A MODEL FOR PEAK TIME DEMAND COINCIDENCE REDUCTION AT RESIDENTIAL CUSTOMER LEVEL USING BEHAVIOURAL DEMAND RESPONSE STRATEGY

Coincidence of customer's demand with peak demand at utility level is very expensive due to the additional capacity requirement to meet the additional demand. Hence reduction of demand coincidence with the utility peak demand by the consumer results in huge electricity charge savings at different levels. There is a great scope for peak time energy conservation at residential level with advanced metering along with behavioural interventions at the customer end. One such is Behavioural Demand Response (BDR) that focuses on motivating, goal setting and providing feedback to the customers to achieve the demand shift rather than enticing or coercing the customer. This work proposes a personified BDR model that sets monthly peak time energy consumption reduction target to individual customer based on the individual smart meter forecasted data and real-time feedback to measure consumer's target achievement. The model has been explored on 20 individual consumers synthetic smart meter data. Consumers are clustered into different categories based on their peak time energy consumption as well as their contribution to the utility level peak load using machine learning algorithm K-means. with the proposed model ,15.4% of peak time energy consumption reduction can be achieved at utility level if the customers reach their individual goals