AI emerges as key tool in managing grid variability for RTC clean energy supply

Time of India

21-04-2025

New Delhi: India's energy sector is undergoing a structural shift as the country targets 500 GW of non-fossil fuel capacity by 2030. As power demand surges from growing industrialisation, electrification of mobility, and data centre expansion, hybrid and round-the-clock (RTC) renewable energy systems are being prioritised for their ability to deliver continuous clean power. Artificial Intelligence (AI) is emerging as a central pillar in this transition, enabling developers to manage grid variability, optimise dispatch, and maintain supply reliability.
Hybrid clean energy systems—combining solar, wind and battery storage—are complex by design. Managing the dynamic interaction between intermittent generation and fluctuating demand requires predictive tools and adaptive control systems. AI platforms such as BrightNight's PowerAlpha are being deployed across the project lifecycle to bridge this gap.
Forecasting and Predictability in Renewable Projects
AI-based forecasting tools use weather data, satellite imaging, and historical trends to predict solar irradiance, wind velocity and demand profiles with greater precision. These inputs support better design of integrated energy systems and enable accurate power scheduling. According to BrightNight, improved forecasting helps meet dispatch commitments, avoids penalties and enhances grid compliance.
'AI is not just a value-add—it is becoming a necessity in the evolving world of hybrid and round-the-clock renewable energy projects,' said Sunil Kumawat, Vice President, Engineering at BrightNight India. 'We are stepping into the intelligent age—where AI and energy are not just connected, but co-dependent.'
Smart Dispatch and Energy Management
At the heart of RTC operations lies the ability to dispatch clean energy consistently. BrightNight's PowerAlpha platform uses machine learning algorithms to manage generation assets based on real-time price signals, demand curves, and weather variability. The software coordinates solar, wind and battery assets to ensure delivery during peak periods, such as evening hours when solar dips and demand spikes.
This capability is especially critical for utilities and large industrial consumers entering into RTC renewable Power Purchase Agreements (PPAs) to ensure energy reliability.
Optimising Storage Utilisation
Storage is a key enabler of RTC power. AI platforms manage battery charging and discharging to ensure optimal usage, longer lifecycle, and enhanced grid integration. By factoring in time-of-day pricing, forecasted generation, and grid stability needs, storage systems can perform peak shaving, ramp support and frequency regulation.
'AI algorithms optimise charging and discharging cycles based on demand patterns, renewable generation forecasts, and grid tariffs. This not only maximises battery life and efficiency but also enhances revenue through smart participation in ancillary service markets,' Kumawat added.
Predictive Maintenance and Asset Health
AI is also transforming asset maintenance in large-scale renewable installations. Sensor data from wind turbines and solar panels is analysed in real-time to detect early signs of wear or malfunction. Predictive maintenance schedules are then generated to minimise downtime and avoid catastrophic failures.
This is particularly effective for assets spread across vast geographies, where physical inspection and reactive repairs can be time-consuming and cost-intensive.
Siting and Resource Mapping with Geospatial AI
The use of AI in geospatial planning is helping project developers identify optimal sites for
hybrid energy systems
. BrightNight's tools analyse terrain data, weather patterns, and grid connectivity to support site selection. The improved siting process reduces project risks, accelerates timelines and supports project bankability during financial closure.
Grid Flexibility and Market Integration
With growing participation in short-term and real-time power markets, AI-enabled tools are supporting intelligent bidding strategies. Real-time market signals, price forecasts and grid constraints are continuously assessed to schedule dispatch.
Grid flexibility is enhanced by AI's ability to autonomously manage intermittency and coordinate with system operators. This supports dynamic balancing of renewable supply and system demand, particularly in states with high renewable penetration.
BrightNight's PowerAlpha Platform
BrightNight describes itself as the first AI-native enterprise in the energy sector. Its proprietary PowerAlpha platform is designed to support project design, development, and real-time operations using cognitive automation and big data.
The company is deploying the platform across its hybrid and RTC energy assets in India. With India's renewable energy capacity at over 125 GW and storage systems gaining policy focus, such platforms are expected to scale further.
'The synergy between AI and clean power isn't theoretical. It's already here—and it's shaping the next chapter of sustainable progress,' Kumawat said.
Energy Demand and the Role of AI
AI itself is a power-intensive technology. Data centres supporting AI applications can consume as much electricity as 100,000 homes. The dual role of AI—as both a driver of energy demand and an enabler of smarter energy usage—makes it central to the clean energy transition.
India's rising electricity consumption from digitalisation, mobility and urbanisation will require power systems that are flexible, intelligent and resilient. Hybrid clean energy systems backed by AI will be integral to this roadmap.
The way ahead
India's journey to a net-zero future hinges not only on deploying renewable assets but also on managing them efficiently. AI offers the tools to address intermittency, reduce curtailment, optimise storage and enable cost-effective power delivery. As policy evolves to support hybrid and RTC models, the integration of AI will become foundational in the clean energy infrastructure of tomorrow.