A unified decision making framework for supply and demand management in microgrid networks

Abstract

This paper considers two important problems -on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while at the same time not deviating much from the customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the time-adjustable demand -i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide a unified Markov decision process (MDP) framework for these problems. We then apply the Q-learning algorithm, a popular model-free reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that the policy obtained by solving our MDP model provides more profit to the microgrids.

A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks

“…Deep Reinforcement Learning algorithms have been successfully applied for computing optimal solutions in the context of energy trading between microgrids in [13], [14], for storage device management in [15], and for energy management in [16], [17]. The closest work to ours is [18], where an energy trading model for a microgrid network has been proposed that also considers job scheduling for customers. We extend this model considerably to include dynamic pricing for transactions between microgrids and apply the independent learners Deep Q-learning algorithm that is shown to have a good empirical performance in literature [19].…”