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How better weather forecasts will allow capacity increases on electricity transmission network

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The Met Office is to begin providing highly detailed weather forecasts to National Grid Electricity Transmission (NGET), which will lead to expanded capacity on the electricity transmission network for wind and solar power. 

For those not in the know, the electricity transmission system is limited by the individual power rating or capacity of the circuits connecting everyone across the country and, with the growth in renewable electricity generation, power flows across the existing grid infrastructure are changing, particularly at peak periods. It’s hoped that this new project will help the grid be more resilient. 

Currently assets are operating at ratings lower than their actual capacity, that’s because NGET is making conservative assumptions about weather conditions. However, the company believes that it could boost its operating capacity by adding in more accurate forecasts to its calculations. 

The calculations that it needs to make are what NGET calls the Dynamic line rating, or DLR for short. The DLR is essentially the capacity of an overhead transmission power line when taking into account all the physical factors of the conductor, alongside the environmental factors. 

Environmental factors can drastically change the DLR for a transmission line, as they are susceptible to changing wind speeds, wind directions, ambient temperature and even solar radiation. At the moment, NGET makes conservative estimates as to what those factors will be and sets the capacity accordingly, if it could get a more accurate reading, it’s thought that it could increase capacity without any dire consequences. 

What NGET is looking for is environmental conditions that are favourable enough to cool the conductors sufficiently. The more cooling that the conductors have, the more power can be pushed through the line, thus relieving some of the network constraints and meeting the country’s energy needs. 

Ofgem is funding the partnership between NGET and the Met Office through its Network Innovation Allowance mechanism, with £545,000 being set aside for the project. 

Anusha Arva, Innovation Engineer for National Grid Electricity Transmission, commented,  “We’ve explored the feasibility of integrating high specification weather forecasting data into our overhead line rating calculation methodology along with starting the initial work towards a demonstrator system. We’re now looking to take the research forward through a subsequent phase of innovation, with the aim to validate this updated methodology against measurements from specific sections of the transmission network, using devices such as local weather stations, and overhead line measurement sensors. The specific sections of the transmission network will be chosen based on a range of scenarios under which Dynamic line rating methodology would need to operate, with focus on sections shaded by dense vegetation and civil structures.”

“With the knowledge and expertise from the Met Office, more accurate and granular weather data can better determine where and when we can boost capacity. Inevitably, this means we can become less reliant on fossil fuel-based generation, avoiding constraint payments to generators when we need to address grid congestion.”

Ian Pearman, Senior Scientist for the Met Office, added, “With electricity generation increasingly dependent on the weather, we’re working ever more closely with National Grid. This innovative project has the potential to provide significant improvements in weather forecast accuracy for the energy sector alongside novel approaches to quantifying uncertainty in the forecast. These can be applied to increase confidence in uprating decisions to help achieve the flexibility requirements on the path towards net zero.”

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  • by P. R..Padmanabhan
    Posted October 3, 2021 2:07 am 0Likes

    Very useful innovation. Putting Science to study nature(weather) and bring efficiency in transmission is a great advancement. Congratulations to Ian Pearman and Anusha Arva.

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