Design tool to make network tariffs more cost-reflective

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A simple visual tool shows how networks can make tariffs more cost-reflective, and address peak network demand.

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This article follows on from our earlier articles here, here and here, and firstly describes a simple tool that can be used to visualise a tariff’s level of cost-reflectivity. It then uses the tool to illustrate the steps that can be used to make a tariff significantly more cost-reflective.

This work, as well as the views of other experts, will be presented at the APVI’s workshop ‘Cost-Reflective Pricing – some different perspectives’ on Wed 1 June.

Figure 1 shows the visual tool, which compares the unitised demand charge (y axis) with the customer demand at the time of the network peak (x axis). Where the demand charges have been unitised, they have been converted to an equivalent kW value.[1] We do this so that there is a more direct visual correlation between what a customer pays and the costs they impose on the network, and because it makes different demand tariffs easier to compare.

All the households above the red line would receive a unitised demand charge that is greater than their demand during the network’s annual peak. Thus, the correlation coefficient between payments under the demand charge and responsibility for the network peak is very low.

passeyim[1] In this case, the net annual demand charge was taken to be $1/kW but the months that have a higher demand charge are given a proportionally higher weighting. The addition of all the monthly demand charge rates equals 1. The unitised demand charge does not include any usage (c/kWh) charges or any separate fixed ($/day) charges.

Figure 2 shows the aggregated half hourly demand for the 3,876 households from the Smart Grid Smart City database for 2013. Although the annual peak is in summer, of the top 50 days, only six days are in summer 1st, 2nd, 5th, 11th, 24th, and 27th). All the rest are in winter. This means that if only the summer days are targeted in the demand charge, and customers respond, the winter days will quickly become the peak days.

Of course, if the network has already been sized to meet the summer peaks, the winter peaks will not be a problem – unless demand in general increases, or, if there is a genuine desire to reduce customer costs and so reduce the size of the network over time.

Figure 2. Annual aggregated SGSC load profile
Figure 2. Annual aggregated SGSC load profile

In this case, the unitised demand charge should not be compared to a single network peak. In other work we have calculated that the correlation coefficient is greatest when the unitised demand charge is compared to each customer’s averaged demand during the first five network peaks (for this SGSC dataset). Figure 3 is equivalent to Figure 1 but for this five network peak comparison, and it can be seen the correlation is indeed better.

Figure 3. Unitised Standard Demand Charge vs Average Demand at Time of Five Highest Network Peaks
Figure 3. Unitised Standard Demand Charge vs Average Demand at Time of Five Highest Network Peaks

The correlation is further improved when the demand charge is applied only during the summer and winter months instead of every month of the year – Figure 4.

Figure 4. Unitised Demand Charge (applied only in summer and winter months) vs Average Demand at Time of Five Highest Network Peaks
Figure 4. Unitised Demand Charge (applied only in summer and winter months) vs Average Demand at Time of Five Highest Network Peaks

Then if the demand charge is applied to the household demand at the time of the network peak (as recommended here) in each of the 12 months, the correlation improves further – Figure 5. Note that although residential network peaks are almost exclusively between 5pm and 7pm, a customer’s demand peaks can be at any time of the day (with two thirds of the SGSC households being outside the 5-7pm window). This means that a customer can be told when the network peak is likely to be, but will have little idea when their own peak is likely to be, and so it is easier for them to take action if the demand charge is based on the time of the network peak.

rsz_screen_shot_2016-05-18_at_113323_am
Figure 5. Unitised Demand Charge (applied to household demand at time of 12 monthly network peaks) vs Average Demand at Time of Five Highest Network Peaks

 

Then if the demand charge is applied to the household demand at the time of the network peak but only during the summer and winter months, the correlation improves further – Figure 6.

rsz_screen_shot_2016-05-18_at_113537_am
Figure 6. Unitised Demand Charge (applied to household demand at time of summer and winter monthly network peaks) vs Average Demand at Time of Five Highest Network Peaks

 

The tariff being assessed here included a minimum 1kW demand charge in each month, which acted as a proxy fixed charge, and if this is removed we get Figure 7.

rsz_screen_shot_2016-05-18_at_113656_am
Figure 7. Unitised Demand Charge (applied to household demand at time of summer and winter monthly network peaks, with minimum kW charge removed) vs Average Demand at Time of Five Highest Network Peaks

Thus, for the load data used here, it would appear that the cost-reflectivity of the demand charge component can be improved significantly by simply applying it to the customer demand at the time of each summer and winter month’s network peaks.

Some points:

  • From the customers point of view this would look like a standard demand charge tariff applied only during summer and winter.
  • This analysis assumes that customers will respond to a demand charge and so decrease the highest network peaks.
  • This design would work equally well with a ‘rebate-based’ tariff
  • This process can be applied to any types of load profiles, no matter which season they peak in, it is simply a matter of applying the tariff periods to the appropriate seasons.

Rob Passey is a Senior Research Associate at the Centre for Energy and Environmental Markets (CEEM) at the University of NSW, Policy Analyst at the Australian PV Institute and Senior Consultant at IT Power (Australia).

Navid Haghdadi is a PhD Candidate at CEEM UNSW.

This work was part-funded by the Energy Consumers Australia.

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7 Comments
  1. Davd Woodgrove 4 years ago

    Brilliant data chaps. Hopefully those retailers and utilities are reading or have done their own similar deep analysis.

  2. John Herbst 4 years ago

    What about a peak or critical-peak volume charge? The tighter the definition of peak, the stronger the signal gets.

    • Rob Passey 4 years ago

      Hi John, I totally agree. For me CPP is the way to go, most likely with a rebate approach rather than a penalty. However, I expect we’ll end up with a range of different tariff options depending on what different people want, and demand charges will be one of those options, so we need to get that design as good as we can.

  3. Jeremy 4 years ago

    Hi Rob and Navid, what is wrong with simple volumetric pricing? For example, eliminate standing (or daily) charges and replace them with a fixed c/kWh charge added onto the volumetric electricity charge. Each year each DNSP can update the network charge, based on the expected total kWh for the next year, and the total network maintenance cost. This will increase the c/kWh charge, e.g. it will rise from 30 c/kWh to 35 c/kWh, and thus encourage solar PV, batteries and negawatts (efficiency). In turn, these will reduce the network peak. Each year, if total kWh reduces, the c/kWh will increase, further encouraging solar PV, batteries and negawatts.

    Wouldn’t this be more readily accepted by voters?

    The effect could be strengthened by imposing time of use pricing, e.g. 70 c/kWh during a known 2 hour time period each day (e.g. 5-7 pm) on known months of the year (e.g. 3 months in summer, 3 in winter).

    • Rob Passey 4 years ago

      Hi Jeremy, this is what is currently in place but it doesn’t reflect the costs faced by the networks (and passed on to customers). Only 2% (or less) of the costs faced by networks relate to the kWh transported, the rest is dictated by the annual demand peaks. This means that people who cause peaks in demand (which are generally disproportionately greater than their usage) are subsidised by people who don’t. To make things more equitable, the charges need to be based on a customer’s demand peaks, hence the use of kW demand charges.

      • Jeremy 4 years ago

        Hi Rob, I understand the principles of network costs, from the perspective of an economic rationalist. I disagree that we currently have volumetric pricing in place – we still pay separate daily charges. The situation is even worse with water charges – 2/3 of my bill is fixed charges, only 1/3 is volumetric, so there is little incentive to reduce consumption.

        With regard to electricity tariffs, I agree that we are attempting to reduce the peaks, as this is the cost we all bear. However, we need a mechanism that is supported by the broader population, not just the economic rationalists. For this to occur, the system should be easily understood and perceived to be fair.

        Also, in a representative democracy, we have the choice to design the system in a manner of our choosing – it does not have to be strict economic rationalism. Tariffs can also be designed to reduce consumption, not just reflect network costs.

        Consider this scenario – we introduce full volumetric pricing with no fixed charges. What behavioural changes do you think will occur? I suggest that significantly more people will install solar + storage, to minimise the kWh they import from the grid. They will try to “beat the system”, and target a zero bill. The presence of storage will significantly reduce network peaks, and thus the costs we all bear.

        Again, I suggest that volumetric pricing (with no fixed charges) will lead to (1) a greater reduction in network peaks than other tariff designs, owing to broader acceptance and a faster uptake of storage, and (2) greater equity with less cross subsidy. This can be achieved while retaining the ability of DNSPs to recover the costs they need to maintain the grid.

        Can you consider this in your research?

        • Rob Passey 4 years ago

          Hi Jeremy,

          Quite a few groups, including us, have done work on volumetric charging, and the problem is that it is just too inequitable. There’s a poor correlation between elec use and contribution to demand peaks (ie. peaks are disproportionately higher), which means that people who are least responsible for the size of the network and the recent increases in costs pay disproportionately more. There are quite a few estimates of the amounts by which people who don’t own air conditioners subsidise those who do, but the one that comes to mind is $300/year (Productivity Commission), and this is because of the current volumetric pricing.

          Rob

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