We present our video of the online discussion from February 24, 2021 on smartgrid response. Digital, automated, data-driven smart response systems can play a key role in grid security and stability going forward. This makes asset monitoring and controllability – underpinned by the Smart Grid Indicator which is now part of the EU Electricity Directive (Article 59) – a vital link in the chain. Taking part were Vera Silva, COT, General Electric and Head of Innovation, T&D Europe; Norela Constantinescu, Head of Research and Innovation, ENTSO-E; Ercole de Luca, Engineering and Innovation, ARETI (DSO, ACEA GROUP); Fréderic Vassort, CEO, AMPACIMON (sponsor). Moderated by Energy Post’s Matthew James. At the end of this post Fréderic Vassort answers specific questions about the importance of this increasingly important field.
A marketplace for energy data will enable Europe’s grid expansion by Veronika Spurná and Helena Uhde at the EU-China Energy Cooperation Platform
Background to the event
The triple drivers of increased demand, mass electrification and the huge ramp-up of variable renewables cannot be catered for by our existing electricity grid/s. However, every day, we learn of new innovations in storage, renewable generation, cross border regulation and market improvements which will come online as the pressure increases.
Central to all this, the grid itself is also innovating. Not least in the area of smartgrid response. Digital, automated, data-driven smart response systems are key to grid security and stability going forward. This makes asset monitoring and controllability, underpinned by the Smart Grid Indicator, which is now part of the EU Electricity Directive (Article 59), a key link in the chain and a subject worthy of discussion – being of great interest to our readers.
Here is a brief outline of the event, sponsored by Ampacimon:
The discussion and presentations help distribution grid operators understand the implications of increased demand due to mass electrification, and transition to a carbon neutral energy mix on grids. Discussion focused on how they can improve the reliability and resilience of their networks; in particular, by increasing grid monitoring and applying AI/ML analytics as advocated by Smart Grid Indicators. The expected main benefits are highlighted below, namely:
- Improve SAIFI/SAIDI statistics – better supply quality
- Faster emergency response – in case of extreme weather/natural disaster/terrorist threat
- Prevent outages – identify weak spots (asset deterioration) and assess likelihood of breakdown (combining with weather forecast and heuristic statistics) to enable predictive maintenance
- Efficient grid upgrades – Prioritisation of metrics for which assets to replace/upgrade first
- Optimal use of grid capacity for DER (renewables, EV and storage) integration
Q&A with Fréderic Vassort, CEO, AMPACIMON
Why is resilience such an important factor for modern electricity grids?
Grids are more and more subject to extreme weather events (for example Texas earlier this year, heat waves and resulting bush fires in California, Australia etc.), making them more vulnerable.
It comes at the worst time, as peak demand is expected to increase due to increased electrification (EVs, Heat Pumps, the switch from fossil fuels to electricity for industrial usages), mostly at distribution level (precisely where grids are more exposed because of the very high mileage of lines).
….and grids are ageing. Grid asset managers are already facing and “investment wall” in years to come, only to replace assets reaching their end of life.
The combination of these three trends makes grid resilience a pressing issue for most DSOs.
What are the drivers that make it such an important feature?
Global warming increases the frequency and severity of extreme weather events.
Their impact on grids is higher than ever before because electrical grids have become an essential part of our economies’ infrastructure.
Moves towards decentralised generation don’t mean that grids will be redundant and that consumers will completely go “off grid”. Quite the contrary, it means that a higher number of smaller, decentralised generators will need to be connected 24×7 in a reliable way.
Likewise, a fast move towards transport electrification (Electrical Vehicles) implies that grids’ peak capacity will have to increase significantly at lower voltage levels.
The same applies to a switch from conventional heating to heat pumps.
Distribution grids are bound to become more and more critical to a reliable functioning of the system, as they need to accommodate higher peak loads, reverse flows and be able to manage local voltage fluctuations induced by these new usages.
This will require significant reinforcement investments, but, above all, better observability and controllability of distribution grids (as opposed to the “fit and forget” principle applied so far).
Clearly, a full “copper plate” approach isn’t financially feasible (nor technically deployable within the relatively short time span required. For example, burying distribution overhead lines cost an order of magnitude more than OHL lines, and takes years to implement).
Fitting the grid with smart sensors, smart actuators, and AI-based data analytics to better detect faults, forecast them, and increase the level of close to real time control is essential.
How can DSOs ensure reliability and stability under such conditions?
While infrastructure reinforcement (underground lines, substations capacity increases) is needed in view of the required capacity increases we believe – and Ercole de Luca at ARETI (DSO, ACEA GROUP) also talks about eloquently in the video (from 21mins 30secs to 30mins 55secs) regarding the Rome grid – “smart” investments must be made in the fields of…
1] Grid observability: measuring in quasi-real time key electrical parameters on all critical branches (if not all branches) of distribution grids. So far, this is achieved only in areas with close to 100% smart meter coverage, and only with relatively poor time resolution (typically 15 mins): not enough to control an active grid.
Likewise, having better visibility on grid asset conditions is key to improve their resilience. It will enable operators to detect faults in real time, locate them, and identify the root cause of the problem as soon as possible, in order to be able to restore operations quickly (For example Gridvisor fault location).
In turn, better observability generates knowledge for a utility in the form of vast amounts of data that can be mined to better understand the behaviour of the grid, particularly when confronted with exceptional events (such as storms or failures). Incorporating this knowledge through AI based solutions into grid operations management will contribute to increasing grid reliability and resilience.
2] “Controllability”: moving from “fit and forget” to active grid management, even at low levels of distribution networks, either centrally or though decentralised solutions, to manage voltage fluctuations and reverse flows induced by large scale decentalised generation (and storage). This required advances Distribution Management systems, using real time data (as mentioned above) to feed them.
3] Forecast: this is the next frontier. Using data generated by measurement points widely dispersed on the grid, together with third party data (weather forecasts, maintenance data, load forecast inputs obtained from third parties), in order to (i) improve close to real time active management of the grid, and (ii) perform smart preventive maintenance in order to mobilise teams (and investments) when and where they maximise their impact.