Biblio

The Smart Grid offers many benefits due to the bidirectional communication between the users and the utility company, which makes it possible to perform a fine-grain consumption metering. This can be used for Demand Response purposes with the generation and delivery of electricity in real time. It is essential to rapidly anticipate high peaks of demand or potential attacks, so as to avoid power outages and denial of service, while effectively supplying consumption areas. In this paper, we propose a novel architecture where cloud computing resources are leveraged (and tested in practice) to enable, on the one hand, the consumption prediction through time series forecasting, as well as load balancing to uniformly distribute the demand over a set of available generators. On the other and, it also allows the detection of connectivity losses and intrusions within the control network by using controllability concepts.

Nowadays, Smart Grid is envisaged to provide several benefits to both customers and grid operators. However, Smart Meters introduce many privacy issues if consumption data is analysed. In this paper we analyse the main techniques that address privacy when collecting electricity readings. In addition to privacy, it is equally important to preserve efficiency to carry on with monitoring operations, so further control requirements and communication protocols are also studied. Our aim is to provide guidance to installers who intend to integrate such mechanisms on the grid, presenting an expert system to recommend an appropriate deployment strategy.