Alliander projects

Alliander has provided funding for two PhD projects:

Dynamic Capacity Control and Balancing at MV

PhD student: Frits de Nijs
Supervisors: Matthijs Spaan and Mathijs de Weerdt

The rise of renewable energy sources such as solar panels puts high demands on our electricity grid. In particular, the demand on the medium-voltage (MV) level of the power grid may increase above current capacity at peak times. TU Delft researchers in collaboration with Alliander investigate how large customers can be involved in staying within network capacity. Combining information about their flexibility in power consumption with smart planning can alleviate some of the capacity problems. The main question of study is what financial incentives are required to entice customers to share this information and if necessary use this flexibility. By rewarding customers for their contribution to keeping the power grid within capacity, the need for investment in the physical grid infrastructure may be reduced.

Big Energy data

PhD student: Hale Cetinay
Supervisor: Fernando Kuipers

The "smart" in smart energy systems is facilitated by ICT and refers to dynamically controlling the system in the face of load fluctuations and anomalies or network failures. Within Alliander's MS-LiveLab project, a test environment has been realized to gain knowledge on digitising the (existing) Medium Voltage networks. MS-LiveLab is installed in the city of Zaltbommel and is expected to generate a large amount of data. The objectives of this project are threefold:

  1. What kind of data is needed and can be collected, how and where can it be efficiently stored, and how frequently - and to where - does it need to be communicated?
  2. What kind of communications and computations infrastructure is needed? There is a strong need for dynamic access to scalable computing and network resources.
  3. Efficiently processing vast amounts of fine-grained data in near-real-time, commonly referred to as data analytics.

Developing algorithms for dealing with big energy data and complex networks constitutes a fairly new area within the field of algorithm design and will require a tailor-made multi-disciplinary solution.