Jakarta, INTI - A research team from Universitas Sebelas Maret (UNS) Surakarta, in collaboration with Anurag University, Hyderabad, India, has developed an artificial intelligence (AI)-based battery energy storage system to optimize renewable energy utilization.
This research is designed to address the uncertainty of electricity supply from sources such as solar and wind power, while also accounting for battery performance degradation due to long-term use.
The system is now entering the testing phase at the Center for Standardization and Services for the Materials and Engineering Tools Industry (B4T) Bandung, as well as the product certification process.
Supporting Indonesia’s Net Zero Emissions 2060
Team leader Chico Hermanu Brillianto Apribowo emphasized that this technology not only improves electricity supply reliability but also has the potential to replace generators and support Indonesia's target of achieving Net Zero Emissions by 2060.
This system integrates battery degradation modeling, renewable energy integration, and grid operational constraints into a single integrated model controlled by a Smart Battery Management System (BMS).
Srikanth Goud B, a researcher from Anurag University, added that large-scale renewable energy integration requires storage technology capable of adaptive operation based on battery conditions and grid requirements.
Using a machine learning optimization approach, the system is able to determine optimal battery charging, discharge, and operation strategies to minimize costs, increase flexibility, and reduce carbon emissions.
Chico explained that this development is relevant to the government's plan to increase electricity generation capacity by 69.5 gigawatta (GW) by 2034, with 76 percent coming from renewable energy.
Beyond the technical aspects, this system also opens up new economic opportunities in the clean energy ecosystem, including storage system design, smart grid management, and environmentally friendly energy services.
Conclusion
A research team from UNS collaborated with Anurag University to develop an artificial intelligence (AI)-based battery energy storage system to optimize renewable energy utilization. Using a Smart Battery Management System (BMS) and machine learning optimization, the technology aims to reduce costs, increase the reliability and flexibility of electricity supply, and reduce carbon emissions. This development is considered relevant to the plan to increase electricity capacity by 69.5 GW by 2034.
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