RENEWCONNECT - All about renewables & power sector

Renewable portfolios (wind, hydro, solar) operate under stringent 15‑minute block forecasting and scheduling regimes— week‑ahead, day‑ahead, and intra‑day —to ensure grid stability and market discipline. Historically, this required 24×7 manual workflows across commercial and scheduling teams, exposing utilities and IPPs to human error, missed gates, and compliance risk . An Automation Process with AI/ML replatforms this end‑to‑end scheduling chain— ingest → predict → compile → validate → submit → reconcile —into a resilient, policy‑aware digital process that is on‑time, accurate, auditable, and secure , freeing scarce human capacity for value‑adding analysis and portfolio optimization. International evidence shows that grid digitalization cuts outages and integration costs; India’s CERC/SLDC procedures explicitly codify forecasting/scheduling at 15‑minute granularity and encourage improved accuracy and discipline—creating the perfect context to industrialize AI/ML scheduling automa...

Identifying ramp-ups and ramp-downs in real-time generation data for solar and wind involves monitoring fluctuations in generation output over a set period. Here’s a step-by-step approach for detecting these changes over daily, monthly, or yearly timeframes: 1. Data Collection and Pre-processing Collect Real-Time Generation Data : Capture real-time or near-real-time data for both solar and wind generation at an appropriate resolution (e.g., minute-by-minute or hourly). Clean Data : Address any data gaps, outliers, or anomalies due to sensor issues, downtime, or maintenance. Convert to Uniform Units : Ensure all data is in a consistent format, such as MW or kW. 2. Define Ramp-Up and Ramp-Down Events Ramp-Up : A positive change in power output over a specified time interval (e.g., 10% increase over 10 minutes). Ramp-Down : A negative change in power output over a specified time interval (e.g., 10% decrease over 10 minutes). The threshold values for ramp-ups and ramp-downs will vary based...

Trend analysis is the process of examining data over a specific period to identify patterns or trends that can indicate potential future outcomes. By looking at how data points have changed historically, trend analysis helps forecast possible future changes and make informed decisions. This method is widely used in various fields such as finance, economics, business, and technology to track performance metrics, identify growth or decline patterns, and predict future behavior. Key Components of Trend Analysis: Data Collection : Gathering historical data relevant to the area being analyzed, such as sales data, stock prices, or climate data. Trend Identification : Observing the general direction—whether upward, downward, or stable. Pattern Recognition : Detecting any regular fluctuations, such as seasonal changes, cyclical patterns, or random irregularities. Forecasting : Using the identified trends and patterns to make predictions about future values or events. Evaluation : Continuously ...