outcomes assume that load was not curtailed during high price events. It is not surprising that this strategy alone would have been costly for a business in Queensland in Q1 irrespective of load profile due to the very large number of price spikes during the Quarter.

Retail pricing is generally set at a flat level for a 12-month period. Typically Q1 pool pricing is much higher than other periods due to hot temperatures and the retailer balances these higher prices with lower expected prices for the remainder of the year. An assessment of the strategy needs to look at a full 12-month period, or multiples of years. However, if monthly cash flow is important to a business then this needs to be taken into consideration.

Despite Q1 typically having higher spot pricing, a pool pass-through strategy would have resulted in a small gain in NSW and a modest loss in VIC in Q1.

The major opportunity was in South Australia. Many customers signed up to contracts that had pricing set at post Northern Power Station closure prices. Whilst it is most likely that prices will increase post closure they are not expected to increase to the extent that the retail prices have currently factored in (see discussion here on likely price increases). Customers are paying the full risk premium of price uncertainty and the retailers have fully covered their own risk.

If the businesses with the four different load profiles were willing and able to curtail load during high price events then they would have achieved a much better outcome. In this analysis we assume that all high price events are forecast (which they are not) and the operations staff curtailed load to zero perfectly (which they do not). We will assume that the businesses curtail load at a spot pricing of $1,000/MWh.

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Curtailing load at $1,000/MWh results in a significant improvement in savings in Queensland but results in a day’s lost production over the full quarter for Load profiles A, B and C. Victoria had a modest improvement in savings for very little down-time as did South Australia. NSW only improved marginally as they had just one price spike.

The analysis assumed a modest load of 1 MW. For a large industrial load of, say, 20 MW running continuously in South Australia the savings for the Quarter would have been more than $2m. In NSW and QLD the savings would have been $175k for the Quarter and in Victoria the savings would have been $35k.

The analysis shows that a pool pass-through strategy combined with Demand Response can deliver savings versus fixed retail pricing. In only two scenarios (NSW and VIC Load D) did the strategy result in a very small increased cost. Given that the period examined was the historically higher price Q1 period then those increased small costs would expect to flip to modest savings for the full year.

There are costs and risks associated with a pool pass-through and Demand Response strategy that are not included in the analysis. These include:

  1. Historical prices do not guarantee future prices – there will be annual volatility
  2. The retailer will charge a management fee to provide the pool pass-through service
  3. Not all high price events are forecast – some will occur during or toward the end of the half-hour period and can only be partially avoided
  4. Operations staff may miss taking action on high price events as training, processes and procedures are not well locked in
  5. Modest annual software and data charges for pool price monitoring and alerts

The successful implementation of a pool pass-through and Demand Response strategy relies on minimising the costs and risks of the above. Altus Energy specialise in assisting businesses to develop and implement pool pass-through and Demand Response systems, procedures and training to ensure implementation success and maximise savings.

For assistance contact us at This email address is being protected from spambots. You need JavaScript enabled to view it.  or through our website atwww.altusenergy.com.au.

Pool Pass-through and DSM Opportunity and Risks in Victoria

Pool Pass-through and DSM Opportunity and Risks in Victoria

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This is the last of an initial series of articles about the opportunities for end users in the mainland regions of the National Electricity Market to save costs by purchasing electricity at pool prices and adopting a Demand Response strategy to save more. The previous articles can be found through the following links for South Australia, Queensland and New South Wales. This week we have a look at the opportunity in Victoria.

Table 1 shows that VIC along with NSW have been the two regions that have consistently had the benefit of relatively low and stable prices over the last, almost two, decades.

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Table 1 – Financial Year Average Annual Spot Prices by Region

Source: http://www.aemo.com.au/Electricity/Data/Price-and-Demand/Average-Price-Tables/Average-Price-Tables-Annual

Victorian spot prices are far less volatile than in South Australia and Queensland as shown below in Figure 1 with very few spikes over the last 26-month period. In fact, over the 2014-15 period there were only 8 price spikes above $1,000, four of them lasting only half an hour, three of them lasted 1 hour and one lasted an hour and a half. Most of these occurred in January 2014. There was one price spike above $1,000 in the first two months of 2016 that lasted for one hour.

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Figure 1 – VIC Half Hour Electricity Spot Prices January 2014 – February 2016 Data Source: NEM-Review

In theory, most of the time the spot price is meant to reflect the short run marginal cost of generation (variable cost) and the price spikes are a feature of the market that both helps to increase the average price towards the long run marginal cost of generation (capital cost recovery + fixed cost recovery + variable cost) and also send an investment signal to the market for new generation. In the case of VIC the market signals (price spikes) are saying no new investment in generation is required.

Having a closer look at the data, Figure 2 shows a Box Plot of the distribution of half-hourly spot prices in VIC from Jan-14 to Feb-16. As the potential range of spot prices spans nearly $10,000 but most price data points are below $60, the outliers are omitted from the graphical display. They are however represented in the average prices shown in red. If outliers are present they will move the average well above the median price and sometimes the whole box range.

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Figure 2 – Monthly Distribution of VIC Half-hour Spot Prices (Data Source: NEM-Review)

The graph shows a few interesting features:

  1. As expected, average prices fell after the repeal of the carbon tax but not by as much as they initially went up by (see Table 1). This may have been a hangover of some longer term bilateral contracts or hedges and this feature has applied across the NEM.
  2. The half-hour volatility increased after the repeal of the tax.
  3. Average monthly prices increased in the second half of 2015 but not necessarily due to price spikes. The median prices increased in the second half.
  4. In December 2015 and January 2016 price spikes did drag the average up above the median.

One would expect that a customer going to the market in very late 2015 for fixed retail prices for 2016 would have seen higher prices than those that went to the market in early 2015. As previously stated, timing is critical when going to the market for pricing.

Over the 12-month period from March 2015 to February 2016 the following price outcomes were experienced (Peak is defined as Mon-Fri 7am – 11pm):

Peak                Average price $44.78/MWh                        Median price $37.99/MWh

Off-Peak          Average price $28.42/MWh                        Median price $27.23/MWh

Flat                 Average price $36.20/MWh        Median price $33.66/MWh 

A consumer with little ability or desire to curtail load during high price events would need to compare the price offers they receive from retailers against the above price outcomes. If the prices are close then there is little incentive to switch to a pool price exposure strategy. If there is a difference that equates to tens or hundreds of thousands of dollars a year then a switch to a pool price exposure strategy with or without demand response would be attractive. Either way, it is important to analyse what the market is doing before going to the market for pricing.

How much better off would a consumer with the ability and willingness to curtail load during high price events be than one who could not curtail?

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Table 2 – VIC Price Frequency Analysis March 2015 – February 2016 (Data source: NEM-Review)

Table 2 shows the frequency of different threshold price intervals over the period from March 2015 to February 2016 and the corresponding average price that would have been achieved if load were curtailed at that level.

The number of half-hour periods exceeding $100/MWh (10 c/kWh) was 214 for the 12-month period. Had a 1 MW flat load profile business been willing and able to curtail load prices exceeded $100 they could have saved up to 7.4%* in cost versus the spot price and achieved an average price of $33.53/MWh (3.35 c/kWh) for a saving of $23,000 per year.

For a small business this may be a significant saving. In reality a business of this size with the ability to curtail would be very unlikely to have a flat load profile (24-hours a day, 7-days a week). A large number of clients that I work with have a normal operational period of 16 hours per day over day-shift and afternoon-shift on predominantly weekdays. If we apply the same analysis as above but from 7:00am to 11:00pm (16 hours operation during peak periods) the savings are even greater.

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Table 3 – VIC Price Frequency Analysis March 2015 – February 2016 (Data source: NEM-Review)

7:00am – 11:00pm On Weekdays

Table 3 shows that the business that operates at 1 MW load for 16-hours per day on weekdays during peak periods would be exposed to a higher average price but the savings of $44,873* (11.4%) with a curtailment strategy would have been significant for that size business. The downside would have been 186 curtailment events equivalent to 93 hours over the year.

A similar sized business that did not have the same ability to curtail load could have chosen $1,000/MWh as the curtailment price threshold. This would have resulted in four half-hour curtailment intervals and a saving of $21,000 (5.4%) versus the spot price.

A much larger business with a 10 MW load would save ten times that amount.

In VIC the opportunities for savings using a pool price pass-through and DSM strategy will depend on the customer’s load profile, the ability to curtail load and the competiveness of the retailer offers.

If your retail price offers sits above the more recent pool historical pricing or you are interested in investigating how a pool/DSM strategy may reduce costs and wanting to get a better understanding of the opportunities and risks with your particular load profile then contact me at This email address is being protected from spambots. You need JavaScript enabled to view it. or through the Altus Energy website.

Notes:

* This analysis assumes that all high price events were forecast and the user was able to curtail immediately on the commencement of the half-hour period. Some high price events are not forecast prior to the half-hour period and occur once the half-hour has commenced. This may be due to unexpected events such as a power plant failure. The 5-minute increment within the half-hour period when the price first spikes will determine how effective curtailment will be. For example, if the spike occurs in the last 5-10 minutes curtailment will not be effective at significantly reducing prices as the load has been run for most of the half-hour.

Pool Pass-through and DSM Opportunity and Risks in New South Wales

Pool Pass-through and DSM Opportunity and Risks in New South Wales

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Last month I wrote about what happened with pool prices over 2014-15 in South Australia and Queensland and the opportunity for savings with pool price pass-through strategy and DSM. This week we will have a look at the opportunity in NSW based on 2014-15 historical data.

Table 1 shows that NSW and VIC have been the two regions that have consistently had the benefit of relatively low and stable prices over the last, almost two, decades.

Image1

Table 1 – Financial Year Average Annual Spot Prices by Region

Source: http://www.aemo.com.au/Electricity/Data/Price-and-Demand/Average-Price-Tables/Average-Price-Tables-Annual

The relatively lower NSW pricing is explained by the fact that there is a larger market, more generator competition, a large supply of lower short-run marginal cost black coal generation and a less “peaky” market demand than SA all culminating in less price spikes driving the average price up above the median price.

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Figure 1 – NSW Half Hour Electricity Spot Prices January 2014 – February 2016

Data Source: NEM-Review

Figure 1 shows that in contrast to SA and QLD there are very few high price spikes. In fact, over the 2014-15 period there were only 5 price spikes above $1,000, three of them lasting only half an hour and 2 lasted 1 hour. There was one price spike above $1,000 in the first two months of 2016.

Once again, a good way to visualise the distribution of prices within each year is to represent them on a box plot. A box plot is a way of displaying the distribution of data based on a five number summary: minimum, first quartile, median, third quartile, and maximum. In the box plot the central rectangle spans the first quartile to the third quartile (the interquartile range or IQR). A horizontal line inside the rectangle shows the median and “whiskers” above and below the box show the locations of the minimum and maximum.

Image3

Figure 2 – Diagrammatical Representation of a Box Plot

Source: http://www.physics.csbsju.edu/stats/box2.html

Data will usually display surprisingly high maximums or surprisingly low minimums called outliers. Outliers are defined as either 3×IQR or more above the third quartile or 3×IQR or more below the first quartile. Suspected outliers are slightly more central versions of outliers: either 1.5×IQR or more above the third quartile or 1.5×IQR or more below the first quartile. If either type of outlier is present the whisker on the appropriate side is taken to 1.5×IQR from the quartile rather than the maximum or minimum.

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Figure 3 – Monthly Distribution of QLD Half-hour Spot Prices (Data Source: NEM-Review)

Figure 3 shows a Box Plot of the distribution of half-hourly spot prices in NSW from Jan-14 to Feb-16. As the potential range of spot prices spans nearly $15,000 but most price data points are below $60, the outliers are omitted from the graphical display. They are however represented in the average prices shown in red. If outliers are present they will move the average well above the median price and sometimes the whole box range.

The graph shows a few interesting features. The first is the obvious change after the repeal of the carbon tax. As expected, average prices fell but not by as much as they initially went up by (see Table 1). This may have been a hangover of some longer term bilateral contracts or hedges. The second feature is the half-hour volatility increased after the repeal of the tax. The third feature is that from September 2015 the average monthly price has been well above the median in all months except October 2015 meaning that there were more price spikes dragging the average up. One would expect that a customer going to the market in very late 2015 for fixed retail prices for 2016 would have seen higher prices than those that went to the market in early 2015. Timing is very important when going to the market for pricing.

For the purposes of carrying out an assessment of a pool pass-through strategy we will examine the 12-month period from March 2015 to February 2016 as this period is both post carbon tax and contains the more volatile periods commencing in September 2015 and running through into 2016.

Over this period the following price outcomes were experienced:

Peak                Average price $53.80/MWh                        Median price $40.40/MWh

Shoulder        Average price $45.29/MWh                        Median price $37.38/MWh

Off-Peak          Average price $33.95/MWh                        Median price $33.55/MWh

Flat                 Average price $40.26/MWh        Median price $35.90/MWh

A consumer with little ability or desire to curtail load during high price events would need to compare the price offers they receive against these price outcomes. If they are close then there is little incentive to switch to a pool price exposure strategy. If there is a difference that equates to hundreds of thousands of dollars a year when applied to their specific load profile, then they have data to either negotiate a better outcome with their retailer or to support a business case to switch to a pool price exposure strategy. Either way, it is important to analyse what the market is doing before going to the market for pricing.

Would a consumer who has the ability and willingness to curtail load during high price events be much better positioned to adopt a pool price pass-through and DSM approach?

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Table 2 – NSW Price Frequency Analysis March 2015 – February 2016 (Data source: NEM-Review)

Table 2 shows the frequency of different threshold price intervals over the period from March 2015 to February 2016. The number of half-hour periods exceeding $100/MWh (10 c/kWh) was 98 for the 12-month period. Had a 1 MW flat load profile business been willing and able to curtail if spot prices exceeded $100 they could have saved up to 8.2%* in cost versus the spot price and achieved an average price of $36.95/MWh (3.7 c/kWh) or a saving of almost $30,000 per year. For a small business this may be a significant saving. In reality a business of this size with the ability to curtail would be very unlikely to have a flat load profile (24-hours a day, 7-days a week). A large number of clients that I work with have a normal operational period of 16 hours per day over day-shift and afternoon-shift on predominantly weekdays. If we apply the same analysis as above but from 8:00am to midnight the results are far different.

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Table 3 – NSW Price Frequency Analysis March 2015 – February 2016 (Data source: NEM-Review)

8:00am – Midnight On Weekdays

Table 3 shows that the business that operates at 1 MW load for 16-hours per day on weekdays would obviously be exposed to a higher average price as it operates largely during peak and shoulder periods but the savings of $26,967* with a curtailment strategy would still have been appreciable for that size business.

A much larger business with a 10 MW load would save ten times that amount.

In NSW the opportunities for savings using a pool price pass-through and DSM strategy will depend on the customer’s load profile and the competiveness of the retailer offers.

If your retail price offers sit well above the more recent pool historical pricing or you are interested in investigating how a pool/DSM strategy may reduce costs and wanting to get a better understanding of the opportunities and risks with your particular load profile then contact me at This email address is being protected from spambots. You need JavaScript enabled to view it. .

Notes:

* This analysis assumes that all high price events were forecast and the user was able to curtail immediately on the commencement of the half-hour period. Some high price events are not forecast prior to the half-hour period and occur once the half-hour has commenced. This may be due to unexpected events such as a power plant failure. The 5-minute increment within the half-hour period when the price first spikes will determine how effective curtailment will be. For example, if the spike occurs in the last 5-10 minutes curtailment will not be effective at significantly reducing prices as the load has been run for most of the half-hour.

Pool Pass-through and Demand Response – Opportunity and Risks in Queensland

Pool Pass-through and Demand Response – Opportunity and Risks in Queensland

In early February I wrote this article about the electricity cost tsunami hitting SA businesses and what they could do to avoid the enormous price increases.  I followed this up this month with a review of what January and February would have delivered a spot-exposed business in SA, in terms of benefits.

I received a lot of feedback about these articles including questions as to what the minimum load threshold for this strategy to be effective is and would it be possible to make similar savings in other states.

In terms of threshold, a business with a total annual consumption of 3GWh or more would certainly benefit from this strategy. This may include businesses with a peak demand of around 500 kW or more. Smaller loads would also benefit but the returns might not be large enough to generate strong interest.

In this article I will focus on the opportunity in Queensland after having a brief view of what has happened with spot prices nationally.

2016-03-07-table1-spotpricesbyregion

Table 1 – Financial Year Average Annual Spot Prices by Region
Source: http://www.aemo.com.au/Electricity/Data/Price-and-Demand/Average-Price-Tables/Average-Price-Tables-Annual

Table 1 above shows the financial year annual average market spot prices for all of the NEM regions. There is volatility evident from year-to-year but it can be seen that pre Carbon Tax average prices were around the $30-$40/MWh mark or 3-4c/kWh in most regions. In Queensland, in the decade preceding the carbon tax, eight out of ten years had pricing in the 3-4c/kWh range. After the Carbon Tax repeal the different regions have diverged in price, each with their own set of issues. I have already written about SA here and will now have a closer look at what is happening in Queensland.

A good way to visualise the distribution of prices is to represent them on a “Box Plot”.

A box plot is a way of displaying the distribution of data based on a five number summary: minimum, first quartile, median, third quartile, and maximum. In the box plot the central rectangle spans the first quartile to the third quartile (the Interquartile Range or IQR). A horizontal line inside the rectangle shows the median and “whiskers” above and below the box show the locations of the minimum and maximum.

2016-03-07-fig1-boxplot

Figure 1 – Diagrammatical Representation of a Box Plot
Source: http://www.physics.csbsju.edu/stats/box2.html

Data will usually display surprisingly high maximums or surprisingly low minimums called outliers. Outliers are defined as either 3×IQR or more above the third quartile or 3×IQR or more below the first quartile. Suspected outliers are slightly more central versions of outliers: either 1.5×IQR or more above the third quartile or 1.5×IQR or more below the first quartile. If either type of outlier is present the whisker on the appropriate side is taken to 1.5×IQR from the quartile rather than the maximum or minimum.

2016-03-07-fig2-trendedbox

Figure 2 – Monthly Distribution of QLD Half-hour Spot Prices
(Data Source: NEM-Review)

Figure 2 shows a Box Plot of the distribution of half-hourly spot prices in Queensland from July-14 to January-16.  As the potential range of spot prices spans nearly $15,000 (from -$1,000/MWh to $13,800/MWh) but most price data points are below $100, the outliers are omitted from the graphical display.  The effect of these outliers is shown in the average prices listed in red.

As can be seen, if spot price outliers are present, they will move the average well above the median price and sometimes the whole box range.

The 12-month average spot prices in the Queensland region have been around the $52/MWh (5.2 c/kWh) over the last 19-months whilst most average monthly prices have been significantly below that. There have been some significant price spikes in Dec-14, Jan-15, Mar-15 and Feb-16 (not shown) that have dragged the average monthly price well above the typical distribution. See here for a discussion about how upstream electric compression at the gas fields for LNG export combined with El Nino hot weather are driving higher summer demand in Queensland.

Typical flat retail price offers for Queensland for the 2015-2016 period have been around the 4.75 c/kWh mark and so have been fairly valued compared with the actual average spot prices of 5.2 c/kWh. A consumer with little ability or desire to curtail load during high price events would likely be better off on a fixed retail price (as opposed to SA where they would still be likely to make considerable savings if they were exposed to the pool – see previous article about the SA market).

Potential benefits of Demand Response (DR)

Would a consumer who has the ability to curtail load during high price events be better off taking a pool price pass-through and DSM approach or sticking with fixed retail prices?

2016-03-07-table2-sithcurtailment

Table 2 – 2015 QLD Price Frequency Analysis
(Data Source: NEM-Review)

Table 2 shows the frequency of different threshold price intervals over the 2015 calendar year. The number of half-hour periods exceeding $100/MWh (10 c/kWh) was 516 for the full year. Had a business been willing and able to curtail load if spot prices exceeded $100 they would have saved up to 33%* in cost versus the spot price and achieved an average price of $34.69/MWh (3.5 c/kWh). However they would have lost the equivalent of 258 hours of production.

(a)  For a factory that is running at near capacity this downtime would likely not have been acceptable.

(b)  If the factory was not running at full capacity the downtime would have had no material effect but the significant cost savings would have gone straight to the bottom line.

A business that was willing and able to curtail but did not want to shut down some or all of their operations for that period of time could choose a different price threshold to curtail load. If the business chooses $1,000 as the curtailment threshold then they would have shut down for a total duration of 33 hours over the whole of 2015 or 0.7 days. This would have reduced their annual availability by a small 0.4% but achieved an annual average price of $38.49/MWh (3.8 c/kWh).

So how would they have fared compared with a typical retail offer?

2016-03-07-table3-curtailmentsavings

Table 3 – Curtailment Savings for a 1 MW Flat Load

Table 3 shows the annual savings for a flat 1 MW load with perfect load curtailment and perfect forecasting. The factory with the underutilised capacity and curtailing at $100 would have saved $112,000 compared with the typical retail pricing. The factory that curtailed at $1,000 would have saved $79,000 for the year compared with the typical fixed retail price.

A flat 10 MW load would have saved $1.12m with a $100 threshold and $790,000 with a $1,000 threshold.

It is evident that there are significant electricity cost savings to be made in Queensland by adopting a pool price pass-through approach along with a Demand Response (DR) strategy.

Setting Up A Pool Price Pass-through and DR Strategy

Before embarking on a pool price pass-through arrangement it is important to understand how the market operates, the opportunities, the risks and your own constraints. It is also critical to educate any staff and employees that are involved.

All businesses are different.
(a)  Some operate 24/7 and any small disruption has a high opportunity cost.
(b)  Some businesses operate all of their equipment well under capacity and are not sensitive at all to short stops.
Most are in between.

It is important to do a study on all of the major equipment, determine which equipment are bottlenecks (constraints), which are utilised below full capacity, the implications of shutting down a constrained asset for a short period of time and the consequential break-even cost of shutting down. Some equipment cannot be shutdown at all, other equipment can only be shut down if stocks allow.

From this analysis a load-shedding schedule can be constructed with a list of equipment to be shut down at different price thresholds. Some of these may have discretionary actions dependent on product stock levels or actions may require escalation for approval prior to shutting down. Some businesses may choose to shut all equipment down at one price level, some will have different levels for different assets and some may choose not to shut down at all.

When constructing the load-shedding schedule it is necessary to have an understanding of the likely number of curtailment events and the duration of those events.

2016-03-07-table4-spikeduration

Table 4 – 2015 QLD Price Event Frequency

Table 4 shows the frequency and duration of high price events in QLD in 2015 exceeding $1,000. Nearly 80% of the events were just one half-hour. It is important to note however that there were two very long duration events, one being 5.5 hours and the other being 7 hours.

2016-03-07-fig3-probabilitydistribution

Figure 3 – Probability distribution Plot of 2015 QLD Half-hour spot Prices

Figure 3 shows a probability plot of actual half-hour spot prices for 2015. It clearly shows the small number of high price intervals compared with the bulk of the prices. 90% of all prices sit below $50/MWh.

Most businesses that I work with initially believe that they have no ability to curtail load.

When their load data is analysed though it is clear that they are frequently curtailing load for many other reasons such as meal breaks, scheduled maintenance, full stocks, product changeovers, shift changeovers, clean-ups, inspections, meetings, training etc. The level of curtailment as a percentage of time for these other reasons far outweighs the curtailment time required for a DSM strategy.

Another question that is frequently asked is what are they going to do with the operators when the plant is curtailed. The answer is that it provides a great opportunity to do all of the things that people never seem to have the time to do including team meetings, tool box meetings, communication sessions, clean ups and even training if a whole afternoon of high prices are expected.

The ability to curtail load in the event of high prices is often much greater than businesses first realise and the rewards are enormous.

Real time, accurate market information is crucial to a DSM strategy.

(a)  NEM-Watch is a great tool that enables the operations staff to monitor the market, prepare for high price events and get alerts in the event of sudden high price events. It provides the big picture in what is happening across the entire network and a detailed picture of what is happening in the hub that the business is operating in with regards to forecast and actual demand and price and generation mix including wind and solar PV.

(b)  A number of energy users who have operated in this way for a number of years choose to use the deSide software for effective warning, and ease of control of their Demand Response.

Load Planning

DSM not only involves a strategy to react to high price events but also incudes planning ahead to shift load to periods that are expected to have lower prices and avoiding running maximum load during periods that are expected to have high prices.

This can include organising scheduled shutdowns in periods that have a higher likelihood of high prices. Figure 4 shows that high price events in Queensland occurred largely in the warmer months of November through to March during the 2014-2015 period.

Major shutdowns should be planned for those months where possible.

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Figure 4: High Price events in QLD > $100/MWh By Month of Year 2014-2015

Figure 5 shows typical price distributions by time-of-day over the 2015 period (excluding outliers). By maximising load during the night and minimising load during the day, particularly late afternoon and early evening the average price can be reduced. Scheduling short maintenance outages on major equipment in the afternoons rather than back shifts can help achieve that outcome. Businesses that pump water such as irrigators or water utilities can schedule their pumping for these lower price periods. Any guess what time most people wake up? What about get home from work?

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 Figure 5 – Price distribution By Time of Day
(Data Source: NEM-Review)

Call To Action

Pool exposure and DSM is not for all businesses.

In Queensland the benefit largely goes to those consumers who are willing and able to curtail load in the event of high prices.  Some businesses require absolute certainty in their cost inputs even if they are at a higher price.  For some the savings may be too small to spend time and resources in setting it up.  Others may not be able to tolerate the risk or potential fluctuations in monthly bills.

For those businesses with significant electricity spend and are in the relentless pursuit of finding cost savings then a pool pass-through arrangement coupled with a DSM strategy may deliver significant savings.

DSM response of a level of up to 345 MW has been observed in Queensland during periods of high prices indicating that smart businesses are already using this strategy.

If you are interested in employing a pool/DSM strategy to reduce costs and wanting to get a better understanding of the risks and potential rewards then contact me at This email address is being protected from spambots. You need JavaScript enabled to view it. .

Notes:

* This analysis assumes that all high price events were forecast and the user was able to curtail immediately on the commencement of the half-hour period. Some high price events are not forecast prior to the half-hour period and occur once the half-hour has commenced. This may be due to unexpected events such as a power plant failure. The 5-minute increment within the half-hour period when the price first spikes will determine how effective curtailment will be. For example, if the spike occurs in the last 5-10 minutes curtailment will not be effective at significantly reducing prices as the load has been run for most of the half-hour.