In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to...
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In the standard formulation of a game, a player's payoff function depends on the states and actions of all the players. Yet, real world applications suggest to consider also a functional of the probability measure...
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In the standard formulation of a game, a player's payoff function depends on the states and actions of all the players. Yet, real world applications suggest to consider also a functional of the probability measure of states and actions of all the players. In this paper, we consider cooperative mean-field type games in which the state dynamics and the payoffs depend not only on the state and actions but also on their probability measure. We establish stochastic maximum principle and provide a time-dependent payoff allocation procedure for coalitions. The allocated payoff considers not only fairness property but also the cost of making the coalition. Finally, time consistency and subgame perfectness solution concept equations are established.
In this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle for optimal control of stochastic differential equations of mean-field t...
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ISBN:
(纸本)9781467360890
In this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle for optimal control of stochastic differential equations of mean-field type, in which the drift and the diffusion coefficients as well as the performance functional depend not only on the state and the control but also on the mean of the distribution of the state. Our result extends to optimal control problems for non-Markovian dynamics which may be time-inconsistent in the sense that the Bellman optimality principle does not hold. For a general action space a Peng's type stochastic maximum principle is derived, specifying the necessary conditions for optimality. Two examples are carried out to illustrate the proposed risk-sensitive mean-field type under linear stochastic dynamics with exponential quadratic cost function. Explicit characterizations are given for both mean-field free and mean-field risk-sensitive models.
In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand t...
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In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to off-peak time. In contrast to most other mechanisms proposed in the literature, the key feature of our mechanism is its modest requirements on user preferences, i.e., it does not require exact knowledge of user responsiveness to rewards for shifting their demand from the peak to the off-peak time. Specifically, our mechanism utilizes a probabilistic reward structure for users who shift their demand to the off-peak time, and is robust to incomplete information about user demand and/or risk preferences. We approach the problem from the public good perspective, and demonstrate that the mechanism can be implemented via lottery-like schemes. Our mechanism permits to reduce the distribution losses, and thus improve efficiency of electricity distribution. Finally, the mechanism can be readily incorporated into the emerging demand response schemes (e.g., the time-of-day pricing, and critical peak pricing schemes), and has security and privacy-preserving properties.
作者：
Hon-Yu MaComputer
Electrical and Mathematical Sciences and Engineering (CEMSE) Division KAUST SRI Center for Uncertainty Quantification in Computational Science and Engineering Thuwal Makkah Province Saudi Arabia
Although many scholars have proposed all kinds of key successful factors (KSFs) for ERP activity to smoothly enhance the implementation of ERP, the KSFs are based too much on concept and protocol cannot help an enterp...
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Although many scholars have proposed all kinds of key successful factors (KSFs) for ERP activity to smoothly enhance the implementation of ERP, the KSFs are based too much on concept and protocol cannot help an enterprise to achieve this objective. Therefore, this research through qualitative interview method, integrating the KSFs and dynamic capability to set up a model so as to exhibit the kind of dynamic capability needed for each factor. This result not only creates practical value for the KSFs and displays implementable effectiveness for the dynamic capability concept, but also brings a new direction to the academic research.
One of the fundamental challenges in distributed interactive systems is to design efficient, accurate, and fair solutions. In such systems, a satisfactory solution is an innovative approach that aims to provide all pl...
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ISBN:
(纸本)9781467357159
One of the fundamental challenges in distributed interactive systems is to design efficient, accurate, and fair solutions. In such systems, a satisfactory solution is an innovative approach that aims to provide all players with a satisfactory payoff anytime and anywhere. In this paper we study fully distributed learning schemes for satisfactory solutions in games with continuous action space. Considering games where the payoff function depends only on own-action and an aggregate term, we show that the complexity of learning systems can be significantly reduced, leading to the so-called mean-field learning. We provide sufficient conditions for convergence to a satisfactory solution and we give explicit convergence time bounds. Then, several acceleration techniques are used in order to improve the convergence rate. We illustrate numerically the proposed mean-field learning schemes for quality-of-service management in communication networks.
Providing realistic opposing forces is critical to the successful use of military training simulations. Unfortunately, a number of issues can make the manual control of realistic opposing forces difficult or unattaina...
Providing realistic opposing forces is critical to the successful use of military training simulations. Unfortunately, a number of issues can make the manual control of realistic opposing forces difficult or unattainable, This paper explores these issues while discussing how Automatic Interactive Targets (AITs) can assist Training Exercise Controllers (TECs) in providing validated and realistic opposing forces in highly interactive situations. The features of the prototype Remote AIT Processing System (RAPS) are used to demonstrate how an AIT system can be designed to meet TEC requirements for automated entities, RAPS can provide remote control of AITs for existing or new systems while providing sufficient features to allow a TEC to appropriately select and control AITs for individual training exercises and crew proficiencies.
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