  Modern power systems are experiencing a variety of challenges driven by
renewable energy, which calls for developing novel dispatch methods such as
reinforcement learning (RL). Evaluation of these methods as well as the RL
agents are largely under explored. In this paper, we propose an evaluation
approach to analyze the performance of RL agents in a look-ahead economic
dispatch scheme. This approach is conducted by scanning multiple operational
scenarios. In particular, a scenario generation method is developed to generate
the network scenarios and demand scenarios for evaluation, and network
structures are aggregated according to the change rates of power flow. Then
several metrics are defined to evaluate the agents' performance from the
perspective of economy and security. In the case study, we use a modified IEEE
30-bus system to illustrate the effectiveness of the proposed evaluation
approach, and the simulation results reveal good and rapid adaptation to
different scenarios. The comparison between different RL agents is also
informative to offer advice for a better design of the learning strategies.
