Designing a critical peak pricing scheme for the profit maximization objective considering price responsiveness of customers. (1st April 2015)

• Record Type: Journal Article
• Authors: Park, S.C.; Jin, Y.G.; Song, H.Y.; Yoon, Y.T.
• Is Part Of: Energy. Volume 83(2015)
• Page Start: 521
• Page End: 531
• Publication Date: 2015-04-01
• Subjects: Demand response -- Critical peak pricing -- Deregulation -- Load serving entity -- Profit maximization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.02.057
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: A deregulated market environment in power industries offers utilities or load serving entities the chance to make profit by pursuing a suitable operational strategy. However, the volatility of the real-time market clearing price raises a price risk issue because the load serving entity sells electricity to customers at a relatively frozen retail rate. One method to hedge price risk is to implement various dynamic pricing schemes in the retail sector in order to reflect the volatility of the real-time market clearing price to the retail rate. This paper presents several analyses for designing one such pricing scheme, namely critical peak pricing for a profit-maximizing load serving entity. Specifically, how the parameters of critical peak pricing affect profit based on the price responsiveness model of customers is analyzed. In this process, a method for solving the events scheduling problem is used as a tool for the analyses. Furthermore, we offer intuitive guidelines and rules for selecting those parameters that maximize the profit of the load serving entity. Finally, the suitability and practicality of the presented analyses are verified by numerical simulations with forecasted data on the real-time market clearing price and demand. Highlights: Effects of the critical peak pricing parameters on profit are analyzed. The profit index is introduced to examine the effects analytically. The optimal peak rate changes little with the number of critical events. There exists the minimum number of critical events to avoid losing profit. Suggested guides for a proper critical peak pricing design are verified by examples.