Blog of Martin Russmann

This whitepaper presents a decentralized negotiation framework for local energy and resource markets and guides the reader from first principles to implementation. We extend classical mean-field games (MFGs) to typed mean-field-type games (MFTGs) that accommodate finite, heterogeneous agent populations and relax anonymity assumptions. Reinforcement learning (RL) is integrated with a two-timescale scheme to adapt strategies under real-time price signals and changing market conditions. A heteroscedastic, heavy-tailed probabilistic forecasting module models uncertainty from intermittent renewables and stochastic demand. The trading layer uses improved Lightning Network protocols (splicing, watchtowers, validating signers). Under standard Lipschitz and measure-Lipschitz assumptions, we establish finite-sample convergence of the empirical law toward the mean-field limit at rate O(N −1/2). In simulations, the framework maintains millisecond-scale latencies (median 47 ms, P95 111 ms), attains 90–95% of the Pareto optimum, reduces peak load by 40–45%, improves revenues by ≈ 15% over deterministic baselines, and remains robust with up to 20% malicious agents. The explanatory sections include an intuitive walkthrough of MFTG, a minimal linear–quadratic example, implementation checklists, routing pseudocode, and an ENTSO-E validation protocol.