  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.
