The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Jinwen Wang - One of the best experts on this subject based on the ideXlab platform.
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Optimize Pricing Policy in Evolutionary Market with Multiple Proactive Competing Cloud Providers
2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017Co-Authors: Jinwen WangAbstract:In the cloud computing market with multiple competing cloud providers, each provider needs to run an optimize Pricing Policy to determine the price for cloud resources in order to beat other providers and maximize the long-term revenue. In this paper, we intend to design an optimal Pricing Policy for the cloud provider by taking into account the following two facts: (1) the market is evolutionary in terms of the increased amount of cloud users and the decreased marginal cost of providers; (2) the market is fully competitive since all providers expect to receive more profits while users expect to pay less for their requirements. By considering these realistic situations, we analyze how the cloud provider designs an optimal Pricing Policy while competing against the other provider. Specifically, we model the Pricing issue in the competing environment as a Markov games, and then adopt the Minimax-Q and Q-learning algorithm to find the optimal Pricing Policy. Finally, we evaluate our algorithms and compare the Pricing Policy with other existing polices. Numerical simulations demonstrate the effectiveness of our proposed approach.
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A Game-Theoretic Analysis of Pricing Strategies for Competing Cloud Platforms
2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), 2016Co-Authors: Yalong Huang, Jinwen Wang, Shengwu XiongAbstract:In this paper, we analyse how multiple competing cloud platforms set effective service prices between Web service providers and consumers. We propose a novel economic framework to model this problem. Cloud platforms run double auction mechanisms, where Web service is commodity traded by service providers (sellers) and service consumers (buyers). Multiple cloud platforms compete against each other to attract service providers and consumers. Specifically, we use game theory to analyse the Pricing policies of competing cloud platforms, where service providers and consumers can choose to participate in any of the platforms, and bid or ask for the Web service. The platform selection and bidding strategies of service providers and consumers are affected by the Pricing policies and vice versa, and so we propose a co-learning algorithm based on fictitious play to analyse this problem. In more detail, we investigate a setting with two competing cloud platforms who can adopt either equilibrium k Pricing Policy or discriminatory k Pricing Policy. We find that, when both cloud platforms use the same type of Pricing Policy, they can co-exist in equilibrium, and they have an extreme bias to service providers or consumers when setting k. When both platforms adopt different types of policies, we find that all service providers and consumers converge to the discriminatory k Pricing Policy and so the two competing platforms can no longer co-exist.
Xiaoya Wang - One of the best experts on this subject based on the ideXlab platform.
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the impact of china s differential electricity Pricing Policy on power sector co2 emissions
Energy Policy, 2012Co-Authors: Junfeng Hu, Fredrich Kahrl, Xiaoya WangAbstract:This article investigates the impact of China's differential electricity Pricing Policy on power sector CO2 emissions using the logarithmic mean divisia index method. The differential Pricing Policy, intended to reduce energy intensity in manufacturing, is being implemented in eight electricity-intensive industries. The study finds that, during 2004–2009, the Policy accounted for a drop of roughly 115TWh in electricity use, which amounted to a reduction of 82 million tons of CO2 emissions. The Policy has been most effective in reducing electricity use in the nonferrous metal smelting and rolling industry, and least effective in the ferrous metal smelting and rolling industry. Because the differential Pricing Policy has had significantly different effects across industries, improving the Policy's design and implementation going forward will require a more detailed understanding and analysis of how it can be better tailored to individual industries.
Eilyan Bitar - One of the best experts on this subject based on the ideXlab platform.
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Risk-Sensitive Learning and Pricing for Demand Response
IEEE Transactions on Smart Grid, 2018Co-Authors: Kia Khezeli, Eilyan BitarAbstract:We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of T days. In order to do so effectively, the utility must design its Pricing Policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a Pricing Policy, which is shown to exhibit an expected payoff loss over T days that is at most O(√T log(T)), relative to an oracle Pricing Policy that knows the underlying demand model. Moreover, the proposed Pricing Policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense.
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Data-driven Pricing of demand response
2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2016Co-Authors: Kia Khezeli, Eilyan BitarAbstract:We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of T days. In order to do so effectively, the utility must design its Pricing Policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a Pricing Policy, which is shown to exhibit an expected payoff loss over T days that is at most O(√T), relative to an oracle who knows the underlying demand model. Moreover, the proposed Pricing Policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense.
Christos Verikoukis - One of the best experts on this subject based on the ideXlab platform.
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Performance Evaluation of a Multi-Standard Fast Charging Station for Electric Vehicles
IEEE Transactions on Smart Grid, 2018Co-Authors: Ioannis Zenginis, John Vardakas, Nizar Zorba, Christos VerikoukisAbstract:Electric vehicles (EVs) have been considered as a feasible solution to deal with the high fuel consumption and greenhouse gas emissions caused by conventional vehicles. However, long charging times and drivers' range anxiety are the main disadvantages of EVs. A key factor that is expected to mitigate these problems and facilitate the wide adoption of EVs will be the effective operation of fast charging stations (FCSs). In this paper, the operation of a FCS is evaluated, in terms of operator's profits and customers' waiting time in the queue. The FCS contains both dc and ac outlets that provide high power levels, while the various EV models are classified by their battery size and the fast charging option they use (dc or ac). The operator's daily profits and the queue waiting time are initially computed by considering that the EVs recharge under a flat-rate Pricing Policy. In order to avoid a long queue build-up at the FCS, a new Pricing Policy is then proposed. The intuition behind the scheduled Pricing Policy is that users are deterred to charge more than an arranged energy threshold, thus reducing the load and the waiting time at the FCS.
Yanjin Li - One of the best experts on this subject based on the ideXlab platform.
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Railway passenger ticket Pricing Policy portfolio
2016 International Conference on Logistics Informatics and Service Sciences (LISS), 2016Co-Authors: Yanjin LiAbstract:Through summarizing the previous research about railway passenger fares, this paper proposed a new idea of fare Policy portfolio. Considering the various characteristics of fare Policy, this paper combined the traditional fare policies to form a Pricing Policy portfolio, established some mathematic models and also proved the existence of equilibrium price. Combination of market segmentation, market competition and dynamic Pricing Policy is a suitable railway fare Policy portfolio which applies to China railway passenger transport development situation.
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LISS - Railway passenger ticket Pricing Policy portfolio
2016 International Conference on Logistics Informatics and Service Sciences (LISS), 2016Co-Authors: Yanjin LiAbstract:Through summarizing the previous research about railway passenger fares, this paper proposed a new idea of fare Policy portfolio. Considering the various characteristics of fare Policy, this paper combined the traditional fare policies to form a Pricing Policy portfolio, established some mathematic models and also proved the existence of equilibrium price. Combination of market segmentation, market competition and dynamic Pricing Policy is a suitable railway fare Policy portfolio which applies to China railway passenger transport development situation.
