RENewable Energy Integration with Efficiency of Smartgrid (RENEWIES)

The transformation of the European energy system poses considerable challenges to electrical 
grids, including increased resilience to climate change, the capacity to meet escalating energy 
demands of modern societies, and the integration of a greater share of variable renewable energy 
sources. The EU aims for climate neutrality by 2050, pursuing a net-zero greenhouse gas econ
omy and a connectivity objective of at least 15% by 2030 to enhance the interconnection of in
stalled electrical generation capacity among member states. Grid expansion is a viable alternative 
for achieving these objectives, offering significant benefits in integrating, transporting, and distrib
uting renewable energy sources. 
In the RENEWIES project-based lab access at the Austrian Institute of Technology (AIT) in Vi
enna, we investigate control stability mechanisms and renewable energy integration in a real-time 
Microgrid (MG) system, incorporating power line communication infrastructure, distributed energy 
resources (DER), and Smart Grid Converters (SGC). The simulation framework, developed using 
MATLAB Simulink, includes photovoltaic (PV) energy sources, with a detailed analysis of power 
quality under varying MG conditions. 
To provide comprehensive validation of the multi-domain and large-scale smart grid, HIL ap
proaches could be integrated with additional simulations and infrastructures. The concept of in
corporating real-time Hardware-in-the-Loop (HIL) into a comprehensive framework underpins the 
ensuing ERIGrid methodologies. These solutions provide a comprehensive understanding of the 
communication network's behaviour and the power system's states. The study commences here, 
as monitoring grid stability necessitates the examination of changeable dynamic factors within a 
comprehensive framework utilising various approaches in the HIL infrastructure. 
With this aim, this study focuses on improving grid stability through non-parametric control meth
ods, particularly PRBS-based impedance measurement, active damping, virtual impedance inte
gration, and real-time solar panel data incorporation for high-penetration scenarios. PRBS signal 
injection is applied via a SGC to measure the system's impedance response, enabling the evalu
ation of grid behaviors. Furthermore, active damping strategies have been explored to enhance 
grid stability, particularly in scenarios involving high renewable penetration. The study also incor
porates Hardware-in-the-Loop (HIL) testing using the Typhoon HIL 602+ platform to validate the 
simulation outcomes under real-world conditions. The HIL setup replicates power converter 
switching states, grid-connected harmonic loads, and network dynamics, allowing for a compre
hensive evaluation of the proposed control methodologies. No issues arose during the utilisation 
of SGC, which may substitute the WFZ device in the project proposal and possesses a more 
sophisticated structure than the WFZ device. It was determined that SGC, offering both frequency 
control and enhanced regulation, can be employed in stability analyses in lieu of WFZ. The effec
tiveness of the SGC in dynamic stability enhancement is tested through impedance-based stabil
ity analysis, Nyquist diagrams, and phase/gain margin calculations. 
A non-parametric harmonic stability monitoring method applicable to both single and multiple con
verter systems was aimed to be developed. For this purpose, a non-parametric stability algorithm 
was successfully modeled and tested in real-time on Typhoon HIL to detect the grid stability of 
the MG network, which had previously been designed in MATLAB with real-time data. 
The findings indicate that active damping combined with virtual impedance provides the most 
effective solution for enhancing grid stability, particularly in high R/X ratio networks. These meth
ods demonstrated superior performance in mitigating oscillations, improving impedance match
ing, and stabilizing the system under fluctuating DER inputs. Further improvements in grid stability
can be achieved through adaptive PRBS injection and real-time optimization of active damping 
parameters. 
This research contributes to the advancement of MG stability analysis, bridging the gap between 
simulation-based studies and real-world grid implementation. Future work will focus on extending 
impedance-based stability assessments to larger-scale networks and enhancing real-time control 
adaptability through machine learning-assisted optimization techniques.