Demand Response through Price-setting Multi-agent Reinforcement Learning

Demand Response through Price-setting Multi-agent Reinforcement Learning

연구 분야: Artificial Intelligence

학회: RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities

Price based demand response is a cost-effective way of obtaining flexibility needed in power systems with high penetration of intermittent renewable energy sources. Model-free deep reinforcement learning is proposed as a way to train autonomous agents for enabling buildings to participate in demand response programs as well as coordinating such programs though price setting in a multiagent setup. First, we show price responsive control of buildings with electric heat pumps using deep deterministic policy gradient. Then a coordinating agent is trained to manage a population of buildings by adjusting the price in order to keep the total load from exceeding the available capacity considering also the non-flexible base load.