Energy Management for Energy Suppliers, Grid Operators, and Energy Service Providers

Challenges

• Individual energy portfolios require individual solutions (energy management is required to support different business models)
• High-quality EMS functions form the basis for sustainable, data-driven services and business models

Our solution and research work

• Integration and development of modelling and optimisation solutions for power station and resource deployment planning, procurement, direct marketing, storage management, virtual power plants and operational management
• Integration/development of solutions for modelling and forecasting consumption and feed-in
• Linear and non-linear modelling approaches for forecasting and optimisation
  • Forecasting: automated data analysis, knowledge-based models, probabilistic forecasting, current AI approaches and self-learning methods (e.g. LSTM, deep learning, reinforcement learning, federated learning, etc.), assistance functions
  • Optimisation: linear and mixed-integer-linear optimisation, stochastic and robust optimisation, agent-based optimisation, fundamental models, automated model generation
• Use of AI for automation and scaling during commissioning/initialisation and during operation
• Resilience in business management