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Smart Grid AI-Powered Simulator - Technical Specification

Table of Contents

  1. Core Objective
  2. System Architecture
  3. Core Subsystems
  4. Generation Model
  5. Load Model
  6. Network & Zone Model
  7. Frequency Model
  8. Operating Limits
  9. Load Shedding System
  10. Dispatch & Control Logic
  11. AI Integration
  12. Early Warning System
  13. Data Logging & Metrics
  14. Simulation Engine
  15. Implementation Phases

Core Objective

The simulator must maintain grid stability by ensuring:

Σ Generation(t) − Σ Load(t) − Losses(t) ≈ 0

If this fails → frequency deviates → grid collapses.

The system must: - Predict load imbalances before they occur - Estimate time-to-load-shedding accurately - Minimize total load shed while maintaining stability - Prevent cascading blackouts - Optimize generation dispatch economically

System Architecture

High-Level Architecture

flowchart TB
    subgraph Simulator["Smart Grid Simulator"]
        Historical["Historical Load Data"]
        Realtime["Real-time Load Data"]
        Forecast["Forecast Engine"]
        GenModel["Generator Models"]
        FreqModel["Frequency Model"]
        LoadForecast["Load Forecast (AI)"]
        Network["Network Zones"]
        RiskEst["Deficit & Risk Est."]
        RiskPred["Shedding Risk Predictor"]
        TimeEst["Time-to-Shedding Est."]
        OptimalShed["Optimal Shedding"]
        SheddingAI["Load Shedding AI (RL)"]
        Control["Control Logic"]
        Dispatch["Dispatch Engine"]
    end

    Historical --> Realtime
    Realtime --> Forecast
    Forecast --> LoadForecast
    GenModel --> FreqModel
    LoadForecast --> FreqModel
    LoadForecast --> RiskEst
    Network --> RiskEst
    RiskPred --> RiskEst
    RiskEst --> OptimalShed
    TimeEst --> OptimalShed
    SheddingAI --> OptimalShed
    OptimalShed --> Dispatch
    Dispatch --> Control
    Control --> GenModel
    Control --> Network

Design Principles

  1. AI Suggests, Physics Enforces, Protection Acts Last
  2. AI optimizes decisions but does not replace physics
  3. All safety thresholds remain rule-based

  4. Protection systems have final authority

  5. Modular & Scalable

  6. Start with single power station, single load bus
  7. Scale to multi-zone, multi-generator systems

  8. Support incremental complexity

  9. Realistic Constraints

  10. Generator ramp limits
  11. Startup delays
  12. Network transfer capacity
  13. Frequency dynamics

Core Subsystems

The system requires 6 core subsystems:

  1. Generation Model - Models power plants with realistic constraints
  2. Load Model - Bottom-up load aggregation with time-dependent profiles
  3. Network/Zones - Geographic and electrical zones with transfer limits
  4. Frequency Model - Swing equation-based frequency dynamics
  5. Load Shedding Logic - Priority-based, selective load disconnection
  6. Control & Dispatch Logic - Real-time decision engine

Generation Model

Generator Parameters

Each generator must have the following parameters:

Parameter Type Description Example Values
id string Unique identifier "GEN_001"
type enum Generator type steam, gas, diesel, hydro, renewable
P_max float (MW) Maximum output capacity 1400 MW
P_min float (MW) Minimum stable output 300 MW
ramp_rate float (MW/sec) Maximum ramp rate 10 MW/min
inertia_constant float (sec) Inertia constant H 5-10 seconds
fuel_cost float ($/MWh) Operating cost 50-100 $/MWh
startup_time float (sec) Time to start from cold 30 min
status enum Current status ON, OFF, STARTING, TRIPPED
efficiency float (0-1) Conversion efficiency 0.35-0.55

Generator State

State Variable Type Description
current_output float (MW) Current power output
available_reserve float (MW) P_max - current_output
ramp_direction enum UP, DOWN, HOLD
time_since_start float (sec) For startup tracking

Generator Constraints

Power Limits:

P_min ≤ P(t) ≤ P_max

Ramp Rate Limits:

|dP/dt| ≤ ramp_rate

Update Rule:

P(t+Δt) = clamp(
    P(t) ± ramp_rate * Δt,
    P_min,
    P_max
)

Generator Types & Characteristics

Type Ramp Rate Startup Time Inertia Typical Use
Steam 5-10 MW/min 30-60 min High (8-10s) Base load
Gas 20-50 MW/min 5-15 min Medium (4-6s) Peak load
Diesel 30-60 MW/min 2-5 min Low (2-4s) Emergency
Hydro 50-100 MW/min 1-3 min Medium (4-6s) Flexible
Renewable Variable N/A Low (1-2s) Intermittent

Load Model

Load Aggregation Hierarchy

flowchart LR
    Room["Room"] --> Building["Building"]
    Building --> Feeder["Feeder"]
    Feeder --> Sector["Sector"]
    Sector --> Town["Town"]
    Town --> Grid["Grid"]

    style Room fill:#e1f5ff
    style Building fill:#b3e5fc
    style Feeder fill:#81d4fa
    style Sector fill:#4fc3f7
    style Town fill:#29b6f6
    style Grid fill:#03a9f4

Load Parameters

Parameter Type Description Example
id string Unique identifier "LOAD_RES_001"
type enum Load category residential, commercial, industrial
priority int Shedding priority (1=highest) 1-5
P_nominal float (MW) Nominal power demand 50 MW
load_profile(t) function Time-dependent factor 0.5-1.5
sheddable bool Can be disconnected true/false
zone_id string Geographic zone "ZONE_A"

Load Profile (Time Dependent)

P_load(t) = P_nominal × usage_factor(t)

Typical Usage Patterns

Residential: - Peak: 18:00-22:00 (evening) - Low: 02:00-06:00 (night) - Daily cycle: 200 * sin(2π * hour / 24)

Commercial: - Peak: 09:00-17:00 (business hours) - Low: 20:00-08:00 (off-hours)

Industrial: - Constant base load with shift changes - Event spikes during startup (300 MW spike)

Load Components

Load(t) = BaseLoad
        + DailyCycle
        + RandomNoise
        + EventSpikes

Example:

daily = 200 * sin(2π * hour / 24)
noise = random.normal(0, 20)
event = 300 if industrial_start else 0
load = 800 + daily + noise + event

Network & Zone Model

Zone Parameters

Parameter Type Description
zone_id string Unique zone identifier
max_transfer_capacity float (MW) Maximum power transfer
connected_zones list Adjacent zones
critical_loads list Non-sheddable loads
total_load float (MW) Current zone load

Zone Constraints

  • Each zone has maximum transfer capacity to adjacent zones
  • Load shedding must respect zonal boundaries
  • Prevents unrealistic global shedding
  • Enables realistic network congestion modeling

Example Zone Structure

graph TB
    subgraph ZoneA["Zone A (Residential)"]
        A_Transfer["Max Transfer: 500 MW"]
        A_Critical["Critical Loads:<br/>Hospital (20 MW)"]
    end

    subgraph ZoneB["Zone B (Commercial)"]
        B_Transfer["Max Transfer: 800 MW"]
        B_Critical["Critical Loads:<br/>Data Center (50 MW)"]
    end

    subgraph ZoneC["Zone C (Industrial)"]
        C_Transfer["Max Transfer: 1200 MW"]
        C_Critical["Critical Loads:<br/>Water Treatment (30 MW)"]
    end

    subgraph ZoneD["Zone D"]
        D_Transfer["Max Transfer: 1000 MW"]
    end

    ZoneA <--> ZoneB
    ZoneA <--> ZoneC
    ZoneB <--> ZoneD
    ZoneC <--> ZoneD

    style ZoneA fill:#ffebee
    style ZoneB fill:#e8f5e9
    style ZoneC fill:#fff3e0
    style ZoneD fill:#f3e5f5

Frequency Model

Swing Equation (Simplified)

The frequency dynamics follow the swing equation:

df/dt = (P_gen - P_load - D·(f - f_nominal)) / (2H)

Where: - H = system inertia constant (seconds) - D = damping coefficient - f_nominal = nominal frequency (50.0 Hz)

Practical Simplified Model

For simulation purposes:

Δf = K × (P_gen - P_load)
f = f_nominal + Δf

Where: - K ≈ -0.005 Hz per MW imbalance (scaled to system size)

Frequency Calculation

def calculate_frequency(total_generation, total_load, f_nominal=50.0):
    """
    Calculate system frequency based on power imbalance.

    Args:
        total_generation: Total generation (MW)
        total_load: Total load (MW)
        f_nominal: Nominal frequency (Hz)

    Returns:
        Current frequency (Hz)
    """
    imbalance = total_generation - total_load
    K = -0.005  # Hz per MW (scaled)
    delta_f = K * imbalance
    return f_nominal + delta_f

Rate of Change of Frequency (RoCoF)

RoCoF = df/dt

Critical for early warning: - Negative RoCoF → frequency dropping → load shedding likely - Large magnitude → fast collapse → urgent action needed

Operating Limits

Frequency Operating Limits

Zone Frequency Range Status Action Required
Normal 49.8 - 50.2 Hz ✅ Stable None
Alert 49.5 - 49.8 Hz ⚠️ Warning Monitor closely
Emergency 49.0 - 49.5 Hz 🔴 Critical Prepare shedding
UFLS Stage 1 48.8 - 49.0 Hz 🔴 Action Shed 5% load
UFLS Stage 2 48.5 - 48.8 Hz 🔴 Action Shed 10% load
UFLS Stage 3 < 48.5 Hz 🔴 Critical Shed 15% load
Blackout Risk < 48.0 Hz ⛔ Danger Island grid, shed 30-40%

Reserve Margin Requirements

  • Spinning Reserve: 5-10% of peak load
  • Operating Reserve: 10-15% of peak load
  • Emergency Reserve: 15-20% of peak load

Load Shedding System

Load Priority Classes

Priority Class Examples Shedding Order
1 Critical Hospitals, Water, Telecom NEVER shed
2 Essential Industry (key processes) Last to shed
3 Important Commercial (offices) Middle priority
4 Standard Residential First to shed
5 Non-essential Entertainment, Advertising First to shed

Under-Frequency Load Shedding (UFLS) Stages

Stage Frequency Trigger Shedding % Typical MW
Stage 1 f < 49.5 Hz 5% 50-100 MW
Stage 2 f < 49.2 Hz 10% 100-200 MW
Stage 3 f < 49.0 Hz 15% 150-300 MW
Emergency f < 48.8 Hz 30-40% 300-600 MW

Load Shedding Algorithm

flowchart TD
    Start([Load Shedding Start]) --> Input[Input: total_deficit, loads]
    Input --> Sort[Sort Loads by Priority<br/>Priority 4-5 first, then 3, then 2<br/>Within same priority: smaller first]
    Sort --> Init[Initialize: shed_amount = 0<br/>loads_to_shed = []]
    Init --> Loop{For each load}

    Loop --> CheckSheddable{Sheddable AND<br/>Priority > 1?}
    CheckSheddable -->|No| NextLoad[Next Load]
    CheckSheddable -->|Yes| AddLoad[Add to loads_to_shed<br/>shed_amount += load.current_load]

    AddLoad --> CheckDeficit{shed_amount >=<br/>total_deficit?}
    CheckDeficit -->|Yes| Return[Return loads_to_shed]
    CheckDeficit -->|No| NextLoad

    NextLoad --> MoreLoads{More Loads?}
    MoreLoads -->|Yes| Loop
    MoreLoads -->|No| Return

    Return --> End([End])

    style Start fill:#c8e6c9
    style End fill:#ffcdd2
    style CheckSheddable fill:#fff9c4
    style CheckDeficit fill:#fff9c4
    style AddLoad fill:#ffccbc

Key Principles

  1. Fast Response: Shedding must occur within seconds
  2. Selective: Only shed necessary loads
  3. Priority-Based: Protect critical infrastructure
  4. Minimum Necessary: Shed only what's needed to restore balance

Dispatch & Control Logic

Control Loop (High Level)

flowchart TD
    Start([Simulation Start]) --> Measure[Measure Current State]
    Measure --> CalcFreq[Calculate Frequency]
    CalcFreq --> Forecast[AI: Predict Future Load]
    Forecast --> CalcReq[Calculate Required Generation]
    CalcReq --> CheckFreq{Frequency<br/>Check}

    CheckFreq -->|f < f_min| TryGen[Try Fast Generation]
    CheckFreq -->|f > f_max| ReduceGen[Reduce Generation]
    CheckFreq -->|Normal| EnforceRamp

    TryGen --> StillDeficit{Still<br/>Deficit?}
    StillDeficit -->|Yes| ShedLoad[Execute Load Shedding]
    StillDeficit -->|No| EnforceRamp

    ReduceGen --> EnforceRamp[Enforce Ramp Limits]
    ShedLoad --> EnforceRamp
    EnforceRamp --> LogState[Log State]
    LogState --> UpdateTime[Update Time: t += Δt]
    UpdateTime --> CheckEnd{Simulation<br/>Running?}
    CheckEnd -->|Yes| Measure
    CheckEnd -->|No| End([End])

    style Start fill:#c8e6c9
    style End fill:#ffcdd2
    style CheckFreq fill:#fff9c4
    style StillDeficit fill:#fff9c4
    style ShedLoad fill:#ffccbc

Fast Generation Support (Before Shedding)

Priority Order: 1. Fast generators (gas, diesel, hydro) - ramp up available reserve 2. Spinning reserve - increase output from online generators 3. Start fast-start units - if time permits 4. Load shedding - only if generation cannot catch up

flowchart TD
    Start([Fast Generation Start]) --> Input[Input: deficit]
    Input --> Sort[Sort Generators by Ramp Rate<br/>Fastest first]
    Sort --> Loop{For each generator}

    Loop --> CheckStatus{Status == ON AND<br/>available_reserve > 0?}
    CheckStatus -->|No| NextGen[Next Generator]
    CheckStatus -->|Yes| RampUp[Ramp Up Generator<br/>deficit -= available_reserve]

    RampUp --> CheckDeficit{deficit <= 0?}
    CheckDeficit -->|Yes| Return[Return deficit = 0]
    CheckDeficit -->|No| NextGen

    NextGen --> MoreGens{More Generators?}
    MoreGens -->|Yes| Loop
    MoreGens -->|No| ReturnRemaining[Return Remaining Deficit]

    Return --> End([End])
    ReturnRemaining --> End

    style Start fill:#c8e6c9
    style End fill:#ffcdd2
    style CheckStatus fill:#fff9c4
    style CheckDeficit fill:#fff9c4
    style RampUp fill:#c5e1a5

Blackout Prevention Logic

if frequency < 48.8:
    # Critical: Island grid and shed aggressively
    island_grid()
    emergency_shedding(percentage=0.35)  # Shed 35% load
    log_critical_event("BLACKOUT_PREVENTION")

AI Integration

AI Model #1: Load Forecasting

Purpose: Predict future demand to enable preventive action

Input Features: - Past load (15s / 1m / 5m resolution) - Time of day (hour, minute) - Day type (weekday, weekend, holiday) - Recent frequency deviation - Temperature (optional, synthetic in simulation) - Historical patterns

Output: - Load(t + 5min) - Load(t + 10min) - Load(t + 30min)

Recommended Models: 1. LSTM/GRU - Best for sequential load patterns 2. Temporal CNN - Faster, stable performance 3. XGBoost - Excellent baseline, interpretable 4. Prophet - Too coarse for real-time control

Training Data: - Synthetic load profiles with realistic patterns - Diurnal cycles, noise, event spikes - Minimum 30 days of data (1-minute resolution)

AI Model #2: Shedding Risk Predictor

Purpose: Answer "Will load shedding happen soon?"

Formulation: Binary/probabilistic classification

Input Features: - Forecast load (from Model #1) - Available generation - Spinning reserve - Ramp limits - Rate of change of frequency (RoCoF) - Time to frequency limit

Output: - P(load_shedding in next 10 min) - Probability [0, 1] - risk_level - LOW, MEDIUM, HIGH, CRITICAL

Model: XGBoost classifier or neural network

AI Model #3: Optimal Load Shedding (RL)

Purpose: Minimize total load shed while maintaining stability

Approach: Reinforcement Learning (RL)

State Space:

state = {
    "frequency": 49.4,           # Hz
    "df_dt": -0.03,              # Hz/s (RoCoF)
    "deficit_mw": 220,            # MW
    "zones": [
        {"zone_id": "A", "mw": 40, "priority": 1},
        {"zone_id": "B", "mw": 60, "priority": 3},
        {"zone_id": "C", "mw": 100, "priority": 5}
    ],
    "available_reserve": 50,      # MW
    "time_to_limit": 45           # seconds
}

Action Space: - Discrete: {shed_zone_A, shed_zone_B, shed_zone_C, ...} - Continuous: [shed_mw_zone_A, shed_mw_zone_B, ...]

Reward Function:

reward = (
    - MW_shed * 1.0                    # Minimize shedding
    - critical_zone_shed * 10.0        # Heavy penalty for critical loads
    - frequency_violation * 100.0      # Prevent frequency collapse
    - switching_penalty * 0.5          # Avoid frequent switching
    + stability_bonus * 5.0            # Reward stable operation
)

RL Algorithm: PPO (Proximal Policy Optimization) or DQN

AI Model #4: Time-to-Shedding Estimator

Purpose: Estimate time remaining before UFLS triggers

Hybrid Approach: Physics-based with AI correction

Physics Estimate:

t = (f_current - f_shed) / |df/dt|

AI Correction: - Learns generator response delays - Accounts for load volatility - Adjusts for forecast error patterns

Output: - time_to_shedding (seconds) - confidence_interval (seconds)

Early Warning System

Early Warning Signals

Signal Threshold Warning Level Action
Load forecast > available gen Deficit > 50 MW 🟡 Warning Prepare ramping
Spinning reserve < 5% Reserve < threshold 🟠 Alert Start fast units
RoCoF < -0.02 Hz/s Negative trend 🟠 Alert Monitor closely
Time to limit < 60s Critical timing 🔴 Critical Prepare shedding
Frequency < 49.5 Hz Below normal 🔴 Action Execute shedding

Early Warning Dashboard

Example Alerts:

🟡 Warning: Load exceeds ramp capability in 5 minutes
   Forecast: 1200 MW | Available: 1150 MW | Deficit: 50 MW

🟠 Critical: Frequency trend indicates UFLS in 40 seconds
   Current: 49.6 Hz | RoCoF: -0.03 Hz/s | Time to 49.5 Hz: 40s

🔴 Action: Shed 200 MW in Zone B
   Deficit: 220 MW | Recommended: Shed Zone B (150 MW) + Zone C (50 MW)

Early Warning Logic

flowchart TD
    Start([Early Warning System]) --> Input[Input: System State]
    Input --> CheckForecast{Forecast Load ><br/>Available Gen + 50 MW?}
    CheckForecast -->|Yes| AddWarning1[Add WARNING:<br/>Load forecast exceeds generation]
    CheckForecast -->|No| CheckRoCoF

    AddWarning1 --> CheckRoCoF{RoCoF < -0.02<br/>Hz/s?}
    CheckRoCoF -->|Yes| CalcTime[Calculate Time to Limit]
    CalcTime --> CheckTime{Time to Limit<br/>< 60s?}
    CheckTime -->|Yes| AddWarning2[Add CRITICAL:<br/>Frequency dropping, UFLS imminent]
    CheckTime -->|No| CheckReserve
    CheckRoCoF -->|No| CheckReserve

    AddWarning2 --> CheckReserve{Spinning Reserve<br/>< 5% of Total Load?}
    CheckReserve -->|Yes| AddWarning3[Add ALERT:<br/>Spinning reserve below threshold]
    CheckReserve -->|No| Return

    AddWarning3 --> Return[Return Warnings]
    Return --> End([End])

    style Start fill:#c8e6c9
    style End fill:#ffcdd2
    style CheckForecast fill:#fff9c4
    style CheckRoCoF fill:#fff9c4
    style CheckTime fill:#fff9c4
    style CheckReserve fill:#fff9c4
    style AddWarning1 fill:#fff9c4
    style AddWarning2 fill:#ffccbc
    style AddWarning3 fill:#ffccbc

Data Logging & Metrics

Required Metrics

Metric Unit Purpose
Time timestamp Temporal reference
Total Load MW Current demand
Total Generation MW Current supply
Frequency Hz System stability indicator
RoCoF Hz/s Rate of frequency change
Shedded Load MW, MWh Load shedding magnitude
Unserved Energy MWh Total energy not supplied
Reserve Margin MW, % Available capacity
Prediction Error MW, MAE Forecast accuracy
Generator Utilization % Efficiency metric
Ramp Events count Generator start/stop events
Shedding Events count Load disconnection events
Frequency Violations count Times frequency exceeded limits

Logging Format

log_entry = {
    "timestamp": "2024-01-15T14:30:00",
    "load": {
        "total_mw": 1200.5,
        "residential_mw": 400.2,
        "commercial_mw": 500.3,
        "industrial_mw": 300.0
    },
    "generation": {
        "total_mw": 1180.0,
        "gen_001_mw": 800.0,
        "gen_002_mw": 380.0
    },
    "frequency": {
        "hz": 49.6,
        "rocof": -0.02
    },
    "shedding": {
        "shedded_mw": 20.5,
        "shedded_mwh": 0.34,
        "zones_shed": ["ZONE_C"]
    },
    "forecast": {
        "predicted_load_5min": 1220.0,
        "prediction_error": 15.2
    },
    "reserves": {
        "spinning_mw": 50.0,
        "spinning_percent": 4.2
    }
}

Analysis Metrics

Key Performance Indicators (KPIs): 1. Load Shedding Reduction: % reduction vs. baseline 2. Early Warning Accuracy: % of correctly predicted events 3. Frequency Stability: % time in normal range 4. Economic Efficiency: Total cost (generation + shedding penalties) 5. System Reliability: Unserved energy / total energy demand

Simulation Engine

Time Engine

Time Step Options: - Real-time: 1 second (for frequency dynamics) - Control: 5-15 minutes (for dispatch decisions) - Forecast: 30 minutes (prediction horizon)

Recommended Configuration: - Simulation step: 5 minutes (initial) - Control update: 1 minute - Forecast horizon: 30 minutes

Simulation Loop (Pseudocode)

def simulation_loop():
    # Initialize
    grid = initialize_grid()
    generators = initialize_generators()
    loads = initialize_loads()
    ai_models = load_ai_models()

    t = 0
    dt = 300  # 5 minutes in seconds

    while t < simulation_duration:
        # 1. Update load profiles
        update_load_profiles(t, loads)
        total_load = sum(load.current_load for load in loads)

        # 2. AI: Forecast future load
        load_forecast = ai_models.load_forecast.predict(
            historical_load=recent_load_history,
            time_features=extract_time_features(t)
        )

        # 3. Dispatch generators
        required_generation = load_forecast + reserve_margin
        dispatch_generators(generators, required_generation)

        # 4. Calculate total generation
        total_generation = sum(gen.current_output for gen in generators)

        # 5. Calculate frequency
        frequency = calculate_frequency(total_generation, total_load)
        rocof = calculate_rocof(frequency_history)

        # 6. Early warning
        warnings = early_warning_system({
            "forecast_load": load_forecast,
            "available_generation": total_generation,
            "frequency": frequency,
            "rocof": rocof
        })

        # 7. Check frequency limits
        if frequency < 49.5:
            # Try fast generation first
            remaining_deficit = try_fast_generation(
                total_load - total_generation
            )

            # If still deficit, shed load
            if remaining_deficit > 0:
                shedding_plan = ai_models.shedding_agent.decide({
                    "deficit": remaining_deficit,
                    "zones": get_zone_states(),
                    "frequency": frequency
                })
                execute_shedding(shedding_plan)

        # 8. Enforce generator ramp limits
        enforce_ramp_limits(generators, dt)

        # 9. Log state
        log_state(t, {
            "load": total_load,
            "generation": total_generation,
            "frequency": frequency,
            "shedding": get_shedding_state()
        })

        # 10. Update time
        t += dt

Start Small: - Single power station (1-2 generators) - Single load bus (aggregate demand) - Time step: 5 minutes - Prediction horizon: 30 minutes - Ramp delay: 30 minutes

Scale Later: - Multiple generators - Multiple zones - Shorter time steps (1 second for frequency) - Longer horizons (1-4 hours)

Implementation Phases

Phase 1: Foundation (Simple, Strong)

Goal: Basic simulation with rule-based control

Components: - ✅ Generator model with ramp limits - ✅ Load model with time profiles - ✅ Frequency calculation - ✅ Rule-based load shedding - ✅ Basic logging

Deliverables: - Working simulation engine - Frequency-based shedding - Data collection pipeline

Timeline: 2-3 weeks

Phase 2: AI Integration (Smart)

Goal: Add AI for forecasting and optimization

Components: - ✅ Load forecasting (LSTM/XGBoost) - ✅ Risk classifier - ✅ Early warning system - ✅ Time-to-shedding estimator

Deliverables: - Trained forecasting model - Early warning dashboard - Improved shedding decisions

Timeline: 3-4 weeks

Phase 3: Advanced Control (Optimal)

Goal: RL-based optimal shedding

Components: - ✅ Reinforcement learning agent - ✅ Multi-zone coordination - ✅ Economic dispatch - ✅ Restoration optimization

Deliverables: - Trained RL agent - Optimal shedding policies - Cost optimization

Timeline: 4-6 weeks

Phase 4: Advanced Features (Production)

Goal: Real-world realism

Components: - ✅ N-1 contingency modeling - ✅ Automatic Generation Control (AGC) - ✅ Market-aware dispatch - ✅ Multi-area coordination

Deliverables: - Production-ready simulator - Comprehensive testing - Performance optimization

Timeline: 6-8 weeks

Technical Stack

Core Libraries

Library Purpose Version
numpy Numerical computations >= 1.24.0
pandas Data handling >= 2.0.0
scikit-learn ML models (baseline) >= 1.3.0
xgboost Gradient boosting >= 2.0.0
tensorflow/pytorch Deep learning >= 2.13.0
matplotlib Visualization >= 3.7.0
stable-baselines3 RL algorithms >= 2.0.0

Language

  • Primary: Python 3.10+
  • Future: Rust (if performance critical)

File Structure

graph TD
    Root["smart_grid/"] --> Src["src/"]
    Root --> Data["data/"]
    Root --> Tests["tests/"]
    Root --> Docs["docs/"]
    Root --> Req["requirements.txt"]

    Src --> GridSim["grid_simulator/"]
    Src --> AIModels["ai_models/"]
    Src --> Utils["utils/"]

    GridSim --> GenPy["generator.py"]
    GridSim --> LoadPy["load_model.py"]
    GridSim --> NetPy["network.py"]
    GridSim --> FreqPy["frequency.py"]
    GridSim --> ShedPy["shedding.py"]
    GridSim --> CtrlPy["controller.py"]
    GridSim --> SimPy["simulation.py"]
    GridSim --> MetPy["metrics.py"]

    AIModels --> ForecastPy["load_forecast.py"]
    AIModels --> RiskPy["risk_predictor.py"]
    AIModels --> TimePy["time_estimator.py"]
    AIModels --> AgentPy["shedding_agent.py"]

    Utils --> DataGenPy["data_generator.py"]
    Utils --> VizPy["visualization.py"]
    Utils --> ConfigPy["config.py"]

    Data --> Training["training/"]
    Data --> Logs["logs/"]
    Data --> Models["models/"]

    Tests --> TestGen["test_generator.py"]
    Tests --> TestLoad["test_load_model.py"]
    Tests --> TestFreq["test_frequency.py"]
    Tests --> TestShed["test_shedding.py"]

    Docs --> TechSpec["technical_specification.md"]
    Docs --> API["api_reference.md"]

    style Root fill:#e3f2fd
    style Src fill:#fff3e0
    style Data fill:#e8f5e9
    style Tests fill:#fce4ec
    style Docs fill:#f3e5f5

Sanity Checks

Before AI Integration

  • [ ] Can generator follow load with ramp limits?
  • [ ] Does ramp delay create realistic shortages?
  • [ ] Does shedding occur naturally when needed?
  • [ ] Is frequency calculation physically correct?
  • [ ] Are priority classes respected?

After AI Integration

  • [ ] Is load shedding reduced vs. baseline?
  • [ ] Are ramps initiated earlier?
  • [ ] Is overgeneration controlled?
  • [ ] Is prediction error acceptable (< 5%)?
  • [ ] Does early warning provide actionable alerts?

Validation Tests

  1. Ramp Test: Sudden load increase → generator ramps correctly
  2. Shedding Test: Large deficit → priority-based shedding
  3. Frequency Test: Imbalance → frequency responds correctly
  4. Forecast Test: Historical data → accurate predictions
  5. RL Test: Training → improved shedding decisions

Key Insights

Critical Principles

  1. Load shedding is not failure — it's protection
  2. Controlled mechanism to prevent cascading collapse

  3. Goal: Minimize shed while maintaining stability

  4. AI optimizes, physics enforces

  5. AI suggests optimal actions
  6. Physics-based models enforce constraints

  7. Protection systems have final authority

  8. Early warning enables prevention

  9. Predict imbalances before they occur
  10. Estimate time-to-shedding accurately

  11. Take preventive action when possible

  12. Realistic constraints are essential

  13. Without ramp limits, delays, and noise → AI gives false confidence
  14. Simulation must capture real-world physics

Success Metrics

  • Load Shedding Reduction: 30-50% vs. reactive baseline
  • Early Warning Accuracy: > 80% true positive rate
  • Frequency Stability: > 95% time in normal range
  • Prediction Error: < 5% MAE for load forecast

References & Standards

Power System Standards

  • IEC 61850: Communication protocols
  • IEEE 1547: Grid interconnection
  • NERC: Reliability standards
  • IEC 61970: Energy management systems

Frequency Standards

  • Nominal Frequency: 50.0 Hz (Asia, Europe) or 60.0 Hz (Americas)
  • Normal Range: ±0.2 Hz
  • UFLS Thresholds: Typically 49.0-49.5 Hz

Load Shedding Standards

  • UFLS Stages: 3-5 stages typical
  • Shedding Amount: 5-15% per stage
  • Response Time: < 1 second for automatic

Conclusion

This technical specification provides a comprehensive foundation for building a realistic, AI-powered smart grid simulator. The system balances:

  • Realism: Physics-based models with realistic constraints
  • Intelligence: AI-driven forecasting and optimization
  • Safety: Rule-based protection with AI assistance
  • Scalability: Start simple, scale to complexity

The key to success is implementing realistic constraints (ramp limits, delays, noise) so that AI learns meaningful patterns and provides genuine value in preventing load shedding and maintaining grid stability.

Document Version: 1.0
Last Updated: 2024-01-15
Status: Draft for Review