Gridcognition was recently named in the Climate Tech 1000 “a list of the 1000 most promising Climate Tech startups and companies around the world”
Powerful Simulation & Optimization Engine for complete energy project modeling.
Our cutting edge Simulation & Optimisation Engine integrates rigorously tested parametric models for multiple kinds of energy resources, a billing-grade pricing and rating engine for commercial calculations, and an advanced optimiser to accurately represent the control system that will orchestrate and optimize your energy resources. Cloud computing means every simulation and optimization job runs on dedicated and scalable compute resources.
Gridcog has an extensible library of pre-built parametric asset models that you can drop into your projects and configure. These include solar generation, wind generation, thermal generation, battery energy storage, demand flexibility and load shifting, electric vehicle charging infrastructure, and electric vehicle fleets. Each model is carefully tested and calibrated using real-world asset data, and fully documented in our online user manual.
Modern energy projects integrate smart, intelligently-controlled and price-responsive energy resources. Gridcog can model the operational control systems that optimize these assets. This includes modeling co-optimization – orchestrating the turn-down of renewables, the discharging of batteries, the dispatch of backup gensets, the shifting of loads, and the charging of vehicles in a coordinated way to maximize commercial and environmental outcomes.
Gridcog includes a billing-grade tariff engine for pricing and rating and to enable economic optimization. Complex utility, regulated network, and competitive retail supply rate structures can be configured; wholesale energy, network support, reserve capacity, ancillary services, and environmental certificate pricing can be configured; and, future rate changes and price escalations can be configured. Commercial calculations are done at a granular-level, down to 5-minute settlement intervals, and down to the individual tariff item level.
When simulating future site loads and intermittent renewable generation, Gridcog can incorporate future variability in a statistically rigorous way, and when modeling operational control systems (optimizers), Gridcog can incorporate forecasting uncertainty (avoiding the ‘perfect foresight’ trap). Together these uncertainty modeling approaches help investors consider the possible range of commercial outcomes that might arise from their project options.