With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch.

Chapter 1: Introduction for Smart Grid Forecast and Dispatch.- Chapter 2: Review for Smart Grid Forecast.- Chapter 3: Review for Smart Grid Dispatch.- Chapter 4: Deep Learning Based Densely Connected Network for Load Forecast.- Chapter 5: Reinforcement Learning Assisted Deep Learning for Probabilistic Charging Power Forecast of Electric Vehicles.- Chapter 6: Dense Skip Attention based Deep Learning for Day-Ahead Electricity Price Forecast with a Drop-Connected Structure.- Chapter 7: Dirichlet Process Mixture Model Based on Relevant Data for Uncertainty Characterization of Net Load.- Chapter 8: Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power.- Chapter 9: Data-driven Bayesian Assisted Optimization Algorithm for Dispatch of Highly Renewable Energy Power Systems.- Chapter 10: Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems.- Chapter 11: Deep Reinforcement Learning Assisted OptimizationAlgorithm for Many-Objective Distribution Network Reconfiguration.- Chapter 12: Federated Multi-Agent Deep Reinforcement Learning Approach via Physic-Informed Reward for Multi-Microgrid Energy Management.- Chapter 13: Supply Function Game Based Energy Management Between Electric Vehicle Charging Stations and Electricity Distribution System.