IET Generation, Transmission & Distribution’s latest article, “Multi-agent reinforcement learning in a new transactive energy mechanism,” explores the potential of reinforcement learning (RL) for decision-making in high-uncertainty environments. The study proposes a novel framework where prosumers utilize RL to maximize profits in the transactive energy market (TEM). This research integrates original results with previous studies, providing fresh insights into the practical applications of RL in TEM.
Novel Framework and Algorithm
A newly designed environment represents an innovative TEM framework where participants submit bids and receive profits. Both sellers and buyers are introduced to new state-action spaces, allowing the application of the Soft Actor-Critic (SAC) algorithm. The SAC algorithm, suitable for continuous state-action spaces, is detailed, highlighting its implementation for single-agent scenarios involving a seller and a buyer. This approach aims to determine the best policy for each participant.
Extending beyond single-agent applications, the study explores multi-agent scenarios where all participants, including multiple sellers and buyers, employ the SAC algorithm. This creates a comprehensive game environment among participants, analyzed to understand if players reach the Nash equilibrium (NE). The investigation delves into the dynamics of multi-agent interactions and the convergence patterns of the involved players.
Numerical Results and Effectiveness
The effectiveness of the new TEM framework is illustrated through numerical results using the IEEE 33-bus distribution power system. By applying SAC with the redesigned state-action spaces, significant profit increases for both sellers and buyers are observed. The Multi-Agent implementation of SAC demonstrates that participants converge to either a single NE or one of multiple NEs within the game context. Specifically, buyers reach their optimal policies within 80 days, while sellers achieve optimality after 150 days, underscoring the algorithm’s impact on strategic decision-making in TEM.
Similar studies in the past have focused on the potential benefits of RL in various market mechanisms. However, the innovative application of SAC within the TEM context and the detailed analysis of its multi-agent dynamics set this study apart. Previous research often concentrated on single-agent scenarios or different RL algorithms, whereas this study expands the scope to multi-agent interactions, providing a more comprehensive view of RL’s utility in TEM.
Earlier research highlighted the challenges of implementing RL in real-world energy markets due to factors like computational complexity and convergence issues. This study addresses these challenges by demonstrating convergence to optimal policies within a reasonable timeframe, offering a practical solution for RL application in TEM. The integration of SAC and the exploration of multi-agent scenarios offer new perspectives and solutions to these longstanding challenges.
Overall, this research offers valuable insights into the practical application of RL in TEM, particularly through the innovative use of the SAC algorithm. By examining both single-agent and multi-agent scenarios, the study provides a nuanced understanding of how RL can optimize decision-making and profitability in energy markets. The detailed analysis of convergence to Nash equilibrium further enriches the existing body of knowledge, offering a robust framework for future research and practical implementations in TEM.
