Abstract
The HEMS algorithm schedules residential appliance operation to minimize the weighted sum of occupant discomfort, total energy cost, peak electricity consumption and carbon footprint, weighted according to occupant preferences. A centralized mixed integer linear programming (MILP) based optimization approach is taken, with the HEMS managing all appliances directly, following a Model Predictive Control (MPC) methodology which determines the optimal schedule over the user-specified scheduling horizon and repeats the optimization at configurable time intervals. The HEMS is developed on the General Algebraic Modeling System (GAMS) using the MILP solver. This system is able to effectively deal with the stochasticity in the distributed system by utilizing Monte Carlo simulation where uncertainties in future electricity price, outdoor temperature, water usage, solar and wind generation can be accounted for. The HEMS controls the operation of the heating, ventilation and air conditioning (HVAC) system, water heater, refrigerator, residential batteries, electric vehicles, standalone micro combined heat and power (CHP) generator, dishwasher, washer/dryer, pool pumps, and lighting.
- Developers:
-
Pratt, Annabelle [1] ; Wu, Hongyu [1] ; Chakraborty, Sudipta [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2015-05-21
- Project Type:
- Closed Source
- Software Type:
- Scientific
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 44948
- Site Accession Number:
- NREL SWR-15-12
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Pratt, Annabelle, Wu, Hongyu, and Chakraborty, Sudipta.
HEMS: Home Energy Management System [SWR-15-12] - Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources.
Computer Software.
USDOE Laboratory Directed Research and Development (LDRD) Program.
21 May. 2015.
Web.
doi:10.11578/dc.20200925.12.
Pratt, Annabelle, Wu, Hongyu, & Chakraborty, Sudipta.
(2015, May 21).
HEMS: Home Energy Management System [SWR-15-12] - Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources.
[Computer software].
https://doi.org/10.11578/dc.20200925.12.
Pratt, Annabelle, Wu, Hongyu, and Chakraborty, Sudipta.
"HEMS: Home Energy Management System [SWR-15-12] - Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources." Computer software.
May 21, 2015.
https://doi.org/10.11578/dc.20200925.12.
@misc{
doecode_44948,
title = {HEMS: Home Energy Management System [SWR-15-12] - Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources},
author = {Pratt, Annabelle and Wu, Hongyu and Chakraborty, Sudipta},
abstractNote = {The HEMS algorithm schedules residential appliance operation to minimize the weighted sum of occupant discomfort, total energy cost, peak electricity consumption and carbon footprint, weighted according to occupant preferences. A centralized mixed integer linear programming (MILP) based optimization approach is taken, with the HEMS managing all appliances directly, following a Model Predictive Control (MPC) methodology which determines the optimal schedule over the user-specified scheduling horizon and repeats the optimization at configurable time intervals. The HEMS is developed on the General Algebraic Modeling System (GAMS) using the MILP solver. This system is able to effectively deal with the stochasticity in the distributed system by utilizing Monte Carlo simulation where uncertainties in future electricity price, outdoor temperature, water usage, solar and wind generation can be accounted for. The HEMS controls the operation of the heating, ventilation and air conditioning (HVAC) system, water heater, refrigerator, residential batteries, electric vehicles, standalone micro combined heat and power (CHP) generator, dishwasher, washer/dryer, pool pumps, and lighting.},
doi = {10.11578/dc.20200925.12},
url = {https://doi.org/10.11578/dc.20200925.12},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20200925.12}},
year = {2015},
month = {may}
}