Optimization of day-ahead and real-time prices for smart home community

The day-ahead and real-time energy prices offered to end consumers do not provide much incentive. Day-ahead prices are available for the following day, but end consumers are charged at real-time prices calculated at the end of the day. The problem is the significant difference between the day-ahead and real-time prices on the same day. Therefore, although consumers may have scheduled their appliances to those times when rates are low, according to day-ahead prices, they still might be charged at a high rate. In this work, we propose an effective day-ahead hourly pricing scheme which depends upon the previous load distribution. We also project real-time prices which are based on day-ahead prices and the difference in the current day's load distribution and previous combined load distribution. The prices are transmitted and implemented via the proposed smart home community controller in smart home community architecture. The simulation results confirm that the proposed pricing scheme minimizes the difference between day-ahead and real-time prices to benefit the end consumer by decreasing their consumption cost. Also, at the same time, the power peak-to-average ratio is reduced, hence maximizing the payoffs of energy generators.