Residential Demand Response: Generation, Storage, and Load Management
- Author(s): Ahmed, Nadia
- Advisor(s): Levorato, Marco
- Li, GP
- et al.
An Energy Management System (EMS) control framework driven by resident behavior patterns is developed. Using hidden Markov modeling techniques, the EMS detects consumer behavior from real-time aggregate consumption and a pre-built dictionary of reference models. These models capture variations in consumer habits as a function of daily living activity sequence. Following a training period, the system identifies the best fit model which is used to estimate the current state of the resident. When a request to activate a time-shiftable appliance is made, the control agent compares grid signals, user convenience constraints, and the current consumer state estimate to predict the likelihood that the future aggregate load exceeds a consumption threshold during the operating cycle of the requested device. Based on the outcome, the control agent initiates or defers the activation request. Using three consumer reference models, a case study assessing EMS performance with respect to model detection, state estimation, and control as a function of consumer comfort and grid-informed consumption constraints is presented. A tradeoff analysis between comfort, consumption threshold, and appliance activation delay is demonstrated.
The EMS system is then extended to include the residential distributed energy management system (DER). In this cyber-physical system, the consumer home generates and stores energy for utilization by the load to decrease peaks in demand on the power grid. Using historical irradiance datasets a solar irradiance model based on weather forecast data is built to predict the potential future harvested energy of the system in addition to the load profile. The harvested energy and the load are used to assess the amount of energy that may be stored in the energy storage unit of a household. Based on the cost associated with the power rate and the degradation of the battery during the charge discharge cycle, a control policy based on a Markov decision process framework is assessed. A case study for Boulder, CO is presented. Results using the rain flow counting method illustrate a significant reduction in material damage to the battery bank.