Abstract
This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.
| Original language | American English |
|---|---|
| Number of pages | 5 |
| DOIs | |
| State | Published - 21 Dec 2018 |
| Event | 2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States Duration: 5 Aug 2018 → 10 Aug 2018 |
Conference
| Conference | 2018 IEEE Power and Energy Society General Meeting, PESGM 2018 |
|---|---|
| Country/Territory | United States |
| City | Portland |
| Period | 5/08/18 → 10/08/18 |
Bibliographical note
See NREL/CP-5500-70492 for preprintNREL Publication Number
- NREL/CP-5500-73330
Keywords
- demand response
- DR
- HEMS
- home energy management system
