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Author ORCID Identifier

https://orcid.org/0000-0003-2896-7005

Abstract

The global shift toward clean energy is accelerating, and by 2050 renewable sources are expected to supply more than 85% of the world’s electricity. This transition, however, introduces new layers of complexity. Wind and solar energy behave as interconnected subsystems whose output fluctuates with weather, season, and geography. Their interaction with fixed hourly demand, capital-intensive investments, and financing structures creates a multi-dimensional system in which small changes can trigger significant operational and economic consequences.  This study presents a simulation-driven framework designed to understand and optimize this complex behaviour. The framework models hourly wind and solar generation alongside projected demand to examine how intermittency influences system reliability. A linear programming model forms the analytical core, identifying the minimum required installed capacity while still meeting demand across varying conditions. To reflect the real-world behaviour of energy infrastructure, the framework evaluates financial components such as Levelized Cost of Energy (LCOE), working capital requirements, depreciation, and long-term debt repayment based on the optimized system configuration. Using realistic capacity utilization factors, 37.89% for wind and 30.42% for solar, the model estimates LCOEs of ₹2.3672/kWh and ₹2.3089/kWh, respectively. Despite the higher capital cost of wind installations, its stronger utilization compensates for the investment, leading to comparable long-term energy costs. This outcome highlights how site-specific resource patterns shape overall system performance.   By combining technical modelling with financial analytics, the study conceptualizes renewable energy planning as a complex socio-technical system. The proposed framework equips developers, investors, and policymakers with practical insights to design renewable portfolios that remain reliable, cost-effective, and adaptive in an increasingly dynamic energy landscape.

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