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Dynamic pricing

About: Dynamic pricing is a research topic. Over the lifetime, 4144 publications have been published within this topic receiving 91390 citations. The topic is also known as: surge pricing & demand pricing.


Papers
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Proceedings ArticleDOI
22 Jul 2012
TL;DR: In this paper, the authors describe a smart grid technology that integrates demand and supply flexibility in the operation of the electricity system through the use of dynamic pricing, which has been researched and developed into a market-ready system, and has been deployed in a number of successful field trials.
Abstract: Response of demand, distributed generation and electricity storage (e.g. vehicle to grid) will be crucial for power systems management in the future smart electricity grid. In this paper, we describe a smart grid technology that integrates demand and supply flexibility in the operation of the electricity system through the use of dynamic pricing. Over the last few years, this technology has been researched and developed into a market-ready system, and has been deployed in a number of successful field trials. Recent field experiences and simulation studies show the potential of the technology for network operations (e.g. congestion management and black-start support), for market operations (e.g. virtual power plant operations), and integration of large-scale wind power generation. The scalability of the technology, i.e. the ability to perform well under mass-application circumstances, has been proved in a targeted field experiment. This paper gives an overview of the results of two field trials and three simulation studies. In these trials and simulations, demand and supply response from real and simulated electrical vehicles, household appliances and heating systems (heat pumps and micro co-generation) has been successfully coordinated to reach specific smart grid goals.

133 citations

Book
21 Sep 2010
TL;DR: In this paper, the Calculus of Variations and Optimal Control are used to define nonlinear programming and discrete-time optimal control, and infinite Dimensional Mathematical Programming (IDMP) is used.
Abstract: Nonlinear Programming and Discrete-Time Optimal Control.- Foundations of the Calculus of Variations and Optimal Control.- Infinite Dimensional Mathematical Programming.- Finite Dimensional Variational Inequalities and Nash Equilibria.- Differential Variational Inequalities and Differential Nash Games.- Optimal Economic Growth.- Production Planning, Oligopoly and Supply Chains.- Dynamic User Equilibrium.- Dynamic Pricing and Revenue Management.

133 citations

Journal ArticleDOI
TL;DR: A stochastic model, based on queueing theory, is proposed, which can provide more accurate forecasts of the load using real-time sub-metering data and a mathematical description of load, along with the level of demand flexibility that accompanies this load, at the wholesale level.
Abstract: In this paper we propose a stochastic model, based on queueing theory, for electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV) charging demand. Compared to previous studies, our model can provide 1) more accurate forecasts of the load using real-time sub-metering data, along with the level of uncertainty that accompanies these forecasts; 2) a mathematical description of load, along with the level of demand flexibility that accompanies this load, at the wholesale level. This can be useful when designing demand response and dynamic pricing schemes. Our numerical experiments tune the proposed statistics on real PHEV charging data and demonstrate that the forecasting method we propose is more accurate than standard load prediction techniques.

133 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide a comprehensive analysis of dynamic pricing by Airbnb hosts, using attribute and sales information from 39,837 listings and hotel data from 1,025 hotels across five markets to test different hypotheses which explore the extent to which Airbnb hosts use dynamic pricing and how their pricing strategies compare to those of hotels.
Abstract: The purpose of this paper is to provide a comprehensive analysis of dynamic pricing by Airbnb hosts.,This study uses attribute and sales information from 39,837 Airbnb listings and hotel data from 1,025 hotels across five markets to test different hypotheses which explore the extent to which Airbnb hosts use dynamic pricing and how their pricing strategies compare to those of hotels.,Airbnb is a unique and complex platform in terms of dynamic pricing where hosts make limited use of dynamic pricing strategies, especially as compared to hotels. Notwithstanding their limited use, hosts who own listings in high-demand leisure markets, manage entire places, manage more listings and have more experience vary prices the most.,This study identified a great need for Airbnb to encourage dynamic pricing among its hosts, but also warned of the potential perils of dynamic pricing in the sharing economy context. The findings also demonstrated challenges for hotel managers interested in actionable information related to Airbnb as a competitor.,This is the first Airbnb study to use a comprehensive set of data over a continuous period in multiple markets to look at a number of listing and host factors and determine their relation with dynamic pricing strategies.

132 citations

Journal ArticleDOI
TL;DR: A dynamic discriminatory pricing mechanism design is proposed and it is shown that it effectively controls congestion while ensuring the efficient allocation of network capacity and is robust to strategic manipulation.
Abstract: We introduce a new dynamic pricing mechanism for controlling congestion in a network shared by non-cooperative users. The network exhibits a congestion externality and users have private information regarding their willingness to pay for network use. The externalities imply that many simple uniform price adjustment processes (e.g., tatonnement) either fail to effectively control flow demands and/or are subject to strategic manipulation. We propose a dynamic discriminatory pricing mechanism design and show that it effectively controls congestion while ensuring the efficient allocation of network capacity. We show the proposed mechanism is robust to strategic manipulation. To the best of our knowledge, there is no other dynamic pricing mechanism in the literature with these properties.

132 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023140
2022262
2021307
2020324
2019346
2018314