An Online Stochastic Learning Strategy for Demand Response in Smart Microgrid
提出一种基于随机学习和优化的在线自适应需求响应策略，用于含可再生能源的智能微电网，通过事件驱动马尔可夫控制过程模型和随机学习算法，实时优化运行利润。
Demand response (DR) plays an essential role in smart grids to reconfigure the load profile to match the energy supply for reliable and economic operation. This article presents an online adaptive DR strategy based on stochastic learning and optimization for smart microgrids with renewable energy to maximize the operation profit. To enable prompt interaction with the stochastic dynamics of usage demands, renewable generations and real-time pricing, a novel event-driven continuous-time Markov control processes model is introduced to formulate the DR optimization. Based on performance sensitivity analysis, a stochastic learning algorithm that combines potentials estimate and policy iteration is developed to find the optimal DR policy online. The presented strategy is environment-adaptive and computation-efficient, which is competent for real-time control and optimization in unknown environments. Simulations have been conducted to evaluate the performance of the presented strategy, and the results demonstrate the effectiveness.