Resilience- and Uncertainty-Aware Optimization Framework for Vehicle-to-Home-Enabled Smart Residential Energy Systems Using a Quantum-Inspired Gradient-Based Optimizer

Abstract

The increasing integration of electric vehicles (EVs) and renewable energy sources is transforming modern residential energy systems, creating new opportunities for cost reduction and energy resilience. However, uncertainty in renewable generation and EV behavior, together with grid outage events, makes optimal operation challenging. This paper proposes a resilience- and uncertainty-aware optimization framework for vehicle-to-home (V2H)-enabled smart residential energy systems integrating solar photovoltaic (PV), EVs, and battery energy storage systems (BESS). The problem is formulated as a constrained optimization model to minimize the dailyized total system cost, including grid energy trading, battery degradation, and investment costs. Uncertainty in PV generation and EV arrival state of charge is modeled using a scenario-based approach, while a resilience-oriented formulation ensures uninterrupted supply for critical loads during grid outages. The EV arrival state of charge is used to capture the uncertainty associated with daily travel behavior and energy consumption. To solve the nonlinear optimization problem, a Quantum-Inspired Gradient-Based Optimizer (QI-GBO) is developed. The results show that QI-GBO achieves the lowest cost of 43.880 USD/day, outperforming a conventional heuristic rule-based strategy, 52.639 USD/day; Gradient-Based Optimizer (GBO), 47.900 USD/day; Grey Wolf Optimization (GWO), 49.310 USD/day; Particle Swarm Optimization (PSO), 49.580 USD/day; and Genetic Algorithm (GA), 50.390 USD/day. This corresponds to cost reductions of 8.39%, 11.01%, 11.50%, and 12.92%, respectively. In addition, QI-GBO exhibits improved convergence behavior, requiring only 82 search iterations to attain the best solution, compared with 112, 161, 208, and 257 iterations for GBO, GWO, PSO, and GA, respectively. Under uncertainty conditions, the system cost varies between 42.15 USD/day and 46.69 USD/day, with a maximum cost reduction of 11.62% between worst-case and best-case scenarios. During grid outage scenarios, the proposed framework guarantees full supply for critical loads and quantifies resilience using the energy not supplied (ENS), which reaches up to 34.99 kWh under severe conditions. The results demonstrate that the proposed QI-GBO-based framework provides superior economic performance, improved convergence behavior, and strong robustness under both uncertainty and outage conditions, making it suitable for practical V2H-enabled smart residential energy systems.