How TCS’s Intelligent Power Plant solution on AWS helps utilities optimize operations and drive energy transition

Solution overview

Advanced digital technologies are revolutionizing the energy industry, enabling organizations to achieve their sustainability goals while reducing carbon footprint and emissions. According to McKinsey, digital transformation in energy could unlock $1.6 trillion of value by 2035, delivering 20-30% reduction in operating costs and up to 5% decrease in carbon emissions. As the industry evolves toward distributed generation systems with renewable integration, organizations are implementing enterprise-grade solutions such as smart grids, artificial intelligence (AI)-driven management systems, and real-time monitoring platforms to orchestrate their energy assets with remarkable precision. The impact is already evident: a 1000 MW Thermal Power Station in Japan achieved cost savings of $2.5 million, while an Offshore Wind Farm in the UK increased its revenue by 6%, demonstrating how data-driven decisions can simultaneously optimize business performance and advance sustainability goals.

The TCS Intelligent Power Plant solution transforms distributed power generation management through a comprehensive, centralized platform built for today’s complex energy landscape. It offers 0.5% efficiency improvement, 8% reduction in NOx (Nitrogen oxide) for thermal power plants, and 1.5% curtailment and 8-10% improvement in generation forecasting accuracy for renewable generation systems. Built on the Amazon Web Services (AWS) secure and scalable cloud infrastructure, this innovative solution uses advanced AI/machine learning (ML) algorithms to process real-time data from distributed energy resources across multiple locations. The solution integrates data streams from diverse power generation assets, such as solar arrays, wind farms, and storage systems, enabling comprehensive monitoring and intelligent decision making at scale.

In this post we explore how this powerful solution delivers both operational excellence and sustainable business outcomes through intelligent energy management.

Solution architecture and data flow

The architecture follows a systematic data flow process, using AWS services throughout the workflow.

1. Data ingestion: Data is ingested from multiple industrial data sources in OPC-UA format (real-time equipment data), on-premises historian systems (historical performance data), and enterprise data lakes using Amazon S3 (organizational datasets). Up to 4,000 sensor tag values per minute can be ingested from a single power plant unit, with a capacity ranging from 600 to 900 MW.
2. Data reception and orchestration: AWS IoT Core securely processes the incoming data streams, triggering AWS Step Functions to coordinate automated data processing workflows.
3. Data processing: Multiple AWS Lambda Functions are triggered by Step Functions to perform automated data processing tasks, such as data cleaning, alert generation, and real-time KPI calculations.
4. Data storage: Amazon DocumentDB manages structured operational data (KPIs, alerts, and transactions), and is complemented by Amazon S3, which handles raw sensor inputs, processed data, and ML training sets and models.
5. ML model training: ML algorithms for predictive analytics capabilities are trained using Amazon SageMaker and containerized models are stored in Amazon Elastic Container Registry (Amazon ECR).
6. ML operations: Trained ML models are deployed to TCS InTwin online (runtime analytics engine) running on Amazon Elastic Container Service (Amazon ECS) for real-time inference and predictions.
7. Application deployment: The solution’s full application stack (front-end and back-end) runs as containerized services on Amazon ECS, making sure of consistent and scalable operations.

Key capabilities

The solution delivers four core capabilities that transform power plant operations.

• Self-learning AI digital twins: Our solution combines data and physics-based AI models to create hybrid digital twins that continuously learn and adapt. Unlike traditional thermodynamic models, these twins are real-time, cost-effective, and self-adjusting to changing operational conditions. The unique self-learning capability enables continuous adaptation to new data, maintaining high accuracy across different operating scenarios, even after major process changes or shutdowns.
• Open and scalable solution: A modular, customizable, and scalable solution that can be integrated with existing plant software and AI/thermodynamic models built by clients. It has open architecture and explainable AI as compared to traditional OEM products.
• Low code digital twin workbench: A low code digital twin workbench to create, customize, import, and manage AI models throughout their lifetime. Workbench to create KPIs, FMEAs, and use cases.
• Pre-built platform: Pre-built platform and use cases configurable for each plant, thereby shortening the deployment time and enabling quick scaling across plants for new use cases.

Key use cases and implementations

In this section we demonstrate how the previously mentioned capabilities are converging into use cases that can solve complex industrial problems. The following are a few case studies where a particular problem is solved, and results are delivered.

Solar generation forecasting: Our advanced analytics and ML models accurately predict renewable energy generation by analyzing weather patterns and historical performance data. In a successful implementation at a UK offshore wind farm, our proprietary algorithms improved forecasting accuracy by 3.2% for wind speed and 15.1% for wind power, resulting in a 6% revenue increase. These enhanced predictions enable better resource planning, enhance grid commitment, and improve the integration of renewable energy sources to their power generation mix.

Combustion optimization in thermal generation: Our solution uses historical plant data and real-time monitoring to optimize thermal power operations through sophisticated objective function modeling. At a Japanese thermal power plant, this data-driven approach delivered significant results: 0.5% improvement in efficiency, 8% reduction in NOx emissions, and 1.6% reduction in unburnt carbon, resulting in $2.5M operational cost savings. The solution continuously provides real-time recommendations for optimal setpoints, balancing efficiency gains with emissions control while making sure of environmental compliance.

Predictive maintenance solution of gas turbine components: Our asset digital twin technology analyzes historical operational and maintenance data to predict potential failures of critical components such as combustor casings and turbine blades. In an Australian power plant implementation, the solution successfully predicted gas turbine failures 8-12 months in advance, enabling proactive maintenance scheduling and significantly reducing outage duration and service costs. This predictive approach optimizes maintenance costs while extending equipment lifecycle and minimizing unplanned downtime.

Business benefits

This comprehensive solution delivers transformative value across multiple dimensions of power generation operations. Through low-code descriptive analytics and AI-driven insights, operators gain deeper real-time understanding of plant performance, enabling the optimization of KPI targets across diverse operating conditions. This leads to reduced operational costs and carbon footprint while improving grid commitment through accurate generation forecasting, which is particularly beneficial for fleet-level optimization of renewable assets. The platform’s predictive capabilities demonstrate a significant impact in asset management, achieving up to 20% reduction in maintenance costs through optimized strategies and predicting asset failures with up to 85% accuracy.

Furthermore, the technology addresses crucial workforce challenges by supporting the aging workforce with AI-guided decision making, facilitating a shift from human-centric to data-centric operations. Integrating with enterprise systems allows it to eliminate traditional data silos, thereby enabling standardized practices and enhanced productivity across the organization. These comprehensive benefits position organizations for both immediate operational improvements and long-term strategic success in sustainable power generation.

Conclusion

TCS Intelligent Power Plant built on AWS is transforming the energy sector’s journey toward sustainability through advanced analytics and AI-driven solutions. Enabling precise performance optimization, predictive maintenance, and seamless integration of renewable energy sources allows this comprehensive platform to help organizations achieve their operational excellence and sustainability goals while driving digital transformation at scale. With deep industry expertise and a team of AWS-certified professionals, TCS has demonstrated success in delivering innovative solutions that accelerate the sustainable energy transition. TCS’s proven track record spans diverse power generation environments, from traditional thermal plants to renewable energy installations.

To learn more about Intelligent Power Plant solution, read this TCS post.