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The rise of distributed power generation could cause gridlock on grids without further upgrades
The global energy transition is driving a massive increase in renewable energy capacity delivered at unprecedented speed, yet the effort will be wasted without an equally dramatic increase in electricity capacity.
Net zero will require power grids to form the backbone of the future energy system and supply half of all energy consumption by 2050 through the electrification of sectors from transport to industrial manufacturing.
Yet, few realise that existing networks are already leaking massive amounts of power and running significantly below capacity, imposing needless constraints on renewable generation and impeding the energy transition.
The power sector is yet to match the digital transformation of other industries which would enable it to harness smart data from its networks to boost grid capacity and reduce waste, unlocking more renewable energy resources without excessive build-outs.
Accurate data on everything from power line temperatures to wind conditions could hold the key to safely increasing capacity, sharing loads between lines or preventing power loss.
With enormous renewable energy capacity already being curtailed, caught in congestion or facing lengthy interconnection queues, the electricity grid is rapidly becoming the real bottleneck in the race to net zero.
Even worse, attempts to alleviate the capacity crunch and avoid the risk of gridlock through build-outs of new networks could run into planning restrictions or land-use conflicts with communities and cost upwards of $14 trillion globally.
With power grid expansion running behind net zero targets, we need a dramatic shift in network efficiency to help operators meet their targets for grid decarbonisation and capacity expansion without unsustainable costs.
There is a imminent risk of gridlock, power outages or major curtailments of renewable energy generation due to the growing squeeze on network capacity. Significant amounts of renewable energy is caught in interconnection queues and even more renewable supply is being curtailed because of capacity constraints.
Combined with the current lack of utility-scale storage solutions, this creates a growing risk that the grid will lack the flexibility to support sudden surges in demand from millions of electric cars and heating systems.
And global grid expansion is falling far behind target due to soaring supply chain costs, bureaucratic permitting processes and land-use conflicts with local communities over the potential impact on everything from flora and fauna to water pollution.
The wasteful, inefficient use of existing networks is one of the key roadblocks to increased incorporation of renewable energy and a major driver behind the demand for new infrastructure.
The problem is that the growing decentralisation of power grids and diversification of power sources has left network operators with limited visibility over their networks.
Many utilities have very limited oversight of primary circuits using basic Supervisory Control and Data Acquisition (SCADA) systems, and little to no visibility of the secondary circuits where renewable power sources are widespread.
Many operators currently rely on rough estimates of network conditions or capacity from a few basic parameters such as temperature or weather forecasts, leaving major data blind spots across their networks.
This denies them the opportunity to help reduce power loss by finding the site and source of electricity losses or seeing where they could share loads between lines. Crucially, limited data prevents them taking advantage of cooler weather to increase power flows while staying within safe conductor temperatures.
For instance, grid operators currently estimate the ampacity or maximum current that overhead power lines can carry without overheating by using crude calculations based on limited parameters.
This means they set excessively cautious capacity limits that fail to take account of the much greater thermal capacity of overhead lines during cooler conditions. As a result, many power grids run 20% below their true capacity.
Under-estimating ampacity means renewable generators are being needlessly replaced with dirty power sources such as gas-fired peaker plants because operators mistakenly believe their long-distance transmission lines are overloaded.
Accurately calculating ampacity would therefore help operators achieve their net-zero targets by reducing reliance on backup power from fossil fuel plants or diesel-powered generators.
The lack of network visibility also means utilities are missing other opportunities to maximise efficiency and output without new infrastructure. For example, the lack of current, comprehensive data on loads across feeder lines is hampering operators from balancing loads between parallel lines to enhance grid reliability and flexibility.
Outdated grid monitoring systems relying on limited, late information from call centres or technicians mean that power loss and theft are going undetected across many networks.
A NEW APPROACH
Utilities in the Middle East are harnessing “multi-sensing” technologies that accurately analyse myriad parameters across networks in real-time to minimise power losses, optimise usage and maximise grid capacity. This could help operators enhance grid performance while deferring or reducing capital investment in new infrastructure.
A recent pilot in the Middle East saw the world’s first deployment of multi-sensing systems on transmission lines to enable Dynamic Line Rating, which safely increases power flows without exceeding safe temperature limits during cooler weather conditions.
The system’s sensors analysed data from over 60 parameters, helping to predict the maximum current that overhead lines could carry several days ahead. This is helping operators harness favourable weather conditions to safely increase capacity, relieve congestion and integrate more renewable power into networks without unnecessary extra infrastructure.
Unlocking spare capacity on long-distance high-voltage transmission lines, could also remove the need to curtail distant renewable energy generators and turn on fossil-fuelled ‘peaker plants’ during peak periods.
The same technology can also use feeder sensors to identify unusually low power levels or leaking grid components and even find common causes of power loss across multiple sites, helping networks conserve power.
This could ultimately be combined with machine learning systems to create smart “self-healing grids” that can anticipate and avert power loss or other faults before they occur.
Cumulatively, a more digitally-drive approach to grid operation could enhance network capacity, flexibility and reliability by giving grid operators a 24-hour, full-spectrum window to dynamically improve network performance.
This could create smart, self-improving grids that continuously increase flexibility and capacity, helping integrate more renewable energy without excessive new infrastructure. A transformation in network efficiency must take place in parallel with new infrastructure development to help achieve the energy transition without unsustainable costs. •
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