Demonstration Projects with Solar and Storage Lead To Efficient and Resilient Control of Microgrids


Swanbarton has 20 years of experience in energy storage, microgrids and local energy markets. In this session, we will report our results from solar and storage demonstration projects and other work.

We demonstrated our ability to increase energy efficiency within microgrids in lab-scale demonstrations and in the field. On a network-connected microgrid at a UK port, we increased battery revenues by ~12%, despite unfavourable tariffs and infrastructure restrictions. Lab modelling shows possible improvements to ~80%.

Our current Energy Catalyst project aims to demonstrate how a small, network-connected, but islanding-capable local microgrid with energy storage and renewable generation can provide an economical and secure electricity supply. An enhanced microgrid controller using multi-objective optimisation maximises energy storage performance against financial or carbon-based parameters.

We have demonstrated that intelligent microgrid generation control saves 13-27% fuel through efficiency gains from load sharing. Additional benefits include switching loads between alternative generation assets, shedding non-critical loads, ensuring power is available when and where needed to protect critical infrastructure, reducing risk and enhancing reliability and security. Combining our planning, optimising and control technologies creates efficient and resilient microgrid systems.

Key Learning Points:
  • Plan and size assets in a microgrid
  • Use multi-objective energy storage-enabled energy system optimisation
  • Distributed asset control and prioritised load shedding
Speaker:

James Hancock, microgrids expert
James Hancock
Innovation Manager
Swanbarton Limited

James Hancock, Innovation Manager, is a data scientist focusing on artificial intelligence with over 10 years' experience in the energy sector. James is writing his thesis "Reinforcement learning to optimise energy storage systems" to complete his MSc in Artificial Intelligence at Bath University. James' work at Swanbarton includes model building and implementing AI-enabled systems and undertaking consultancy work from a data-orientated stance. Before working at Swanbarton, James worked in the offshore energy industry.

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