Multi-Objective Particle Swarm Optimization for Site Selection and Policy Subsidy Maximization of Foreign Renewable Energy Enterprises in the United States

Abstract

Foreign renewable energy enterprises face unprecedented complexity when selecting investment locations across the United States due to fragmented policy landscapes spanning federal, state, and county jurisdictions. This research develops a multi-objective particle swarm optimization framework that simultaneously maximizes policy subsidy acquisition and minimizes operational costs for renewable energy facility placement. The proposed methodology integrates a comprehensive policy database encompassing 11 subsidy mechanisms across 50 states and 3,000+ counties, including Investment Tax Credits, Job Creation Tax Credits, Payment In Lieu of Taxes agreements, and New Markets Tax Credits. The database architecture supports automated policy update mechanisms employing web scraping and natural language processing technologies to maintain current incentive information. Experimental validation using real-world data from 20 counties across 5 U.S. states demonstrates that the optimized site selection achieves 19% government subsidy-to-investment ratios compared to industry averages of 9%, while improving project Internal Rate of Return from 13% to 18%. The Pareto frontier analysis reveals three distinct optimal solution clusters representing cost-minimization, rapid-profitability, and employment-maximization strategies that provide decision-makers with transparent trade-off visualization. Comparative analysis against traditional sequential filtering methodologies confirms statistically significant performance improvements across subsidy capture, financial returns, and risk-adjusted metrics. This framework provides actionable decision support for foreign investors navigating the intricate U.S. renewable energy policy environment and offers policymakers empirical insights into competitive advantage creation through strategic subsidy allocation.