قیمت گذاری عملی حلقه بسته پویا در گرید هوشمند برای متعادل سازی عرضه و تقاضا Practical closed-loop dynamic pricing in smart grid for supply and demand balancing

1. Introduction Enabled by new technologies, such as the intelligent and autonomous control, two-way communications between the power supplier and customers, and the advanced software-based data management, traditional power grids can be upgraded to smart grids that can intelligently incorporate distributed energy sources and deliver the power to customers efficiently (Fang, Misra, Xue, & Yang, 2012). Different from the traditional power grid, in smart grids, the supply and demand sides interact with each other by exchanging the price and demand information, aiming to minimize over-provisioning at the supply side (Yu & Hong, 2016). To improve efficiency, reduce peak load and balance the demand and supply, dynamic pricing has been advocated and become a promising technology (Borenstein, Jaske, & Rosenfeld, 2002; Chen, Wei, & Hu, 2013; Liang, Li, Lu, Lin, & Shen, 2013; Liu, Liu, Low, & Wierman, 2014; Samadi, Mohsenian-Rad, Schober,Wong,&Jatskevich, 2010; Sen, Joe-Wong, Ha, & Chiang, 2013; Tarasak, 2011). Based on dynamic pricing, considerable benefits will be gained by encouraging the customers to consume energy in a more efficient way (Deng, Yang, Hou, Chow, & Chen, 2015; Kim, Zhang, Schaar, & Lee, 2014; Wen et al., 2013; Zhang & Papachristodoulou, 2015). A proper dynamic pricing strategy cannot only smooth load demand curves to enhance the robustness and lower the generation cost of the power grid, but also reduce the electricity expenditures of the customers by reasonably scheduling their flexible electricity usage. However, how to design a proper dynamic pricing strategy is still a challenging problem given the difficulty in estimating the load accurately. The estimation errors are unavoidable due to the random demand, and the lack of knowledge in customers’ preference and their reactions to price change (Joe-Wong, Sen, Ha, & Chiang, 2012; Qian, Zhang, Huang, & Wu, 2013; Wu et al., 2015). We refer the readers to the survey papers (Annaswamy, Hussainy, Chakrabortty, & Cvetkovic, 2016; Khan, Mahmood, Safdar, Khan, & Khan, 2016) for more details about dynamic pricing, price-based control and the corresponding open issues in smart grids. In the past few years, dynamic pricing in smart grids has attracted extensive attention, and many pricing schemes were developed in the literature, including real time pricing (Joe-Wong et al., 2012; Mohsenian-Rad & Leon-Garcia, 2010; MohsenianRad, Wong, Jatskevich, Schober, & Leon-Garcia, 2010; Qian et al., 2013), time of use (Braithwait, Hansen, & Sheasy, 2007), and critical peak pricing (Kii, Sakamoto, Hangai, & Doi, 2014), and many more as discussed in Khan et al. (2016). The existing pricing schemes can be divided into two categories. The first one aims to maximize the profits of customers, and deals with how the customers schedule their flexible electricity usage to achieve their desired level of comfort with a lower electricity bill payment based on the prediction of future price (Mohsenian-Rad & Leon-Garcia, 2010; Mohsenian-Rad et al., 2010).