The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration. This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction, achieving 28% RMSE reduction on real-world datasets (CAISO, PJM), coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4% while ensuring stability constraints. The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility, validated through grid-scale case studies. Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems. These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms, emphasizing computational efficiency and interoperability with legacy infrastructure.