Near off-grid solutions using renewable energy technologies and demand side prediction

The rapid growth of electricity demand, coupled with insufficient supply due to geographical constraints, has limited energy access for remote communities in countries such as Indonesia. However, recent advances in renewable energy and storage technologies provide promising means to supply power to communities under near off-grid conditions. Energy management solutions that effectively combine storage systems with multiple renewable energy sources, such as solar and geothermal, can increase the reliability of energy supply for remote communities.

This project aimed to study the optimisation of such systems. Along with conducting significant research into theoretical and numerical analyses related to controlling renewable energy integrated storage systems, the project undertook hardware experimentation. 

The main experimental focus was to test how the operation of a solar PV-integrated battery system impacts its lifetime. We developed a prototype to test the battery lifetime value with three different strategies: simple set-point control, optimised operation disregarding battery degradation, and optimized operation considering degradation. The prototype was tested indoors, and six units were to be installed around Melbourne University for further assessment. Due to the problems of procuring locations at the university, however, testing was delayed. 



Journal articles

Abdulla, K., De Hoog, J., Steer, K., Wirth, A., & Halgamuge S. (2017). Multi-resolution dynamic programming for the receding horizon control of energy storage. IEEE Transactions on Sustainable Energy, 10(1), 333-343.

DOI: 10.1109/TSTE.2017.2754505

Abdulla, K.,  et al. (2016). Optimal operation of energy storage systems considering forecasts and battery degradation. IEEE Transactions on Smart Grid, 9(3), 2086-2096.

DOI: 10.1109/TSG.2016.2606490

Abdulla, K., Steer, K., Wirth, A., & Halgamuge, S. (2016). Improving the on-line control of energy storage via forecast error metric customization. Journal of Energy Storage, 8, 51-59.

Weeratunge, H., Narsilio, G., de Hoog, J., Dunstall, S., & Halgamuge, S. (2018). Model predictive control for a solar assisted ground source heat pump system. Energy , 152, 974-984.