PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Xuebo Liu,Hongyu Wu,Yingying Wu

doi:10.3390/su132413826

Abstract

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.

Highlights

There are about 100 million single-family homes in the U.S that consume 36% of total electricity while causing the peak system load in hot summer days [1]
A vast body of studies have demonstrated that home energy management system (HEMS) can efficiently reduce the electricity cost [2,3,4,5,6,7,8] and provide residential demand flexibility and demand response (DR) in response to the DR signal from distribution system operators to minimize grid congestion and violations at peak load conditions [5]
This paper studies the HEMS, consisting of HVAC, electrical vehicles (EV), and PV, by taking into account three distinct categories of occupant behaviors: (1) clothing behaviors, (2) EV SOC concerns, and (3) predicted mean vote (PMV)-based thermal comforts

Summary

Introduction

There are about 100 million single-family homes in the U.S that consume 36% of total electricity while causing the peak system load in hot summer days [1]. The. ADP-based MPC model is an algorithmic function in the application layer to determine the optimal actions for the controllable appliances. ADP-based MPC model is an algorithmic function in the application layer to determine the optimal actions for the controllable appliances The details of this model are provided in the subsequent sections. The occupant’s daily schedules, such as commute and travel plans, can be collected from the smartphone. Such data will be feedback to the occupant-centric environment information module for data analysis and calibration. The optimal schedule for HVAC and EV generated by the HEMS is directly sent to the corresponding appliances, whereas the best actions for clothing behaviors are sent to the occupant’s smartphone application as notifications

Objectives

Results

Discussion

Conclusion