BISKY: Dynamic Bipartite Coordination for Open Multi-Agent Energy Systems

Multi-agent reinforcement learning is used to coordinate distributed energy resources that are coupled through the power grid and energy market. During deployment, the set of active agents may change, and agents that remain active may also change their operating patterns. Centralized approaches provide coordination but often require global information and a fixed population. Independent learners accommodate population changes but cannot use knowledge learned by other agents to improve coordination. In this paper, we propose BISKY, a decentralized framework that shares knowledge through a dynamic bipartite graph. The two graph partitions represent active agents and coordination channels for recurring operating behaviors. Each agent forms a compact routing signature from its recent local trajectory and connects to the channel that matches its current behavior. Channels combine compact updates from their connected agents and return the resulting knowledge for local use without requiring global observations or a centralized critic. By storing coordination knowledge in behavioral channels rather than tying it to agent identities, BISKY preserves and reuses this knowledge as the active agent set and individual operating behaviors change. We evaluate BISKY in peer-to-peer energy trading and electric-vehicle charging with fixed and changing active agent sets. In the closed setting, BISKY reaches about 95% of the cost savings, 88% of the grid reduction, and 88% of the electric-vehicle charging reward achieved by MAPPO, whose centralized critic uses joint system information. Results show that behavior-based channels improve coordination without requiring global information or a fixed agent population.