Integration of Electric Vehicles in Home Energy Management Considering Urgent Charging and Battery Degradation

Abdelaal, Galal; Gilany, Mahmoud; Elshahed, Mostafa; Sharaf, Hebatallah Mohamed; El’Gharably, Aboul’Fotouh

doi:10.1109/access.2021.3068421

Cited by 24 publications

(18 citation statements)

References 47 publications

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“…The RTP data is obtained from Rezaee Jordehi, 12 while the PV data is obtained from Zuhaib et al 40 Figures 3 and 4 show the two datasets, RTP and PV power. The parameters chosen for EV and ESS are obtained from Abdelaal et al 41 and are given in Table 3. The HEMS may operate in both G2H and H2G modes.…”

Section: Resultsmentioning

confidence: 99%

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Mohammad

Zuhaib

Ashraf

2022

Intl J of Energy Research

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Summary Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home‐to‐grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low‐price period, the ESS and EV are charged and discharged during the high‐price period. The paper built a multi‐objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi‐objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits.

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Section: Resultsmentioning

confidence: 99%

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Mohammad

Zuhaib

Ashraf

2022

Intl J of Energy Research

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“…The controller must receive information to execute the scheduling. Among these, at connection time, the initial SoC is computed for each EV n, as in Equation (13). The current SoC of each EV connected at any instant of time can be obtained considering the battery SoC in the previous time step, accounting for the increase or decrease of SoC as a result of charging or discharging, as denoted by Equation ( 14).…”

Section: Constraintsmentioning

confidence: 99%

“…User profit maximization can be achieved using heuristic algorithms accounting for battery degradation, as well as variable electricity costs and incentives, in a large distribution system [12]. A house energy management system strategy was proposed in [13] to coordinate the operations associated with the household load demand, including charging and discharging. A fixed charging rate and fixed battery degradation cost were considered for all SoC levels, in order to reduce the daily energy cost and peak-to-average ratio, as well as alleviating distribution transformer stresses.…”

Section: Introductionmentioning

confidence: 99%

Battery-Conscious, Economic, and Prioritization-Based Electric Vehicle Residential Scheduling

et al. 2022

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Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up within a specific state-of-charge range, degradation can be reduced by 5.3%. All charging requests are completed before the next-day departure time, with 16.35% cost reduction achieved by scheduling charging during lower tariff prices, in addition to prevention of overloading of the distribution transformer.

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“…EVs, as the home energy storage, will play a more important role in providing vehicle-to-home (V2H) services in the presence of residential PV. To coordinate the scheduling of the house load, a PV-EV integrated HEMS could positively affect the household and the distribution network, creating a 'win-win' scenario for both [15]. Moreover, Shafie-Khah et al [6] incorporate the uncertainties of distributed renewable energy (DRE) and EV to schedule the appliances in smart houses.…”

Section: Introductionmentioning

confidence: 99%

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Liu

2021

Sustainability

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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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Integration of Electric Vehicles in Home Energy Management Considering Urgent Charging and Battery Degradation

Cited by 24 publications

References 47 publications

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Battery-Conscious, Economic, and Prioritization-Based Electric Vehicle Residential Scheduling

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

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