Grid edge innovation driving the electricity market towards net zero


Grid edge technologies are an essential tool in the transformation of global energy – facilitating the adoption of renewables, turning consumers in to prosumers, and mitigating climate change, as Michael Nelson explains.

Innovations in the electricity market are rapidly changing the way the industry thinks about grid infrastructure and their relationship with consumers. A once centralised energy market based on fossil fuels is slowly being disrupted, with new energy technologies such as solar and wind power, microgrids and energy storage being adopted to satisfy changing consumer preferences and progressive green policies.

New technologies are generally pioneered and innovated upon on the margin of stable industries before being adopted on a wider scale. In the electricity industry this is known as the ‘Grid Edge’. Technologies developed at the grid edge are currently being utilised to explore ways to transition to a decentralised and transactive electric grid.

Up to this point, electricity markets have mainly comprised of one-way energy transactions, where centralised electricity power stations produce power which is then sold to consumers via a network of distributors and service providers. Grid edge technology is redefining this dynamic – end-users are now able to become power producers or prosumers, while traditional energy producers are becoming service providers.

“Technological innovations that connect producers and consumers of distributed energy allow anyone – from businesses and municipalities to other communities – to become green utilities and start, grow and evolve their energy business,” says Dr Christian Chudoba, founder and CEO at leading energy platform Lumenaza.

“The ensuing decentralisation encourages customer-centric, innovative offers such as dynamic renewable energy tariffs. Consumers are increasingly aware of options outside of traditional suppliers and seek transparency over their consumption as well as a personalised price based on demand. Utilities that transform their offerings – businesses, communities, individuals – and, of course, the environment are all winners of the disrupted energy market.”

Chudoba says that because the urgency of the situation regarding the climate crisis has now reached the mainstream, more people than ever are looking for ways to make a personal contribution to the energy transition.

“Policies such as the EU’s Green Deal and the UK’s Plan for Growth are paving the way towards a sustainable economic recovery after the pandemic, encouraging and even requiring the uptake of grid edge technologies.”

The importance of grid intelligence

Much of the innovation in grid edge technology is devoted to system intelligence. Legacy systems tend to struggle to handle the enormous amounts of data generated by modern energy market participants, and further innovation in artificial intelligence (AI) and machine learning (ML) will be necessary to model, forecast and analyse resilience across the network.

Joel Jaton, CCTO and co-founder at Swiss cleantech Depsys, says that a transition to clean energies will be impossible without grid intelligence technologies at the ‘near edge’ of the distribution grid, providing distribution system operators (DSOs) with the information needed to always ensure operational excellence.

“Today’s grids are mostly stable and predictable. However, the energy transition brings more factors that influence the number of ‘events’ happening on the network. As more intermittent, decentralised energy resources and electric vehicles connect to the grid, the more complicated it becomes to balance the flow to ensure reliable and stable power quality,” says Jaton.

By 2030, it is estimated that there will be 50 to 70 million more electric vehicles in circulation in the UK and Europe alone. Grid operators can also expect more than 470 gigawatts (GW) of additional decentralised renewables, and over 40 GW of self-consumption which will need to be integrated.

All of the ‘events’ triggered by this evolution will need to be identified, processed, categorised, interpreted and shared by applying the intelligent edge architectural principles. If not, then the DSOs will very quickly find themselves unnecessarily overwhelmed.

“Treating the data locally at strategic nodes in the grid helps separate the signals from the noise in real-time, enabling swifter and more targeted action since only the data that matters is shared,” Jaton continues. “This is true for any event whether it relates to congestion, over or under-voltage or interruptions. The more data points that are available, the more opportunity there is for data analysis. But making sure to identify the important nodes in the grid and capturing reliable data is the foundation for stability.”

Once the data sources are set, then the edge processing and intelligence data treatment can begin.

“From the outset, thresholds are set to, for example, notify of a voltage violation,” Jaton concludes. “This helps uncover anomalies from day one. As more data is captured and processed, it becomes relevant to look at trends and patterns that can help decide on appropriate actions as well as ultimately help predict future grid behaviour.”

“In addition, DSOs will save on costs for data transfer and storage, as well as reduce their need to hire data experts.”

Optimising grid monitoring near the edge

As DSOs evolve both their physical infrastructure and their operational procedures, questions need to be answered about where the grid needs to be intelligent.

“It is costly to add intelligence everywhere, and frankly unnecessary, adding more complexities that are not needed. The very edge of the distribution grid are the end points where we consume electricity – our homes, offices and streetlights, for example,” says Jaton.

“It is useful for both the consumer and electricity provider to know how much is being consumed, and when, allowing for more conscious consumer behaviour as well as fairer billing, which is the main purpose that smart meters serve.

“Moving slightly inwards, the end points are typically connected in cabinets which are themselves connected to low or medium voltage transformer substations. These nodes are crucial hubs for capturing and processing data from the grid. Therefore, this is where the intelligence needs to reside for the DSO to get a reliable, actionable overview of grid performance and acquire the ability to optimise across the grid by acting in targeted locations.”

In the absence of this real grid data, DSOs have so far relied on the knowledge and experience of their staff as well as on scenario-based simulations. Traditionally they have opted for the ‘worst case’ scenario to be sure to not compromise their ability to continue to deliver a quality electricity supply.

The consequence has been pre-emptive grid build-out with often over dimensioned transformers, cables, and other assets.

Using AI, ML, and smart meter technologies, DNOs can deliver accurate, reliable, and efficient data far superior to the manual analysis currently performed by their network planners. Utilities will be able to better understand their network reinforcement needs, plan more accurately and optimise their investments.

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