In late 2014, the Australian Energy Market Commission (AEMC) introduced a new rule requiring network businesses to set prices that reflect the efficient cost of providing network services to individual consumers. As the cost of network provision is impacted more by maximum electricity demand than usage, this typically translates to higher prices in periods of high demand rather than the flat usage charges that predominate today’s market. Network prices based on these new pricing objectives and pricing principles are being gradually phased in from 2017 – but primarily on an opt-in basis. The Victorian Government’s recent move to veto the “opt-out” approaches proposed in that State, leaves only South Australia and the ACT where residential solar customers could be directly assigned to new demand tariffs – at least in the short term.
For most residential customers with solar PV, the maximum output of their system does not coincide with their maximum demand, so while the solar system is effective at reducing a network charge based on usage, it will be less effective against a network charge based on demand – hence, their bills are likely to increase under the new tariffs.
However, network tariff reform should in theory also present a range of opportunities particularly with respect to demand management or peak shifting technologies such as battery storage and home energy management systems which, if combined with solar PV, could effectively convert a demand tariff into a pseudo premium feed-in tariff (FiT), enabling customers to get more value from their self-generated electricity.
Marchment Hill Consulting has undertaken detailed modelling of the impact on solar PV customers based on the proposed new network tariffs for all distribution businesses in the NEM, as detailed in their Tariff Structure Statements (TSS) released in late 2015. We have also assessed the impact of battery storage and home energy management systems (HEMS) and considered:
- What is the optimal mix of distributed energy resources to eliminate or reverse the bill impact of cost reflective network tariffs for solar customers?
- Under what conditions (e.g. storage costs, HEMS costs) does an average solar customer become better off under the new network tariffs?
The modelling compares the customer’s bills under current flat tariffs to that under future maximum demand tariffs (MDT) and provides the customer with options (i.e. storage and/or HEMS/DM) to reduce their peak demand. Each customer is assumed to have a 3kW solar PV system installed and a $0.08/kWh FiT.
Table 1 and Table 2 below show the tariffs modelled.
The model uses 30-minute interval data for 20 randomly selected customers from the Reward Based Tariffs (RBT) Trial in Queensland, as shown in Figure 1.
Figure 1: Average Daily Load Profiles (Gross)
Key findings from the modelling include:
- Finding 1: The majority of solar PV customers are worse off under demand tariffs modelled and would need relatively significant peak demand reductions (average 44%) to achieve bill neutrality.
- Finding 2: The impact of demand tariffs varies greatly depending on the customer profile with more ‘peaky’ loads worse off, which illustrates the importance of understanding a customer’s individual load profile and tailoring products, services and solutions accordingly.
- Finding 3: The relatively long peak period proposed by the Victorian networks and current high cost of appropriate technologies (e.g. storage) makes it challenging (and costly) to target peaks.
- Finding 4: HEMS, in combination with more informed decision making, shows potential to reduce peaks at a relatively low cost. This would, however, require a behavioural change for the customer, which is not the case for storage.
- Finding 5: A combination of technologies does not provide a simple additive effect and highlights the need for further modelling, research and product development to arrive at the appropriate solutions that truly add value to the customer.
Each key finding is explored in more detail below.
Finding 1: The majority of solar PV customers are worse off under demand tariffs modelled and would need relatively significant peak demand reductions (average 44%) to achieve bill neutrality.
Installing residential solar PV has the effect of reducing a customer’s grid consumption during the day, while its generation profile has limited alignment to the customer’s peak period. As MD Tariffs are designed for customers to incur a higher charge during these peak periods, customers with solar PV would be assumed to see their bills increase under such an arrangement.
Table 3 below shows the relative bill increase for solar PV customers under a MDT. The average increase is roughly 23%(1).
Table 3: MDL Solar PV Customer Bill Increase
* The relatively higher increase for UE customers is due to their tariff being modelled is a ‘pure’ demand tariff, i.e. without a residual consumption charge.
As the consumption charge is generally relatively low under MD tariffs, the customer’s best option to reduce their bill is to reduce their peak demand. Table 4 below shows the peak demand reduction required for customers to reach bill neutrality (equal to 2016 bill). The average reduction is approximately 37%.
Table 4: Required Peak Demand Reduction for Bill Neutrality
Finding 2: The impact of demand tariffs varies greatly depending on the customer profile with more ‘peaky’ loads worse off, which illustrates the importance of understanding a customer’s individual load profile and tailoring products, services and solutions accordingly.
The above analysis highlights that, in order to have any significant impact on their bill, the majority of customers would need to reduce their peak demand. However, for certain customers the demand tariff has the opposite effect – this is the case where the customer has a relatively small peak demand.
The two highlighted customers below have similar annual consumption, but the relative ‘peakiness’ of customer 5’s load profile makes them significantly worse off under a MD tariff.
Figure 2: Customer 12 and 5 Average Load Profile and Peak Demand
This highlights the importance for stakeholders to have a much better understanding of their customers’ load profiles and other relative information (major appliances, AC, pool pumps etc.) to tailor products and services to their individual needs.
Finding 3: The relatively long peak period proposed by the Victorian networks and current high cost of appropriate technologies (e.g. storage) makes it challenging (and costly) to target peaks.
As the bill for the majority of solar PV customers would increase under an MD tariff, this presents an opportunity for these customers to invest in (and for service providers to sell) products that allow them to reduce their peak demand. One of the prime candidates includes energy storage.
The tariff put forward in the VIC TSS proposes a peak window between 3pm and 9pm on weekdays and the storage algorithm has been configured to allow the customer to offset as much of their consumption as possible within that window. The net effect on an example customer’s average load profile is shown in Figure 3.
Figure 3: Average Load Profile with 3pm – 9pm Discharge
As show in Figure 3, on average, the storage algorithm modelled is reasonably successful in reducing the customer’s peak demand. However, this is not sufficient for the customer to achieve bill neutrality (equal to 2016 bill) due to high technology costs (assumed $1,200/kWh), reduced FiT revenue and the extensive peak period which results in a relatively large battery being required.
An example is provided in Figure 4.
Figure 4: Storage Net Benefit
The resulting annual net benefit for all customers is shown in table 5.
Table 5: Storage Annual Net Benefit
As made clear in table 5, no customer receives an overall positive business case from investing in storage. It should be noted that, to some extent, this is due to the bill reduction being compared to the 2016 bill (flat rate). If compared to a customer’s bill under a MDT, the business case would improve for the majority of customers.
Part of the reason the business case is less appealing is due to the relatively long peak period proposed in the VIC TSS makes it challenging to effectively target and reduce a customer’s peak.
Intuitively it would seem that the discharge period could be shortened for certain customers that generally peak within a shorter window in the peak period. This would then allow the customer to invest in a relatively smaller battery while achieving similar bill reductions – a clear win-win.
This would further appear to be the case from analysing the peak times of each individual customer. Figure 5 shows the distribution of the time of day that each customer weekday peak falls on during summer (December – March).
Figure 5: Peak Distribution
As shown in Figure 5, a number of customers seem to generally peak within a shorter window than 3pm – 9pm. However, the modelling undertaken by Marchment Hill Consulting (MHC) has found that individually – and on aggregate – customers are no better off under a scenario where the storage system discharges during a shorter window.
This outcome can be illustrated by having a closer look at one example customer: Customer 19. This customer has a relatively average annual consumption (9 MWh) with a rather high peak demand (6 kW summer peak). Their high peak demand makes it a prime candidate for storage as their potential savings are relatively high under a MD Tariff, as highlighted in Table 3.
Customer 19 further seems to peak within a shorter window, which would allow the storage discharge period to be reduced and potentially for the storage system to reduce more of the peak demand due to additional capacity being available.
Based on the load data, a discharge period of 5pm – 8pm was implemented, as shown in Figure 6.
Figure 6: Average Load Profile with 5pm – 8pm Discharge
This approach does not, however, result in an overall lower bill for the customer, as highlighted in Table 6.
Table 6: Customer 19 Results
This is due to the storage system essentially missing the customer’s peak, or creating a ‘new’ monthly maximum demand outside of the discharge period, but within the 3pm – 9pm peak window.
This also highlights that the duration of the peak period set by the network is likely to have a relatively major impact on the cost-effectiveness of storage and other technologies that may be utilised for customers to reduce their peak demand.
Finding 4: HEMS, in combination with more informed decision making, shows potential to reduce peaks at a relatively low cost. This would, however, require a behavioural change for the customer, which is not the case for storage.
The HEMS system modelled has a relatively simplified algorithm where it reduces the customer’s peak by 25% through load shifting. This returns a positive business case for certain customers, as shown in table 7.
Table 7: HEMS Net Benefit
It should be noted that this is a relatively simplistic analysis and to actually shift a customer’s load continuously would have added complexities.
Additionally, HEMS deliver the greatest value to a customer that is willing to change their behaviour and accept the potential inconvenience of, for example, pre-cooling and heating and not being able to use certain appliances at peak times. Energy storage, on the other hand, requires no real behavioural change by the customer to deliver value.
Although the technology in itself is relatively mature, the challenge to facilitate widespread adoption is to encourage consumers to fully embrace the behavioural changes required to deliver the benefits. This is different to, for example, solar PV where early adoption was triggered by relatively generous government incentives and which has more of a ‘set-and-forget’ operating mode.
Finding 5: A combination of technologies does not provide a simple additive effect and highlights the need for further modelling, research and product development to arrive at the appropriate solutions that truly add value to the customer.
MHC’s modelling has further found that a combination of technologies does not provide a simple additive benefit effect. This is because the storage system in some instances is able to reduce a customer’s monthly demand to less than the 25% the HEMS is targeting (and in some cases eliminates the peak entirely). The net effect is shown in table 8.
Table 8: Storage and HEMS Combination Net Benefit
This again shows the importance of truly understanding the specific characteristics of the individual customer – there is not a one-size-fits-all.
Conclusion
MHC’s modelling has found that it is challenging for solar PV customers with existing technology prices to achieve bill neutrality, even under relatively strong MD tariffs. The modelling has further determined that the relatively long peak windows proposed, make it challenging for demand response technologies to target customers’ peaks effectively and that a combination of technologies do not provide a simple additive benefits effect.
The fact that the majority of solar PV customers are worse off under a MD tariff, makes it unlikely that many of them would ‘opt-in’ to this type of tariffs. This is particularly true if the price signal is not strong enough for them to invest in a technology to achieve necessary peak demand reductions, which is the case with current MD tariffs proposed.
There is a case to be made for networks to be required to offer strong, opt-in truly cost-reflective tariffs – including localised pricing – to provide an added incentive for storage and demand management technologies and support efficient network expenditure.
Footnote
(1) It should be noted that all customers (solar PV, or no solar PV) that have a relatively high ratio of peak demand to consumption, would be worse off under a maximum demand tariff.
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