The agony and ecstasy of solar & storage quoting
The battery storage revolution has long been the subject of hype and speculation but homeowners have now taken to embrace the smart energy model in droves.
Battery storage technology requires a much higher level of sophistication than grid-connect solar to model energy flows and provide an accurate picture of the energy, component lifecycles and, thereafter, financial benefits to the owner.
To provide a recommendation encompassing grid consumption, solar & storage, and supplying the consumer well-informed options, requires a greater level of modelling power.
The spreadsheet is history
For years many installation companies have prided themselves on their tailored spreadsheets to size solar systems.
These may have served to produce a rough estimate for small solar panel installations but can a spreadsheet really model the factors below?
Firstly, the most variable factor in home energy systems is the habits of the energy users themselves.
How can you assess the optimum size, components and configuration of a solar and storage system without load profile data and the year round variability?
Ideally, actual ‘interval data’ of this consumption will be available either from an electricity provider or via a monitoring device installed to gather this data.
Next the tariff plan should be available to measure the financial benefit including the variability from hour to hour and weekday to weekend.
For a thorough analysis, a variety of time-of-use plans available to the consumer from competing retailers is required to compare for the best option.
With this information in hand we can begin to run a generation and energy flow model.
What’s so difficult about that?
A proper modeling algorithm will evaluate solar production for each hour in each day of the year, using a ‘typical’ year profile, which builds in hour-to-hour variability based on cloudiness indexes and using satellite derived solar radiation statistics within a close proximity.
While the solar radiation is in hourly intervals, if consumption data is in 15 or 30 minute intervals, this is the resolution that our modeling should occur to best model battery charge and discharge cycles.
Within each interval, the algorithm should evaluate beam, diffuse and ground reflected irradiance impinging on the panel, while factoring any shading and transmittance losses.
Solar panel output should be deriving using hourly temperature and other derating factors, then power losses through cabling, peak power clipping and efficiency losses at the inverter before arriving at the usable power.
Then self-consumption of solar can be calculated and battery charge and/or solar export allocated, with various user or configuration limits applied to each.
Batteries – quantifying a dynamic system
One of the reasons for the success story that is solar panel systems are the most predictable of generation and component lifecycles.
While the technologies vary greatly, batteries generally share the dynamic nature of the electro-chemical conversion process.
The measurement of the state-of-charge of a battery at any given time is an estimation based on known factors and in some cases unknown factors.
The variability of which is primarily affected by:
- the depth of cycling as set in the inverter configuration, varying by daily generation and consumption changes,
- charge current levels with consideration of battery loss factors,
- discharge current levels, with effect varying by battery technology according to Peukerts law, and with current varying as different appliances are turned on and off, and
- the temperature of the battery.
Therefore a model that repeats this energy analysis in smaller time intervals provides a better evaluation of battery cycling.
Analysing these energy flows in each time interval throughout a year and applying the various tariffs allows us to estimate not only the self-consumption, but the storage cycling, and the financial advantages of one system over another.
But it’s not that simple…
Tariffs aren’t tariffs
We are blessed in Australia with a history of electricity monopolies and policy that has resulted in consumers facing huge variations and complexities in the tariff structures.
We’ll leave the greatly increased complexity of commercial tariffs and gross vs net feed-in tariffs for another time.
To summarise, most consumers have a single rate tariff, or time-of-use tariffs in which higher rates are applicable during afternoon/evening peak periods, or some will have a block tariff with variable rates based on the overall level of usage over a given period.
In addition, daily supply charges and discounts may applied, along with variations in feed-in tariffs paid for solar power export.
But wait, there’s more
Having arrived at production, cycling and financial values for a typical year is well and good but a value proposition must also include the cash flow variations over the lifetime of the system and the lifecycle of each component and costs of replacement within the period of this return on investment.
Again batteries add much complexity due to their dynamic nature and technology variability in the estimation of their lifecycle. There is little consistency in warranty conditions so each battery product must be evaluated on its own terms to arrive at the use by date for modelling purposes.
Finally it gets simpler
A few larger companies may have the resources to model of all these factors with a good degree of reliability and provide consumers with the level of transparency that they deserve in an investment such as this.
Consumers have begun to get educated in the factors in their buying choice and are demanding more of sales and install companies, as they should.
Nobody will benefit from budget solar battery operators taking a hold in today’s market.
There are always challenges in responding to each new technology coming onto the market but that’s the pain we take on for our users to experience the ecstasy.