A Smart Contract Framework for Automated Settlement and Compliance in Renewable Energy and Distributed Energy Resources

Abstract

This study addresses the persistent problem that renewable-energy and distributed energy resource (DER) transactions generate high-volume, interval-based micro-transactions that strain conventional centralized billing, reconciliation, and compliance reporting, creating delays, disputes, and weak audit trails. The purpose was to evaluate a smart contract framework that integrates automated settlement and compliance evidence generation within cloud-hosted, enterprise-grade DER transaction workflows using a quantitative, cross-sectional, case-based design. A purposive case sample of n = 214 informed stakeholders was drawn from enterprise settlement and compliance settings, including settlement and billing personnel (31.3%), compliance and audit professionals (24.8%), DER operations and aggregation roles (22.4%), and platform/ICT specialists (21.5%). Key variables were modeled as composite 5-point Likert constructs: Smart Contract Quality (SCQ), Data Integrity and Oracle Reliability (DIO), Interoperability (INT), Security and Privacy Controls (SP), Regulatory Alignment (RA), Automated Settlement Effectiveness (ASE), Compliance Automation Performance (CAP), and Operational or Market Performance (OMP). The analysis plan applied descriptive statistics, internal consistency reliability (Cronbach’s alpha), Pearson correlations, and multiple regression hypothesis testing. Findings showed high capability and outcome perceptions, including SCQ M = 4.12 (SD = 0.61), DIO M = 4.05 (SD = 0.67), SP M = 4.18 (SD = 0.58), ASE M = 4.15 (SD = 0.60), and CAP M = 4.22 (SD = 0.55), with strong reliabilities (α = 0.82–0.91 across constructs). Correlations supported the framework pathways, notably SCQ–ASE r = 0.63, DIO–ASE r = 0.58, SP–CAP r = 0.66, RA–CAP r = 0.61, and CAP–OMP r = 0.68 (all p < 0.001). Regression results confirmed that settlement effectiveness was predicted by SCQ (β = 0.34, p < 0.001), DIO (β = 0.29, p < 0.001), and INT (β = 0.21, p = 0.002) with R² = 0.496, while compliance performance was driven by SP (β = 0.38, p < 0.001) and RA (β = 0.31, p < 0.001) plus organizational readiness (β = 0.19, p = 0.002) with R² = 0.538; together ASE (β = 0.29) and CAP (β = 0.41) explained OMP (R² = 0.572). Audit evidence readiness was also strong (ARS M = 4.25) and artifact coverage exceeded 82.7%–88.3% for trace logs, access logs, dispute logs, and provenance links, indicating compliance-grade outputs at scale. Implications suggest that enterprise implementations should prioritize contract correctness assurance, oracle and meter-data governance, secure per missioning with privacy-aware transparency, and explicit regulatory alignment to convert automation into measurable operational and audit benefits.