Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors.
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Home Energy Management with Clothing Integrated Thermal Comfort and EV SOC Concern
This paper proposes a home energy management
system (HEMS) while considering the residential occupant’s
clothing integrated thermal comfort and electrical vehicles (EV)
state-of-charge (SOC) concern. An adaptive dynamic program-
ming (ADP) based HEMS model is proposed to optimally
determine the setpoints of heating, ventilation, air conditioning
(HVAC), the donning/doffing decisions for the clothing conditions
and charging/discharging of EV while taking into account the
uncertainties in outside temperature and EV arrival SOC. We use
model predictive control (MPC) to simulate a multi-day energy
management of a residential house equipped with the proposed
HEMS. The proposed HEMS is compared with a baseline case
without the HEMS. The simulation results show that a 47.5% of
energy cost saving can be achieved by the proposed HEMS while
maintaining satisfactory occupant thermal comfort and negligible
EV SOC concerns.
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- Award ID(s):
- 1856084
- NSF-PAR ID:
- 10403122
- Date Published:
- Journal Name:
- 2022 IEEE Power & Energy Society General Meeting (PESGM)
- Page Range / eLocation ID:
- 01 to 05
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PESGM48719.2022.9917003
