Being able to build accurate pictures of household solar generation and overall energy consumption down to 5-minute blocks gives Solar Analytics some powerful analytical opportunities.
A team of our people have been turning their attention to one of the big questions of our times for the distributed clean energy sector: when will the financial pay-off period for home battery storage reach a market tipping point?
By that I mean when the economics are so good that investing in storage becomes a compelling investment. Some early movers are already installing storage before then, just as a relatively small number of people invested in rooftop solar before price reductions and government incentives drove the solar boom five years ago.
The headline financial returns aren’t yet great for anyone who wants to rush into buying battery storage straight away. Although there’ll always be special situations, the Solar Analytics simulation model didn’t find any pay-back period better than 15 years-plus. Which just doesn’t make the grade on most spreadsheets.
Our team was made up of Rui Tang, a recent recruit to Solar Analytics from the UNSW pool of solar graduate talent; our Head of Data Analytics Dr Jonathon Dore; and our Chief Technology Officer Dr John Laird. Their work was presented at the Australian Photovoltaic Institute (APVI) conference in Brisbane in December 2015.
Based on a sample of homes, the team focused on determining the optimal battery system size, and financial returns based on actual household energy consumption and local tariff pricing.
Two types of homes were tested:
Type 1 – uses most of its daily energy consumption in the daytime 6am to 6pm
Type 2 – uses most of its daily energy consumption in the nighttime 6pm to 6am
Here’s what Rui had to say: ‘This simulation model takes advantage of the high resolution energy data collected from hundreds of existing Solar Analytics customers to determine the economic feasibility of installing a battery system for these residential PV consumers. In contrast to previous studies, we use actual PV generation and consumption data over a 12-month period to obtain a consumption/generation pattern.
‘Then by considering different charging scenarios and battery parameters, a charge/discharge pattern is determined. Optimal battery sizes and configurations are determined by taking account technology cost and electricity price, thereby enabling the potential profitability of battery storage to be calculated for each customer with a high degree of certainty.
‘Results from simulations indicate that based on the current battery costs, the estimated payback period of installing a battery system is still over 15 years even for Type 1 home owners. However, the payback period is rapidly decreasing as battery costs reduce.’
Surprisingly to me, for most households the optimal battery size to get the best financial return was only 4kWh. This was smaller than I expected, and is significantly smaller than the average size installed in UK homes to date. Perhaps this is being driven by the tech savvy early adopters who want to maximize their grid independence and use of their solar system.
So when it comes to batteries, and their cost and payback, size and how you use your energy really does matter.
You can see the full paper here: Site Specific Battery Simulation Model.