Virtual Lab: Study of Demand Response in Smart Grid

Interactive simulation of residential load management, peak shaving, smart metering and demand response

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Experiment Objective

The objective of this virtual laboratory is to study the effect of demand response on electrical demand in a smart-grid environment. The simulation represents residential consumers with fixed and flexible electrical loads.

During a selected peak-demand interval, the demand-response controller reduces flexible consumption such as air-conditioning, water-heating and electric-vehicle charging loads.

The resulting load profile is compared with the baseline profile to determine peak-demand reduction, daily energy consumption and electricity cost.

Learning Outcomes

• Understand residential load aggregation.
• Identify peak-demand periods.
• Understand smart-meter operation.
• Observe automatic demand response.
• Calculate peak shaving.
• Analyse time-of-use tariffs.
• Interpret demand-response graphs.
1. Simulation Controls
2. Smart Grid Single-Line Diagram
Smart Grid Demand Response — Single Line Diagram Electrical power path and demand-response communication path ELECTRICAL POWER NETWORK Generation → Transmission → Distribution → Consumer UTILITY GRID Generation AC Generation TRANSMISSION High-voltage network DISTRIBUTION Substation 33 kV / 415 V Transformer SMART METER Measurement + communication REAL-TIME DEMAND 10.30 kW Automatic metering SMART HOME Residential flexible demand AC Cooling WATER Heater EV CHARGER Flexible charging load DEMAND RESPONSE / COMMUNICATION NETWORK DR AGGREGATOR / CONTROLLER Price signal • Peak event • Load control DR CONTROL SIGNAL Electrical power flow Demand-response communication Animated power packet Flexible / controllable load
3. Real-Time Simulation Results
Baseline mode — demand response inactive
Baseline peak
0
kW
DR peak
0
kW
Peak reduction
0
kW
Reduction
0
%
Daily energy
0
kWh
Electricity cost
0
₹ / day
4. Load Profile and Tariff Analysis
5. Mathematical Model and Calculation

Aggregate electrical demand

P (t) = P base + P AC (t) + P WH (t) + P EV (t)

Demand-response controlled load

P DR (t) = P base + P flex (t) × ( 1 − r )

where r is the selected demand-response reduction fraction.

Peak-demand reduction

Δ P peak = P peak,baseline − P peak,DR

Energy consumption

E = ∑ t N P (t) Δ t

Electricity cost

C = ∑ t N P (t) × τ (t) × Δ t
Simulation assumption: The demand-response controller acts primarily on flexible loads during the selected peak interval. Fixed household demand remains unchanged.
6. Hourly Numerical Calculation
Time Base Load Flexible Load Baseline DR Load Tariff DR State
7. Laboratory Procedure and Observations
Procedure
  1. Set the residential load parameters.
  2. Select the peak-demand period.
  3. Run the baseline simulation.
  4. Observe the evening demand peak.
  5. Set the desired DR reduction.
  6. Apply demand response.
  7. Compare the baseline and DR curves.
  8. Observe the change in peak demand.
  9. Change the tariff and repeat the experiment.
Expected Observation

When demand response is activated, flexible loads are reduced during the selected peak interval. Therefore, the maximum grid demand decreases.

Increasing the DR percentage increases the amount of peak shaving. Large flexible loads such as air conditioners, water heaters and EV chargers have the greatest influence on the peak-demand response.


Conclusion: Demand response provides a mechanism for coordinating flexible electricity consumption with grid requirements. By reducing controllable demand during high-load periods, smart-grid systems can reduce peak demand, improve network utilization and support more efficient electricity management.

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