EnergySHR: Solving Energy Transition Challenges

EnergySHR aims at practically experiencing how data and algorithms, preferably tensely linked to each other, can be shared, as a basis for better cooperation on energy transition research and innovation between scientists from academia, public and private sector, to accelerate research, collaboration and innovation in the energy transition.

AMBITION: A platform for sharing (SHR) open datasets and AI algorithms that contribute to solving challenges of the energy transition.

Objectives

Implement an open-source, practical example to experience and discuss future-proof data and algorithm sharing for research and innovation in the energy transition.
Provide a base infrastructure that may be leveraged for facilitating proof of concepts collaborations between academia and other organizations;

The topics above apply to many other public and private organizations in the Netherlands and other countries. In upcoming phases interested public and private organizations can start participating in this initiative. For instance, by sharing or accessing datasets or algorithms through EnergySHR.

The EnerygSHR platform has been initiated for TU Delft and Erasmus University Rotterdam as part of their cooperation in Convergence.

In the first phase (mid 2023 – mid 2024), a pilot version of EnergySHR was tested, based on an innovative technological platform that aimed to support all use cases: open data, restricted data, and closed data (coupled to algorithms). The pilot identified some challenges associated with the platform technology (design choices, development and deployment requirements) that resulted in a reconsideration of the chosen technology.

EnergySHR will continue to develop a solution focussing on the use case of open data and algorithms, but the pilot EnergySHR platform is closed until further notice as we evaluate our options and explore the best avenues for future work. In the meantime, if you have energy-related data to share, we recommend the following platforms:

Open data (including data under EULA or other restrictions): Upload to [4TU.ResearchData]
Sensitive data: Upload and analyse in SURF's Tinker SANE and Blind SANE secure environments.

Examples of topics for data and AI algorithms

Congestion management
Predictive maintenance
Decision making support tools
Forecasting energy production and consumption
Real-time system optimisation (electricity, heat and gas)
Monitoring system capacity and balance
Flexibility and energy storage
Move to Energy as a Service
Ensuring inclusivity
Citizens’ privacy

Challenges in the energy transition

The energy system is transforming from a centralized, fossil fuel-based, automatic control-dominated system into a system with increased complexity due to decentralised produced renewable energy. The integration between the electrical grid, gases (like hydrogen) and heat systems and reduced time-limits for system control adds to this complexity.

Advanced computational and Artificial Intelligence (AI) solutions are required

This transition requires advanced computational and artificial intelligence (AI) solutions to support the operation framework and simulation models, and fully exploit the information behind the multi-dimensional, historical, operational data inside energy systems. These solutions contribute to solving issues related to decision making for coordination & control of energy systems (balancing the grid, congestion, and flexibility), long-term planning and support interactions in the energy system.

The need for a data and algorithms sharing platform

EnergySHR matches supply and demand of open source data and algorithms that can be relevant for research, collaboration, and innovation supporting the energy transition. It allows data scientists, data consumers and data providers, like grid operators, local government, energy service providers and energy companies to share and collaborate.