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

TL;DR

This paper presents a unified decision-making framework for microgrid supply and demand management using reinforcement learning, improving profit outcomes by integrating energy sharing, purchasing, and flexible load scheduling.

Contribution

It introduces a combined MDP model for both supply and demand problems in microgrids and applies Q-learning to optimize policies, which was not previously done.

Findings

The unified framework yields higher profits for microgrids.

Q-learning effectively finds optimal policies in the proposed model.

Simulation results demonstrate improved microgrid performance.

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.