Game-Theory based dynamic pricing strategies for demand side management in smart grids Dipti Srinivasan a, * , Sanjana Rajgarhia a , Bharat Menon Radhakrishnan a , Anurag Sharma a , H.P. Khincha b a Department of Electrical and Computer Engineering, National University of Singapore 4, Engineering Drive 3,117583, Singapore b Department of Electrical Engineering, Indian Institute of Science, C V Raman Ave, Bengaluru, Karnataka, 560012, India article info Article history: Received 5 May 2016 Received in revised form 29 November 2016 Accepted 30 November 2016 Keywords: Demand-Side Management Dynamic pricing Game theory Residential smart grids abstract With the increasing demand for electricity and the advent of smart grids, developed countries are establishing demand side management (DSM) techniques to influence consumption patterns. The use of dynamic pricing strategies has emerged as a powerful DSM tool to optimize the energy consumption pattern of consumers and simultaneously improve the overall efficacy of the energy market. The main objective of the dynamic pricing strategy is to encourage consumers to participate in peak load reduction and obtain respective incentives in return. In this work, a game theory based dynamic pricing strategy is evaluated for Singapore electricity market, with focus on the residential and commercial sector. The proposed pricing model is tested with five load and price datasets to spread across all possible scenarios including weekdays, weekends, public holidays and the highest/lowest demand in the year. Three pricing strategies are evaluated and compared, namely, the half-hourly Real-Time Pricing (RTP), Time-of-Use (TOU) Pricing and Day-Night (DN) Pricing. The results demonstrate that RTP maximizes peak load reduction for the residential sector and commercial sector by 10% and 5%, respectively. Moreover, the profits are increased by 15.5% and 18.7%, respectively, while total load reduction is minimized to ensure a realistic scenario. © 2016 Elsevier Ltd. All rights reserved. 1. Introduction Electricity has grown to become an essential part of human life. A reliable and seamless supply is required to facilitate economic and industrial growth as well as to improve quality of life. Global electricity demand has been increasing exponentially and is ex- pected to double in value between 2002 and 2030 [1]. Electricity is a non-storable commodity; its wholesale price varies across time periods depending on demands [2]. In most cases however, the consumer is charged a fixed price and the price fluctuations are borne by the utility company. Since consumers are unaffected by wholesale price changes, their demand shows drastic fluctuations with low valleys at night and high peaks during the day. These fluctuations decrease supply reliability, system efficiency and reduce profits for utility companies. Moreover, many countries have also chosen to restructure their power industry and introduce deregulation in their electricity markets. Hence, companies need to establish Demand-Side Management (DSM) strategies to influence user consumption patterns and thereby achieve peak-load reduc- tion. The increasing penetration of renewables and market dereg- ulation has further bolstered the need for operational flexibility in the grid and resulted in development of efficient DSM techniques [3e6]. It is noted that the availability of renewable generation will impact the dynamic pricing strategy and thus, the DSM techniques based on its intermittency and cheaper generation cost. Demand response techniques can control and modify user consumption patterns through incentive based dynamic pricing techniques. Demand response algorithms have been widely adop- ted in the literature as they result in significant electricity bill savings and avoid undesirable peaks in the daily load demand, thereby improving the efficiency of the system [7e14]. Today, several developed countries such as USA, Canada and many parts of Europe have successfully developed and implemented dynamic pricing strategies to perform DSM. A 2010 survey conducted by the Federal Energy Regulatory Commission of USA shows that demand response methods could lead to a 7.6% decrease in peak load * Corresponding author. E-mail addresses: elesd@nus.edu.sg (D. Srinivasan), anurag@u.nus.edu (A. Sharma). Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy http://dx.doi.org/10.1016/j.energy.2016.11.142 0360-5442/© 2016 Elsevier Ltd. All rights reserved. Energy 126 (2017) 132e143