Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach

Many current studies on smart grid in electricity market are indicative of the key role it plays in electricity generation, distribution, retailing and end-user management. Demand response programs (DRPs) can be used to lower high energy prices in wholesale electricity markets, and ensure the security of power systems when at risk. The concern of most researchers in this field is to further unearth the potential of smart grid in the direction of demand response (DR) through enhanced demand side management (DSM) centering on the behavior of the end-user. Our model proposes a more effective way in using incentive based demand response program to help residential customers derive more benefits from smart grid. The constrained non-linear programming (CNLP) model optimizes residential consumption of electricity by shaving of load at peak and increasing of load at off-peak to help generators reduce production cost at peak times and increase revenue at of off-peaks. The model uses a credit function to regulate consumption and reward end-users for load shedding and load shifting at peaks and also at off-peaks reward end-users for increasing load. The simulated results show that, high consumption appliances are best used at dawn, midday, and at night, if consumers want to cut down cost. The corresponding effect is that, generating and distribution companies derive the right revenue from their investments by not producing beyond their capacity during peaks at high cost and maintain constant power supply in and environmentally friendly manner.