  Demand Response (DR) has a widely recognized potential for improving grid
stability and reliability while reducing customers energy bills. However, the
conventional DR techniques come with several shortcomings, such as inability to
handle operational uncertainties and incurring customer disutility, impeding
their wide spread adoption in real-world applications. This paper proposes a
new multiagent Reinforcement Learning (RL) based decision-making environment
for implementing a Real-Time Pricing (RTP) DR technique in a prosumer dominated
microgrid. The proposed technique addresses several shortcomings common to
traditional DR methods and provides significant economic benefits to the grid
operator and prosumers. To show its better efficacy, the proposed DR method is
compared to a baseline traditional operation scenario in a small-scale
microgrid system. Finally, investigations on the use of prosumers energy
storage capacity in this microgrid highlight the advantages of the proposed
method in establishing a balanced market setup.
