Solvyn AURA: AI-driven Energy Management Solution

Solvyn AURA: AI-driven Energy Management Solution

India flag

India

Environment

Implementing Organisation

Smart Grid Analytics Pvt. Ltd

India, Karnataka

Private Sector

Implementing Point of Contact

Kumar M.

Contributor of the Impact Story

International Energy Agency

Year of implementation

2026

Problem statement

Renewable energy operations are fragmented, with forecasting, plant control, and commercial trading handled separately by different teams and tools, while battery energy storage is used reactively rather than as a planned economic asset. This siloed approach creates operational stress where trading teams lack visibility into plant capability and battery constraints, and operators execute schedules that don't reflect weather uncertainty, battery limits, or market dynamics. The result is last-minute schedule changes, avoidable deviations, conservative bidding, aggressive battery cycling, missed market opportunities, higher imbalance exposure, and increased battery degradation from high C-rate events, deep state-of-charge swings, and thermal excursions. Strict market timelines at India Energy Exchange (IEX) leave little room for manual reconciliation, forcing teams to choose between protecting battery assets and maximizing value. This fragmentation also leads to avoidable curtailment of renewables and inefficient balancing actions that increase emissions from the electricity sector. The Smart Grid Analytics solution addresses this by unifying renewable generation, battery storage, and market participation into one continuous AI-driven system that integrates with existing infrastructure, combines live telemetry with weather inputs for 95% forecast reliability, uses digital twin models to evaluate battery limits and grid constraints alongside IEX price signals, and produces optimized dispatch schedules that continuously adapt to changing conditions—enabling planned, lower-stress battery cycling, reducing deviations and renewable curtailment, improving market discipline, and allowing operators to achieve both asset protection and value maximization simultaneously.

Submission Overview

Smart Grid Analytics Pvt. Ltd is a company that develops and operates AI-driven energy management solutions. They own and operate the Solvyn AURA platform, which is commercially deployed across renewable and hybrid energy projects in India. The company specializes in integrating renewable operations with battery storage and electricity market trading.

AI Technology Used

Machine Learning
Predictive Analytics
IoT & Sensor Analytics

Key Outcomes

Efficiency & Productivity

Resource Efficiency

Economic Value Creation

Renewable energy operations in India were fragmented, with forecasting, plant control, and market trading managed separately and battery storage used reactively. This led to poor coordination, rushed manual decisions, unnecessary battery stress, and a constant trade-off between protecting assets and maximizing revenue. Solvyn AURA by Smart Grid Analytics has transformed this approach. By integrating renewable generation, battery storage, and market trading into a single AI-driven platform, Solvyn AURA connects live plant data, weather forecasts, and digital twin models to optimize dispatch and trading decisions in real time. It works with existing infrastructure and provides clear dashboards so operators remain in control. As a result, forecast reliability has improved to around 95%, last-minute schedule changes have reduced, and batteries are now operated as planned strategic assets rather than reactive tools—extending equipment life while increasing revenue. Independent power producers and renewable developers can now protect their assets and maximize returns simultaneously, while reducing renewable energy curtailment and improving overall grid efficiency.

Impact Metrics

Forecast reliability for near-term generation and market price predictions.

Baseline Value

NA

Post-Implementation

Forecast reliability improved to around 95% post-implementation.

Battery operation pattern.

Baseline Value

NA

Post-Implementation

NA

Implementation Context

Deployed

India with plans of future expansion across across Asia, the Middle East and Africa

Independent power producers, renewable energy developers, asset owners operating utility-scale solar, wind, and hybrid projects

Key Partnerships

Replicability & Adaptation

Not specified

1. Scales across solar, wind, and battery storage assets. 2. Can be deployed in regions with similar market and grid structures. 3. Runs on standard industrial computing systems. 4. Requires minimal additional hardware. 5. Integrates with existing equipment using standard SCADA and industry protocols. 6. Works with current infrastructure without major replacement. 7. Operated by existing plant and trading teams. 8. Does not require new specialized teams. 9. Requires minimal retraining. 10. Adoption is supported by data standardization policies.

* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.