Among many key subjects in the smart grid technology, Demand Side Management (DSM) is one of the common and popular subjects that interests researchers who are working in controlling and monitoring customers’ activities. In reality, DSM involves any activities that impress customer’s consumption levels to meet demand response mutuality in a power grid system. This usually happens by means of employing new policies by utility companies, defining suitable pricing schemas that guarantee grid’s continual working or hiring effective scheduling approaches to adjust hourly customer’s consumption levels, especially on peak-time hours. Among them, pricing methods are very important and effective in controlling customer’s consumption patterns. Real-Time Pricing (RTP), Time of Use (TOU) pricing and dynamic pricing are common approaches which are being employed by many utility companies and are mostly dependent on the grid’s dynamics load behavior. Very effectively, real-time pricing methods adjust real-time prices based on real-time grid demand’s level dynamics. In this paper, we propose a new pricing method that not only makes use of grid’s real-time consumption data but also considers consumption levels of each customers and define real-time prices individually (Personalized Pricing). This means that the consumption price for each individual customer will be adjusted by the changes that occur during the course of power consumption to reflect each individual customer’s habit of using electricity. In this way, our proposed method can consider both grid and individual customer’s consumption level to adjust real-time prices. Generally personalized pricing is a type of incentive-based DSM models that can be able to impress customer’s consumption levels by persuading them to decrease their consumption levels during peak-time hours by updating each customer’s consumption prices individually. However, there is a more important capability that lies at the heart of Personalized Pricing, individual satisfaction to be more exact. Our results also intensify that most of our customers in the grid will decrease their consumption levels during peak-time hours to reduce their electricity consumption costs.

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