How to manage the risk surrounding Australia’s energy future
In the face of global climate change, Australia’s energy future is one of the most important issues facing our government and business leaders. Last Summer, short-term attention has focused on the need to avoid blackouts, which in-turn has translated into pressure to continue to operate ageing assets such as AGL’s Liddell power station in the Hunter Valley. With that as our present reality, how do we ensure these assets are operated in a manner which provides consistent and reliable service?
At a recent roundtable with The Australian Financial Review industry leaders discussed the uncertainty facing the energy industry. There was a general consensus that the industry needs greater clarity on government policy before making some crucial investment decisions. Alinta Energy CEO Jeff Dimery compared the current situation to playing football with invisible goalposts.
This puts the ball in the government’s court. While there’s much more debating to be done on recently announced policy which places emphasis on reliability and affordability of supply, it’s safe to assume coal-fired power stations have a role to play for the foreseeable future. This might be another 20 years. It might be longer than that.
So, how do the operators of these ageing assets keep them operating efficiently, minimising the risk of outages that have very serious implications for business, government and the wider population?
This is crucially important as the year moves towards another hot summer. A repeat of the blackout that swept across South Australia in 2016 would be a very bad result for all concerned.
Managing risk profile
Operating an asset throughout its’ design life presents challenges; where to invest in predictive and preventive maintenance, how to minimise unplanned downtime, where might the failures occur? These questions become even more critical when the asset life is extended beyond design. That’s where intimate knowledge of the individual items of equipment becomes a business imperative. Imagine if you had 185 years of loss experience across thousands of businesses, with the ability to challenge that data to learn where the weaknesses might exist.
By partnering with an insurer who can use analytics in combination with highly trained engineering staff businesses can drive investment to address any concerns about reliability. This makes business more resilient and risk management more efficient and effective because you’re spending money in the right places.
Let’s use a turbine as an example. The original equipment manufacturer (OEM) specifies inspection frequencies and these periodical checks are used to detect and manage any major maintenance issues. Predictive analytics provides another layer of detail so, as a risk manager, you’re able to leverage the knowledge gleaned from the operating experience of thousands of other similar pieces of equipment.
If you’ve been running a large fleet of turbines across a number of plants for many years, you’ll have data that lets you know when it’s time to check equipment such as over-speed protection. This is accurate because it’s based on many hours of operation. But, what if you have inexperienced operators; how might the delay in checking overspeed protection become a higher priority if you have less experience in your control room? If you don’t have this data, what’s most important in ensuring you avoid a significant equipment failure?
Loss history data
Access to loss history data will give you better visibility of where your business is exposed and which investments you should prioritise. Maybe you need to invest in automatic protection, or perhaps a focus on operator training to make sure testing is being done at the right time and in the right manner. Insightful response to an incident can make the difference between a significant loss and a minimal interruption.
Common examples where analytics are relevant in the utilities industry including dissolved gas analysis in transformers, flame detection in boilers or crack propagation monitoring on turbines. Managing the lifecycle of all industrial equipment is just like running your car – the more kilometres you travel, the greater the risk that something will fail.
Armed with the knowledge predictive analytics provides you’ll know what losses have occurred in the past and what you need to do to minimise the risk of them happening in your business. Anything else is just guesswork.