Our Services
We provide expert consultancy and advanced analytical solutions for the optimization, control, and profitability assessment of energy storage systems and energy market operations.
Our work integrates engineering expertise with machine learning and predictive analytics, helping our clients make smarter, data-driven decisions in a rapidly evolving energy landscape.
We support both industrial energy users and trading companies in developing optimal operational strategies, forecasting renewable generation and consumption, and evaluating investment opportunities in energy storage.
Our Focus
We help energy producers, industrial consumers, and trading companies to:
- Maximize the profitability of energy storage assets,
- Develop and implement optimal dispatch and control strategies,
- Improve accuracy of renewable generation and consumption forecasts,
- Use data-driven analytics to enhance trading decisions,
- Assess technical and financial feasibility of energy storage investments,
- Build the foundation for AI-based decision support in energy markets.
What We Do
- Energy storage control strategies and optimization – We develop algorithms for real-time and forecast-based management of energy storage systems.
Our models take into account energy market prices, weather forecasts, and load predictions to define optimal charge and discharge strategies.
Dispatch strategies are updated dynamically (e.g. every six hours) as more accurate data becomes available. - Technical and investment analyses – We perform detailed profitability studies for energy storage projects, including:
- Revenue modeling from Day-Ahead Market (DAM) arbitrage, capacity market participation, and Demand Side Response (DSR) services,
- Assessment of energy storage degradation, efficiency, and operational strategy,
- Determination of optimal storage capacity and configuration,
- Integration with renewable sources (PV, wind) or industrial processes.
- Prediction of renewable energy generation (RES) – We build calibrated forecasting models for PV and wind installations.
These models correct systematic weather forecast errors using historical SCADA data, ensuring highly accurate production forecasts for the next day or week. - Consumption prediction and clustering – We use machine learning to automatically cluster energy consumers into characteristic groups and create accurate short- and long-term consumption forecasts for trading or balancing purposes.
- Price forecasting and market analytics – We design and train neural network-based models to predict hourly electricity prices on the Day-Ahead Market, improving contracting and trading strategies.
Our models achieve high accuracy by incorporating PSE coordination plan data, weather variables, and generation forecasts. - Support for trading companies – We assist trading entities in developing analytical and forecasting tools, improving market models, and evaluating energy storage co-location opportunities at RES sites.