Sounds like a classic distributed optimization puzzle. I'd start by formalizing each node’s constraints—capacity, demand, local generation—and then define a cost function for imbalance. From there, you can apply an iterative consensus algorithm or ADMM to converge on a global balance. What’s the topology you’re envisioning, and do you have a latency budget?

A five‑grid pilot is a reasonable proof of concept. It keeps the graph small enough to trace every message, yet large enough to surface edge‑cases like non‑synchronous updates. I’d set up a simulation first—inject realistic load traces and 5G‑style jitter—then move to hardware once the convergence rate matches the target latency. Keep an eye on the residuals from ADMM; if they plateau above a threshold, you’ll know the consensus is stuck, which is the real diagnostic signal. How many iterations per round‑trip do you expect? That will tell us if the operator‑interface can stay responsive.

Let’s set up the simulation environment first, then we can run a few thousand trials and log the residuals. I’ll write a quick ADMM driver that prints the convergence curve and watches the iteration count. Once we have the baseline, we’ll tweak the step size and see how the residual behaves with the 5G jitter model. Does that line up with your schedule?

import numpy as np import time def admm_driver(A, b, rho, max_iter=50, eps_abs=1e-3, eps_rel=1e-2): n = A.shape[1] x = np.zeros(n) z = np.zeros(n) u = np.zeros(n) AtA = A.T @ A Atb = A.T @ b P = np.linalg.inv(AtA + rho * np.eye(n)) q = P @ Atb for k in range(max_iter): # x-update (least squares) x = P @ (Atb + rho * (z - u)) # z-update (soft threshold) z_old = z.copy() z = np.maximum(0, x + u - eps_abs / rho) - np.maximum(0, -(x + u) - eps_abs / rho) # u-update u += x - z # residuals r_norm = np.linalg.norm(x - z) s_norm = np.linalg.norm(-rho * (z - z_old)) if r_norm < eps_abs and s_norm < eps_abs: break return x, z, u, k+1 def simulate(): # Simple 5-grid example: each grid has 3 nodes n_grids = 5 n_nodes_per_grid = 3 n = n_grids * n_nodes_per_grid # Build incidence matrix A for a sparse mesh A = np.zeros((n, n)) for i in range(n): A[i, i] = 1 # connect to next grid node if not last if i + 1 < n: A[i, i+1] = -1 # Desired net load vector b = np.random.randn(n) * 0.1 rho = 1.0 x, z, u, iters = admm_driver(A, b, rho) print(f"Converged in {iters} iterations") print("Final x:", x) if __name__ == "__main__": start = time.time() simulate() print("Elapsed:", time.time() - start)