Smart microgrid planning with unified probabilistic forecasting and deep reinforcement learning control

Increasing energy demands and climate challenges require innovative approaches to renewable energy integration, particularly in remote regions where grid extension is economically unfeasible. Traditional microgrid planning methods suffer from poor renewable forecasting integration and static component sizing that cannot adapt to changing conditions. This study presents a unified framework that integrates probabilistic wind forecasting with intelligent microgrid planning and control. The framework combines three core innovations: a hybrid convolutional neural network-long short-term memory ensemble forecasting model with uncertainty quantification, multi-objective optimization using evolutionary algorithms with dynamic component resizing, and a Deep Reinforcement Learning-based Energy Management System. Unlike existing approaches that treat forecasting and control as isolated components, this framework propagates uncertainty from prediction through infrastructure planning to real-time operational decisions. Validation through a real-world case study in Sujawal, Pakistan, demonstrates significant performance improvements where the system cuts levelized cost of energy (LCOE) by 25%, boosts reliability by 60%, reduces diesel use by 41%, and maintains 97.6% grid stability. The probabilistic forecasting enables uncertainty-aware decision-making, while adaptive sizing allows infrastructure to evolve with changing conditions. This research advances the integration of forecasting and smart grid control by providing a mathematical foundation for resilient, data-driven renewable energy planning. The modular architecture ensures generalizability across diverse geographical contexts and renewable sources. The framework offers a viable alternative to grid extension in developing economies, demonstrating that forecast-informed microgrids can achieve superior economic and environmental performance while maintaining high reliability standards.