PROMINT Results to Date
Objective 1. Design, simulation and evaluation of the communications layer for distributed power systems operating in microgrids
- State-of-the-art report on communications in Smart Energy Grids
- Assessment report on the available tecnologies and the use cases
- Identification of the IEC 61850 standard as the ideal candidate to address the creation of a distributed P2P communications architecture in microgrids
- Photovoltaic experimental plant: simulates a microgrid node; battery energy storage capacity; controllable loads; possibility of connection and disconnection from the main grid; Raspberry Pi as a protocol converter
- Identification of Blockchain technology as a key tool to create P2P communication networks in Microgrids
- Designing a local market for Microgrids using smart contracts
- Study of the main cybersecurity threats that can affect smart grids and microgrids: cybersecurity analysis of a prototype microgrid; identification of assets with C4 Model; threat assessment with STRIDE; risk quantification with MAGERIT
- Development of a novel induction energy harvesting technique applicable to low energy sensors for hard-to-reach lines
Objective 2: Modelling, control and energy management in hybrid DC/AC smart grids
- State-of-the-art reports and current projects related to the operation of islanded DC and AC grids and microgrids applied in the railway sector and in urban electricity grids.
- Analysis of the stability of distribution networks and their possible bifurcation points using non-linear methods
- Preparation of models for SIMULINK representation of different elements with possible presence in smart microgrids
- Studies for the improvement of transient stability in hybrid DC/AC systemns by coordinating station control
- Study of the application of the calculation of the eigenvalue sensitivities of the resulting linear model for the study of the stability of droop control of electronic converters in a microgrid
- Modal analysis applied to microgrids: proposed "state relevance coefficient"
- Modal analysis applied to microgrids in unbalanced systems: this analysis produces exactly the same result as the impedance criterion and produces richer results, but requires more complex models
- Establishing the foundations for the analysis of microgrids with strong imbalances
- Multi-agent control in microgrids: Synchronisation of a microgrid - Experimental results in the IMDEA Energy Laboratory (SEIL).
- Deep Learning by Reinforcement for generation management in smart grids: Optimisation of operation through a deep (multi-layer) neural network that serves to learn by trial and error. It has been tested in a simple, small system and in a typical CIGRE distribution network.
- Modelling networks with reduced inertia: investigating the interaction between converters and the low-inertia network
- Swing cancellation in microgrids: use of storage to cancel the impact of sub-synchronous swings on the local network
- Virtual impedance for optimal power quality design in microgrids
- Parallel VSM: control of Virtual Synchronous Machines in parallel connection for isolated microgrids
- Integration of renewable energies in electricity grids: Grid-formers and grid-followers, safe joint operation
- Predictive control for renewables integration
- Supporting voltage control using virtual admittance
Objective 3: Energy recovery in rail transport networks and their integration in urban microgrids
- Study of the small-signal stability of DC microgrids in the railway sector and their connections to electricity grids
- Study for the selection of the optimal number of reversible substations for braking energy recovery in urban netwoks.
- Development of an algorithm for the optimisation of the location and capacity determination of Energy Storage Systems (ESS) on electrified railway lines
- Design of an optimisation based on an evolutionary type algorithm that unifies different search methods, the Substrate Layer Coral Reefs Optimisation algorith (SLCRO) and has been tested by combining simulations of the "Metro de Madrid" line.
- Smart substation management
Objective 4: Design and implementation of an energy management system for hybrid renewable generation and battery storage systems
- Design and implementation of an energy management (EMS) for hybrid renewable generation and battery storage systems (BESS)
- Development of a planning tool for the optimal dimensioning of the BEES
- Design and simulation of the energy management system for the operation of the hybrid system
- EMS implementation on a real-time control platform (PC with RTDS)
Objective 5: Machine learning applied to microgrids, electric vehicles and energy management
- Analysis of the state of the art in renewable resource prediction applicable to microgrids
- Generation of machine learning algorithms for short and long term prediction of water level in a hydroelectric reservoir
- Optimal location and dimensioning of ESSs on railway lines
- Programming of charging and discharging of electric vehicles. Inclusion of CO2 saving measures in the optimisation.
