The SmartGrids feasibility study aims to identify how space based assets (satellite imagery, communications, navigation etc.) can be utilised for the benefit of stakeholders in the energy network sector. The objective is to develop services which can improve energy network performance, reduce costs, and mitigate problems which arise from the management, maintenance and operation of energy network infrastructures.
Services being considered so far include: large scale monitoring, forecasting, and prioritisation support for vegetation control; automatic detection and alert of infrastructure anomalies (subsidence, hot-spots, and foreign objects); emergency satellite communication backup channels; and, remote estimation of electrical consumption.
Users and their needs
Services are targeted at users with needs arising from management, maintenance, and operation of energy networks. Users responsible for infrastructure assets have the potential to benefit from services built on artificial intelligence and space based assets.
Users engaged so far:
WPD are the electricity distribution network operator for the Midlands, South West and Wales within the UK. They deliver electricity to over 7.9 million customers over a 55,500 square kilometres service area.
And, through ESA’s support:
- European Network of Transmission System Operators for Electricity (ENTSO-E) represents 43 electricity transmission system operators from 36 countries across Europe. It was established and given legal mandates by the EU’s Third Legislative Package for the Internal Energy Market in 2009.
- Enel Global Infrastructure and Networks S.r.l belongs to the Enel Group, a multi-national power utility and leading integrated player in the world’s energy markets, with a presence in 37 countries.
- Emergency back-up for critical communication channels.
- Prevention of damage and faults caused by vegetation growth in proximity to infrastructure.
- Automatic monitoring of infrastructure to detect and avoid damage caused by subsidence and extreme weather.
- Automatic detection of foreign objects in powerline corridors.
- Estimation of end user electricity consumption.
- Automatic hot-spot identification which may indicate fault.
Service/ system concept
Service products will be made accessible as per end user needs. This could be via a GMV hosted geospatial web platform, API calls to a cloud hosted server, or data products delivered in a format to be ingested by existing GIS workflows.
User discussions so far indicate the need for services in four application areas:
A – Vegetation monitoring
Service A-1: Provides a map showing vegetation species and vegetation proximity to powerlines. Map is updated as new raw imagery becomes available.
Service A-2: Extends Service A-1 to forecast upcoming problem areas. Uses information on vegetation species and current extent in conjunction with weather data and growth models.
Service A-3: Aids resource deployment, prioritises cutting tasking, monitors work.
B- Electric usage estimation
Service B-1: Provides an estimation of expected electrical consumption based on aerial observations.
Service B-2: Identifies discrepancies between meter reading data and estimates provided by service B-1.
Service B-3: Identifies end user owned renewable power assets and estimates power generation.
C – Satellite communication backup
Service C-1: Emergency backup satellite communications network.
D – Automated Infrastructure monitoring and alert
Service D-1: Hot spot detection.
Service D-2: Pylon displacement monitoring.
Service D-3: Identification of foreign objects in proximity to powerlines.
Space Added Value
By using global positioning services, in conjunction with satellite imagery, labelled features can be located on images captured from space. AI Computer Vision algorithms, capable of learning the signatures of labelled features, can then be trained and used to identify features on a huge scale.
Proposed services will use space assets in three main areas: satellite imagery, GNSS, and satellite communications. These assets will be coupled with technologies such as cloud computation and deep learning. This brings a wealth of benefits over current methods. Namely:
- Substantial reduction in monitoring costs
- Enhanced efficiency and agility in resource deployment
- Early identification of problems
- Increased resilience
- Augmentation of existing practices with new capabilities
- Strong scalability
The feasibility study is being concluded with the consolidation of the business plan.
The consultations done by the consortium at early project stages have derived in a set of service mock-ups with specifications that are aligned with user needs and expectations. Technical and technological details are available as well as the operational procedures to make the services reliable in practical terms.
Over the coming months, the consortia will further engage end users and potential customers to close the market analysis and froze the business plan, and propose a roadmap towards a potential demonstration project.