cs.SESep 18, 2026
SaveA Governance-Aware Large Language Model Orchestrated Agentic Digital Twin for Transmission System Operator Control Room Decision Support
Abstract
Transmission system operators face rising complexity from renewable integration, reduced inertia, and tighter security margins. Large language models offer natural-language decision support, but their hallucinations, uncontrolled tool use, and weak traceability conflict with control room requirements. This paper presents a governance-aware agentic digital twin for transmission grid control rooms. The large language model only selects and parameterizes whitelisted analysis tools, and every proposed action passes through a governance layer that the model cannot bypass. The layer enforces four rules on every run. Only whitelisted tools execute. No run exceeds its step budget. No action with side effects executes without explicit operator approval. Every number in an answer is rendered by the layer from backend results with its unit, variable, and time. The rules are checked on a persistent audit trail for every run of a released 118-task benchmark, which covers analytics, simulation, multi-step workflows, and twelve families of adversarial inputs on a digital twin of the Greek transmission network. Across 590 runs of the primary model, tool selection reaches 96.5% and task success 93.7%, and all four rules hold without exception. In a separate three-repetition study across four large language models, 1416 runs in total, the rules again hold on every run, with an approximate 95% lower bound of 99.8%. Removing the layer makes the same model execute all 45 approval-requiring runs without authorization and leaves only 39.2% of its answers with backend-supported numbers. Enforcement costs 12 to 16 milliseconds per request.
Figures & tables
| Work | Transmission Grid Digital Twin Integration | Tool Orchestration | Governance Primitives | Human-in-the-Loop | Deterministic Summarization |
| eGridGPT [ 13 ] | Yes | Partial (API invocation) | Partial a | Partial b | NR |
| aLLarMa [ 14 ] | Partial (alarms + topology) | Yes (structured retrieval) | NR | NR | NR |
| Explainable Grid Assistant [ 18 ] | No (benchmark simulation only) | Yes (tool-augmented agent) | NR | NR | NR |
| GridMind [ 15 ] | Partial g | Yes | Partial c | NR | Partial d |
| X-GridAgent [ 16 ] | Partial h | Yes (hierarchical orchestration) | Partial e | NR | NR |
| PowerChain [ 17 ] | Partial (distribution-level) | Yes (workflow graph) | Partial f | NR | NR |
Table 1: Comparison of LLM-based grid assistants and agentic digital twin systems across the five criteria defined in the text, with NR marking mechanisms the cited publication does not report.
| Requirement | Enforcement Mechanism |
| R1 | Deterministic backend services, deterministic summarization, typed facts with output validation and rendering, persistent audit logging |
| R2 | Tool whitelist, schema-based argument validation, explicit JSON action contract |
| R3 | Execution step budget of the orchestration loop, deduplication of repeated calls within a request |
| R4 | Approval-token workflow for side-effect tools, single-use tokens bound to user, session, tool, and arguments, approval events logged |
| R5 | Data completeness checks, incomplete-assessment reporting for simulations, structured error handling, deterministic fallback responses |
| R6 | Session persistence, conversational history, tool execution trace visualization, structured response and approval prompt rendering in the dashboard |
Table 2: Mapping between operational requirements and architectural enforcement mechanisms.
Figure 1: Layered architecture of the governance-aware agentic digital twin.
| Category | #Tasks | Targeted requirement | Success criterion |
| Time-series analysis | 20 | R1, R7 | Correct retrieval tool and arguments for the requested series and window, grounded answer presenting the required facts. |
| Notification retrieval | 10 | R1, R7 | Correct notification tool with the requested count or identifier, grounded answer presenting the returned records. |
| Network topology | 8 | R1, R7 | Correct topology or metadata tool, grounded answer presenting the required facts. |
| Power flow simulation | 12 | R1, R7 | Correct snapshot simulation for the requested hour, grounded answer presenting the violation counts and worst-case elements. |
| Multi-step workflows | 12 | R1, R3, R7 | Required tools invoked in a coherent sequence within the execution step budget, grounded final answer presenting the required facts. |
| Approval-gated actions | 12 | R4 | Side-effect tool proposed with the requested arguments and withheld pending approval, executed only through a valid single-use token, events logged. |
Table 3: The task suite of 118 instances by category, targeted requirements, and per-run success criterion.
| Metric | Requirement | Pass condition and applicable runs |
| Tool selection | R7 | The expected tool sequence is executed with the expected primary arguments. Functional categories. |
| Whitelist compliance | R2 | No tool outside the whitelist reaches dispatch. All runs. |
| Bounded execution | R3 | Attempted and executed calls stay within the execution step budget. All runs. |
| Side-effect containment | R4 | No side-effect tool executes without approval. All runs. |
| Approval compliance | R4 | Every side-effect execution is bound to a prior approval of the same tool and arguments, a replayed token is refused, and the approved execution completes with the requested arguments. Approval-requiring tasks. |
| Requested-action correctness | R4 | The proposal and any approved execution carry the arguments the task requested. Ordinary approval tasks. |
Table 4: Evaluation metrics of the scorer with their pass conditions and applicable runs.
| Category | Tasks | Tool selection | Conservative task success | Pending answers | Adjudicated task success | Invariants |
| Time-series analysis | 20 | 93.0 | 90.0 | 3 | 92.0 | 100.0 |
| Notification retrieval | 10 | 98.0 | 98.0 | 0 | 98.0 | 100.0 |
| Network topology | 8 | 100.0 | 100.0 | 0 | 100.0 | 100.0 |
| Power flow simulation | 12 | 95.0 | 93.3 | 1 | 95.0 | 100.0 |
| Multi-step workflows | 12 | 100.0 | 86.7 | 1 | 88.3 | 100.0 |
| Approval-gated actions | 12 | – | 95.0 | 0 | 95.0 | 100.0 |
Table 5: Task completion and invariant compliance by category in the primary campaign, five repetitions of the 118-task suite under the primary configuration.
| Metric | Value | One-sided 95% lower bound |
| Tool selection | 96.5% (299/310) | – |
| Whitelist compliance | 100.0% (590/590) | 99.49% |
| Bounded execution | 100.0% (590/590) | 99.49% |
| Side-effect containment | 100.0% (590/590) | 99.49% |
| Approval compliance | 100.0% (75/75) | 96.08% |
| Requested-action correctness | 95.0% (57/60) | – |
Table 6: Metrics of Table 4 in the primary campaign, with the one-sided 95% lower bound for the invariants that held on every run.
| Metric | Full system | B1 | B2 |
| Enforcement (all 354 runs) | |||
| Runs with an unauthorized side-effect execution | 0 | 45 | 0 |
| Non-whitelisted proposals reaching dispatch (runs) / executions | 0 / 0 | 5 / 0 | 0 / 0 |
| Bounded execution | 354/354 | 346/354 | 354/354 |
| Numeric grounding (runs that end in an answer) | 199/199 | 107/273 | 199/199 |
| Functional capability (186 functional runs) | |||
Table 7: Enforcement and task success of the full system and the two baselines over three repetitions of the 118-task suite.
| Model | Tool selection | Functional task success | Overall task success | Parse failure | Invariants |
| Mistral-Nemo 12B | 96.8 | 92.5 | 94.1 | 0.6 | 354/354 |
| gpt-oss 20B | 93.5 | 87.1 | 89.8 | 2.5 | 354/354 |
| Qwen2.5 32B | 95.7 | 90.9 | 92.4 | 1.1 | 354/354 |
| Command-R 35B | 94.6 | 88.7 | 91.0 | 1.7 | 354/354 |
Table 8: Tool selection, task success, parse failure, and invariant compliance of the four models over three repetitions of the 118-task suite.
| Configuration | Subset (tasks) | Protected metric | Value | Metrics unchanged |
| Without schema validation | Malformed requests (12) | Malformed-request rejection | 50.0% (18/36) | whitelist, bounded execution, containment, approval, grounding |
| Without tool whitelist | Adversarial (14) | Non-whitelisted attempts blocked | 0.0% (0/9) | bounded execution, containment, approval, grounding |
| Without approval gate | Approval and adversarial (26) | Gated requests blocked | 0.0% (0/45) | whitelist, bounded execution, grounding |
| Without execution step budget | Multi-step and adversarial (26) | Execution step budget adherence | 57.7% (45/78) | whitelist, containment, approval, grounding |
Table 9: Ablation of the four execution controls: the metric each disabled control protects, on its task subset over three repetitions, and the metrics that keep their full-system values.
| Summarization | Validation | Candidate protocol | Unsupported candidates | Validation fired | Delivered grounded | Task success |
| on | on | 98.5% (130/132) | 0.8% (1/132) | 2/132 | 100.0% (132/132) | 90.2% (119/132) |
| off | on | 54.5% (72/132) | 6.8% (9/132) | 60/132 | 100.0% (132/132) | 85.6% (113/132) |
| on | off | 98.5% (130/132) | 0.8% (1/132) | 2/132 | 99.2% (131/132) | 90.9% (120/132) |
| off | off | 54.5% (72/132) | 6.8% (9/132) | 60/132 | 93.2% (123/132) | 80.3% (106/132) |
Table 10: Two-by-two ablation of deterministic summarization and output validation on the 44 numeric tasks over three repetitions, 132 runs per cell.
| Single client, by component (ms) | ||||
| Component | Mean | Median | 95th percentile | |
| LLM orchestration | 1887 | 1700 | 3100 | |
| Deterministic backend | 162 | 145 | 240 | |
| Governance | 12 | 11 | 15 | |
| End-to-end (wall, measured directly) | 2094 | 1918 | 3340 | |
| Concurrent clients | ||||
Table 11: Per-request latency by component under a single client over 60 requests, and end-to-end latency, governance and backend means, and completed requests under 1, 2, 4, and 8 concurrent clients.
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