DIGITAL ENERGY

Road to 2030

Unprecedented and systemic change in the energy industry is unfolding. We are working with leading stakeholders from Energy and IT to bridge some gaps of the future energy marketplace. The roadmap to 2030 is emerging at pace.

Implementation

Stakeholders globally are working to define the new digital energy landscape as never before. National implementation programmes on an unprecedented scale are due to start.

Open Data

DSOs are utilising increasing volumes of open network data to understand and react to changes in bi-directional demand and supply. Aggregated, normalised data from multiple sources requires an extensive storage and processing platform.

Engagement

Harnessing the power of aggregated open data through ground-breaking technology, we are working with partners to establish world-class operational control and analytics systems.

The
Challenge

The projected growth of Electric Vehicle charging points and heat pumps are set to grow exponentially over the next 8-9 years.

As a result, there is an increasing need for electricity grid Distribution Network Operators (DNOs or DSOs) to be aware of the locations of these devices, and to know their status at any given time.

There is a clear need for a whole of market aggregation service that will interact with a multitude of Operators and Vendors to extract utilisation data, reformat it, filter by region and provide the output to DSOs. The data should be as near to real-time as possible.

Plugin Power Energy is bridging the gap between the emerging needs of the energy industry and solution provision from major IT service vendors.
This comes at a time when the decades old power industry faces nothing short of a digital revolution in almost every country.

The
Solution

By harnessing the power of Artificial Intelligence learning, your team can more accurately predict utilisation modelling and peak planning. Through our work with industry standards groups globally, we aim to enable your team to load balance clusters of chargers and heat-pumps remotely.

• Through a combination of DSO requirements, experience and from automated learnt behaviour, our AI system will project peaks based on events, weather forecasts, social patterns.
• The more users, the more data, the more accurate the projections will become.

Engagement of EV drivers to assist with pro active load balancing.

 

The future of live
charger data

By harnessing the power of Artificial Intelligence learning, your team can more accurately predict utilisation modelling and peak planning. Through our work with industry standards groups globally, We aim to enable your team to load balance clusters of chargers remotely….

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