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.

Selling through Priceline? On the impact of name-your-own-price in competitive market

Abstract: Priceline.com patented the innovative pricing strategy, Name-Your-Own-Price (NYOP), that sells opaque products through customer-driven pricing. In this article, we study how competitive sellers with substitutable, non-replenishable goods may sell their products (i) as regular goods, through a direct channel at posted prices, and possibly at the same time (ii) as opaque goods, through a third-party channel that engages in NYOP. We establish a stylized model framework that incorporates three sets of stakeholders: two competing sellers, an intermediary NYOP firm, and a sequence of customers. We first characterize customers’ optimal purchasing/bidding decisions under various channel structures and then analyze corresponding sellers’ dynamic pricing equilibrium. We conduct extensive numerical studies to illustrate the impact of inventory and time on equilibrium prices, expected profit, and channel strategies. We find that the implications are highly dependent on channel structure (dual versus single). ...

Optimal power procurement and demand response with quality-of-usage guarantees

Abstract: In this paper, we propose a general operating scheme which allows the utility company to jointly perform power procurement and demand response so as to maximize the social welfare. Our model takes into consideration the effect of the renewable energy and the multi-stage feature of the power procurement process. It also enables the utility company to provide quality-of-usage (QoU) guarantee to the power consumers, which ensures that the average power usage level meets the target value for each user. To maximize the social welfare, we develop a low-complexity algorithm called the welfare maximization algorithm (WMA), which performs joint power procurement and dynamic pricing. WMA is constructed based on a two-timescale Lyapunov optimization technique. We prove that WMA achieves a close-to-optimal utility and ensures that the QoU requirement is met with bounded deficit. WMA can be implemented in a distributed manner and is robust with respect to system dynamics uncertainty.

Dynamic pricing of general insurance in a competitive market

Abstract: A model for general insurance pricing is developed which represents a stochastic generalisation of the discrete model proposed by Taylor (1986). This model determines the insurance premium based both on the breakeven premium and the competing premiums offered by the rest of the insurance market. The optimal premium is determined using stochastic optimal control theory for two objective functions in order to examine how the optimal premium strategy changes with the insurer’s objective. Each of these problems can be formulated in terms of a multi-dimensional Bellman equation. In the first problem the optimal insurance premium is calculated when the insurer maximises its expected terminal wealth. In the second, the premium is found if the insurer maximises the expected total discounted utility of wealth where the utility function is nonlinear in the wealth. The solution to both these problems is built-up from simpler optimisation problems. For the terminal wealth problem with constant loss-ratio the optimal premium strategy can be found analytically. For the total wealth problem the optimal relative premium is found to increase with the insurer’s risk aversion which leads to reduced market exposure and lower overall wealth generation.

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid

Abstract: We present an Open Automated Demand Response Communications Specifications (OpenADR) data model capable of communicating real-time prices to electricity customers. We also show how the same data model could be used to for other types of dynamic pricing tariffs (including peak pricing tariffs, which are common throughout the United States). Customers participating in automated demand response programs with building control systems can respond to dynamic prices by using the actual prices as inputs to their control systems. Alternatively, prices can be mapped into building operation modes, which can act as inputs to control systems. We present several different strategies customers could use to map prices to operation modes. Our results show that OpenADR can be used to communicate dynamic pricing within the Smart Grid and that OpenADR allows for interoperability with existing and future systems, technologies, and electricity markets.