AQE Projects

PROJECT

USE CASE : Machine Learning Powered Trading Optimization for Power & Gas Markets.

We engineer and refine, and you retain full ownership.  

OVERVIEW

Designed and deployed AI-driven trading algorithms for ERCOT, CAISO, PJM, and AESO to optimize power and gas trading strategies. Leveraged ETRM and ISO market data to enhance decision-making, risk management, and market execution.

  • Traditional power and gas trading relies on manual analysis, historical trends, and static models, leading to:

    • Suboptimal trading decisions due to market volatility and dynamic pricing.

    • Inefficient risk management, resulting in exposure to price fluctuations.

    • Limited scalability, enable traders to do what they are best with multiple nodal strategies via augmentation. 

    • Delayed decision-making, impacting profitability in fast-moving markets.

  • Implemented an AI-powered trading system that integrates real-time market data, predictive analytics, and 90% automated backtesting to optimize trading strategies.

    • Connected to ETRM and market APIs for real-time data collection.

    • Deployed a data lake architecture on Azure for structured and unstructured data storage.

    • Leveraged Snowflake & Databricks for scalable ETL and historical data processing.

    • Built machine learning models for price forecasting, volatility prediction, and bid optimization.

    • Used deep reinforcement learning algorithms to simulate and optimize trading strategies.

    • Implemented real-time anomaly detection to flag irregular market conditions.

    • Integrated AI-driven signals with ETRM and CTRM systems for VaR simulations.

    • Deployed Power BI dashboards for real-time market insights and P&L tracking.

    • Ensured compliance and risk mitigation through analytics based position monitoring.

    • Hosted on Azure Kubernetes Services (AKS) for high availability and low-latency execution.

    • Used Databricks for distributed computing, reducing model training time by 65%.

    • Enabled real-time analytics using Snowflake, improving data query speed by 80%.

    • Reduced Market Exposure: Analytics-driven hedging strategies decreased risk exposure by 30%, ensuring optimal portfolio balance.

    • Enhanced Volatility Management: Predictive models improved price volatility forecasting accuracy by 85%, allowing for proactive risk adjustments.

    • Automated Position Limits: AI-enforced compliance rules ensured position limits were never exceeded, reducing regulatory risk incidents by 40%.

    • Real-Time Stress Testing: End-of-day simulated market fluctuations, ensuring portfolio resilience against high-impact market shocks.

    • Increased Trading Profitability: Analytics-driven decision-making improved trade execution efficiency by 30%, maximizing profit margins.

    • Reduced Risk Exposure: Automated risk analysis reporting minimized loss potential by 25%.

    • Enhanced Market Responsiveness: Reduced trade execution time from minutes to seconds, capitalizing on price fluctuations.

    • Operational Efficiency: Reduced manual intervention by 40%, allowing traders to focus on strategy rather than evaluation.

    • Scalability & Flexibility: Cloud-based architecture allowed seamless scaling across multiple ISOs.

PROJECT

USE CASE : Real-Time Command Center & Analytics for LNG Operations.

We develop, optimize, and put full control in your hands.  

OVERVIEW

Developed a real-time command center and analytics platform for LNG commercial and operations teams, integrating PI, SCADA, and market data (Henry Hub, JKM, TTF) to enhance decision-making, risk management, and operational efficiency.

  • Traditional LNG operations often face challenges such as:

    • Limited real-time visibility, leading to delayed responses in market and operational changes.

    • Inefficient data integration, requiring manual reconciliation of SCADA, PI, and external market data.

    • Suboptimal commercial strategies, due to a lack of real-time analytics on pricing, demand, and logistics.

    • High operational risk, stemming from fluctuating LNG market conditions and infrastructure constraints.

  • Designed and implemented a centralized command center powered by real-time analytics, automated workflows, and AI-driven insights to optimize LNG operations.

    • Connected PI & SCADA systems for real-time LNG plant and pipeline monitoring.

    • Integrated Henry Hub, JKM, and TTF market data to align trading and operational strategies.

    • Unified data storage using Snowflake, ensuring high-speed access and analytics scalability.

    • Developed a Databricks-driven ETL pipeline, automating data ingestion and transformation.

    • Built forecasting models for LNG pricing, supply, and demand across key markets.

    • Deployed real-time anomaly detection to flag potential infrastructure issues and market risks.

    • Developed automated P&L tracking dashboards in Power BI, enabling real-time decision-making.

    • Designed a real-time operational dashboard, allowing teams to monitor vessel movements, inventory levels, and plant performance.

    • Automated risk analysis and scenario modeling, enabling proactive responses to supply / demand fluctuations.

    • Integrated SCADA alerts and AI-driven recommendations, reducing downtime and optimizing throughput.

    • Built customized alerting systems, providing early warnings for price volatility and operations.

    • Improved Market Responsiveness: Analytics-driven insights reduced response time to price fluctuations by 50%. 

    • Operational Downtime Reduction: Predictive maintenance and anomaly detection decreased unexpected downtime by 30%. 

    • Enhanced Trade Optimization: Real-time market integration improved LNG trade execution efficiency by 25%.

    • Lower Risk Exposure: Automated risk analytics reduced financial exposure to market fluctuations by 40%.

    • Faster Decision-Making: Command center enabled commercial teams to act on market shifts in real-time, improving profitability.

    • Higher Profit Margins: Enhanced pricing strategies improved trading profits by 20%.

    • Optimized LNG Logistics: Automated scheduling reduced demurrage costs and improved supply chain efficiency.

    • Data-Driven Decision Making: Real-time analytics and dashboards eliminated reliance on manual reports, improving efficiency by 35%.

    • Scalability & Flexibility: Cloud-based architecture allowed seamless integration with new LNG terminals and trading markets.

PROJECT

USE CASE : Adaptive Commercial Model for Combined Cycle Gas Turbine (CCGT) Power Plants.

Expertly crafted, effortlessly managed, to fit your OT needs. 

OVERVIEW

Led the development of a machine learning-driven SCADA alerting system and P&L Spark Spread Models to optimize asset monitoring and financial performance for CCGT power plants. Leveraged PI, SCADA, Databricks, Power BI, and React to enhance operational efficiency, real-time analytics, and profitability. 

  • Traditional CCGT operations face several key challenges:

    • Unoptimized asset performance, leading to inefficiencies in power generation and fuel consumption.

    • Limited real-time monitoring, increasing downtime and reducing plant availability.

    • Lack of advanced financial modeling makes optimizing power trading and dispatch decisions difficult.

    • Reactive maintenance practices, causing higher operational costs and unexpected outages.

  • Designed and implemented an analytics powered system that integrates real-time operational monitoring, predictive analytics, and financial modeling to optimize CCGT operations.

    • Connected SCADA & PI to ingest live turbine and system performance metrics.

    • Integrated market and fuel pricing data to dynamically calculate spark spreads.

    • Leveraged Databricks for scalable data transformation and analytics processing.

    • Developed near real-time alerting system to detect anomalies and potential failures.

    • Implemented machine learning models for predictive maintenance and asset reliability forecasting.

    • Built automated efficiency monitoring to adjust turbine performance parameters in real-time.

    • Developed dynamic Spark Spread Models that optimize dispatch decisions based on fuel costs and electricity pricing.

    • Built Power BI dashboards to visualize profitability, efficiency trends, and real-time financial performance.

    • Automated profitability simulations for better decision-making on plant operations.

    • Built a React-based web application for operators to monitor performance, receive AI-driven alerts, and track financial KPIs.

    • Implemented customized alerting and reporting, allowing proactive management of power generation assets.

    • Enabled mobile accessibility, allowing plant managers to make real-time decisions on the go.

    • Reduced Unplanned Downtime: Predictive maintenance reduced turbine failures by 35%.

    • Optimized Fuel Efficiency: Machine learning-driven tuning improves heat rate efficiency by 5%, lowering fuel costs.

    • Improved Market Responsiveness: Spark spread models enabled optimized dispatch strategies, increasing revenue by 20%.

    • Automated Risk Alerts:  anomaly detection reduced unplanned operational risks by 15%. 

    • Enhanced Operational Transparency: Real-time monitoring decreased response time to performance issues by 50%. 

    • Faster Decision-Making: Integrated financial analytics allowed traders to adjust strategies within an hour during forced outage. 

    • Higher Profit Margins: Improved trading and dispatch efficiency increased annual profits by 15%. 

    • Lower Maintenance Costs: AI-driven asset monitoring reduced maintenance expenses by 10%. 

    • Enhanced Grid Participation: Real-time optimization improved participation in energy markets, securing more favorable contracts. 

    • Scalability & Flexibility: Cloud-based architecture enabled seamless expansion to additional power plant assets.

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