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Decentralized energy markets can coordinate distributed generation, flexible demand and storage, but feeder feasibility, household forecasting and smart-contract settlement are usually assessed separately. This study develops a grid-constrained Decentralized Energy Marketplace (DEM) linking household commitments, bilateral matching, feeder-capacity screening, personalized federated forecasting and auditable energy-quantity settlement in a module-coupled, open-loop configuration with no feedback from settlement outcomes to forecasting or bidding. The principal finding concerns the enforcement mechanism applied when a feeder limit binds. Across 45 paired scenarios of measured Ausgrid households under a 1.5 kW branch-transfer stress cap, whole-trade removal retains 74.9% of the price-only submitted-price surplus, whereas marginal re-clearing retains 93.1%, a paired gain of 0 . 0 2 1 9 ± 0 . 0 0 6 0 EUR per interval ( 𝑡 = 7 . 1 2 ); this ordering holds in eight seasonal and weekday/weekend windows spanning the measured year, with the summer case-study week as the largest-gap case. AC power flow on 4320 accepted schedules shows no voltage or thermal violations; the first demand-side limit occurs at 1.7 times peak demand. In a 30-client non-IID benchmark, plain FedAvg underperforms local training for 87% of client–seed pairs, a known federated-learning effect demonstrated on measured energy data; per-client fine-tuning removes most of the penalty, and forecast-generated commitments materially change deviation-adjusted outcomes. On-chain replay exposed an energy-conservation defect missed by the initial tests; after correction, all 116 tradable intervals (1,325 trades) reconciled within integer rounding and a randomized differential suite matched an integer-exact reference on two EVM clients. Efficient constraint handling, client-level personalization and realistic multi-party settlement testing are central to credible blockchain-enabled local energy trading.
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Nguyen et al. (2026) studied this question.
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