Most solar retailers and installers selling batteries today are not equipped to provide customers with a reasonable statement of that battery’s performance.
In other words, consumers are buying a solar battery that may not be fit for purpose.
In solar, we’ve seen the fallout when performance and savings are not as expected – and some installers did not survive to tell the story.
Will you run the risk of battery sub-standard performance?
Anyone who has been in the industry for a few years might recall how PV was often sold with little to no communication of expected performance.
Since 2010, Standards have required that customers be provided with performance estimates to protect consumer interests.
That hasn’t yet become a requirement for batteries and so a ‘guesstimate’ of battery savings has become the norm.
But new battery standards will change all that, and installers are advised to be prepared.
A battery should be demonstrably fit for purpose before sale.
The reason this is not done consistently is that the calculations required to determine battery cycling can’t be done in a spreadsheet, and still can’t be done by most online quoting tools.
So what is a reasonable set of battery performance metrics for a quality installer?
Detailed explanations of these metrics are below.
But even with these metrics, a deeper visualisation of the day to day cycling of a battery is essential to installer confidence in lifetime performance.
Batteries are completely unlike their predictable solar PV accomplice.
Electrochemistry is a much more dynamic and variable system as anyone who is experienced with off-grid systems understands well after numerous call-backs.
You certainly can’t use generic fixed performance indicators like you might (and yet definitely shouldn’t in 2019) use average solar yield.
If you indicate to a customer that a 10 kWh battery is going to cycle reliably at 10 kWh per day, you’re heading for trouble.
You open yourself to lawsuits and reputation fallout from customers with unrealistic expectations.
The battery nameplate rating, similar to a PV system kilowatt rating is somewhat standard measure of the amount of energy attainable under set conditions.
Those conditions never happen in the real world.
Throughout the year, there are different demands on a battery and the actual energy delivered in a single battery cycle will depend on these factors.
Variability in solar resource available for charging is a primary consideration in wet months.
But equally important is the energy consumption in those same times as the consumption takes priority on that solar supply, unless control systems allow for set-points to charge from gross solar.
Also introducing variability in the battery cycle is the peak load demands and the temperature of the battery when under these conditions.
If we have interval data for a customer, along with variable solar radiation (not averaged!) we have a means for 30-minute modelling to measure and report state of charge.
This requires a simulation algorithm taking into account all of the location factors, de-rating factors, component specs and configuration details.
This 6 kW solar system with 10 kWh battery is poorly sized for the 35 kWh daily loads in winter
Any customer with time-of-use, block tariffs, or demand tariffs, will require an extra level of assessment.
This means financial simulation within each 30-minute and monthly summing period.
With any low off-peak tariff, grid charge simulation could provide a big benefit to reduce peak grid supply.
In order to model this benefit in a meaningful way, set-points for time ranges, state-of-charge ranges and maximum charge levels are required.
A comparison of monthly bill charges based on historic energy use can be compared to a simulation with solar only, or with solar and batteries for best indications of battery value and benefits.
Each of today’s growing range of battery products has unique qualities, strengths and weaknesses.
A given customer may show peak demand needs that suggest that a battery with higher power performance is better suited.
The load fluctuations in combination with tariff conditions will determine how a particular battery is matching these needs.
The battery or its control system will dictate what control set-points can be utilised and fed into simulation software.
Where storage capacity is not used to its maximum benefit on-site, storage export may be desirable.
Again, the same control over setpoints is required as for grid charge and possibly a separate export tariff for this condition.
With the need to provide reasonable payback for storage within the warranty period, and with premium evening feed-in tariffs coming on offer, this is likely to become more commonplace.
A reasonable question but how do you answer it?
Modelling software must be able to give an estimate of battery backup duration based on a given outage.
Answer: Let’s have a look at a scenario of an outage at 9pm in July. Base on the average for that month, here’s how your battery might keep the lights on.
In the world of storage simulation, annual averages, monthly averages and even daily averages are only indicators.
To get a good sense of battery cycling, we need to visualise the solar supply, consumption and battery state-of-charge and the day-today variability with cloud cover.
The risks of customers will overblown expectations is high with a cost in reputation, time in callouts and possible claims against the sales company and or installer.
A customer should receive a proposal that shows this analysis and gives them a clear indication of the demands expected on the battery system, and the ability of the battery to reasonably sustain performance under these conditions.
With good communication and a commitment to maintain on ongoing relationship with the customer, any issues that follow can be resolved and goodwill maintained.
It’s good business, good customer service, as well as best practice.
A consumer can reasonably expect a thorough value assessment is completed for an installation that is likely to be the 10s of thousands of dollars.
These are some of the metrics that can be used for this assessment.
Average Monthly Maximum State of Charge:
By looking at monthly average peak state-of-charge we get a good indicator of battery demand and the resource available to recharge it. This figure gets the maximum state-of-charge for each day of the month, then averages those peak SoC values.
An average monthly maximum of 60% suggests that more PV is desirable.
Lifetime Energy Throughput under Warranty Conditions:
Battery warranties can include maximum years, cycle life and/or maximum energy throughput over that lifetime.
This can only be given reliably when a complete cycling assessment is included in the simulation.
Customers should see that a battery is modelled at warranty conditions, and should see replacement battery cost allocated after the warranty has expired.
Simulated Cycle Life:
Given a fixed term battery warranty (usually 10 years), we can use cycle modelling to determine the total number of charge-discharge cycles over this lifetime.
Warranty Lifetime Years:
If the warranty includes a cycle life, then the assessment should determine the number of years before the warranty expires due to the cycle limit.
If it shown to be before any given lifetime term in years, then the customer should be aware that the warranty is likely to expire earlier.
Backup Hours of Autonomy
The battery backup hours or hours of autonomy is an indicator of the number of hours a customer might expect to keep the lights on given a grid outage at a particular time of any month.
If the backup-hour rating for a July outage occurring at 6pm is 12 hours, it may get them through the night but may not maintain enough charge to reach the point at which it starts to recharge from solar.
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