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Quantifying the F uture I mpact of D emand R esponse : A Data Driven Approach Miha Grabner Electric Power Research Institute Milan Vidmar, Slovenia, Europe Kuala Lumpur, 3. 9. 2019
Quantifying the Future Impact of Demand Response: A Data Driven Approach
Miha GrabnerElectric Power Research Institute Milan Vidmar, Slovenia, Europe
Kuala Lumpur, 3. 9. 2019
Real Demand Response Project
Critical Peak Pricing - Peak shaving
Send notice one day before
DR event
Before DR Program
DR limited to 50 x 1 hour
SBS Data
Smart Metering Data
Substation Data Analytics
Substation Data Analytics
Clustering Substation Daily Profiles
50 hours of DR activations
Approx. 5 % annual peak demand decrease
Smart Meter Data Analytics
Targeting the Consumers using Clustering
Group similar daily profiles
Approx. 800 small consumers
Without equipment(SMS notification)
Installed equipment(Direct Load Control)
Estimating Baseline Load
Probabilistic Forecasting Backtesting
CONCLUSION
SMS Consumers: 0.25 kW/consumer
DLC Consumers: 2 kW/consumer
Mean Demand Flexibility per Consumer
DLC Consumers: 2300 EUR / consumer
Mean Annual Savings per Consumer
SMS Consumers: 6300 EUR / consumer
DLC Consumers: 23 EUR / consumer
Mean Annual Savings per Consumer
SMS Consumers: 9 EUR / consumer
Satisfied with the Project?
Very Satisfied Consumers!
Demand response is still for enthusiasts!
RECOMMENDATIONS
Hire a Trained Energy Data Scientist
RECOMMENDATIONS
Hire a Trained Energy Data Scientist
Evaluate Responses using ML
RECOMMENDATIONS
Hire a Trained Energy Data Scientist
Social Aspect is Most Important!
Evaluate Responses using ML
AI in Smart Grids
