Digital twins are transformative tools in renewable energy investments, enhancing operational efficiency, predictive maintenance, and grid management. This review explores their economic impact, focusing on how they optimize solar, wind, and storage assets through accurate forecasting, scenario analysis, and real-time data analytics. Digital twins reduce operational risks, improve energy yield, and increase profitability by enabling proactive maintenance, minimizing downtime, and lowering costs. They also support strategic decision-making through detailed financial modeling and risk assessment, increasing investor confidence and reducing capital costs. Additionally, digital twins foster innovation and technological advancements in the renewable energy sector, driving efficiency and performance optimization. Digital twins streamline operational workflows, reduce manual monitoring costs, and enhance asset longevity. They provide transparent data insights, improving regulatory compliance and stakeholder trust. By integrating IoT and AI, digital twins enable real-time monitoring, predictive analytics, and dynamic energy management, supporting grid stability and resilience. They also facilitate virtual testing and validation of renewable energy projects, optimizing resource allocation and project feasibility. Challenges in adopting digital twins include data integration, interoperability, model scalability, and regulatory compliance. Ensuring data security and ethical data use is critical. Future advancements in AI, IoT, and edge computing will further enhance digital twin capabilities, enabling more accurate predictions and optimized energy management. As digital twins evolve, they will play a key role in accelerating the transition to sustainable and economically viable renewable energy systems. Continued investment in digital twin technology, along with industry collaboration and policy support, is essential for maximizing the economic and environmental benefits of renewable energy investments.Digital twins are transformative tools in renewable energy investments, enhancing operational efficiency, predictive maintenance, and grid management. This review explores their economic impact, focusing on how they optimize solar, wind, and storage assets through accurate forecasting, scenario analysis, and real-time data analytics. Digital twins reduce operational risks, improve energy yield, and increase profitability by enabling proactive maintenance, minimizing downtime, and lowering costs. They also support strategic decision-making through detailed financial modeling and risk assessment, increasing investor confidence and reducing capital costs. Additionally, digital twins foster innovation and technological advancements in the renewable energy sector, driving efficiency and performance optimization. Digital twins streamline operational workflows, reduce manual monitoring costs, and enhance asset longevity. They provide transparent data insights, improving regulatory compliance and stakeholder trust. By integrating IoT and AI, digital twins enable real-time monitoring, predictive analytics, and dynamic energy management, supporting grid stability and resilience. They also facilitate virtual testing and validation of renewable energy projects, optimizing resource allocation and project feasibility. Challenges in adopting digital twins include data integration, interoperability, model scalability, and regulatory compliance. Ensuring data security and ethical data use is critical. Future advancements in AI, IoT, and edge computing will further enhance digital twin capabilities, enabling more accurate predictions and optimized energy management. As digital twins evolve, they will play a key role in accelerating the transition to sustainable and economically viable renewable energy systems. Continued investment in digital twin technology, along with industry collaboration and policy support, is essential for maximizing the economic and environmental benefits of renewable energy investments.