Jun 5, 2020

Smarter utilities through machine learning

William Girling
4 min
Jeff Fried
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming many elements of our lives, including the utility industry...

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming many elements of our lives, including the utility industry.

Utilities, such as energy, gas, water, and waste management, already rely on smart devices for optimization of infrastructure and the supply-demand balance. Now smarter utilities – a whole ecosystem of technology-driven, sophisticated marketplaces – are emerging. They have a lot to gain from the use of ML. The energy sector and smart power grids, in particular, will benefit enormously from recent advances in ML and AI. 

The energy sector and the infrastructure it relies on is incredibly complex. As a result, it’s often plagued by maintenance issues, system or equipment failures, and management challenges which can be caused by a variety of factors including inclement weather, surges in demand, and misallocation of resources. In fact, it has been estimated that 61 to 86% of energy on the U.S. grid is wasted due to overloading grids and creating congestion.

The Data Conundrum

Energy grids provide a treasure trove of data, much of which can help operators triage issues as they arise. However, collecting and aggregating this data is a significant challenge given the high volume of data constantly passing through the grid. For instance, think about the signals coming from billions of different pieces of equipment and from millions of sources across the grid. As a result, it’s an incredibly daunting task for operators to keep up with this flood of data, which can often lead to missed insights that may cause a malfunction, or worse, an outage.

Properly collecting this data is only half the challenge. Once it’s collected and organized, making use of this data is a consistent headache for data scientists. A diverse group of algorithms must be built to uncover the insights needed to ensure grids run efficiently. From there, they must be constantly maintained to guarantee accuracy, which requires a significant investment in time and resources for those involved. 

How Automation Can Help

Like many other business applications, harnessing the power of ML to automate processes within data management can provide significant benefits for the energy sector. Some of the most applicable applications include:

  • Predicting failures - With the right algorithms in place, operators can better predict grid failures before they reach the customer. As a result, energy companies can avoid customer dissatisfaction and the corresponding financial losses that come with it.
  • Energy management - Surges in energy usage can cause major problems to the grid. By properly allocating energy based on demand, operators can enable “load management” across the grid, saving resources when needed and ultimately leading to greener, more efficient practices.
  • Maintenance - Energy grids cover massive sections of the country and can often be hit with multiple different weather scenarios at the same time such as high winds in one area, lightning strikes in another, and heavy rain in an entirely different region. Being able to automate the intake of maintenance signals and predict where maintenance may be needed enables operators to prioritize work, save money, and reduce downtime. 

Putting Machine Learning in Practice

The energy sector is rapidly adopting ML capabilities to automate the way the grid is operated, creating new demands on development teams. To accomplish these goals and to stay on pace, developers need fast and easy access to ML capabilities. They can’t afford to sink weeks and weeks and into building the code and architectures required to make automation possible. 

Fortunately, there are solutions available to make this possible, enabling developers to quickly test ML-powered applications seamlessly and without system interruption. By putting ML within the reach of application developers, teams can get to value faster. Embedding ML within data management platforms is a way of enabling developers, and it ultimately empowers data science teams to spend more time innovating and less time building and maintaining. As the sector becomes more advanced, so will the ML operations coming into play, which will lead to a smarter grid, more efficient operators, and happier customers. 

This article was contributed by Jeff Fried, Director of Product Management, InterSystems 

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Jul 26, 2021

Ofwat allows retailers to raise prices from April

Dominic Ellis
3 min
Ofwat confirms levels of bad debt costs across the business retail market are exceeding 2% of non-household revenue

Retailers can recover a portion of excess bad debt by temporarily increasing prices from April 2022, according to an Ofwat statement.

The regulator confirmed its view that levels of bad debt costs across the business retail market are exceeding 2% of non-household revenue, thereby allowing "a temporary increase" in the maximum prices. Adjustments to price caps will apply for a minimum of two years to reduce the step changes in price that customers might experience.

Measures introduced since March 2020 to contain the spread of Covid-19 could lead to retailers facing higher levels of customer bad debt. Retailers’ abilities to respond to this are expected to be constrained by Ofwat strengthening protections for non-household customers during Covid-19 and the presence of price caps.  

In April last year, Ofwat committed to provide additional regulatory protection if bad debt costs across the market exceeded 2% of non-household revenue. 

Georgina Mills, Business Retail Market Director at Ofwat said: “These decisions aim to protect the interests of non-household customers in the short and longer term, including from the risk of systemic Retailer failure as the business retail market continues to feel the impacts of COVID-19. By implementing market-wide adjustments to price caps, we aim to minimise any additional costs for customers in the shorter term by promoting efficiency and supporting competition.”  

There are also three areas where Ofwat has not reached definitive conclusions and is seeking further evidence and views from stakeholders:   

  1. Pooling excess bad debt costs – Ofwat proposes that the recovery of excess bad debt costs is pooled across all non-household customers, via a uniform uplift to price caps. 
  2. Keeping open the option of not pursuing a true up – For example if outturn bad debt costs are not materially higher than the 2% threshold. 
  3. Undertaking the true up – If a 'true up' is required, Ofwat has set out how it expects this to work in practice. 

Further consultation on the proposed adjustments to REC price caps can be expected by December.

Anita Dougall, CEO and Founding Partner at Sagacity, said Ofwat’s decision comes hot on the heels of Ofgem’s price cap rise in April.

"While it’s great that regulators are helping the industry deal with bad debt in the wake of the pandemic, raising prices only treats the symptoms. Instead, water companies should head upstream, using customer data to identify and rectify the causes of bad debt, stop it at source and help prevent it from occurring in the first place," she said.

"While recouping costs is a must, water companies shouldn’t just rely on the regulator. Data can help companies segment customers, identify and assist customers that are struggling financially, avoiding penalising the entire customer in tackling the cause of the issue."

United Utilities picks up pipeline award

A race-against-time plumbing job to connect four huge water pipes into the large Haweswater Aqueduct in Cumbria saw United Utilities awarded Utility Project of the Year by Pipeline Industries Guild.

The Hallbank project, near Kendal, was completed within a tight eight-day deadline, in a storm and during the second COVID lockdown last November – and with three hours to spare. Principal construction manager John Dawson said the project helped boost the resilience of water supplies across the North West.

“I think what made us stand out was the scale, the use of future technology and the fact that we were really just one team, working collaboratively for a common goal," he said.

Camus Energy secures $16m funding

Camus Energy, which provides advanced grid management technology, has secured $16 million in a Series A round, led by Park West Asset Management and joined by Congruent VenturesWave Capital and other investors, including an investor-owned utility. Camus will leverage the operating capital to expand its grid management software platform to meet growing demand from utilities across North America.

As local utilities look to save money and increase their use of clean energy by tapping into low-cost and low-carbon local resources, Camus' grid management platform provides connectivity between the utility's operations team, its grid-connected equipment and customer devices.

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