Biblio

With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.

Security and privacy of smart grid communication data is crucial given the nature of the continuous bidirectional information exchange between the consumer and the utilities. Data security has conventionally been ensured using cryptographic techniques implemented at the upper layers of the network stack. However, it has been shown that security can be further enhanced using physical layer (PHY) methods. To aid and/or complement such PHY and upper layer techniques, in this paper, we propose a PHY design that can detect and locate not only an active intruder but also a passive eavesdropper in the network. Our method can either be used as a stand-alone solution or together with existing techniques to achieve improved smart grid data security. Our machine learning based solution intelligently and automatically detects and locates a possible intruder in the network by reusing power line transmission modems installed in the grid for communication purposes. Simulation results show that our cost-efficient design provides near ideal intruder detection rates and also estimates its location with a high degree of accuracy.

The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.

The display image on the visual display unit (VDU) can be retrieved from the radiated and conducted emission at some distance with no trace. In this paper, the maximum eavesdropping distance for the unintentional radiation and conduction electromagnetic (EM) signals which contain information has been estimated in theory by considering some realistic parameters. Firstly, the maximum eavesdropping distance for the unintentional EM radiation is estimated based on the reception capacity of a log-periodic antenna which connects to a receiver, the experiment data, the attenuation in free-space and the additional attenuation in the propagation path. And then, based on a multi-conductor transmission model and some experiment results, the maximum eavesdropping distance for the conducted emission is theoretically derived. The estimating results demonstrated that the ITE equipment may also exist threat of the information leakage even if it has met the current EMC requirements.