A Metaheuristic Framework for Cost-Effective Renewable Energy Planning: Integrating Green Bonds and Fiscal Incentives

The integration of non-conventional renewable energy sources (NCRES) plays a critical role in achieving sustainable and decentralized power systems. However, accurately assessing the economic feasibility of NCRES projects requires methodologies that account for policy-driven incentives and financing mechanisms. To support the shift towards NCRES, evaluating their financial viability while considering public policies and funding options is important. This study presents an improved version of the Levelized Cost of Electricity (LCOE) that includes government incentives such as tax credits, accelerated depreciation, and green bonds. We apply a flexible investment model that helps to find the most cost-effective financing strategies for different renewable technologies. To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. The model is tested in a case study in Colombia covering battery storage, large- and small-scale solar power, and wind energy. Results show that combining smart financing with policy support can significantly lower electricity costs, especially for technologies with high upfront investments. We also explore how changes in interest rates affect the results. This framework can help policymakers and investors design more affordable and financially sound renewable energy projects.