Converge Hybrid
Converge Hybrid provides software and consulting for designing hybrid renewable energy projects that combine wind, solar, and battery storage. Its platform simulates thousands of configurations, using an Artificial Operator to make hourly dispatch decisions over a 30-year horizon to deliver firm power at the lowest cost. The tool is designed for emerging markets where grid instability deters investment, with pre-loaded data for regions like Kenya, Egypt, and South Africa.
- Energy
- Clean Technology
- Data & Analytics
- Software Only
- Battery Technology
- Artificial Intelligence
Funding
Founders
Founder details are not available yet.
Product
Problem
Grid instability deters investment in emerging economies where clean energy is most needed, and hybrid renewable projects are often sized using manual methods that test only a few configurations. Traditional modelling treats storage as a rough energy balance, which understates what a battery earns while overstating how much storage is actually needed to deliver firm, dependable power.
Solution
Converge Hybrid provides a purpose-built software platform and expert consulting for designing hybrid renewable energy projects—wind, solar, and battery storage optimized together. The application uses advanced algorithms and extensive datasets to analyze thousands of project configurations against historical wind and solar data, grid constraints, demand profiles, and power price curves. An Artificial Operator runs inside every configuration, making hourly dispatch decisions—charging, holding, or discharging—based on a rolling forecast window across a 30-year horizon. The platform outputs technical reports including LCOE, IRR, and NPV, producing bankable configurations that reduce LCOE, maximize yield, and accelerate development timelines from concept to financial close.
Target Audience
Primary users are renewable energy developers, industrial offtakers, grid operators, and consulting engineers who need techno-economic analysis to design hybrid wind-solar-battery projects that deliver firm power at lowest cost, particularly in emerging markets where grid instability deters investment.
Features
- Hybridisation modelling engine that tests thousands of configurations per sweep versus 3-5 manually, covering wind, solar, and battery storage
- Artificial Operator (AO) making 262,800 dispatch decisions per configuration—hour by hour over 30 years—with degradation, round-trip losses, and grid connection limits respected
- Three dispatch strategies: Maximise revenue (day-ahead arbitrage), Match demand (delivery obligations such as data centres), and a reactive baseline to quantify dispatch intelligence value
- Forecast uncertainty as a configurable input, applied to resource, price, and demand across a rolling look-ahead window, with perfect-foresight runs as an upper-bound benchmark
- Pre-loaded resource data and grid profiles for underserved regions including Kenya, Kazakhstan, Egypt, South Africa, and Namibia
- Hourly power-flow visualisations per configuration, with sub-hourly time steps on the roadmap for ancillary revenue modelling