How was the infrastructure evaluated, and what are its main open limitations and next steps?

Question

Accepted Answer

It was tested in simulation under both stable and unstable supply/demand conditions — not in a real deployment — with future work on federated-learning forecasts and higher-performance (DAG-based) blockchains.
Evidence comes from a simulation of a virtual electric grid, exercising the components under two regimes: one where total production roughly equals consumption (stable), and one where they diverge sharply (unstable). The simulation shows the components keeping the grid balanced across both, and compares favourably to Brooklyn Microgrid and Power Ledger on endpoint control, incentive-based coordination, and macro dispatch.
The honest limitations: it is a conceptual design validated in simulation, not a live grid, and it leans on assumptions (accurate AI forecasts, trustworthy meter proofs, cooperative endpoints). Stated next steps:

Federated learning to train the endpoint AI models efficiently and privately;
integrating a high-scalability blockchain (e.g. DAG-based), since PoD is designed to slot into such platforms as a proposer-election mechanism.

Go deeper:

A Blockchain-Based Architecture for Energy Trading (Sun & Weingärtner, HSLU, Electronics 2025) — The primary paper, including the simulation results and stated next steps.
 Federated learning (Wikipedia) — Training the endpoint forecasting models collaboratively without centralising private data.