Key technologies
The energy management platform will feature a variety of components and technologies developed within the project.
Management units and smart grid technologies
The energy-management platform will include 4 types of management units:
- customer management unit (CMU),
- distributed energy resources management units (DERMU),
- low voltage grid management unit (LVGMU) and
- medium voltage grid management unit (MVGMU).
All these units can acquire signals and turn them into data. They store this data locally and feed measurements into the energy-management platform. They also receive commands from the platform to control assets or give local commands.
Data-exchange middleware
From a hardware and software point of view, smart grids are heterogeneous systems formed by different sub-systems and components that need to constantly exchange information to carry out their tasks in an optimal, secure and efficient way.
In the project, a data-exchange middleware will be used for real-time communication and data storage between the different devices in the system. It will provide a high-level programming model that provides ways of storing/getting information from/to the grid and encrypts messages for security purposes.
Cloud computing, Fog computing and Edge devices will be integrated. Fault-tolerance, resiliency and security will be improved.
Security and privacy technology
Smart grid solutions are based on data which belongs to different stakeholders. It is therefore crucial to handle it with care.
Four security and privacy modules will be installed within the platform:
- Trust and groupmanagement (access control)
- Mechanisms for privacy and security
- Node protection and maintenance
- Network security
The project will investigate the data flows and security issues in the whole system and propose holistic solutions. Additionally, it will propose guidelines for developing energy management algorithms that minimise the need to export the data of the individual user outside the user premises, thus limiting potential security and privacy gaps.
Prediction models
New prediction models of electricity production (from renewables) and electricity demand, including potential flexibility in the demand, will be tested.
The project aims to develop models to forecast energy flexibility for a reliable and useful energy management on a fine-grained level.
In addition, the following technologies will be tested to further increase and unlock the electric grid flexibility:
High-efficient power inverters
for the creation of DC microgrids
The project will create novel high-efficient power inverters based on silicon carbide power devices. These will provide for smarter integration of renewable energy generators and electric vehicles into the electricity distribution network. By switching to new silicon carbide-based semiconductor modules, devices will become smaller, quieter and more energy-efficient.
Smart Grid power electronic converters enable power balancing, voltage equalisation, power factor correction and harmonic elimination to reduce heating losses in network assets. They therefore offer an effective method for rapid electric vehicle charging by increasing the capacity of existing network assets dynamically and autonomously.
Vehicle 2 Grid (V2G)
V2G storage capabilities can enable electric vehicles to store and discharge electricity generated from renewable energy sources, such as solar and wind, with output that fluctuates depending on weather and time of day.
The project will test the combination of V2G with local DC networks supported by silicon carbide-based high-efficiency power inverters at district level.
Smart storage for buildings
Three-phase smart energy storage systems will be specifically designed for buildings with large consumption and high rated power, in combination with local renewable sources. Energy storage will increase the energy flexibility of pilot buildings, thus supporting the electricity grid at distribution and transmission level operations.
The energy consumption and generation patterns of the pilot-case buildings will be analysed to define a methodology for sizing energy storage systems for buildings with high rates of power and energy demand that optimises flexibility services and economic viability.