In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents' actions. It is shown that the non-cooperative interactions among the aggregators lead to a subgame perfect @math -Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peak-to-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators.
Strategic decision-making of users has been widely investigated, by modeling user behavior, in many user-centric applications in sociology, economics, and energy markets @cite_38 @cite_34 @cite_14 . Game theory has been a popular analytical platform in demand-side management literature to model how users strategically reason about their neighbors' behavior to determine their own energy strategies such that the system-wide objectives are optimized @cite_15 @cite_36 @cite_33 @cite_32 @cite_2 . The demand-side management approach in @cite_17 optimally schedules the user energy consumption through a non-cooperative game among energy users. The non-cooperative Stackelberg game in @cite_25 explores optimal load interaction between a utility company and residential energy users to balance energy demand and supply. Moreover, the insights of game theory have been used to study the energy trading competition between shared energy storage systems and self-interested solar energy users for small-scale demand-side management @cite_6 @cite_3 .
@cite_38 @cite_14 @cite_33 @cite_36 @cite_32 @cite_6 @cite_3 @cite_2 @cite_15 @cite_34 @cite_25 @cite_17
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We study the demand side management (DSM) problem when customers are equipped with energy storage devices. Two games are discussed: the first is a non-cooperative one played between the residential energy consumers, while the second is a Stackelberg game played between the utility provider and the energy consumers. We introduce a new cost function applicable to the case of users selling back stored energy. The non-cooperative energy consumption game is played between users who schedule their energy use to minimize energy cost. The game is shown to have a unique Nash equilibrium, that is also the global system optimal point. In the Stackelberg game, the utility provider sets the prices to maximize its profit knowing that users will respond by minimizing their cost. We provide existence and uniqueness results for the Stackelberg equilibrium. The Stackelberg game is shown to be the general case of the minimum Peak-to-Average power ratio (PAR) problem. Two algorithms, centralized and distributed, are presented to solve the Stackelberg game. We present results that elucidate the interplay between storage capacity, energy requirements, number of users and system performance measured in total cost and peak-to-average power ratio (PAR). Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges. In this paper, the interactions and energy exchange decisions of a number of geographically distributed storage units are studied under decision-making involving end-users. In particular, a noncooperative game is formulated between customer-owned storage units where each storage unit's owner can decide on whether to charge or discharge energy with a given probability so as to maximize a utility that reflects the tradeoff between the monetary transactions from charging discharging and the penalty from power regulation. Unlike existing game-theoretic works which assume that players make their decisions rationally and objectively, we use the new framework of prospect theory (PT) to explicitly incorporate the users' subjective perceptions of their expected utilities. For the two-player game, we show the existence of a proper mixed Nash equilibrium for both the standard game-theoretic case and the case with PT considerations. Simulation results show that incorporating user behavior via PT reveals several important insights into load management as well as economics of energy storage usage. For instance, the results show that deviations from conventional game theory, as predicted by PT, can lead to undesirable grid loads and revenues thus requiring the power company to revisit its pricing schemes and the customers to reassess their energy storage usage choices. In this paper, the problem of optimally charging or discharging customer-owned storage units in a smart grid is studied. In particular, a noncooperative game is formulated between customers equipped with distributed storage units who seek to decide on their optimal charging strategy so as to optimize a utility that reflects both the monetary benefit from the charging discharging operation and the associated penalty from power regulation. In contrast to the traditional game-theoretic models that are often based on rational and objective decisions, we introduce the behavioral framework of prospect theory (PT) so as to explicitly incorporate how the customers estimate their utilities with respect to their real-world considerations of gains and losses. The existence of the mixed-strategy Nash equilibrium is shown for the PT case and the properties of the game are analyzed under both PT and classical game theory. Simulation results show how the individual customer perceptions on their utilities can significantly impact the overall charging and discharging operations.