A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

Joint energy consumption and trading management is still a major challenge in smart (micro) grids. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. Here, an inclusive formulation for energy management and trading of a micro/nano-grid (M/NG) is proposed in this article. Subsequently, a holistic solution to jointly optimizing the internal energy consumption management and external local energy trading for a smart grid including several M/NGs is provided. As the problem is computationally intractable, the proposed approach involves three hierarchical stages. First, a game-theoretic online stochastic energy management model is provided with a reinforcement learning solution by which the M/NGs can schedule their power consumptions. Second, an effective incentive-compatible double-auction is formulated by which the M/NGs can directly trade with each other. Third, the central controller develops an optimal power allocation program to reduce the power transmission loss and the destructive effects of local energy trading. The simulation results validate the efficiency of the proposed framework.