Search

Autonomous smart grids increasingly rely on AI-driven decisions, yet legal frameworks have not kept pace. Liability for autonomous actions remains ambiguous, threatening both innovation and accountability. No existing conceptual framework systematically links the algorithmic decision layer, operator oversight, and regulatory auditability to address the liability gap in autonomous grid operations. This creates uncertainty when harmful outcomes arise from distributed interactions among software, operators, vendors, and regulated utilities. This article develops a conceptual framework that connects these three layers into a coherent liability-aware governance architecture for autonomous smart grids. The aim is to clarify how responsibility could be assigned, reviewed, and operationalized before and after autonomous grid actions occur. The framework is built through conceptual synthesis of AI law, human-in-the-loop research, and regulatory governance literature. No experiments are conducted, and no quantitative data are presented. The framework defines the roles and interactions of algorithmic decisions, operator oversight, and auditability mechanisms, and derives eight testable propositions. Two tables summarize the decision-liability mapping and the framework logic. The liability-aware framework provides a foundation for designing accountable autonomous grid systems and for future interdisciplinary legal-technical research. It supports legal clarity while preserving the operational benefits of intelligent grid automation.

Algorithmic flexibility markets can support the energy transition by coordinating distributed prosumer resources through automated platforms. Yet participation depends not only on price signals but also on whether prosumers trust platforms, perceive compensation as fair, and believe flexibility provision will not compromise reliability. Existing work has advanced knowledge on peer-to-peer trading, demand response, virtual power plants, and automated energy coordination. However, no unified theory fully explains how trust, fairness, incentives, and grid reliability requirements jointly shape prosumer participation in algorithmic flexibility markets. This article develops a theory-building model of prosumer participation in algorithmic flexibility markets. The model links micro-level perceptions of trust, fairness, and incentives with macro-level grid reliability requirements and market design constraints. The article is conceptual and develops theory by integrating behavioural, institutional, technological, and energy-system perspectives. It defines core constructs, specifies causal relationships, and derives propositions for future empirical testing. The theory proposes that participation emerges from four interlocking mechanisms: trust in algorithmic platforms, perceived fairness of compensation and procedures, incentive salience, and perceived alignment with grid reliability needs. These mechanisms interact dynamically rather than operating as independent determinants. The model provides a foundation for future empirical research and for designing flexibility markets that are technically effective and socially acceptable. It also clarifies why participation may fail even when economic incentives appear attractive.