Work Package 1
The aim of WP 1 is to introduce an energy management system for jointly managed loads/generations (resources) with the aim of minimizing energy usage and energy costs in the system. For this purpose, the system will be implemented in a structured manner in two stages. In the first stage, the energy management system (EMS) is created for a local aggregation of resources. This controller coordinates the generation (solar panels, wind), storage (battery), and usage (bidirectional charging station and other loads) of energy. To do this it requires an IoT-enabled remote monitoring infrastructure on the user side as well as a 5G-enabled, ultra-low latency communications to collect required data in real-time, and incorporate weather and energy price forecasts into the optimization process. This provides a recommendation for the household/customer’s energy configuration and estimates the expected energy usage of the household.
In the second stage, with an EMS Hub, the user is given the option to optimize across multiple EMS platforms. In this way, the total electrical load in the distribution grid can be reduced. At the same time, this reduces the carbon footprint of energy generation by using green, self-generated energy, such as solar and wind, instead of coal and oil-based energy.
Work Package 2
The goal of Work Package 2 is to achieve further optimization of energy management through communication between various individual EMS systems. This optimization involves, for the first time, electric vehicles (EVs) as a means of transporting energy and people from one EMS system to another. The research objective is to explore the possibilities of using such a transport process to smooth the aggregated power demand of EMS systems from the distribution system operator’s perspective. Requirements for power optimization and data transmission, especially latency and data throughput, will be theoretically determined. Likewise, the structure and optimization for the EMS hub in terms of communication will be explored and described.
Work Package 3
The goal of Work Package 3 is to realize self-learning optimization of Energy Management systems through 5G and AI. The objective is to maximize energy usage with local energy and minimize the usage of fossil energies. To achieve this the strong capabilities of AI Watson are taken into account. With its weather forecast data for example its feasible to recognize where and when solar panels or wind turbines generate electricity in order benefit of it. Furthermore, we are planing to set up a green ledger based microgrid. The locally generated energy is the cheapest energy. This energy is to be certified as green energy and integrated into the charging processes via a campus energy management system (EMS).
Work Package 4
- Downscaled testbed under development
- Virtual testbed under development
- Physical testbed in planning.
Work Package 5