SIGNAL AND INFORMATION PROCESSING APPLICATIONS FOR THE SMART GRID

This paper discusses using signal processing to assist in processing of information for the smart grid. This consists of getting information about the electrical grid and environment via sensor networks, interpreting information received via signal processing and machine learning, and then using the information to make intelligent decisions about the grid using control and optimization algorithms. The focus is on the electrical grid beyond the last substation, the distribution grid. For the smart distribution grid there is an increasing amount of distributed renewable energy sources and possible distributed storage. This necessitates gathering more information about the electrical grid, environmental data, and building energy usage. With this information we can forecast distributed renewable energy sources and develop algorithms for distributed state estimation. We can then develop demand response algorithms to control loads (e.g. appliances, thermostats, air conditioners, hot water heaters).