電網 AI 安全

# 負載預測投毒情境
load_forecast_attacks = {
    "under_forecasting": {
        "description": "Cause the AI to predict lower demand than "
                       "actual, resulting in insufficient generation",
        "technique": "Inject historical records showing lower demand "
                     "during conditions similar to the target period",
        "consequence": "Generation shortfall, frequency drop, load "
                       "shedding (rolling blackouts)",
        "nerc_violation": "BAL-001 (Real Power Balancing Control)",
    },
    "over_forecasting": {
        "description": "Cause the AI to predict higher demand than "
                       "actual, resulting in excess generation",
        "technique": "Inject inflated historical demand records",
        "consequence": "Frequency rise, wasted fuel costs, potential "
                       "generator damage from sudden curtailment",
        "financial_impact": "Excess generation costs passed to consumers",
    },
    "volatility_injection": {
        "description": "Cause the AI to produce volatile, unreliable "
                       "forecasts that undermine operator confidence",
        "technique": "Inject inconsistent historical data that causes "
                     "the model to produce wildly varying predictions",
        "consequence": "Operators revert to manual forecasting, losing "
                       "the efficiency benefits of AI",
    },
}

# 負載預測的天氣資料供應鏈
weather_data_chain = {
    "source": "National Weather Service / commercial weather provider",
    "transport": "API calls, FTP downloads, data feeds",
    "processing": "Unit conversion, interpolation, gridding",
    "ingestion": "Feature engineering for the load forecast model",
    "attack_points": [
        "Compromise the weather data API endpoint",
        "Man-in-the-middle the weather data feed",
        "Modify weather data in the processing pipeline",
        "Inject false weather station readings upstream",
    ],
}

# 太陽能發電預測攻擊情境
solar_prediction_attacks = [
    {
        "name": "cloud_cover_manipulation",
        "description": "Modify satellite cloud cover data fed to the "
                       "solar prediction model to create false "
                       "clear-sky or overcast predictions",
        "impact": "If AI predicts clear skies but clouds arrive: "
                  "sudden generation shortfall. If AI predicts clouds "
                  "but skies are clear: excess solar, potential "
                  "over-generation and curtailment costs.",
    },
    {
        "name": "inverter_data_poisoning",
        "description": "Manipulate real-time generation data from "
                       "solar inverters to skew the AI's nowcasting",
        "impact": "AI adjusts short-term predictions based on false "
                  "current generation, causing dispatch errors",
    },
    {
        "name": "panel_degradation_masking",
        "description": "Manipulate generation data to hide panel "
                       "degradation, causing the AI to over-predict "
                       "generation from degraded installations",
        "impact": "Cumulative over-prediction as fleet ages",
    },
]

# 測試 OPF AI 的對抗性穩健性
def test_opf_robustness(opf_ai, grid_model, attack_scenarios):
    """
    Test whether adversarial inputs can cause the OPF AI
    to produce dispatches that violate safety constraints.
    """
    results = []
    for scenario in attack_scenarios:
        adversarial_state = apply_attack(
            grid_model.current_state,
            scenario.modifications
        )
        dispatch = opf_ai.solve(adversarial_state)
        violations = check_constraints(dispatch, grid_model)
        thermal_violations = [v for v in violations
                              if v.type == "thermal"]
        voltage_violations = [v for v in violations
                              if v.type == "voltage"]
        results.append({
            "scenario": scenario.name,
            "thermal_violations": len(thermal_violations),
            "voltage_violations": len(voltage_violations),
            "worst_overload": max(
                (v.overload_percent for v in thermal_violations),
                default=0
            ),
            "dispatch_cost": dispatch.total_cost,
        })
    return results

# 測試 AMI AI 對資料操縱的韌性
def test_ami_data_integrity(ami_ai_system, test_meters):
    """
    Test whether the AMI AI can detect manipulated meter data.
    """
    manipulation_types = [
        {"name": "flat_line", "detection_difficulty": "Easy"},
        {"name": "scaled_reduction", "detection_difficulty": "Moderate"},
        {"name": "pattern_shifting", "detection_difficulty": "Hard"},
        {"name": "neighbor_mimicry", "detection_difficulty": "Very hard"},
    ]
    for manipulation in manipulation_types:
        for meter in test_meters:
            original = ami_ai_system.get_readings(meter.id)
            modified = apply_manipulation(original, manipulation["name"])
            ami_ai_system.inject_readings(meter.id, modified)
            detected = ami_ai_system.run_anomaly_detection(meter.id)
            print(f"Manipulation: {manipulation['name']}, "
                  f"Detected: {detected}")