The broker mechanism is widely applied to serve for interested parties to derive long-term policies in order to reduce costs or gain profits in smart grid. However, a broker is faced with a number of challenging problems such as balancing demand and supply from customers and competing with other coexisting brokers to maximize its profit. In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering. We use real household electricity consumption data to simulate the retail market for evaluating our strategy. The experiments demonstrate the superior performance of the proposed pricing strategy and highlight the effectiveness of our reward shaping mechanism.
Based on a similar MDP, a Recurrent Deep Multiagent Reinforcement Learning framework with sequential clustering is presented by REF .
Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid
IJCAI
Computer Science