Showing papers on "Dynamic pricing published in 2010"

Household response to dynamic pricing of electricity: a survey of 15 experiments

Abstract: Since the energy crisis of 2000–2001 in the western United States, much attention has been given to boosting demand response in electricity markets. One of the best ways to let that happen is to pass through wholesale energy costs to retail customers. This can be accomplished by letting retail prices vary dynamically, either entirely or partly. For the overwhelming majority of customers, that requires a change out of the metering infrastructure, which may cost as much as $40 billion for the US as a whole. While a good portion of this investment can be covered by savings in distribution system costs, about 40% may remain uncovered. This investment gap could be covered by reductions in power generation costs that could be brought about through demand response. Thus, state regulators in many states are investigating whether customers will respond to the higher prices by lowering demand and if so, by how much. To help inform this assessment, this paper surveys the evidence from the 15 most recent pilots, experiments and full-scale implementations of dynamic pricing of electricity. It finds conclusive evidence that households respond to higher prices by lowering usage. The magnitude of price response depends on several factors, such as the magnitude of the price increase, the presence of central air conditioning and the availability of enabling technologies such as two-way programmable communicating thermostats and always-on gateway systems that allow multiple end-uses to be controlled remotely. In addition, the design of the studies, the tools used to analyze the data and the geography of the assessment influence demand response. Across the range of experiments studied, time-of-use rates induce a drop in peak demand that ranges between 3 and 6% and critical-peak pricing (CPP) tariffs induce a drop in peak demand that ranges between 13 and 20%. When accompanied with enabling technologies, the latter set of tariffs lead to a reduction in peak demand in the 27–44% range.

Incentive-Based Energy Consumption Scheduling Algorithms for the Smart Grid

Abstract: In this paper, we study Demand Response (DR) problematics for different levels of information sharing in a smart grid. We propose a dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and study how close they can get to an ideal flat profile depending on how much information they share. When customers can share all their load profiles, we provide a distributed algorithm, set up as a cooperative game between consumers, which significantly reduces the total cost and peak-to-average ratio (PAR) of the system. In the absence of full information sharing (for reasons of privacy), when users have only access to the instantaneous total load on the grid, we provide distributed stochastic strategies that successfully exploit this information to improve the overall load profile. Simulation results confirm that these solutions efficiently benefit from information sharing within the grid and reduce both the total cost and PAR.

Distributed Demand Management in Smart Grid with a Congestion Game

Abstract: In this paper we propose distributed load management in smart grid infrastructures to control the power demand at peak hours, by means of dynamic pricing strategies. The distributed solution that we propose is based on a network congestion game, which can be demonstrated to converge in a finite number of steps to a pure Nash equilibrium solution. We take advantage of the remarkable property of congestion games, according to which they are equivalent to potential games. We define a potential function characterized by a meaningful physical interpretation, so that we obtain the favorable result that the optimal local solution of each selfish consumer is also the solution of a global objective. We evaluate this approach for managing both the demand and the grid load and we show that load control can be effectively achieved implementing a distributed solution, which significantly reduce the signaling burden over the network.

Dynamic Pricing with a Prior on Market Response

Abstract: We study a problem of dynamic pricing faced by a vendor with limited inventory, uncertain about demand, and aiming to maximize expected discounted revenue over an infinite time horizon. The vendor learns from purchase data, so his strategy must take into account the impact of price on both revenue and future observations. We focus on a model in which customers arrive according to a Poisson process of uncertain rate, each with an independent, identically distributed reservation price. Upon arrival, a customer purchases a unit of inventory if and only if his reservation price equals or exceeds the vendor's prevailing price. We propose a simple heuristic approach to pricing in this context, which we refer to as decay balancing. Computational results demonstrate that decay balancing offers significant revenue gains over recently studied certainty equivalent and greedy heuristics. We also establish that changes in inventory and uncertainty in the arrival rate bear appropriate directional impacts on decay balancing prices in contrast to these alternatives, and we derive worst-case bounds on performance loss. We extend the three aforementioned heuristics to address a model involving multiple customer segments and stores, and provide experimental results demonstrating similar relative merits in this context.

Dynamic Resource Pricing on Federated Clouds

Abstract: Current large distributed systems allow users to share and trade resources. In cloud computing, users purchase different types of resources from one or more resource providers using a fixed pricing scheme. Federated clouds, a topic of recent interest, allows different cloud providers to share resources for increased scalability and reliability. However, users and providers of cloud resources are rational and maximize their own interest when consuming and contributing shared resources. In this paper, we present a dyanmic pricing scheme suitable for rational users requests containing multiple resource types. Using simulations, we compare the efficiency of our proposed strategy-proof dynamic scheme with fixed pricing, and show that user welfare and the percentage of successful requests is increased by using dynamic pricing.

Optimal Dynamic Pricing of Perishable Items by a Monopolist Facing Strategic Consumers

Abstract: We introduce a dynamic pricing model for a monopolistic company selling a perishable product to a finite population of strategic consumers (customers who are aware that pricing is dynamic and may time their purchases strategically). This problem is modeled as a stochastic dynamic game in which the company’s objective is to maximize total expected revenues, and each customer maximizes the expected present value of utility. We prove the existence of a unique subgame-perfect equilibrium pricing policy, provide equilibrium optimality conditions for both customer and seller, and prove monotonicity results for special cases. We demonstrate through numerical examples that a company that ignores strategic consumer behavior may receive much lower total revenues than one that uses the strategic equilibrium pricing policy. We also show that, when the initial capacity is a decision variable, it can be used together with the appropriate pricing policy to effectively reduce the impact of strategic consumer behavior. The proposed model is computationally tractable for problems of realistic size.

Dynamic Pricing with Loss Averse Consumers and Peak-End Anchoring

Abstract: We analyze a dynamic pricing problem where consumer's purchase decisions are a ected by representative past prices, summarized in a reference price. We propose a new, behaviorally motivated reference price mechanism, based on the peak-end memory model proposed by Kahneman et al. (1993). Speci cally, we assume that consumers' reference price is a weighted average of the lowest and last price. Gain or loss perceptions with respect to this reference price a ect consumer purchase decisions in the spirit of prospect theory, resulting in a non-smooth demand function.We investigate how these behavioral patterns in consumer anchoring and decision processes a ect the optimal dynamic pricing policy of the rm. In contrast with previous literature, we show that peak-end anchoring leads to a range of optimal constant pricing policies even with loss neutral buyers. This range becomes wider if consumers are loss averse. In general, we show that skimming or penetration strategies are optimal, i.e. the transient pricing policy is monotone, and converges to a steady state, which depends on the initial price perception. The value of the steady state price decreases, the more consumers are sensitive to price changes, and the more they anchor on the lowest price.

Household Response to Dynamic Pricing of Electricity - A Survey of the Empirical Evidence

Abstract: Since the energy crisis of 2000-2001 in the western United States, much attention has been given to boosting demand response in electricity markets. One of the best ways to let that happen is to pass through wholesale energy costs to retail customers. This can be accomplished by letting retail prices vary dynamically, either entirely or partly. For the overwhelming majority of customers, that requires a changeout of the metering infrastructure, which may cost as much as $40 billion for the US as a whole. While a good portion of this investment can be covered by savings in distribution system costs, about 40 percent may remain uncovered. This investment gap could be covered by reductions in power generation costs that could be brought about through demand response. Thus, state regulators in many states are investigating whether customers will respond to the higher prices by lowering demand and if so, by how much. To help inform this assessment, we survey the evidence from the 15 most recent pilots, experiments and full-scale implementations of dynamic pricing of electricity. We find conclusive evidence that households (residential customers) respond to higher prices by lowering usage. The magnitude of price response depends on several factors, such as the magnitude of the price increase, the presence of central air conditioning and the availability of enabling technologies such as two-way programmable communicating thermostats and always-on gateway systems that allow multiple end-uses to be controlled remotely. They also vary with the design of the studies, the tools used to analyze the data and the geography of the assessment. Across the range of experiments studied, time-of-use rates induce a drop in peak demand that ranges between three to six percent and critical-peak pricing tariffs induce a drop in peak demand that ranges between 13 to 20 percent. When accompanied with enabling technologies, the latter set of tariffs lead to a drop in peak demand in the 27 to 44 percent range.

Dynamic Pricing and Stabilization of Supply and Demand in Modern Electric Power Grids

Abstract: The paper proposes a mechanism for real-time pricing of electricity in smart power grids, with price stability as the primary concern. In previous publications the authors argued that relaying the real-time wholesale market prices to the end consumers creates a closed loop feedback system which could be unstable or lack robustness, leading to extreme price volatility. In this paper, a mathematical model is developed for characterization of the dynamic evolution of supply, (elastic) demand, and market clearing (locational marginal) prices under real-time pricing. It is assumed that the real-time prices for retail consumers are derived from the Locational Marginal Prices (LMPs) of the wholesale balancing markets. The main contribution of the paper is in presenting an effective stabilizing pricing algorithm and characterization of its effects on system efficiency. Numerical simulations conform with our analysis and show the stabilizing effect of the mechanism and its robustness to disturbances.

Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice

Abstract: In response to competitive pressures, firms are increasingly adopting revenue management opportunities afforded by advances in information and communication technologies. Motivated by these revenue management initiatives in industry, we consider a dynamic pricing problem facing a firm that sells given initial inventories of multiple substitutable and perishable products over a finite selling horizon. Because the products are substitutable, individual product demands are linked through consumer choice processes. Hence, the seller must formulate a joint dynamic pricing strategy while explicitly incorporating consumer behavior. For an integrative model of consumer choice based on linear random consumer utilities, we model this multiproduct dynamic pricing problem as a stochastic dynamic program and analyze its optimal prices. The consumer choice model allows us to capture the linkage between product differentiation and consumer choice, and readily specializes to the cases of vertically and horizontally differentiated assortments. When products are vertically differentiated, our results show monotonicity properties (with respect to quality, inventory, and time) of the optimal prices and reveal that the optimal price of a product depends on higher quality product inventories only through their aggregate inventory rather than individual availabilities. Furthermore, we show that the price of a product can be decomposed into the price of its adjacent lower quality product and a markup over this price, with the markup depending solely on the aggregate inventory. We exploit these properties to develop a polynomial-time, exact algorithm for determining the optimal prices and the profit. For a horizontally differentiated assortment, we show that the profit function is unimodal in prices. We also show that individual, rather than aggregate, product inventory availability drives pricing. However, we find that monotonicity properties observed in vertically differentiated assortments do not hold.

Information Acquisition for Capacity Planning via Pricing and Advance Selling: When to Stop and Act?

Abstract: This paper investigates a capacity planning strategy that collects commitments to purchase before the capacity decision and uses the acquired advance sales information to decide on the capacity. In particular, we study a profit-maximization model in which a manufacturer collects advance sales information periodically prior to the regular sales season for a capacity decision. Customer demand is stochastic and price sensitive. Once the capacity is set, the manufacturer produces and satisfies customer demand (to the extent possible) from the installed capacity during the regular sales period. We study scenarios in which the advance sales and regular sales season prices are set exogenously and optimally. For both scenarios, we establish the optimality of a control band or a threshold policy that determines when to stop acquiring advance sales information and how much capacity to build. We show that advance selling can improve the manufacturer's profit significantly. We generate insights into how operating conditions (such as the capacity building cost) and market characteristics (such as demand variability) affect the value of information acquired through advance selling. From this analysis, we identify the conditions under which advance selling for capacity planning is most valuable. Finally, we study the joint benefits of acquiring information for capacity planning through advance selling and revenue management of installed capacity through dynamic pricing.

Supply Chain Engineering: Useful Methods and Techniques

Abstract: Supply Chain Engineering considers how modern production and operations management (POM) techniques can respond to the pressures of the competitive global marketplace by integrating all activities in the supply chain, adding flexibility to the system, and drastically reducing production cost. Several POM challenges are answered through a comprehensive analysis of concepts and models that assist the selection of outsourcing strategies and dynamic pricing policies. The ramifications of these topics are discussed from local to global perspectives. Supply Chain Engineering also presents inventory control policies, radio frequency identification (RFID) technologies, flexible and re-configurable manufacturing systems, real-time assignment and scheduling methods, new warehousing techniques. In addition, a significant part of the book is devoted to: lean manufacturing, line balancing (assembly lines, U-lines, and bucket brigades), and dynamic facilities layout approaches. Explanations are given using basic examples and detailed algorithms, while discarding complex and unnecessary theoretical minutiae. Moreover, all the examples have been carefully selected with a view to eventual industrial application. Supply Chain Engineering is written for students and professors in industrial and systems engineering, management science, operations management, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.

Integrating Dynamic Pricing and Replenishment Decisions Under Supply Capacity Uncertainty

Abstract: This paper examines an integrated decision-making process regarding pricing for uncertain demand and sourcing from uncertain supply, which are often studied separately in the literature. Our analysis of the integrated system suggests that the base stock list price policy fails to achieve optimality even under deterministic demand. Instead, the optimal policy is characterized by two critical values: a reorder point and a target safety stock. Under this policy, a positive order is issued if and only if the inventory level is below the reorder point. When this happens, the optimal order and price are coordinated to achieve a constant target safety stock, which aims at hedging the demand uncertainty. We further investigate the profit improvement obtained from deploying dynamic pricing, as opposed to static pricing. Our results indicate that either supply limit or supply uncertainty may induce a significant benefit from dynamic pricing, and the compound effect of supply limit and uncertainty can be much more pronounced than the individual effects. Whether or not the supply capacity is limited has a major implication on the value of dynamic pricing. Under unlimited supply, dynamic pricing is more valuable when procurement cost is high or when demand is more sensitive to price. With limited supply, however, the capacity restriction tends to be relaxed, reducing the value of dynamic pricing.

Supply Chain Engineering

Abstract: Supply Chain Engineering considers how modern production and operations management (POM) techniques can respond to the pressures of the competitive global marketplace by integrating all activities in the supply chain, adding flexibility to the system, and drastically reducing production cost. Several POM challenges are answered through a comprehensive analysis of concepts and models that assist the selection of outsourcing strategies and dynamic pricing policies. The ramifications of these topics are discussed from local to global perspectives. Supply Chain Engineering also presents • inventory control policies, • radio frequency identification (RFID) technologies, • flexible and re-configurable manufacturing systems, • real-time assignment and scheduling methods, • new warehousing techniques. In addition, a significant part of the book is devoted to: lean manufacturing, line balancing (assembly lines, U-lines, and bucket brigades), and dynamic facilities layout approaches. Explanations are given using basic examples and detailed algorithms, while discarding complex and unnecessary theoretical minutiae. Moreover, all the examples have been carefully selected with a view to eventual industrial application. Supply Chain Engineering is written for students and professors in industrial and systems engineering, management science, operations management, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.

Dynamic Optimization and Differential Games

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.

Dynamics of Pricing in the Video Game Console Market: Skimming or Penetration?

Abstract: Nintendo lost its dominant position in the video game industry during the console war between its Nintendo 64 and Sony's PlayStation. However, Nintendo could have made several different strategic decisions to change the outcome. This article develops a structural model and investigates these alternative strategies through policy simulations. In particular, the author provides a framework to study firms' optimal pricing strategies under network effects, consumer heterogeneity, and oligopolistic competition. Consumer heterogeneity provides an incentive for a durable goods manufacturer to price skim, while network effects lead to an opposite motive for penetration pricing. The proposed framework incorporates these two competing motives under oligopolistic competition. The author estimates a demand system that allows for indirect network effects and consumer heterogeneity and then numerically solves for the Markov perfect equilibrium in firms' dynamic pricing game. Policy simulations indicate that Ni...

Dynamic Pricing and Inventory Control: Uncertainty and Competition

Abstract: In this paper, we study a make-to-stock manufacturing system where two firms compete through dynamic pricing and inventory control. Our goal is to address competition (in particular a duopoly setting) together with the presence of demand uncertainty. We consider a dynamic setting where multiple products share production capacity. We introduce a demand-based fluid model where the demand is a linear function of the price of the supplier and of her competitor, the inventory and production costs are quadratic, and all coefficients are time dependent. A key part of the model is that no backorders are allowed and the strategy of a supplier depends on her competitor's strategy. First, we reformulate the robust problem as a fluid model of similar form to the deterministic one and show existence of a Nash equilibrium in continuous time. We then discuss issues of uniqueness and address how to compute a particular Nash equilibrium, i.e., the normalized Nash equilibrium.

Dynamic Pricing: When To Entice Brand Switching And When To Reward Consumer Loyalty

Abstract: This article develops a theory of dynamic pricing in which firms may offer separate prices to different consumers based on their past purchases. Brand preferences over two periods are described by a copula admitting various degrees of positive dependence. When commitment to future prices is infeasible, each firm offers lower prices to its rival’s customers. When firms can commit to future prices, consumer loyalty is rewarded if preference dependence is low, but enticing brand switching occurs if preference dependence is high. Our theory provides a unified treatment of the two pricing policies, and sheds light on observed practices across industries.

Prediction-based charging of PHEVs from the smart grid with dynamic pricing

Abstract: Coexistence of Plug-in Hybrid Vehicles (PHEVs) with the emerging smart grids has been recently an attractive and equally challenging research topic. The existing electricity grids are rapidly evolving into smart grids by utilizing the advances in Information and Communication Technologies (ICT). Meanwhile, advances in Lithium-Ion (Li-ion) battery technologies have made manufacturing of PHEVs cost-wise effective, and PHEVs are expected to be widely adopted in the following years. PHEVs have several benefits over conventional vehicles such as, less fuel dependency, lower operating costs and lower amount of CO 2 emissions. On the other hand, unless PHEVs are powered by off the grid renewable energy resources, they will be drawing electricity from the grid to charge their batteries and they will increase the load on the grid. In the worst case, when the Time Of Charging (TOC) coincides with the critical peak periods, the grid may experience overall or partial failure. For most of the cases, TOC may be during the peak hours when the price of electricity is high. To avoid endangering grid resilience and to avoid high costs, a charging strategy and communication with the smart grid is essential. In this paper, we propose a prediction-based charging scheme which receives dynamic pricing information by wireless communications, predicts the market prices during the charging period and determines an appropriate TOC with low cost. Our prediction-based charging scheme is based-on a simple, light-weight classification technique which is suitable for implementation on a vehicle or a charging station. We show that prediction-based charging provides less operating cost and less CO 2 emissions.

Optimal Pricing with Speculators and Strategic Consumers

Abstract: This paper studies a monopolist firm selling a fixed capacity. The firm sets a price before demand uncertainty is resolved. Speculators may enter the market purely with the intention of resale, which can be profitable if demand turns out to be high. Consumers may strategically choose when to purchase, and they may also choose to purchase from the firm or from the speculators. We characterize equilibrium prices and profits and analyze the long-run capacity decisions of the firm. There are three major findings. First, the presence of speculators increases the firm's expected profits even though the resale market competes with the firm. Second, by facilitating resale, the firm can mimic dynamic pricing outcomes and enjoy the associated benefits while charging a fixed price. Third, speculative behavior may generate incentives for the seller to artificially restrict supply, and thus may lead to lower capacity investments. We also explore several model extensions that highlight the robustness of our results.

Dynamic pricing: when to entice brand switching and when to reward consumer loyalty

Abstract: This article develops a theory of dynamic pricing in which firms may offer separate prices to different consumers based on their past purchases. Brand preferences over two periods are described by a copula admitting various degrees of positive dependence. When commitment to future prices is infeasible, each firm offers lower prices to its rival's customers. When firms can commit to future prices, consumer loyalty is rewarded if preference dependence is low, but enticing brand switching occurs if preference dependence is high. Our theory provides a unified treatment of the two pricing policies and sheds light on observed practices across industries.

An Experimental Comparison of Critical Peak and Hourly Pricing: The PowerCentsDC Program*

Abstract: This paper reports on the results of a dynamic pricing experiment that compares the performance of three popular pricing programs–hourly pricing, critical peak pricing, and critical peak-pricing with a rebate–for a representative sample from the population of households in the District of Columbia. The sampled households differ in terms of their income levels, electricityusing appliance holdings and whether they own a smart thermostat. Using a nonparametric conditional mean estimation framework that allows for customer-specific fixed effects and hourof-sample fixed effects, I find that customers on all of the dynamic pricing programs substantially reduce their electricity consumption during high-priced periods. The hourly average treatment effects associated with each of these dynamic pricing plans are larger in absolute value for households with all-electric heating and households with smart thermostats. Low-income households have significantly larger hourly average treatment effects than higher income households on the same dynamic-pricing tariff. The results of these experiments are also used to investigate two hypotheses about differences in the customer-level demand response to the three dynamic pricing tariffs. Specifically, I find that for roughly the same marginal price during a critical peak period, critical peak pricing yields a larger hourly average demand reduction than critical peak pricing with a rebate. I also find that the demand reduction associated with higher hourly prices is very similar to the predicted demand reduction associated with the same price increase under critical peak pricing.

Intertemporal Pricing and Consumer Stockpiling

Abstract: We study a dynamic pricing problem for a class of products with stable consumption patterns (e.g., household items, staple foods). Consumers may stock up the product at current prices for future consumption, but they incur inventory holding costs. We model this situation as a dynamic game over an infinite time horizon: in each period, the seller sets a price, and each consumer chooses how many units to buy. We develop a solution methodology based on rational expectations. By endowing each player with beliefs, we decouple the dynamic game into individual dynamic programs for each player. We solve for the rational expectations equilibrium, where all players make optimal dynamic decisions given correct beliefs about others' behavior. In equilibrium, the seller may either charge a constant fixed price or offer periodic price promotions at predictable time intervals. We show that promotions are useful when frequent shoppers are willing to pay more for the product than are occasional shoppers. We also develop several model extensions to study the impact of consumer stockpiling on the seller's inventory, production, and rationing strategies.

Minimizing the Electricity Bill of Cooperative Users under a Quasi-Dynamic Pricing Model

Abstract: Dynamic energy pricing is a promising development that addresses the concern of finding an environmentally friendly solution to meeting energy needs of customers while minimizing their electrical energy bill. In this paper, we mathematically formulate the electrical energy bill minimization problem for cooperative networked consumers who have a single energy bill, such as those working in a commercial/industrial building. The idea is to schedule user requests for appliance use at different times during a fixed interval based on dynamic energy prices during that interval. Two different methods are presented to minimize the energy cost of such users under non-interruptible or interruptible jobs. The methods relay on a quasi-dynamic pricing function for unit of energy consumed, which comprises of a base price and a penalty term. The methods minimize the energy cost of the users while meeting all the scheduling constraints and heeding the pricing function. The proposed methods result in significant savings in the energy bill under different usage pricing, and scheduling constraints.

Revenue Management: A Practical Pricing Perspective

Abstract: Introduction PART I: REVENUE MANAGEMENT THEORY AND ISSUES The Applications of Revenue Management and Pricing The Role of Space in Revenue Management Pricing and Revenue Optimization: Maximizing Staff Effectiveness The Era of Convergence in Revenue Management Does the Consumer Trust You? The Changing Meaning of Luxury The Future of Airline Distribution and Revenue Management GDS Capabilities, OD Control and Dynamic Pricing B2B Price Optimization Analytics Fencing in the Practice of Revenue Management PART II: APPLICATIONS Search-Based Advertising: An Overview from a Revenue Management Angle Revenue Management and Air Cargo Practical Pricing for the Hotel Industry Practical Pricing and the Airline Industry Setting Optimal Rents for Apartment Firms Practical Pricing and the Restaurant industry: Application of Revenue Management Principles to Pricing Menus and Services Practical Pricing for the Car Parking Industry Golf Course Revenue Management