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Gökmen, Nuri

PROJECT TITLE: Maximizing Renewable Energy Integration in Distribution Grids

PhD period: 2014.07.01 – 2017.06.30. (has been prolonged till 2017.12.30)

Section: Electric Power Systems
Research Programme: Intelligent Energy Systems and Active Networks 
Supervisor: Zhe Chen
Co-Supervisor: Weihao Hu 
Contact information

Collaborators: Dong Energy, kk-electronic, HEF and AREVA.
Funding: Scholarship from the Department of Energy Technology.


Policy makers accelerate to support integration of renewable energy resources (RES) due to environmental and social concerns such as increased soil and air pollution. Within this scope, Denmark has targets to provide 50% of its national electricity consumption from wind energy in 2020. On the other hand, intermittent nature of renewable energy resources brings a challenging task to reach this goal as generation of energy is highly dependent on meteorological conditions. Another important problem is excess electricity production due to the relatively high wind penetrations. This excess energy must be used both effectively and efficiently in order to reach aforementioned targets together with the best economic benefits. In parallel to this, there has been a significant change in road transport sector in recent years. Usage of electric vehicles (EVs) is expected to increase considerably for reducing fossil fuel consumption and greenhouse gas emissions in the near future. Individual heat pumps (HPs) and space heaters (SHs) will also play an important role in future energy systems. Thus, storing excess energy on these flexible electrical and thermal units is seen as a promising option for increasing renewable energy share in distribution grids.

The main objective of this project is to maximize the share of solar and wind energy by actively controlling the electrical and thermal loads in distribution grids. Mathematical models of electrical vehicles, heat pumps and space heaters as well as grid connected wind turbines and photovoltaic modules will be developed and a virtual power plant will be created to analyze the effects of all active elements for various generation and load scenarios. Intelligent demand side management techniques will be investigated with innovative charging/discharging and heating strategies for EVs and HPs, respectively, by ensuring the grid security, voltage stability and power quality. MATLAB and DIgSILENT Powerfactory simulation tools are going to be used for both modelling and analyzing the power system for different load and generation profiles.


Publications in journals and conference papers may be found at VBN.