The growing share of flexible energy resources such as electric vehicle chargers and heat pumps adds complexity to distribution grid operations. At the same time, the increasing observability and controllability enabled by the implementation of distribution grid control systems promises possibilities for coping with the newly introduced complexity. While automated control manages standard cases, exceptional scenarios characterized by uncertainty or incomplete data require manual intervention. This work proposes a framework for a decision support system (DSS). A DSS built with this structure is able to enhance transparency and provide actionable recommendations that allow operators to resolve incidents - i.e., non-routine situations in distribution grid operation that cannot be handled automatically - with minimal effort and cost. Additionally, included functionalities are outlined for exemplary use cases. These demonstrate the applicability of the DSS framework.

Based on insights from the DISEGO project and regulatory frameworks such as the German §14a EnWG or the French Effacement, the DSS identifies critical issues beyond automated solutions, classifies the nature of incidents (e.g., communication failures, data gaps, model inconsistencies), and suggests tailored actions. The goal is to deliver context-aware, comprehensible, and traceable recommendations that match operator responsibilities and workflows—even under limited information availability.

Incidents are assigned to impact domains, which correspond systematically to the Smart Grid Architecture Model (SGAM) interoperability layers - spanning components to business processes - to ensure comprehensive coverage of operational challenges. This mapping ensures that technical and organizational aspects of decision-making are captured, and that the DSS design accounts for the roles of real-time measurements, forecasts, grid models, and regulatory constraints. Additionally, this work includes a descriptive proposal for functions to identify incidents.

Based on SGAM, available information are categorized to generate actionable recommendations. Typical failure scenarios, such as persistent grid limit violations, failure of autonomous control routines, and mismatches between the physical grid and its Digital Twin, are addressed. For each case, the actionable recommendations are described. These are supported by real-time visualization of affected areas, overlays of measurement uncertainty, prioritized action lists with justifications, and system diagnostics. These outputs aim to preserve operator agency while improving the quality of manual interventions.

Scenario-based validation demonstrates that structured decision support can reduce operator workload, improve grid stability, and accelerate response during critical incidents. The framework emphasizes the relevance of approaches combining automation and expert-driven decision-making and outlines a pathway for strengthening operational resilience.