Game-Theoretic Pricing for Data Services in Smart Grid

We consider the problem of promoting the Real-Time Pricing (RTP) data service in Smart Grid under the threat of likely unauthorized data redistribution between subscribers (i.e., sellers) and non-subscribers (i.e., secondary buyers). The secondary buyers can obtain the RTP copies from neighboring sellers, provided that the data cost can be shared wisely. Such stealthy behaviors could jeopardize the market revenue of Smart Grid Data Service Provider (DSP) and is very difficult to trace due to its understated manner. In this paper, we analyze the optimal price setting for DSP to protect or maximize its profit. We model the behavior between the subscribers and the secondary buyers as an evolutionary game and find the optimal payoffs for both groups of users. Based on the behavior analysis of users in the redistribution network, we investigate the evolutionarily stable fraction of users who decide to subscribe versus different subscription cost. Such analysis can help DSP restrain such unauthorized redistribution behavior, and hence preserve its profit.