  Multi-energy microgrid (MEMG) offers an effective approach to deal with
energy demand diversification and new energy consumption on the consumer side.
In MEMG, it is critical to deploy an energy management system (EMS) for
efficient utilization of energy and reliable operation of the system. To help
EMS formulate optimal dispatching schemes, a deep reinforcement learning
(DRL)-based MEMG energy management scheme with renewable energy source (RES)
uncertainty is proposed in this paper. To accurately describe the operating
state of the MEMG, the off-design performance model of energy conversion
devices is considered in scheduling. The nonlinear optimal dispatching model is
expressed as a Markov decision process (MDP) and is then addressed by the twin
delayed deep deterministic policy gradient (TD3) algorithm. In addition, to
accurately describe the uncertainty of RES, the conditional-least squares
generative adversarial networks (C-LSGANs) method based on RES forecast power
is proposed to construct the scenarios set of RES power generation. The
generated data of RES is used for scheduling to obtain caps and floors for the
purchase of electricity and natural gas. Based on this, the superior energy
supply sector can formulate solutions in advance to tackle the uncertainty of
RES. Finally, the simulation analysis demonstrates the validity and superiority
of the method.
