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Autonomous control is increasingly relevant to smart-grid operation, where control systems may support voltage regulation, frequency control, dispatch, and anomaly response. Yet their trustworthiness remains uncertain when decisions are opaque, intervention options are unclear, and cyber-physical conditions are unstable. Existing research often treats explainability, operator intervention, and cyber-physical uncertainty as separate design or evaluation issues. This separation limits theory development because trust in autonomous grid control depends on how these mechanisms operate together. This article develops a conceptual theory of trustworthy autonomous smart-grid control. The theory links explainability, operator intervention, and cyber-physical uncertainty to both perceived and operational trustworthiness. The article uses theory-building logic rather than empirical testing. It synthesizes concepts from trustworthy AI, human-automation interaction, explainable AI, and cyber-physical security to construct mechanisms and derive propositions. The proposed model specifies causal pathways through which explanation quality, intervention capability, and uncertainty handling shape trustworthiness. It also identifies interaction effects, boundary conditions, and testable propositions for future research. The theory provides a foundation for designing, evaluating, and empirically testing trustworthy autonomous control systems in smart-grid environments. It emphasizes that trustworthiness is not produced by transparency, human oversight, or robustness alone, but by their coordinated operation.

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.

The Smart Grid is an advanced electricity network deeply intertwined with cyber technologies that oversees power generation, long-distance transmission, and final delivery to end users. At its core lies the substation. However, embedding digital systems within substations has dramatically widened the range of potential vulnerabilities. Advanced adversaries, such as those behind the PipeDream APT, deliberately exploit several industrial protocols, including Modbus, DNP3, and IEC61850. Substations face strict operational constraints that complicate the adoption of conventional protective mechanisms, such as data encryption or network intrusion detection systems. To address these difficulties, we present an integrated, trust-centric architecture that significantly strengthens substation defenses. The proposed architecture integrates three main components: a trust evaluation model, a dynamic risk assessment model, and a trust portability model. The trust evaluation model can uncover protocol-specific malicious activities targeting Intelligent Electronic Devices and SCADA human-machine interfaces. The dynamic risk assessment model continuously determines the substation’s current security risk exposure. The trust portability model analyzes whether a device, together with its accumulated trust profile, can be effectively migrated and reused in another substation environment. For a realistic evaluation, we constructed a containerized (Docker) substation replica organized around a multi-agent framework and equipped with a live dashboard modeled after modern Security Operations Centers. Effectiveness was measured by simulating a broad spectrum of adversary behaviors aligned with the MITRE ICS ATT&CK matrix. Experimental results indicate robust resistance to various attack vectors, reliable detection of malicious actions, and appropriate incentivization of well-behaved, trustworthy components. 

Sophisticated machine learning (ML) techniques have demonstrated superior performance over conventional methods across numerous forecasting tasks, particularly in electricity price forecasting (EPF). Nevertheless, the predictive capability of these ML models deteriorates markedly when encountering input data that differs significantly from the examples observed during training. This phenomenon frequently occurs in EPF due to shifts in market conditions, such as surges in fuel costs, higher integration of renewable energy sources, or modifications in regulatory frameworks and operational rules. While the decline in accuracy on unfamiliar data is problematic, an even greater difficulty lies in the inability to anticipate when the model will produce unreliable outputs. This lack of foresight exposes electricity market participants—including bidding agents and retailers—to considerable financial risks stemming from erroneous price predictions. Consequently, it is critical to determine whether a given model prediction can be relied upon in real time. To address this need, the present study introduces a trust evaluation algorithm for EPF applications grounded in explainable artificial intelligence (XAI) methods. The proposed algorithm computes trust scores that indicate the reliability of each new prediction generated by the model. These scores are constructed in two phases: first, a preliminary score is derived from the correlation between local and global explanations; second, the score is refined using SHapley Additive exPlanations (SHAP) values for individual features. Such score-based interpretations are more accessible and actionable for EPF practitioners, such as asset managers and traders, than conventional feature-based visualizations. The effectiveness of the algorithm is assessed using datasets from the Italian electricity market and the Electric Reliability Council of Texas (ERCOT). Empirical results reveal that the method achieves over 85% accuracy in recognizing reliable predictions when the incoming data distribution closely matches the training distribution. Under conditions of distributional shift, the algorithm maintains a comparable level of accuracy in correctly flagging unreliable predictions.