offshore shipping

Houston logistics company works toward software solutions to energy transition challenges

Matthew Costello, CEO and co-founder of Voyager Portal, joins the Houston Innovators Podcast. Photo courtesy of Voyager

For several years now, Matthew Costello has been navigating the maritime shipping industry looking for problems to solve for customers with his company, Voyager Portal.

Initially, that meant designing a software platform to enhance communications and organization of the many massive and intricate global shipments happening every day. Founded in 2018 by Costello and COO Bret Smart, Voyager Portal became a integral tool for the industry that helps users manage the full lifecycle of their voyages — from planning to delivery.

"The software landscape has changed tremendously in the maritime space. Back in 2018, we were one of a small handful of technology startups in this space," Costello, who serves as CEO of Voyager, says on the Houston Innovators Podcast. "Now that's changed. ... There's really a huge wave of innovation happening in maritime right now."

And, predictably, some of those waves are caused by new momentum within the energy transition.

"The energy transition has thrown up a lot of questions for everyone in the maritime industry," Costello says. "The regulations create a lot of questions around cost primarily. ... And that has created a huge number of opportunities for technology."

Fuel as a primary cost for the maritime industry. These cargo ships are traversing the world 24/7 and burning fuel at all times. Costello says there's an increased focus on the fuel process — "all with a goal of essentially reducing carbon intensity usage."

One of the ways to move the needle on reducing the carbon footprint of these ships is optimizing the time spent in port, and specifically the delays associated. Demurrage are charges associated with delays in loading and unloading cargo within maritime shipping, and Costello estimates that the total paid globally in demurrage fees is around $10 billion to $20 billion a year.

"These fees can be huge," Costello says. "What technology has really enabled with this problem of demurrage is helping companies drill down to the true root cause of what something is happening."

All this progress is thanks to the enhancement — and wider range of acceptance — of data analysis and artificial intelligence.

Costello, who says Voyager has been improving its profitability every quarter for the last year, has grown the business to around 40 employees in its headquarters of Houston and three remote offices in Brazil, London, and Singapore. The company's last round of funding was a series A in 2021. Costello says the next round, if needed, would be next year.

In the meantime, Voyager is laser focused on providing optimized, cost-saving, and sustainable solutions for its customers — around half of which are headquartered or have a significant presence in Houston. For Costello, that's all about putting the control back into the hands of his customers.

"If we think back to the real problems the industry faces, a lot of them are controlled by different groups and parties. The fact that a ship cannot get in and out of a port quickly is not necessarily a function of one party's issue — it's a multitude of issues, and there's no one factor," Costello says on the show. "To really make the whole process efficient end-to-end you need to provide the customer to access and options for different means of getting cargo from A to B — and you need to have a sense of control in that process."

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This article originally ran on InnovationMap.

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A View From HETI

Georg Rute ,CEO of Gridraven, discusses the potential of AI and DLR. Photo via Getty Images

From bitter cold and flash flooding to wildfire threats, Texas is no stranger to extreme weather, bringing up concerns about the reliability of its grid. Since the winter freeze of 2021, the state’s leaders and lawmakers have more urgently wrestled with how to strengthen the resilience of the grid while also supporting immense load growth.

As Maeve Allsup at Latitude Media pointed out, many of today’s most pressing energy trends are converging in Texas. In fact, a recent ERCOT report estimates that power demand will nearly double by 2030. This spike is a result of lots of large industries, including AI data centers, looking for power. To meet this growing demand, Texas has abundant natural gas, solar and wind resources, making it a focal point for the future of energy.

Several new initiatives are underway to modernize the grid, but the problem is that they take a long time to complete. While building new power generation facilities and transmission lines is necessary, these processes can take 10-plus years to finish. None of these approaches enables both significantly expanded power and the transmission capacity needed to deliver it in the near future.

Beyond “curtailment-enabled headroom”

A study released by Duke University highlighted the “extensive untapped potential” in U.S. power plants for powering up to 100 gigawatts of large loads “while mitigating the need for costly system upgrades.” In a nutshell: There’s enough generating capacity to meet peak demand, so it’s possible to add new loads as long as they’re not adding to the peak. New data centers must connect flexibly with limited on-site generation or storage to cover those few peak hours. This is what the authors mean by “load flexibility” and “curtailment-enabled headroom.”

As I shared with POWER Magazine, while power plants do have significant untapped capacity, the transmission grid might not. The study doesn’t address transmission constraints that can limit power delivery where it’s needed. Congestion is a real problem already without the extra load and could easily wipe out a majority of that additional capacity.

To illustrate this point, think about where you would build a large data center. Next to a nuclear plant? A nuclear plant will already operate flat out and will not have any extra capacity. The “headroom” is available on average in the whole system, not at any single power plant. A peaking gas plant might indeed be idle most of the time, but not 99.5% of the time as highlighted by the Duke authors as the threshold. Your data center would need to take the extra capacity from a number of plants, which may be hundreds of miles apart. The transmission grid might not be able to cope with it.

However, there is also additional headroom or untapped potential in the transmission grid itself that has not been used so far. Grid operators have not been able to maximize their grids because the technology has not existed to do so.

The problem with existing grid management and static line ratings

Traditionally, power lines are given a static rating throughout the year, which is calculated by assuming the worst possible cooling conditions of a hot summer day with no wind. This method leads to conservative capacity estimates and does not account for environmental factors that can impact how much power can actually flow through a line.

Take the wind-cooling effect, for example. Wind cools down power lines and can significantly increase the capacity of the grid. Even a slight wind blowing around four miles per hour can increase transmission line capacity by 30 percent through cooling.

That’s why dynamic line ratings (DLR) are such a useful tool for grid operators. DLR enables the assessment of individual spans of transmission lines to determine how much capacity they can carry under current conditions. On average, DLR increases capacity by a third, helping utilities sell more power while bringing down energy prices for consumers.

However, DLR is not yet widely used. The core problem is that weather models are not accurate enough for grid operators. Wind is very dependent on the detailed landscape, such as forests or hills, surrounding the power line. A typical weather forecast will tell you the average conditions in the 10 square miles around you, not the wind speed in the forest where the power line is. Without accurate wind data at every section, even a small portion of the line risks overheating unless the line is managed conservatively.

DLR solutions have been forced to rely on sensors installed on transmission lines to collect real-time weather measurements, which are then used to estimate line ratings. However, installing and maintaining hundreds of thousands of sensors is extremely time-consuming, if not practically infeasible.

The Elering case study

Last year, my company, Gridraven, tested our machine learning-powered DLR system, which uses a AI-enabled weather model, on 3,100 miles of 110-kilovolt and 330-kilovolt lines operated by Elering, Estonia’s transmission system operator, predicting ratings in 15,000 individual locations. The power lines run through forests and hills, where conventional forecasting systems cannot predict conditions with precision.

From September to November 2024, our average wind forecast accuracy saw a 60 percent improvement over existing technology, resulting in a 40 percent capacity increase compared to the traditional seasonal rating. These results were further validated against actual measurements on transmission towers.

This pilot not only demonstrated the power of AI solutions against traditional DLR systems but also their reliability in challenging conditions and terrain.

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Georg Rute is the CEO of Gridraven, a software provider for Dynamic Line Ratings based on precision weather forecasting available globally. Prior to Gridraven, Rute founded Sympower, a virtual power plant, and was the head of smart grid development at Elering, Estonia's Transmission System Operator. Rute will be onsite at CERAWeek in Houston, March 10-14.

The views expressed herein are Rute's own. A version of this article originally appeared on LinkedIn.

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