From Bellman to Real-Time: Graph Compression, DCRNN, and MARL for Scalable Energy System Control—Methodology and Initial Validation

The optimal control of complex energy systems via Bellman’s principle of optimality quickly becomes difficult for high-dimensional, path-dependent dynamics because the state space grows exponentially with each time step. We propose a computationally tractable framework based on hierarchical path-dependency decomposition: (i) graph compression that reduces multi-layer topologies to a single directed flow network; (ii) a diffusion convolutional recurrent neural network (DCRNN) that maps historical trajectories into a finite-dimensional hidden state, approximating the transport of past states without storing full trajectories; and (iii) multi-agent reinforcement learning (MARL) for decentralized local control. By restricting full-path online optimization to a rolling one-step horizon and using temperature and flow rate as sufficient statistics for thermal dynamics, the framework preserves physical fidelity while enabling real-time execution. This reduction is justified because thermal constraints constitute the primary active failure mode in energy systems. We provide a proof sketch showing that the diffusion convolution operation in the DCRNN approximates the Green’s function of the underlying transport equation. Using weather data from Palm Springs, California (a region with moderate path dependency), initial numerical experiments achieve 98.4% correlation with reference solutions, a temperature forecasting mean absolute error (MAE) of 1.23 °C, and mostly subsecond (<1 s) inference times using consumer-grade hardware. Despite the thermodynamic advantages of concentrated solar thermal (CST) systems over conventional photovoltaic panels—higher conversion efficiency and integrated thermal storage—their deployment on factory rooftops remains elusive due to the continuous, real-time control burden they impose. The proposed framework directly addresses this barrier by delivering accuracy comparable to classical controllers (MPC, PID) with latency sufficient for real-time intervention, positioning CST for transition from remote desert locations to distributed industrial sites. Beyond solar-thermal generation, the same hierarchical architecture is applicable to integrated HVAC system control (e.g., using movable mirrors to both produce renewable energy and reduce cooling loads in data centers), energy storage management, desalination plant control, and chemical production optimization—any domain where thermal-hydraulic transport must be regulated under tight safety and latency constraints. The framework demonstrates that trading exact Bellman optimality for data-driven approximations enables a shift from offline simulation to sensor-driven real-time regulation across this broader class of energy systems.