Simulating the effects of smart grid technologies on power quality

Master's assignment
Student Gerwin Hoogsteen
Supervisors Albert Molderink, Gerard Smit, and Vincent Bakker
Location Enschede
Finished July 2013


A transition in the energy sector is going on. Formerly, electricity was only generated centrally and distributed to consumers in the low voltage network. Nowadays, more residents in the low voltage network start to produce their own electricity. This thesis is about the effects of this distributed generation in the low voltage on the power quality and how smart grid technology can help to improve the power quality.

A high penetration of distributed generation can lead to increased voltage levels, harmonics and fluctuations of the voltage level in the low voltage network. These aspects influence the power quality of the electricity delivered to the consumers. Overloading of the network might occur as well. Demand side management could help to mitigate these problems and improve the power quality by balancing consumption and production.

To cope with the integration of more distributed generation and large loads in existing networks, demand side management (DSM) methodologies are developed. Network structures and models are added to the DSM methodology and energy stream simulator, TRIANA, developed by the University of Twente. Additionally, a load-flow algorithm is implemented to calculate the voltage levels and currents flowing through the network. These models provide the means to evaluate the effects of distributed generation in the low voltage network and allows the simulator to fully exploit the models for the planning stage. The first implementation with the integration of network models uses load-flow information as feedback to alter the planning.

The accuracy of the implementation is evaluated by comparing the calculated voltage levels with those resulting from the more advanced network simulator Gaia by Phase to Phase. The maxi- mum deviation in voltage level was found to be 1.3 V. The computation time of the implemented algorithm only takes 1.3ms, whereas Gaia usually requires a second to find the values.

An existing Dutch low voltage network is modeled for simulations. A total of 121 households are modeled after a futuristic model with a varying penetration of photovoltaics, electrical vehicles, heat pumps and smart appliances. Simulations show that large loads lead to voltage drops during the winter. With demand side management, the minimum voltage level is locally as low as 204 V, lower then the minimum allowed by the Dutch regulations. Using load-flow information as feedback improves the voltage level with a minimum of 212 V. The maximum cable usage is also reduced from 88.5% to 66.3%. These results are obtained with similar DSM performance results.

The results show that large loads can have influence on the power quality in a network as well. With a high penetration of either distributed generation or large loads the simulated network is close to its limits, or even exceeds these limits. Demand side management does not necessarily lead to a better power quality, but can even decrease the power quality. The main reason for this is the imbalance introduced in the network at certain points. This research shows that incorporating load-flow calculations in DSM can overcome this problem and incorporate power quality optimization objectives in DSM. A smart-grid should therefore balance production and consumption both globally as locally to improve the power quality.