Key features of the gridseer technology platform
Probabilistic Forecasts and Advanced Machine Learning
Harnesses cutting-edge machine learning to generate detailed probabilistic forecasts that adapt to real-time weather and system conditions, ensuring high precision in demand and supply management. Produces detailed scenario-based forecasts of energy demand and renewable energy production, accounting for weather patterns and variability.
Risk-Adjusted Stochastic Unit Commitment (RAS-UC)
A core feature that strategically plans power generation a week in advance, factoring in uncertainty to optimize costs and minimize risk, thereby ensuring stability and reliability. Ensures optimal operation of renewable resources and large-scale energy storage systems.
Energy Management System (EMS)
Implements real-time resource dispatch based on advanced forecasts and risk assessments, maintaining a balance between generation and consumption, even under extreme conditions not ever seen in historical data.
Optimal Utilization of Renewable Resources and Large-Scale Energy Storage
Promotes environmental sustainability by maximizing the use of renewable energy and enhancing the efficiency of large energy storage systems, like pumped hydro.
Quantified Impact on System Risk
Employs a unique Risk Scoring Mechanism to quantify and manage the impacts of different grid resources on the system's risk profile, supporting informed decision-making and enhancing system resilience.