Supergen Network Plus in Artificial Intelligence for Renewable Energy (SuperAIRE)
Lead Research Organisation:
University of Warwick
Department Name: School of Engineering
Abstract
SuperAIRE aims to establish a world-leading network connecting academia, industries, and policymakers across the spectrum of artificial intelligence (AI) for renewable energy (RE), particularly wind, solar, marine, and bio energy. This includes generation, storage, transmission/distribution and demand side management. These represent most of the research areas in the UKRI's Energy and Decarbonisation theme. With SuperAIRE, we aim to create the conditions in which AI for RE can be promoted much more rapidly than at present to boost the development and deployment of RE. We will not only exploit the transformative power of AI in different RE subsectors but also address common challenges and optimise performance across the RE ecosystem. Supported by a broad partnership currently with 30 partners across industry (23), leading R&I organisations (5), and policymakers (2), we will incubate a Supergen AI+RE research community seizing the opportunity to enhance the UK's role as a global leader in the intelligent and digital transformation of the RE sector.
Despite the recent growth in all subsectors, progress in essential technologies supporting the lifecycles of RE systems lags behind. AI offers strategic advantages in overcoming the limitations of traditional methods which struggle to process the increasing complexity and big data in RE systems. It will enable decision-supporting digitalisation, operational efficiency optimisation, cost-effective integration, multi-scenario adaptability, and technological cross-applicability. Though there are some current critical masses in AI for RE, the communities are facing many challenges, e.g., the fragmented nature of the landscape, subsystem isolation, and limited scope. SuperAIRE will address these challenges by enabling shared learning on common research challenges in different RE subsectors through promoting novel generic approaches complemented with refinements tailored to subsector's unique needs; forging a holistic view to facilitate system-wide AI applications; and fostering comprehensive solutions that go beyond single-task focuses to exploit the full potential of AI in enhancing the RE ecosystem.
SuperAIRE will carry out diverse activities to engage with stakeholders, facilitate knowledge exchanges, catalyse community coherence, identify cross-sector opportunities, address skill gaps, support nurturing high-skill professionals with multidisciplinary expertise, and disseminate project outcomes. These activities include four key challenge workshops, bimonthly seminars, flexible funds, outreach activities, an international conference, etc. SuperAIRE will support early career researchers (ECRs) from both academia and industry via a dedicated ECR Forum, a mentoring scheme, secondment opportunities, and ECR grants. We will emphasise Equality, Diversity and Inclusion in all activities. Based on the current critical mass and emerging gaps and opportunities, we have also proposed six pre-defined research themes (RTs) to steer our Network+ activities, especially in guiding discussions, identifying challenges and opportunities, streamlining research coordination efforts, shaping a research landscape report, and developing a whitepaper. This includes RT1 Robust and trustworthy AI; RT2 Prediction and forecasting across scales; RT3 AI-powered digital twins; RT4 Intelligent control and management; RT5 Smart integration; and RT6 Intelligent robotics and autonomous systems in resource assessments, operations, and maintenance.
Bolstered by strong support from project partners, we will consolidate core achievements and pursue the establishment of a new Supergen Hub in AI for RE at the end of SuperAIRE. Through these endeavours, we aim to enhance the efficiency, resilience, and affordability of RE, ultimately transforming the RE sector and addressing environmental challenges via AI.
Despite the recent growth in all subsectors, progress in essential technologies supporting the lifecycles of RE systems lags behind. AI offers strategic advantages in overcoming the limitations of traditional methods which struggle to process the increasing complexity and big data in RE systems. It will enable decision-supporting digitalisation, operational efficiency optimisation, cost-effective integration, multi-scenario adaptability, and technological cross-applicability. Though there are some current critical masses in AI for RE, the communities are facing many challenges, e.g., the fragmented nature of the landscape, subsystem isolation, and limited scope. SuperAIRE will address these challenges by enabling shared learning on common research challenges in different RE subsectors through promoting novel generic approaches complemented with refinements tailored to subsector's unique needs; forging a holistic view to facilitate system-wide AI applications; and fostering comprehensive solutions that go beyond single-task focuses to exploit the full potential of AI in enhancing the RE ecosystem.
SuperAIRE will carry out diverse activities to engage with stakeholders, facilitate knowledge exchanges, catalyse community coherence, identify cross-sector opportunities, address skill gaps, support nurturing high-skill professionals with multidisciplinary expertise, and disseminate project outcomes. These activities include four key challenge workshops, bimonthly seminars, flexible funds, outreach activities, an international conference, etc. SuperAIRE will support early career researchers (ECRs) from both academia and industry via a dedicated ECR Forum, a mentoring scheme, secondment opportunities, and ECR grants. We will emphasise Equality, Diversity and Inclusion in all activities. Based on the current critical mass and emerging gaps and opportunities, we have also proposed six pre-defined research themes (RTs) to steer our Network+ activities, especially in guiding discussions, identifying challenges and opportunities, streamlining research coordination efforts, shaping a research landscape report, and developing a whitepaper. This includes RT1 Robust and trustworthy AI; RT2 Prediction and forecasting across scales; RT3 AI-powered digital twins; RT4 Intelligent control and management; RT5 Smart integration; and RT6 Intelligent robotics and autonomous systems in resource assessments, operations, and maintenance.
Bolstered by strong support from project partners, we will consolidate core achievements and pursue the establishment of a new Supergen Hub in AI for RE at the end of SuperAIRE. Through these endeavours, we aim to enhance the efficiency, resilience, and affordability of RE, ultimately transforming the RE sector and addressing environmental challenges via AI.
Organisations
- University of Warwick (Lead Research Organisation)
- Schlumberger Limited (Collaboration)
- BP (British Petroleum) (Collaboration)
- Five AI (Collaboration)
- Schneider Electric Ltd UK (Collaboration)
- Facebook (Collaboration)
- Vital Energi (Collaboration)
- Mott Macdonald UK Ltd (Collaboration)
- ARUP Group (Collaboration)
- PA Consulting (Collaboration)
- The Mathworks Ltd (Collaboration)
- Opal-Rt Technologies (Collaboration)
- National Energy System Operator (Collaboration)
- Alan Turing Institute (Collaboration)
- Engineering and Physical Sciences Research Council (EPSRC) (Collaboration)
- Vestas Wind Systems A/S (Collaboration, Project Partner)
- Energy Systems Catapult Ltd (Collaboration)
- Nortech (Collaboration)
- Scottish Power Ltd (Collaboration)
- ARUP (Project Partner)
- Mott MacDonald (Project Partner)
- DNV GL (Project Partner)
- National Grid ESO (Project Partner)
- UNIVERSITY OF PLYMOUTH (Project Partner)
- embotech AG (Project Partner)
- THE ALAN TURING INSTITUTE (Project Partner)
- Nortech Management Ltd (Project Partner)
- Vital Energi (Project Partner)
- FIVE AI LIMITED (Project Partner)
- Schneider Electric Limited (Project Partner)
- Dept for Energy Security & Net Zero (Project Partner)
- SP Energy Networks (Project Partner)
- Supergen Energy Networks Hub (Project Partner)
- Global Wind Energy Council (Project Partner)
- OPAL-RT Europe SAS (Project Partner)
- Bays Consulting Ltd (Project Partner)
- Meta (Previously Facebook) (Project Partner)
- OFFSHORE RENEWABLE ENERGY CATAPULT (Project Partner)
- GE Power (Project Partner)
- The MathsWorks, Inc. (Project Partner)
- BP (Project Partner)
- EDRMedeso AS (Project Partner)
- PA Consulting Group (Project Partner)
- ETAP Automation Ltd (Project Partner)
- SCHLUMBERGER CAMBRIDGE RESEARCH LIMITED (Project Partner)
Publications
Huang Y
(2026)
Multiagent Inductive Policy Optimization
in IEEE Transactions on Neural Networks and Learning Systems
Yanchang Liang
(2026)
SimuAgent: An LLM-Based Simulink Modeling Assistant Enhanced with Reinforcement Learning
| Description | Examples of key findings include: (1) the study provides evidence that LLM agents can effectively automate graphical engineering modeling workflows, and provides a practical framework that significantly improves performance, efficiency, and deployability for AI-assisted engineering design. (2) We developed a new multi-agent reinforcement learning method, Multiagent Inductive Policy Optimization (MAIPO), which improves how multiple agents learn coordinated strategies in complex environments. The method introduces a trust-region based training objective that ensures policies improve reliably while encouraging exploration to avoid poor local solutions. Experiments on wind-farm control and standard multi-agent benchmarks show that MAIPO achieves stronger performance than existing policy optimisation approaches |
| Exploitation Route | AI-assisted automation of graphical engineering modelling workflows; Frameworks improving efficiency and deployability of AI-driven engineering design tools; New optimisation methods for multi-agent reinforcement learning; Modelling and control tools for complex multi-agent systems; Improving efficiency of energy systems; Reducing uncertainty in decision-making for distributed control systems; Supporting development of intelligent control in renewable energy applications; Enabling further research on scalable reinforcement learning methods. |
| Sectors | Digital/Communication/Information Technologies (including Software) Education Energy Environment |
| Description | This work has contributed to AIOLUS, an intelligent wind farm management solution currently under exploration for commercialisation. Since November 2025, we have engaged with the AI Strategy for Energy team at DESNZ, whose internal briefings have cited our work. We have also collaborated with DESNZ on the Green Powered Future Mission, contributing to a insight report on AI-Driven Renewable Energy Innovation. In addition, we presented our research on AI for wind energy at the Mission's annual meeting and participated in an AI panel discussion. We have also strengthened community building and capacity development across the AI and renewable energy communities. This included three research challenge workshops bringing together researchers and practitioners, two masterclasses to train early-career researchers (ECRs), several industry workshops -including one focused on equality, diversity and inclusion (EDI)- invited sessions at international conferences, and a range of stakeholder engagement activities and invited talks. The project also supported the research community through three flexible funding awards, alongside targeted support for early-career researchers through seven ECR grants and several ECR conference travel awards. |
| First Year Of Impact | 2025 |
| Sector | Digital/Communication/Information Technologies (including Software),Education,Energy,Environment |
| Impact Types | Policy & public services |
| Description | WindConnect: Wind farm control and integration in sector-coupled power systems |
| Amount | € 4,862,087 (EUR) |
| Funding ID | 101227683 |
| Organisation | Marie Sklodowska-Curie Actions |
| Sector | Charity/Non Profit |
| Country | Global |
| Start | 02/2026 |
| End | 01/2030 |
| Description | Arup |
| Organisation | Arup Group |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | BP |
| Organisation | BP (British Petroleum) |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Energy Systems Catapult |
| Organisation | Energy Systems Catapult Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2025 |
| Description | Five AI |
| Organisation | Five AI |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Mathworks |
| Organisation | The Mathworks Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Meta |
| Organisation | |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Mott MacDonald |
| Organisation | Mott Macdonald UK Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | NESO |
| Organisation | National Energy System Operator |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | This leads to a close collaboration with NESO on grid forming and AI based technology in power systems. |
| Collaborator Contribution | Technical advice, secondment hosting. |
| Impact | A joint journal paper published: Q. Liu, M. Wang, M. Nick, C. Chen, and X. Zhao, "Current-limiting control design for grid-forming capability enhancement of IBRs under asymmetric grid disturbances," IEEE Transactions on Power Electronics, vol. 41, no. 4, pp. 6430-6448, Apr. 2026, doi: 10.1109/TPEL.2025.3632684 Another joint journal paper under review. Models and tools used by NESO. |
| Start Year | 2025 |
| Description | Nortech |
| Organisation | Nortech |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | OPAL-RT |
| Organisation | Opal-Rt Technologies |
| Country | Canada |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | PA Consulting |
| Organisation | PA Consulting |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Schlumberger |
| Organisation | Schlumberger Limited |
| Department | Schlumberger Cambridge Research |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Schneider Electric |
| Organisation | Schneider Electric Ltd UK |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Scottish Power Energy Networks |
| Organisation | Scottish Power Ltd |
| Department | Scottish Power Energy Networks |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Supergen Energy Networks Hub |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Department | Supergen Energy Networks Hub |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | The Alan Turing Institute |
| Organisation | Alan Turing Institute |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | Advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | Vestas |
| Organisation | Vestas Wind Systems A/S |
| Country | Denmark |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2026 |
| Description | Vital Energi |
| Organisation | Vital Energi |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Collaboration and sharing of research outcomes |
| Collaborator Contribution | advisory board |
| Impact | Collaboration and sharing of research outcomes |
| Start Year | 2024 |
| Description | AI workshop with NESO |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | We held a AI knowledge sharing workshop to NESO. Over 10 power system engineers from NESO attended this workshop. We disseminated the project results on AI application in wind energy, hydrogen, microgrid, grid integration, and health monitoring to them and talked about collaborations. |
| Year(s) Of Engagement Activity | 2025 |
| Description | Energy System Catapult |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | We held a AI knowledge sharing workshop to Energy system catapult who visited our lab at Warwick University. We disseminated the project results on AI application in wind energy, hydrogen, microgrid, grid integration, and health monitoring to them and talked about collaborations. |
| Year(s) Of Engagement Activity | 2024 |
| Description | Workshops with National Energy System Operator |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | A workshop was held at University of Warwick on 25 Oct 2024, where 21 Power system engineers from the National Grid ESO (now called NESO) joined us. In this workshop, we shared our research in AI, grid forming, wind energy, microgrid, and grid forming. |
| Year(s) Of Engagement Activity | 2024 |