Power is the most under-rated asset today critical to datacentres, banks, and industries that depend on uninterrupted power. Any disruption not only results in financial loss but also damages customer expectation and brand-value.
While power is crucial so are qualitative parameters such as getting the desired load, and cleanliness. Businesses have stringent rules to follow when it comes to BSR (Business for Social Responsibility) and ESG (Environmental, Social, and Governance) benchmarks. BESS (Battery Energy Storage Systems) have replaced the conventional UPS (Uninterrupted Power Supply) and tethered solutions, but real-time AI-enabled feature could become more-reliable tool for industries.
Why BESS systems have become a mainstay
Be it powering electric vehicles or large data-centers, BESS systems have emerged as a reliable tool. Back in 2013, global adoption rate of BESS systems stood at 1 GW but it increased to 106 GW by 2025 end – proving its reliability in critical operations such as Oil & Gas, heavy industry (steel, automotive, and Cement) and more recently with datacenters.
Overall, datacentres are expected to consume around 40–45 TWh of electricity annually by 2030, compared to roughly 10–15 TWh today, with their share of national power demand rising steadily.US which houses nearly 5,400 datacenters had to adopt BESS systems as the power-demand from AI workflows increased. The US Department of Energy forecast that datacenters alone could account for 12% of America’s electricity demand by 2028.
India, one of the fastest-growing digital economies, is witnessing a rapid expansion of data centre infrastructure as AI workloads and cloud adoption accelerate. The country currently hosts over 240 data centres, and industry projections suggest that total capacity could increase from about 1.5 GW in 2025 to nearly 8–10 GW by 2030, significantly increasing electricity demand from the sector.
As AI-driven computing intensifies, India too is confronting the need for stronger power distribution and grid optimisation systems that can efficiently balance workloads across feeders, renewable sources, and transmission networks while ensuring reliable power supply to hyperscale facilities.
So far, several cases have pointed out to the need for a reliable distribution network – something that could optimise power workloads between feeders, grids, and the power generation sources.
Next frontier of dependability
AI and ML tools are becoming the mainstay in tackling this intermittency and variability – this is a transformation of BESS from passive storage decks of batteries into an adaptive, data-driven system.
AI-powered energy management platforms leverage advanced predictive analytics and forecasting models to accurately anticipate renewable energy generation, load demand, and evolving market signals. By processing and analysing large volumes of meteorological data, historical operational information, and real-time grid inputs, these platforms can identify patterns and predict fluctuations in solar irradiance, wind generation, and electricity consumption with a high degree of precision. This data-driven insight enables operators to optimise battery’ charge–discharge cycles and overall energy storage utilisation, ensuring that stored energy is deployed at the most efficient and economically beneficial times. As a result, such predictive capabilities help minimise the mismatch between energy generation and demand, strengthen grid reliability and stability, and maximise the effective integration and utilisation of renewable energy resources within the power system.
Machine learning also improves real-time operational control. Data-driven optimization frameworks allow BESS to perform critical grid services, such as peak shaving, frequency regulation, and load shifting, efficiently than conventional rule-based systems. For example, ML-based energy management strategies can forecast grid loads and adjust battery dispatch accordingly, smoothing daily demand peaks and filling demand valleys while maintaining optimal battery state-of-charge.
Such approaches have demonstrated measurable improvements in load balancing and overall storage efficiency, strengthening the grid’s ability to accommodate variable renewable energy. Beyond operational optimization, AI enhances the reliability and longevity of battery systems. Intelligent battery management systems use data analytics to monitor parameters such as temperature, voltage, and degradation patterns, enabling precise estimation of state-of-charge (SoC) and state-of-health (SoH). Predictive maintenance algorithms can detect anomalies early, reducing failure risks and lowering operational costs while extending battery life cycles.
Having committed to ambitious renewable energy targets which includes aiming to achieve 500 GW of non-fossil fuel capacity by 2030, India stands at a critical juncture in the energy transition segment. While India has made tectonic achievements in these fields in the last decade, our goals demand a fundamental shift in the manner we generate and consume power. BESS with AI is the next as India ascends towards renewable goals. Replus is proud to be a part of India’s ascent towards her goals having commissioned 100MWh BESS projects ourselves and 1GWh BESS projects under execution.
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
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