We address the problem of mechanism design for two-stage repeated stochastic games -a novel setting using which many emerging problems in next-generation electricity markets can be readily modeled. Repeated playing affords the players a large class of strategies that adapt a player's actions to all past observations and inferences obtained therefrom. In other settings such as iterative auctions or dynamic games where a large strategy space of this sort manifests, it typically has an important implication for mechanism design: It may be impossible to obtain truth-telling as a dominant strategy equilibrium. Consequently, in such scenarios, it is common to settle for mechanisms that render truth-telling only a Nash equilibrium, or variants thereof, even though Nash equilibria are known to be poor models of real-world behavior owing to each player having to make overly specific assumptions about the behaviors of the other players in order for them to employ their Nash equilibrium strategy. In general, the lesser the burden of speculation in an equilibrium, the more plausible it is that it models real-world behavior. Guided by this maxim, we introduce a new notion of equilibrium called Dominant Strategy Non-Bankrupting Equilibrium (DNBE) which requires the players to make very little assumptions about the behavior of the other players to employ their equilibrium strategy. Consequently, a mechanism that renders truth-telling a DNBE as opposed to only a Nash equilibrium could be quite effective in molding real-world behavior along the desired lines. Finally, we present a mechanism for two-stage repeated stochastic games that renders truth-telling a Dominant Strategy Non-Bankrupting Equilibrium. The mechanism also guarantees individual rationality and maximizes social welfare. I. INTRODUCTIONThe power system is on the cusp of a revolution. The coming decade could witness increased renewable energy penetration, Electric Vehicle (EV) penetration, EV energy storage integration, demand response programs, etc. These changes have a profound impact on electricity market operations. New mechanisms must be devised to address a variety of important problems that are anticipated to arise in next-generation electricity markets. Most of the existing mechanism design settings are insufficient to model certain crucial features of these problems. To address this, we introduce the setting of Two-Stage Repeated Stochastic Games using which many problems that arise in the context of electricity markets can be readily modeled. The setting is an extension of the one-shot two-stage stochastic game introduced in [1] to repeated plays.At a high level, a two-stage stochastic game, as the name suggests, consists of two stages. In the first stage, the players do not know their valuation functions precisely, but rather only know the probability distribution of their valuation functions. It is only in the second stage of the game that the valuation functions realize. However, the social planner cannot wait until the second stage to decide on the out...

We present a two-stage mechanism for creating markets for demand response. Demand response involves system operators using incentives to modulate electricity consumption around peak hours or when faced with an incidental supply shortage. However, system operators typically have imperfect information about their customers' counterfactual consumption, that is, their consumption had the incentive been absent. The standard approach to estimate the reduction in a customer's electricity consumption then is to estimate their counterfactual baseline. However, this approach is not robust to estimation errors or strategic exploitation by the consumers and can potentially lead to overpayments to customers who do not reduce their consumption and underpayments to those who do. In addition, the incentive payments are often designed based on models of consumer behavior or other ad-hoc rules that are only partially accurate at best. The two-stage mechanism proposed in this paper circumvents the aforementioned issues. In the dayahead market, the participating loads submit the probability distribution of their next-day consumption. In real-time, if and when called upon for demand response, the loads report the realization of their baseline demand and receive credit for reductions below their reported baseline. The mechanism guarantees ex post incentive compatibility of truthful reporting of the probability distribution in the day-ahead market and truthful reporting of the realized baseline demand in real-time. The mechanism can be viewed as an extension of the celebrated Vickrey-Clarke-Groves mechanism augmented with a carefully crafted second-stage penalty for deviations from the day-ahead bids.

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