The electromobility and energy markets are currently facing a common challenge given that sector coupling is still in the conceptual initial phase. In fact due to the omission of centrally generated control energy with an increasing fluctuating energy originating from solar and wind power, increased energy demand and peak loads, especially due to the ramp-up of electromobility, new distributed control mechanisms must be found to ensure grid stability. However, electromobility not only creates new challenges, with a suitable approach, it also offers the potential to become an integral part of controlling grid stability.

The ramp-up of e-mobility will result in a huge, distributed battery system whose storage capacity can be charged and discharged via the energy interface of bidirectional charging (BDL) in a way that is suitable for the grid. The bundling of the resulting storage capacity creates enormous mobile storage resources and flexibility, which, taking into account the potential of the charging infrastructure and the digital connect interfaces of conventional vehicles, could be used for a range of network-related system services.

Demand-Side Management (DSM) is a framework that addresses these challenges through distributed information sharing, integrated scheduling, and intelligent decision-making across the network. This enables an adjustment of the energy consumption or an optimal use of resources and flexibility, also from electromobility.
 

The solution to the above problem relies on algorithmic approaches for the development and implementation of predictive DSM concepts. The core idea is that the electric vehicles individually or in combination (charging park, parking garage, etc.), or in the form of an "energy cell", actively participate in the overall economic optimization by exchanging power with the network, and also allow future time intervals as a degree of freedom for supply planning. These degrees of freedom can also be taken into account in the long-term for network planning and savings can thus be achieved. In this way, the energy provider optimizes the network operation according to the required system service(s) and calculates charging/discharging points and performance profiles that are optimal in terms of space and time for individual vehicles or a fleet of electric vehicles along a specified time interval.

A local predictive control algorithm of such an energy cell ensures additionally compliance with the specified performance profile. Performance profiles that deviate from the basic profile can generate an additional payment for the energy cell depending on the flexibility offered.

Thanks to the technology developed in DeRIVE, network stability can be better controlled in the longer term, and network expansion can be limited by providing additional storage capacity and dynamically shifting the residual loads. Ultimately, this approach creates new energy markets and policies for assessing consumer flexibility and dynamic adjustment of electricity prices.
 

• Develop novel predictive DSM concepts for electric vehicles which provide optimal time-stamped charging/discharging profiles and charging point connection within a network.
• Real-time emulation of DSM algorithms and ancillary services utilizing a PowerHIL environment with a BDL AC/DC interface as test-under-device.
• Use and extension of the established communication standards of electromobility, including the standards currently available between charging points, electric vehicles mobility operator which will be used in the future with smart charging.
• Experimental validation of ancillary services and bi-directional charging (BDL) in conjunction with the battery management system (BMS) of a test vehicle and a large stack of emulating vehicle batteries.
   

• Electromobility
• Bidirectional charging
• Demand-Side Management
• Ancillary services
• Sector coupling
   

July 2022 - June 2025

AKKA Industry Consulting GmbH 
Stadtwerk Haßfurt GmbH 
TU Kaiserslautern 
Expleo Germany GmbH 
Es Geht!Energiesystme GmbH 
Hubject GmbH 
 

Prof. Dr.-Ing. Naim Bajcinca
Gottlieb-Daimler-Str. 42
67663, Kaiserslautern
+49 (0)631/205-3230
naim.bajcinca(at)mv.uni-kl.de