Self-Determination Theory and Deep Reinforcement Learning for Personalized Energy Trading in Smart Grid

The development of automated home energy management (HEM) and peer-to-peer energy trading mechanisms encourages a greater number of energy consumers to switch roles and become providers. To sustain this trend and maintain their long-term commitment to energy platforms, we face the challenge of aligning the primary psychological motivators of prosumers with our developed energy services. Most existing approaches target maximizing prosumer utility based on extrinsic benefits, such as economic rewards. However, the intrinsic motivations which are inherently satisfying for prosumers, have not been thoroughly analyzed. This article explores both extrinsic and intrinsic motivations of prosumers from a psychological perspective and addresses these within the technological field. Self-determination theory is adopted as a psychological framework to analyze prosumer behavior in energy systems. The study quantifies prosumers’ motivations and proposes a quality-of-energy-service measure to reflect individual preferences. Additionally, a leader-follower-based optimization framework is introduced, enabling individual prosumers to make optimal decisions regarding their energy management and trading strategies in a P2P energy market. The proposed system features a deep reinforcement learning agent as the leader, targeting optimal HEM solutions, while the follower aims to find the optimal trading strategy for prosumers in an auction-based P2P trading environment. Numerical results demonstrate that our proposed model outperforms baseline models.