A smart grid is a complex system consisting of a wide range of electric grid components, entities controlling power distribution, generation and consumption, and a communication network supporting data exchange. This paper focuses on the influence of imperfect network conditions on smart grid controllers, and how this can be counteracted by utilizing Quality of Service (QoS) information from the communication network. Such an interface between grid controller and network QoS is particularly relevant for smart grid scenarios that use third party communication network infrastructure, where modification of networking and lower layer protocols are impossible. This paper defines a middleware solution for adaptation of smart grid control, which uses network QoS information and interacts with the smart grid controller to increase dependability. In order to verify the methodology, an example scenario of a low voltage grid controller is simulated under imperfect network conditions.

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We investigate a centralized wind farm controller which runs periodically. The controller attempts to reduce the damage a wind turbine sustains during operation by estimating fatigue based on the wind turbine state. The investigation focuses on the impact of information access and communication networks on the controller performance. We start by investigating the effects of a communication network that introduces delays in the information access for the central controller. The control performance as measured by accumulated fatigue is shown to be significantly impacted by communication delays and also by the choice of the time instances at which sensor information is accessed. In order to optimize the latter, we introduce an information quality metric and a mathematical model based on Markov chains, which are compared performance-wise to a heuristic approach for finding this parameter. This information quality metric is called mismatch probability, mmPr, and is used to express quantitatively the information accuracy in a given scenario. Lastly, measurements of different communication technologies have been performed in order to carry out the analysis in a practically relevant scenario with respect to the communication network delays. These measurements are done in regard to packet loss and communication delays, and the simulations are rerun using either the traces from the measurements or scenarios constructed from the delay parameters.

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