The Experts below are selected from a list of 29127 Experts worldwide ranked by ideXlab platform
Ahmad Faruqui - One of the best experts on this subject based on the ideXlab platform.
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Arcturus: An International Repository of Evidence on Dynamic Pricing
Green Energy and Technology, 2014Co-Authors: Ahmad Faruqui, Sanem SergiciAbstract:This chapter introduces Arcturus, an international database of Dynamic Pricing and time-of-use Pricing studies. It contains the demand response impacts of 163 Pricing treatments that were offered on an experimental or full-scale basis in 34 projects in seven countries located in four continents. The treatments included various types of Dynamic Pricing rates and simple time-of-use rates, some of which were offered with enabling technologies such as smart thermostats. The demand response impacts of these treatments vary widely, from 0 % to more than 50 %, and this discrepancy has led some observers to conclude that we still do not know whether customers respond to Dynamic Pricing. We find that much of the discrepancy in the results goes away when demand response is expressed as a function of the peak-to-off-peak price ratio. We then observe that customers respond to rising prices by lowering their peak demand in a fairly consistent fashion across the studies. The response curve is nonlinear and is shaped in the form of an arc: as the price incentive to reduce peak use is raised, customers respond by lowering peak use, but at a decreasing rate. We also find that the use of enabling technologies boosts the amount of demand response. Overall, we find a significant amount of consistency in the experimental results, especially when the results are disaggregated into two categories of rates: time-of-use rates and Dynamic Pricing rates. This consistency evokes the consistency that was found in earlier analysis of time-of-use Pricing studies that was carried out by EPRI in the early 1980s. Our analysis supports the case for the rollout of Dynamic Pricing wherever advanced metering infrastructure is in place.
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Arcturus: International Evidence on Dynamic Pricing
The Electricity Journal, 2013Co-Authors: Ahmad Faruqui, Sanem SergiciAbstract:This paper introduces Arcturus, an international database of Dynamic Pricing and time-of-use Pricing studies. It contains the demand response impacts of 163 Pricing treatments that were offered on an experimental or full-scale basis in 34 projects in seven countries located in four continents. The treatments included various types of Dynamic Pricing rates and simple time-of-use rates, some of which were offered with enabling technologies such as smart thermostats. The demand response impacts of these treatments vary widely, from 0% to more than 50%, and this discrepancy has led some observers to conclude that we still don’t know whether customers respond to Dynamic Pricing. We find that much of the discrepancy in the results goes away when demand response is expressed as a function of the peak to off-peak price ratio. We then observe that customers respond to rising prices by lowering their peak demand in a fairly consistency fashion across the studies. The response curve is nonlinear and is shaped in the form of an arc: as the price incentive to reduce peak use is raised, customers respond by lowering peak use, but at a decreasing rate. We also find that the use of enabling technologies boosts the amount of demand response. Overall, we find a significant amount of consistency in the experimental results, especially when the results are disaggregated into two categories of rates: time-of-use rates and Dynamic Pricing rates. This consistency evokes the consistency that was found in earlier analysis of time-of-use Pricing studies that was carried out by EPRI in the early 1980s. Our analysis supports the case for the rollout of Dynamic Pricing wherever advanced metering infrastructure is in place.
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Dynamic Pricing of Electricity for Residential Customers: The Evidence from Michigan
Energy Efficiency, 2013Co-Authors: Ahmad Faruqui, Sanem Sergici, Lamine AkabaAbstract:The rollout of smart meters has enabled the provision of Dynamic Pricing to residential customers. However, doubts remain whether households can respond to time-varying price signals and that is preventing the full-scale rollout of Dynamic Pricing and the attainment of economic efficiency. Experiments are being conducted to test price responsiveness. We analyze data from a pilot in Michigan which featured two Dynamic Pricing rates and an enabling technology. Unlike most other pilots, it also included a group of “information only” customers who were provided information on time-varying prices but billed on standard rates. Similarly, unlike most other pilots, it also included two control groups, one of whom knew they were in the pilot and one of whom did not. This was designed to test for the presence of a Hawthorne effect. Consistent with the large body of experimental literature, we find that customers, including low-income participants, do respond to Dynamic Pricing. We also find that the response to critical peak Pricing rates is similar to the response to peak time rebates, consistent with the finding of one prior experiment but inconsistent with the finding of two prior experiments. We also find that the “information only” customers respond to the provision of Pricing information but at a substantially lower rate than the customers on Dynamic Pricing. We find that the response to enabling technology is muted. We do not find any evidence to suggest that a Hawthorne effect existed in this experiment.
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Chapter 3 – The Ethics of Dynamic Pricing
Smart Grid, 2012Co-Authors: Ahmad FaruquiAbstract:Publisher Summary This chapter focuses on Dynamic Pricing, which conveys the time-varying nature of electricity costs to consumers. The idea of time-variable Pricing has been widely practiced in many markets for large commercial and industrial customers. Its application to residential and small commercial and industrial customers is in the nascent stage. Dynamic Pricing is a form of time-of-use (TOU) Pricing where prices during the peak period on a limited number of days can vary to reflect market conditions on a day-a head or day-of basis. One popular variant of Dynamic Pricing is critical-peak Pricing (CPP)in which prices during the top 40–150 hours of the year rise to previously specified levels designed to recover the full capacity and energy cost of power plants that run primarily during those hours. During all other hours of the year, prices are lower than existing rates by an amount sufficient to leave the bill unchanged for a customer whose load shape mirrors that of the rate class.
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Dynamic Pricing and Its Discontents
2011Co-Authors: Ahmad Faruqui, Jennifer PalmerAbstract:Energy economists and others have long advocated Dynamic Pricing — the use of different prices that reflect actual costs — for electricity supply. Politicians and some consumer advocates have been reluctant to embrace Dynamic Pricing because, they claim, it would be harmful to consumers, especially low-income consumers. This paper examines the results of a large number of Dynamic Pricing programs in the United States and around the world, and find that the concerns about consumers are misplaced. In these pilot programs, Dynamic Pricing regularly results in lower consumer costs, strong consumer response, and positive consumer feedback — even for low-income consumers.
Jang-won Lee - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning
IEEE Transactions on Smart Grid, 2016Co-Authors: Byung-gook Kim, Mihaela Van Der Schaar, Yu Zhang, Jang-won LeeAbstract:In this paper, we study a Dynamic Pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though Dynamic Pricing is an efficient tool to manage the microgrid, the implementation of Dynamic Pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing Dynamic Pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based Dynamic Pricing algorithm can effectively work without a priori information about the system Dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.
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INFOCOM Workshops - Dynamic Pricing for smart grid with reinforcement learning
2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2014Co-Authors: Byung-gook Kim, Mihaela Van Der Schaar, Yu Zhang, Jang-won LeeAbstract:In the smart grid system, Dynamic Pricing can be an efficient tool for the service provider which enables efficient and automated management of the grid. However, in practice, the lack of information about the customers’ time-varying load demand and energy consumption patterns and the volatility of electricity price in the wholesale market make the implementation of Dynamic Pricing highly challenging. In this paper, we study a Dynamic Pricing problem in the smart grid system where the service provider decides the electricity price in the retail market. In order to overcome the challenges in implementing Dynamic Pricing, we develop a reinforcement learning algorithm. To resolve the drawbacks of the conventional reinforcement learning algorithm such as high computational complexity and low convergence speed, we propose an approximate state definition and adopt virtual experience. Numerical results show that the proposed reinforcement learning algorithm can effectively work without a priori information of the system Dynamics.
Ram Rajagopal - One of the best experts on this subject based on the ideXlab platform.
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CDC - Stochastic Dynamic Pricing: Utilizing demand response in an adaptive manner
53rd IEEE Conference on Decision and Control, 2014Co-Authors: Wenyuan Tang, Rahul Jain, Ram RajagopalAbstract:Dynamic Pricing to residential customers has been proposed recently, as extensions of static network utility maximization problems. Those deterministic models do not exploit the refined information as time advances. To address this issue, we formulate a stochastic Dynamic Pricing framework, in which we show the existence of an optimal price process that incentives the agents to choose the socially optimal decisions. We develop a distributed algorithm and investigate the information structure of the involved stochastic processes via a numerical example, which also illustrates the advantage of stochastic Dynamic Pricing over deterministic Dynamic Pricing.
Alper Şen - One of the best experts on this subject based on the ideXlab platform.
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A Comparison of Fixed and Dynamic Pricing Policies in Revenue Management
Omega-international Journal of Management Science, 2013Co-Authors: Alper ŞenAbstract:We consider the problem of selling a fixed capacity or inventory of items over a finite selling period. Earlier research has shown that using a properly set fixed price during the selling period is asymptotically optimal as the demand potential and capacity grows large and that Dynamic Pricing has only a secondary effect on revenues. However, additional revenue improvements through Dynamic Pricing can be important in practice and need to be further explored. For example, in 2009, increasing the average price by one percent would increase the profitability of the largest airlines and rental car companies in the US by 67 percent and 30 percent, respectively. We suggest two simple Dynamic heuristic heuristics that continuously updates prices based on remaining inventory and time in the selling period. The first heuristic is based on approximating the optimal expected revenue function and the second heuristic is based on the solution of the deterministic version of the problem. We show through a numerical study that the revenue impact of using these Dynamic Pricing heuristics rather than fixed Pricing may be substantial. In particular, the revenue approximation heuristic has a consistent and remarkable performance leading to at most 0.2 percent gap compared to optimal Dynamic Pricing. We also show that the benefits of these Dynamic Pricing heuristics persist under a periodic setting. This is especially true for the revenue approximation heuristic for which the performance is monotone in the frequency of price changes.We finally show that both of these heuristics can be easily implemented for the multiple products case. We conclude that Dynamic Pricing should be considered as a more favorable option in practice.
Erik Elmroth - One of the best experts on this subject based on the ideXlab platform.
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UCC - Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes
2013 IEEE ACM 6th International Conference on Utility and Cloud Computing, 2013Co-Authors: Pette Svärd, Johan Tordsson, Erik ElmrothAbstract:Until now, most research on cloud service placement has focused on static Pricing scenarios, where cloud providers offer fixed prices for their resources. However, with the recent trend of Dynamic Pricing of cloud resources, where the price of a compute resource can vary depending on the free capacity and load of the provider, new placement algorithms are needed. In this paper, we investigate service placement in Dynamic Pricing scenarios by evaluating a set of placement algorithms, tuned for Dynamic Pricing. The algorithms range from simple heuristics to combinatorial optimization solutions. The studied algorithms are evaluated by deploying a set of services across multiple providers. Finally, we analyse the strengths and weaknesses of the algorithms considered. The evaluation suggests that exhaustive search based approach is good at finding optimal solutions for service placement under Dynamic Pricing schemes, but the execution times are usually long. In contrast, greedy approaches perform surprisingly well with fast execution times and acceptable solutions, and thus can be a suitable compromise considering the tradeoffs between quality of solution and execution time.
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Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes
2013 IEEE ACM 6th International Conference on Utility and Cloud Computing, 2013Co-Authors: Pette Svärd, Joha Tordsso, Erik ElmrothAbstract:Until now, most research on cloud service placement has focused on static Pricing scenarios, where cloud providers offer fixed prices for their resources. However, with the recent trend of Dynamic Pricing of cloud resources, where the price of a compute resource can vary depending on the free capacity and load of the provider, new placement algorithms are needed. In this paper, we investigate service placement in Dynamic Pricing scenarios by evaluating a set of placement algorithms, tuned for Dynamic Pricing. The algorithms range from simple heuristics to combinatorial optimization solutions. The studied algorithms are evaluated by deploying a set of services across multiple providers. Finally, we analyse the strengths and weaknesses of the algorithms considered. The evaluation suggests that exhaustive search based approach is good at finding optimal solutions for service placement under Dynamic Pricing schemes, but the execution times are usually long. In contrast, greedy approaches perform surprisingly well with fast execution times and acceptable solutions, and thus can be a suitable compromise considering the tradeoffs between quality of solution and execution time.
