Yuanzhang Xiao

Demand Side Management in Smart Grids Using a Repeated Game Framework

Demand-side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers the differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off-peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested and foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

Dynamic Spectrum Sharing Among Repeatedly Interacting Selfish Users With Imperfect Monitoring

We develop a novel design framework for dynamic distributed spectrum sharing among secondary users (SUs), who adjust their power levels to compete for spectrum opportunities while satisfying the interference temperature (IT) constraints imposed by primary users. The considered interaction among the SUs is characterized by the following three unique features. First, the SUs are interacting with each other repeatedly and they can coexist in the system for a long time. Second, the SUs have limited and imperfect monitoring ability: they only observe whether the IT constraints are violated, and their observation is imperfect due to the erroneous measurements. Third, since the SUs are decentralized, they are selfish and aim to maximize their own long-term payoffs from utilizing the network rather than obeying the prescribed allocation of a centralized controller. To capture these unique features, we model the interaction of the SUs as a repeated game with imperfect monitoring. We first characterize the set of Pareto optimal operating points that can be achieved by deviation-proof spectrum sharing policies, which are policies that the selfish users find it in their interest to comply with. Next, for any given operating point in this set, we show how to construct a deviation-proof policy to achieve it. The constructed deviation-proof policy is amenable to distributed implementation, and allows users to transmit in a time-division multiple-access (TDMA) fashion. In the presence of strong multi-user interference, our policy outperforms existing spectrum sharing policies that dictate users to transmit at constant power levels simultaneously. Moreover, our policy can achieve Pareto optimality even when the SUs have limited and imperfect monitoring ability, as opposed to existing solutions based on repeated game models, which require perfect monitoring abilities. Simulation results validate our analytical results and quantify the performance gains enabled by the proposed spectrum sharing policies.

Dynamic Incentive Design for Participation in Direct Load Scheduling Programs

Interruptible Load (IL) programs have long been an accepted measure to intelligently and reliably shed demand in case of contingencies in the power grid. However, the emerging market for Electric Vehicles (EV) and the notion of providing non-emergency ancillary services through the demand side have sparked new interest in designing direct load scheduling programs that manage the consumption of appliances on a day-to-day basis. In this paper, we define a mechanism for a Load Serving Entity (LSE) to strategically compensate customers that allow the LSE to directly schedule their consumption, every time they want to use an eligible appliance. We study how the LSE can compute such incentives by forecasting its profits from shifting the load of recruited appliances to hours when electricity is cheap, or by providing ancillary services, such as regulation and load following. To make the problem scalable and tractable we use a novel clustering approach to describe appliance load and laxity. In our model, customers choose to participate in this program strategically, in response to incentives posted by the LSE in publicly available menus. Since 1) appliances have different levels of demand flexibility; and 2) demand flexibility has a time-varying value to the LSE due to changing wholesale prices, we allow the incentives to vary dynamically with time and appliance cluster. We study the economic effects of the implementation of such program on a population of EVs, using real-world data for vehicle arrival and charge patterns.

Incentive design for Direct Load Control programs

We study the problem of optimal incentive design for voluntary participation of electricity customers in a Direct Load Scheduling (DLS) program, a new form of Direct Load Control (DLC) based on a three way communication protocol between customers, embedded controls in flexible appliances, and the central entity in charge of the program. Participation decisions are made in real-time on an event-based basis, with every customer that needs to use a flexible appliance considering whether to join the program given current incentives. Customers have different interpretations of the level of risk associated with committing to pass over the control over the consumption schedule of their devices to an operator, and these risk levels are only privately known. The operator maximizes his expected profit of operating the DLS program by posting the right participation incentives for different appliance types, in a publicly available and dynamically updated table. Customers are then faced with the dynamic decision making problem of whether to take the incentives and participate or not. We define an optimization framework to determine the profit-maximizing incentives for the operator. In doing so, we also investigate the utility that the operator expects to gain from recruiting different types of devices. These utilities also provide an upper-bound on the benefits that can be attained from any type of demand response program.

Intervention in Power Control Games With Selfish Users

We study the power control problem in single-hop wireless ad hoc networks with selfish users. Without incentive schemes, selfish users tend to transmit at their maximum power levels, causing excessive interference to each other. In this paper, we study a class of incentive schemes based on intervention to induce selfish users to transmit at desired power levels. In a power control scenario, an intervention scheme can be implemented by introducing an intervention device that can monitor the power levels of users and then transmit power to cause interference to users if necessary. Focusing on first-order intervention rules based on individual transmit powers, we derive conditions on the intervention rates and the power budget to achieve a desired outcome as a (unique) Nash equilibrium with intervention and propose a dynamic adjustment process to guide users and the intervention device to the desired outcome. We also analyze the effect of using aggregate receive power instead of individual transmit powers. Our results show that intervention schemes can be designed to achieve any positive power profile while using interference from the intervention device only as a threat. Lastly, simulation results are presented to illustrate the performance improvement from using intervention schemes and the theoretical results.

Socially-Optimal Design of Service Exchange Platforms with Imperfect Monitoring

We study the design of service exchange platforms in which long-lived anonymous users exchange services with each other. The users are randomly and repeatedly matched into pairs of clients and servers, and each server can choose to provide high-quality or low-quality services to the client with whom it is matched. Since the users are anonymous and incur high costs (e.g., exert high effort) in providing high-quality services, it is crucial that the platform incentivizes users to provide high-quality services. Rating mechanisms have been shown to work effectively as incentive schemes in such platforms. A rating mechanism labels each user by a rating, which summarizes the users past behaviors, recommends a desirable behavior to each server (e.g., provide higher-quality services for clients with higher ratings), and updates each servers rating based on the recommendation and its clients report on the service quality. Based on this recommendation, a low-rating user is less likely to obtain high-quality services, thereby providing users with incentives to obtain high ratings by providing high-quality services. However, if monitoring or reporting is imperfect—clients do not perfectly assess the quality or the reports are lost—a users rating may not be updated correctly. In the presence of such errors, existing rating mechanisms cannot achieve the social optimum. In this article, we propose the first rating mechanism that does achieve the social optimum, even in the presence of monitoring or reporting errors. On one hand, the socially-optimal rating mechanism needs to be complicated enough, because the optimal recommended behavior depends not only on the current rating distribution, but also (necessarily) on the history of past rating distributions in the platform. On the other hand, we prove that the social optimum can be achieved by “simple” rating mechanisms that use binary rating labels and a small set of (three) recommended behaviors. We provide design guidelines of socially-optimal rating mechanisms and a low-complexity online algorithm for the rating mechanism to determine the optimal recommended behavior.

Efficient Interference Management Policies for Femtocell Networks

Managing interference in a network of macrocells underlaid with femtocells presents an important, yet challenging problem. A majority of spatial (frequency/time) reuse based approaches partition the users based on coloring the interference graph, which is shown to be suboptimal. Some spatial time reuse based approaches schedule the maximal independent sets (MISs) in a cyclic, (weighted) round-robin fashion, which is inefficient for delay-sensitive applications. Our proposed policies schedule the MISs in a non-cyclic fashion, which aim to optimize any given network performance criterion for delay-sensitive applications while fulfilling minimum throughput requirements of the users. Importantly, we do not take the interference graph as given as in existing works; we propose an optimal construction of the interference graph. We prove that under certain conditions, the proposed policy achieves the optimal network performance. For large networks, we propose a low-complexity algorithm for computing the proposed policy. We show that the policy computed achieves a constant competitive ratio (with respect to the optimal network performance), which is independent of the network size, under wide range of deployment scenarios. The policy can be implemented in a decentralized manner by the users. Compared to the existing policies, our proposed policies can achieve improvement of up to 130% in large-scale deployments.

Intervention with Complete and Incomplete Information: Application to Flow Control

Most congestion control schemes are based on user cooperation, i.e., they implicitly assume that users are willing to share their private information and to take actions such that the network operates efficiently. However, a self-interested and strategic user might exploit such schemes to obtain an individual gain at the expenses of the other users, misrepresenting its private information and overusing the resources. We first quantify the inefficiency of the network in the presence of selfish users for two different scenario: in the complete information case - in which the users have no private information - and in the incomplete information case - in which the users have private information. Then, we ask whether the congestion control scheme can be designed to be robust to self-interested strategic users. To reach this objective, we use an intervention scheme. For the complete information scenario we describe a scheme that is able to give the users an incentive to optimally use the resources. For the incomplete information scenario we describe two schemes that provide the users with an incentive to report truthfully and to use the resources efficiently, although not always optimally. Illustrative results show that the considered schemes can considerably improve the efficiency of the network.

Non-stationary demand side management method for smart grids

Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. The consumers choose their power consumption patterns according to different prices charged at different times of the day. Importantly, consumers incur discomfort costs from altering their power consumption patterns. Existing works propose stationary strategies for consumers that myopically minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested consumers as a repeated energy scheduling game which foresightedly minimizes their long-term total costs. We then propose a novel methodology for determining optimal nonstationary DSM strategies in which consumers can choose different daily power consumption patterns depending on their preferences and routines, as well as on their past history of actions. We prove that the existing stationary strategies are suboptimal in terms of long-term total billing and discomfort costs and that the proposed strategies are optimal and incentive-compatible (strategy-proof). Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

Repeated resource sharing among selfish players with imperfect binary feedback

We develop a novel design framework for resource sharing among self-interested players, who adjust their resource usage levels to compete for a common resource. We model the interaction among the players as a repeated resource sharing game with imperfect monitoring, which captures four unique features of the considered interaction. First, the players inflict negative externality to each other due to the interference/congestion among them. Second, the players interact with each other repeatedly because of their long-term coexistence in the system. Third, since the players are decentralized, they are selfish and aim to maximize their own long-term payoffs from utilizing the resource rather than obeying any prescribed sharing rule. Finally, the players are informed of the interference/congestion level through a binary feedback signal, which is quantized from imperfect observation about the interference/congestion level. We first characterize the set of Pareto optimal operating points that can be achieved by deviation-proof resource sharing policies, which are policies that the selfish players find it in their self-interests to comply with. Next, for any given operating point in this set, we show how to construct a deviation-proof policy to achieve it. The constructed deviation-proof policy is amenable to distributed implementation, and allows players to use the resource in an alternating fashion. In the presence of strong negative externality, our policy outperforms existing resource sharing policies that dictate constant resource usage levels by the players. Moreover, our policy can achieve Pareto optimality even when the players have imperfect binary feedback, as opposed to existing solutions based on repeated game models, which require a large amount of feedback. The proposed design framework applies to many resource sharing systems, such as power control, medium access control (MAC), and flow control.