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The stochastic and dynamic nature of renewable energy sources and powerelectronic devices are creating unique challenges for modern power systems. Onesuch challenge is that the conventional mathematical systems models-basedoptimal active power dispatch (OAPD) method is limited in its ability to handleuncertainties caused by renewables and other system contingencies. In thispaper, a deep reinforcement learning-based (DRL) method is presented to providea near-optimal solution to the OAPD problem without system modeling. The DRLagent undergoes offline training, based on which, it is able to obtain the OAPDpoints under unseen scenarios, e.g., different load patterns. The DRL-basedOAPD method is tested on the IEEE 14-bus system, thereby validating itsfeasibility to solve the OAPD problem. Its utility is further confirmed in thatit can be leveraged as a key component for solving future model-free AC-OPFproblems.

A Deep Reinforcement Learning Based Approach for Optimal Active Power Dispatch