Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.

The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is designed to extract temporal features of uncertainty and generate the optimal scheduling decisions in an end-to-end manner. To optimize the policy with high-dimensional and continuous actions, proximal policy optimization (PPO) is employed to train the neural network-based policy in a data-driven fashion. The proposed method does not require any forecasting information on the uncertainty or a prior knowledge of the physical model of the microgrid. Simulation results using realistic power system data of California Independent System Operator (CAISO) demonstrate the effectiveness of the proposed method.