Energy

For thermostatically controlled loads (TCLs) to perform demand response services in real-time markets, recursive methods for parameter estimation are needed. As the physical characteristics of a TCL change (e.g. the contents of a refrigerator or the occupancy of a conditioned room), it is necessary to update the parameters of the TCL model. Otherwise, the TCL will be incapable of accurately predicting its potential energy demand, thereby decreasing the reliability of a TCL aggregation to perform demand response. In this paper, we investigate the potential of an unscented Kalman filter (UKF...

This paper proposes three novel distributed algorithms to optimally schedule Plug-in Electric Vehicle (PEV) charging. We first define the global optimization problem, where we seek to control large heterogeneous fleets of PEVs to flatten a net Load Curve. We demonstrate that the aggregated objective can be distributed, via a new consensus variable. This leads to a dual maximization problem that can be solved in an iterative and decentralized manner: at each iteration, PEVs solve their optimal problem, communicate their response to the aggregator, which then updates a price signal. We...

The short-term forecasting of building electricity demand is certain to play a vital role in the future power grid. Given the deployment of intermittent renewable energy sources and the ever increasing consumption of electricity, the generation of accurate demand-side electricity forecasts will be valuable to both grid operators and building energy management systems. The literature is rich with forecasting models for individual buildings. However, an ongoing challenge is the development of a broadly applicable method for electricity forecasting across geographic locations, seasons, and...

Energy systems (e.g. ventilation fans, refrigerators, and electrical vehicle chargers) often have binary or discrete states due to hardware limitations and efficiency characteristics. Typically, such systems have additional programmatic constraints, such as minimum dwell times to prevent short cycling. As a result, non-convex techniques, like dynamic programming, are generally required for optimization. Recognizing developments in the field of distributed convex optimization and the potential for energy systems to participate in ancillary power system services, it is advantageous to...