  The global energy landscape is undergoing a transformation towards
decarbonization, sustainability, and cost-efficiency. In this transition,
microgrid systems integrated with renewable energy sources (RES) and energy
storage systems (ESS) have emerged as a crucial component. However, optimizing
the operational control of such an integrated energy system lacks a holistic
view of multiple environmental, infrastructural and economic considerations,
not to mention the need to factor in the uncertainties from both the supply and
demand. This paper presents a holistic datadriven power optimization approach
based on deep reinforcement learning (DRL) for microgrid control considering
the multiple needs of decarbonization, sustainability and cost-efficiency.
First, two data-driven control schemes, namely the prediction-based (PB) and
prediction-free (PF) schemes, are devised to formulate the control problem
within a Markov decision process (MDP). Second, a multivariate objective
(reward) function is designed to account for the market profits, carbon
emissions, peak load, and battery degradation of the microgrid system. Third,
we develop a Double Dueling Deep Q Network (D3QN) architecture to optimize the
power flows for real-time energy management and determine charging/discharging
strategies of ESS. Finally, extensive simulations are conducted to demonstrate
the effectiveness and superiority of the proposed approach through a
comparative analysis. The results and analysis also suggest the respective
circumstances for using the two control schemes in practical implementations
with uncertainties.
