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.

On the Efficacy of Static Prices for Revenue Management in the Face of Strategic Customers

Abstract: The present paper considers a canonical revenue management problem wherein a monopolist seller seeks to maximize revenues from selling a fixed inventory of a product to customers who arrive over time. We assume that customers are forward looking and strategize on the timing of their purchase, an empirically confirmed aspect of modern customer behavior. In the event that customers were myopic, foundational work by Gallego and van Ryzin [1994] established that static prices were asymptotically optimal for this problem. In stark contrast, for the case where customers are forward looking, available results in mechanism design and dynamic pricing offer no such simple solution and are also constrained by restrictive assumptions on customer type. The present paper studies the revenue management problem while assuming forward looking customers. We demonstrate that for a broad class of customer utility models, static prices surprisingly continue to remain asymptotically optimal in the scaling regime where inventory and demand grow large. We further show that irrespective of regime, an optimally set static price guarantees the seller revenues that are within at least 63.2% of that under an optimal dynamic mechanism. The class of customer utility models we consider is parsimonious and enjoys empirical support. It also subsumes many of the utility models considered for this problem in existing mechanism design research; we allow for multi-dimensional customer types. We also allow for a customer's disutility from waiting to be positively correlated with his valuation. Our conclusions are thus robust and provide a simple solution to what is considered a challenging problem of dynamic mechanism design.

A market-based mechanism for providing demand-side regulation service reserves

Abstract: We develop a market-based mechanism that enables a building Smart Microgrid Operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market Independent System Operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e.g., a one hour long time-scale). During this period the ISO repeatedly requests from the SMO to decrease or increase its consumption. We model the operational task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated non-linear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is tight and the static policy provides an efficient approximation of the dynamic pricing policy. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.

Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid

Abstract: Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.

A dynamic pricing strategy for high occupancy toll lanes

Abstract: High Occupancy Toll (HOT) lanes are emerging as a solution to the underutilization of High Occupancy Vehicle (HOV) lanes and also a means to generate revenue for the State Departments of Transportation. This paper proposes a method to determine the toll price dynamically in response to the changes in traffic condition, and describes the procedures for estimating the essential parameters. Such parameters include expected delays, available capacity for toll-paying vehicles and distribution of travelers’ value of time (VOT). The objective function of the proposed pricing strategy can be flexibly modified to minimize delay, maximize revenue or combinations of specified levels of delay and revenue. Real-world data from a 14-mile of freeway segment in the San Francisco Bay Area are used to demonstrate the applicability and feasibility of the proposed method, and findings and implications from this case study are discussed.