Multi-Objective Optimization-Based Operational Strategy for Highway Service Area Integrated Energy Systems
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Abstract
Highway service area integrated energy systems are facing increasing operational challenges caused by the rapid growth of electric vehicle charging demand, renewable energy uncertainty, and low-carbon operation requirements. To address these challenges, this study proposes a multi-objective optimization framework for HSA-IES operation considering operational cost, carbon emissions, and renewable energy utilization. The proposed system integrates photovoltaic generation, combined heat and power units, battery energy storage, flexible electric vehicle charging, heating and cooling subsystems, and grid interaction. A multi-energy coupling model is established, and an enhanced NSGA-II algorithm is adopted to obtain Pareto-optimal scheduling solutions under stochastic renewable generation and traffic-induced load fluctuations. Simulation results show that the proposed strategy reduces operational cost by approximately 12.7% and carbon emissions by approximately 18.2% compared with a conventional rule-based strategy, while renewable energy utilization increases from 72.6%±2.4% to 86.1%±2.3%. From an advanced engineering perspective, the system involves power-electromagnetic coupling, grid-connected conversion, EV charging interfaces, and intelligent energy-management communication. The results provide decision support for low-carbon and robust highway energy operation.
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