Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning
This addresses grid stability and cost reduction for microgrid operators and prosumers, but appears incremental as it builds on existing demand response and reinforcement learning methods.
The paper tackles the problem of implementing demand response in microgrids with prosumers by proposing a multiagent reinforcement learning framework for real-time pricing, which shows economic benefits compared to a baseline scenario.
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.