How social learning affects customer behavior under the implementation of TOU in the electricity retailing market

As a representative motivative scheme in demand response implementation, time-of-use (TOU) pricing has significant potentials in achieving load-shifting goals. Meanwhile, social learning among customers, becoming popular with the development of information technologies, also profoundly affects customer consumption decisions. Still, its impacts on power customer consumption behaviors under implementing the TOU scheme remain unexplored. We propose an evolutionary game-theoretical model that incorporated social learning to illustrate how customer power consumption behaviors evolve if they learn from each other, where consuming relations between power consumers and retailers are formulated on account of the bipartite graphs. In every period, the retailers establish the TOU scheme based on the historical demand to optimize its current profits and load-shifting effects, and the customers, in turn, dispatch their consumptions for utility maximizations subjected to the proposed prices. We simulate the different scenarios of consumer behavior decisions verify our model by empirical data. Our results demonstrate how social learning changes consumer behavior and maximizes all consumers' utility. Noteworthy, Social learning brings volatility to the market, which incurs fluctuations for both firms' payoffs and customer utilities. Besides, we derive that the rigid price caps can guarantee customers positive utilities, and more communications among customers will lead to the decay of the entire retailing profits. This research provides decision support for consumer behavior and pricing of power retailing companies in the context of social learning.