The dynamic nature of sustainable energy and electric systems can vary significantly along with the environment and load change, and they represent the features of multivariate, high complexity and uncertainty of the nonlinear system. Moreover, the integration of intermittent renewable energy sources and energy consumption behaviours of households introduce more uncertainty into sustainable energy and electric systems. The operation, control and decision-making in such an environment definitely require increasing intelligence and flexibility in the control and optimization to ensure the quality of service of sustainable energy and electric systems. Reinforcement learning is a wide class of optimal control strategies that uses estimating value functions from experience, simulation, or search to learn in highly dynamic, stochastic environment. The interactive context enables reinforcement learning to develop strong learning ability and high adaptability. Reinforcement learning does not require the use of the model of system dynamics, which makes it suitable for sustainable energy and electric systems with complex nonlinearity and uncertainty. The use of reinforcement learning in sustainable energy and electric systems will certainly change the traditional energy utilization mode and bring more intelligence into the system. In this survey, an overview of reinforcement learning, the demand for reinforcement learning in sustainable energy and electric systems, reinforcement learning applications in sustainable energy and electric systems, and future challenges and opportunities will be explicitly addressed.

可持续能源和电力系统的动态性质会随着环境和负载的变化而显着变化，它们代表了非线性系统的多元，高复杂性和不确定性的特征。此外，间歇性可再生能源与家庭能源消费行为的结合为可持续能源和电力系统带来了更多不确定性。在这样的环境中进行操作，控制和决策肯定需要在控制和优化方面增加智能和灵活性，以确保可持续能源和电力系统的服务质量。强化学习是一类最佳的控制策略，它使用经验，模拟或搜索来估计价值函数，以在高度动态，随机的环境中学习。交互式上下文使强化学习能够发展出强大的学习能力和高度适应性。强化学习不需要使用系统动力学模型，这使其适用于具有复杂非线性和不确定性的可持续能源和电力系统。在可持续能源和电力系统中使用强化学习肯定会改变传统的能源利用模式，并为系统带来更多智能。在本次调查中，将明确解决强化学习的概述，可持续能源和电力系统中强化学习的需求，可持续能源和电力系统中强化学习的应用以及未来的挑战和机遇。