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Author ORCID Identifier

https://orcid.org/0009-0007-3572-6169

Date Available

4-21-2026

Year of Publication

2026

Document Type

Master's Thesis

Degree Name

Master of Computer Engineering (MCompE)

College

Engineering

Department/School/Program

Computer Science

Faculty

Simone Silvestri

Abstract

Electric vehicles (EVs) and rooftop solar photovoltaic (PV) systems are increasingly being integrated into residential settings, creating new opportunities for vehicle-to-grid (V2G) and vehicle-to-home (V2H) operations. In these systems, the EV battery functions as a controllable energy storage unit that can charge from the grid or PV and discharge energy to supply household load or export to the grid for a profit. By intelligently scheduling this bidirectional power exchange, households can reduce electricity costs and enhance PV utilization. Realizing these benefits requires optimization strategies that balance cost reduction with EV battery health preservation. However, existing V2G/V2H studies largely emphasize cost minimization while neglecting or oversimplifying battery degradation, resulting in aggressive cycling and accelerated battery wear. This thesis proposes a multi-objective optimization framework using a constraint-aware genetic algorithm to jointly minimize household energy costs and EV battery degradation in PV-integrated homes. The framework incorporates realistic residential load and PV data, stochastic EV arrival behavior, and cycle-life-based degradation modeling to generate Pareto-optimal charging and discharging schedules under varying system conditions. Simulation results demonstrate up to 44% energy cost savings and up to 3x less battery degradation compared to state-of-the-art methods, significantly improving the economic and practical feasibility of residential V2G/V2H operation.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2026.104

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Funding Information

This work is partially supported by the National Science Foundation CAREER grant nr. 1943035.

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