SolarChain: A Physics-Grounded Embodied IoT System for Verifiable Urban Solar Market Design

Shilin Ou, Yifan Xu, Zhenshan Zhang, Luyao Zhang and Ming-Chun Huang

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Abstract: Distributed solar markets must coordinate physical reports, economic allocation, and public settlement even when IoT data can be manipulated. We present SolarChain, a controlled Embodied Intelligence of Things (EIoT) prototype that integrates four functions: physics-bounded screening of photovoltaic reports, persistent agent and planner coordination, configurable allocation between producer rewards and market liquidity, and replayable hash-linked auditing of settlement decisions. The benchmark combines city-level historical weather inputs with physics-modeled generation bounds and synthetic nodes, demand, trades, and scripted attacks. On 36,000 monthly records, an IQR/MAD baseline attains F1=1.000, while the rule-based adaptive verifier attains F1=0.988 and provides physically interpretable decision evidence; it is not uniformly superior across attack classes. A sensitivity sweep selects a 20/80 reward/liquidity default under the stated simulation assumptions, while showing the incentive-liquidity trade-off. These results provide reproducible evidence from a controlled prototype, not proof of deployment readiness. We release the code, data, and audit artifacts in the spirit of open science.

Date: 2026-05, Revised 2026-08
New Economics Papers: this item is included in nep-ene and nep-env
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