Energy Management: Forecast-Based Optimization and Model-Predictive Control

Flexibility is an essential resource in the 100% renewable electricity system of the future. To harness existing flexibility, we are developing strategies for forecast-based optimization and model-predictive control.

With our optimization software OptiCharge, we create optimized charging schedules for battery electric vehicles that promote the integration of renewable energy while reducing operating costs. This intelligent planning maximizes energy efficiency and reduces dependence on conventional energy sources.

OptiCharge combines various use cases, including PV optimization and price optimization, both unidirectional and bidirectional, for individual vehicles and larger fleets.

Another optimization software solution is our Dynamic Energy System Optimization (DESO). Through multi-criteria battery optimization – which also accounts for uncertainties in forecasts – electricity costs can be reduced, self-consumption increased, and battery degradation minimized.

In addition to our specific software frameworks OptiCharge and DESO, we are engaged in several research projects focused on the optimization of decentralized energy systems. The focus here is on the control of battery electric vehicles, heat pumps, and battery storage systems in any combination and for various applications both in front of and behind the meter.

Our R&D Services in this Field Include:

• Optimization algorithms as SaaS for improved performance and scalability.
• PV- and price-optimized bidirectional charging of battery electric vehicles and battery systems
• Real-time control on embedded systems for efficient implementation
• Accounting for uncertainties in real-time control; possible states are modeled and integrated into the cost function with appropriate weighting