The master's thesis deals with the design of a flexibility control algorithm for demand and production in distribution networks. The need for such a control algorithm arises from the increasing connection of new types of devices, such as electric vehicles, heat pumps, or solar power plants, which pose great challenges to the operation of modern distribution networks.
There are two approaches to addressing these challenges. The first approach is the traditional approach that does not consider the flexibility that new types of devices can provide. Network planners mainly upgrade grids by replacing existing elements, which leads to the underutilization of newly installed elements. An alternative approach could be to consider the flexibility potential of newly connected devices, which puts traditionally passive consumers in the role of active users or prosumers who can positively influence network conditions by adjusting their consumption or production. The goal of the master's thesis is the design of an algorithm that takes into account the potential of demand and production flexibility in the operation of the distribution network.
The first part of the thesis briefly describes the characteristics and features of low-voltage distribution networks and their operating limitations. Voltage and thermal limitations of the network, as well as losses that occur during the operation of distribution networks, are described in detail. These limitations form the basis for calculating the hosting capacity of the distribution network.
Then, the concept of the hosting capacity of the distribution network, which has so far been used in the planning of the distribution network, is described. Several types of methods can be used to calculate the hosting capacity, including deterministic, stochastic, and optimization methods, whose advantages and disadvantages are presented in this work.
The following section introduces both methods that were tested within the master's thesis. As potential methods for designing the flexibility control algorithm, the particle swarm optimization method, which belongs to optimization methods, and the method based on sensitivity theory, which has been mainly used for voltage regulation at the high voltage level, were identified.
In the final part of the master's thesis, both methods are tested on a real low-voltage distribution network within a simulation environment. In the first part of the test, the methods are used to ensure, that the network operates within established operational criteria. Simulation results show that the results of both methods successfully ensure that the considered network operates within the established criteria. In the second part of the test, we used the sensitivity coefficient-based method to determine the nodal capacity that an aggregator could utilize to provide system services.
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