Showing papers on "Dynamic pricing published in 2015"
TL;DR: In this paper, the authors present a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones, to reduce the consumer's electricity bill whilst minimizing the daily volume of curtailed energy, and therefore considering the user's comfort level.
Abstract: This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer’s electricity bill whilst minimizing the daily volume of curtailed energy, and therefore considering the user’s comfort level. To avoid shifting a large portion of consumer demand toward the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer’s demand goes beyond a prescribed power threshold. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household’s daily electricity bill while preserving comfort level, as well as preventing creation of new least-price peaks.
311 citations
01 Jul 2015
TL;DR: The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household's daily electricity bill while preserving comfort level, as well as preventing creation of new least-price peaks.
Abstract: This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer�s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user�s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer�s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller to minimize the household�s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks.
277 citations
TL;DR: In this paper, the authors provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an in-depth overview of the available literature on dynamic pricing and learning.
245 citations
TL;DR: In this paper, the optimal storage management and sizing problem in the presence of renewable energy and dynamic pricing associated with electricity from the grid is formulated as a stochastic dynamic program that aims to minimize the long-run average cost of electricity used and investment in storage, if any, while satisfying all the demand.
Abstract: We address the optimal energy storage management and sizing problem in the presence of renewable energy and dynamic pricing associated with electricity from the grid. We formulate the problem as a stochastic dynamic program that aims to minimize the long-run average cost of electricity used and investment in storage, if any, while satisfying all the demand. We model storage with ramp constraints, conversion losses, dissipation losses and an investment cost. We prove the existence of an optimal storage management policy under mild assumptions and show that it has a dual threshold structure. Under this policy, we derive structural results, which indicate that the marginal value from storage decreases with its size and that the optimal storage size can be computed efficiently. We prove a rather surprising result, as we characterize the maximum value of storage under constant prices and i.i.d. net-demand processes: if the storage is a profitable investment, then the ratio of the amortized cost of storage to the constant price is less than 1/4. We further perform sensitivity analysis on the size of optimal storage and its gain via a case study. Finally, with a computational study on real data, we demonstrate significant savings with energy storage.
243 citations
15 Jun 2015
TL;DR: This work shows that profit under any dynamic pricing strategy cannot exceed profit under the optimal static pricing policy (i.e., one which is agnostic of stochastic fluctuations in the system load), and explains the apparent paradox.
Abstract: We study optimal pricing strategies for ride-sharing platforms, using a queueing-theoretic economic model. Analysis of pricing in such settings is complex: On one hand these platforms are two-sided - this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support very high temporal-resolution for data collection and pricing - this requires stochastic models that capture the dynamics of drivers and passengers in the system. We focus our attention on the value of dynamic pricing: where prices can react to instantaneous imbalances between available supply and incoming demand. We find two main results: We first show that profit under any dynamic pricing strategy cannot exceed profit under the optimal static pricing policy (i.e., one which is agnostic of stochastic fluctuations in the system load). This result belies the prevalence of dynamic pricing in practice. Our second result explains the apparent paradox: we show that dynamic pricing is much more robust to fluctuations in system parameters compared to static pricing. Moreover, these results hold even if the monopolist maximizes welfare or throughput. Thus dynamic pricing does not necessarily yield higher performance than static pricing - however, it lets platforms realize the benefits of optimal static pricing, even with imperfect knowledge of system parameters.
235 citations
TL;DR: A queueing-theoretic economic model is built that lets platforms realize the benefits of optimal static pricing, even with imperfect knowledge of system parameters, and shows that dynamic pricing is much more robust to fluctuations in system parameters compared to static pricing.
Abstract: We study optimal pricing strategies for ride-sharing platforms, such as Lyft, Sidecar, and Uber. Analysis of pricing in such settings is complex: On one hand these platforms are two-sided -- this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support high temporal-resolution for data collection and pricing -- this requires stochastic models that capture the dynamics of drivers and passengers in the system.In this paper we build a queueing-theoretic economic model to study optimal platform pricing. In particular, we focus our attention on the value of dynamic pricing: where prices can react to instantaneous imbalances between available supply and incoming demand. We find two main results: We first show that performance (throughput and revenue) under any dynamic pricing strategy cannot exceed that under the optimal static pricing policy (i.e., one which is agnostic of stochastic fluctuations in the system load). This result belies the prevalence of dynamic pricing in practice. Our second result explains the apparent paradox: we show that dynamic pricing is much more robust to fluctuations in system parameters compared to static pricing. Thus dynamic pricing does not necessarily yield higher performance than static pricing -- however, it lets platforms realize the benefits of optimal static pricing, even with imperfect knowledge of system parameters.
168 citations
TL;DR: In this paper, the authors identify most recent trends in dynamic pricing research involving such problems and identify a number of possible directions for future research, including problems with multiple products, problems with competition, and problems with limited demand information.
Abstract: Dynamic pricing enables a firm to increase revenue by better matching supply with demand, responding to shifting demand patterns, and achieving customer segmentation. In the last 20 years, numerous success stories of dynamic pricing applications have motivated a rapidly growing research interest in a variety of dynamic pricing problems in the academic literature. A large class of problems that arise in various revenue management applications involve selling a given amount of inventory over a finite time horizon without inventory replenishment. In this study, we identify most recent trends in dynamic pricing research involving such problems. We review existing research on three new classes of problems that have attracted a rapidly growing interest in the last several years, namely, problems with multiple products, problems with competition, and problems with limited demand information. We also identify a number of possible directions for future research.
154 citations
TL;DR: Simulation results show that the proposed scheduling method leads to significant reduction in energy costs for diverse load scenarios with the electricity demand from the grid, and the deployment of the proposed method is advantageous for both users and utilities.
151 citations
TL;DR: Simulation results show that the proposed schemes can provide effective management for household electricity usage and bidirectional transactions.
Abstract: In this paper, the electricity cost minimization problem is considered for a residential microgrid which consists of multiple households (users) equipped with renewable-based distributed energy resource (DER). Each user has a set of nonshiftable and shiftable loads. Bidirectional electricity transactions are allowed, and a dynamic pricing model for the purchasing/selling of electricity from/to the grid is proposed. In order to reduce the electricity cost, the following decisions needed to be made: 1) scheduling decisions for the shiftable loads; 2) purchasing/selling decisions for each user at each time slot; and 3) amount decisions of the electricity purchased/sold by the users. An optimization problem to minimize the total electricity cost is formulated to obtain the optimal amount of electricity consumed, sold, and purchased for each user, respectively. A centralized algorithm based on dynamic programming, $Q$ -learning, and Lyapunov methods are proposed to solve the optimization problem with perfect information, with partial information, and without information of any time-varying parameters, respectively. For the latter two cases, distributed algorithms are designed for practical implementation. Simulation results show that the proposed schemes can provide effective management for household electricity usage and bidirectional transactions.
140 citations
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
TL;DR: In this article, Cachon et al. consider a multi-period single product pricing problem with an unknown demand curve and investigate the price trajectory induced by a misspecified model and quantifying the magnitude of revenue losses as a function of the time horizon relative to an oracle that knows the true demand curve.
Abstract: We consider a multiperiod single product pricing problem with an unknown demand curve. The seller's objective is to adjust prices in each period so as to maximize cumulative expected revenues over a given finite time horizon; in doing so, the seller needs to resolve the tension between learning the unknown demand curve and maximizing earned revenues. The main question that we investigate is the following: How large of a revenue loss is incurred if the seller uses a simple parametric model that differs significantly i.e., is misspecified relative to the underlying demand curve? We measure performance by analyzing the price trajectory induced by this misspecified model and quantifying the magnitude of revenue losses as a function of the time horizon relative to an oracle that knows the true underlying demand curve. The "price of misspecification" is expected to be significant if the parametric model is overly restrictive. Somewhat surprisingly, we show under reasonably general conditions that this need not be the case.
This paper was accepted by Gerard Cachon, stochastic models and simulation.
TL;DR: An inventory model for perishable foods, in which the demand depends on the price and quality that decays continuously, is formulated to determine a joint dynamic pricing and preservation technology investment strategy while maximizing the total profit from selling a given initial inventory of foods.
Abstract: The food quality has always played an important role in the retail process since it has been considered as a direct factor to influence a consumer’s purchase decision. In this paper, we formulate an inventory model for perishable foods, in which the demand depends on the price and quality that decays continuously. The objective is to determine a joint dynamic pricing and preservation technology investment strategy while maximizing the total profit from selling a given initial inventory of foods. We first prove the existence of an optimal solution based on Filippov–Cesari theorem. Then, we obtain all the candidates and provide the conditions that make a certain candidate be an optimal solution according to Pontryagin’s maximum principle. Next, we present an effective algorithm to search for the optimal strategy. Finally, two numerical examples are employed to illustrate the solution procedure and the results, followed by sensitivity analysis and managerial insights.
TL;DR: In this paper, a detailed storage model linking together technical, economic and electricity market parameters is developed to maximize the profit of the storage owner (electricity customer) under simplifying assumptions, by determining the optimal charge/discharge schedule.
Abstract: Price arbitrage involves taking advantage of an electricity price difference, storing electricity during low-prices times, and selling it back to the grid during high-prices periods. This strategy can be exploited by customers in presence of dynamic pricing schemes, such as hourly electricity prices, where the customer electricity cost may vary at any hour of day, and power consumption can be managed in a more flexible and economical manner, taking advantage of the price differential. Instead of modifying their energy consumption, customers can install storage systems to reduce their electricity bill, shifting the energy consumption from on-peak to off-peak hours. This paper develops a detailed storage model linking together technical, economic and electricity market parameters. The proposed operating strategy aims to maximize the profit of the storage owner (electricity customer) under simplifying assumptions, by determining the optimal charge/discharge schedule. The model can be applied to several kinds of storages, although the simulations refer to three kinds of batteries: lead-acid, lithium-ion (Li-ion) and sodium-sulfur (NaS) batteries. Unlike literature reviews, often requiring an estimate of the end-user load profile, the proposed operation strategy is able to properly identify the battery-charging schedule, relying only on the hourly price profile, regardless of the specific facility’s consumption, thanks to some simplifying assumptions in the sizing and the operation of the battery. This could be particularly useful when the customer load profile cannot be scheduled with sufficient reliability, because of the uncertainty inherent in load forecasting. The motivation behind this research is that storage devices can help to lower the average electricity prices, increasing flexibility and fostering the integration of renewable sources into the power system.
TL;DR: In this article, the authors evaluate various dynamic pricing programs in the U.S. and Europe, and provide insights into various aspects including risks and rewards, enabling technologies, lower-income groups and customer types surrounding programs such as Time-of-Use (TOU), Critical Peak Pricing (CPP), Peak Time Rebates (PTR), and Real Time Pricing (RTP).
Abstract: With the development of demand response (DR) technologies and increasing electricity demand, dynamic pricing has been a popular topic in many countries. This paper evaluates various dynamic pricing programs in the U.S. and Europe, and provides insights into various aspects including risks and rewards, enabling technologies, lower-income groups and customer types surrounding programs such as Time-of-Use (TOU), Critical Peak Pricing (CPP), Peak Time Rebates (PTR) and Real Time Pricing (RTP). We conclude this paper with three main findings: (1) policy coordination in promoting dynamic pricing programs between federal and state regulatory agencies is very critical; (2) customer engagement is very important and can be enhanced via more accessible educational programs and policy adjustments; and (3) more investment in related R&D is required to construct a commonly accepted methodology for measuring the effectiveness of dynamic pricing programs.
01 Jan 2015
TL;DR: In this article, the authors proposed an aggregated dynamic model for multimodal mobility with the consideration of parking, and utilize the model to evaluate management policies, such as parking pricing.
Abstract: Cruising-for-parking is a critical mobility issue in urban cities. The cost and accessibility of parking significantly influence people’s travel behavior (such as mode choice) and facility choice (on-street or garage parking). Furthermore, parking affects traffic performance for all users of a city. Car-users may have to cruise for on-street parking space before reaching destinations and cause delays eventually to everyone, even users with destinations outside limited parking areas. It is therefore crucial to understand the impact of parking on mobility and identify traffic management policies to avoid the negative externalities. Most existing studies of parking either fall short in reproducing the dynamic spatiotemporal features of traffic congestion in general and the cruising-for-parking phenomenon, or require data that are expensive and difficult to collect. In this paper, the authors propose an aggregated dynamic model for multimodal mobility with the consideration of parking, and utilize the model to evaluate management policies, such as parking pricing. The proposed approach is based on the recent development of the low-scattered Macroscopic Fundamental Diagram (MFD), which demonstrated decent representation of the complex dynamics of transport system at network-level for single-mode and bi-modal (car and bus) urban networks. The MFD-based bi-modal modeling framework is extended with a parking module where cruising delay and change of behavior (e.g. mode choice and parking facility choice) caused by parking are taken into account. Pricing strategies of parking are then developed to reduce congestion and travel cost. Result of a case study shows that traffic performance under various types of parking policies can be investigated and close-to-optimum pricing schemes can be obtained. Furthermore, parking market competition can be simulated and studied with the proposed modeling approach.
IBM1
TL;DR: A Mixed-Integer Non-linear Programming (MINLP) formulation of the optimal pump scheduling problem is presented and a Lagrangian decomposition approach is presented that exploits the structure of the problem leading to smaller problems that are solved independently.
TL;DR: In this paper, a new dynamic dial-a-ride policy is introduced, one that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method proposed by Hyttia et al. (2012) for large-scale systems.
Abstract: Non-myopic dial-a-ride problem and other related dynamic vehicle routing problems often ignore the need for non-myopic pricing under the assumption of elastic demand, which leads to an overestimation of the benefits in level of service and resulting inefficiencies. To correct this problem, a new dynamic dial-a-ride policy is introduced, one that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method proposed by Hyttia et al. (2012) for large-scale systems. By including social optimal pricing, the social welfare of the resulting system outperforms the marginal pricing assumed for previous approaches over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20–31% range. For a given demand function, we can derive the optimal fleet size to maximize social welfare. Sensitivity tests to the optimal price confirm that it leads to an optimal social welfare while the marginal pricing policy does not. A comparison of single passenger taxis to shared-taxis shows that system cost may reduce at the expense of decreased social welfare, which agrees with the results of Jung et al. (2013).
TL;DR: This paper discusses the formulation of crowding in public transport and its implications for pricing, seating capacity and optimal scheduling, and derives the optimal dynamic pricing and optimal share of seats for the one OD pair case.
Abstract: This paper discusses the formulation of crowding in public transport and its implications for pricing, seating capacity and optimal scheduling. An analytical model is used to describe the user equilibrium and the optimal equilibrium for different stylized conditions. For the one OD pair case with identical desired arrival time, we derive the optimal dynamic pricing and optimal share of seats. For the uniformly distributed desired arrival times case, we derive the optimal time table and the optimal pricing. Next we generalize the results to the case of a small network with several stations, stochastic choice and allocation of seats.
TL;DR: An intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture-an effort towards optimizing the energy consumption profile of PH EVs users is proposed.
Abstract: Future large-scale deployment of plug-in hybrid electric vehicles (PHEVs) will render massive energy demand on the electric grid during peak-hours. We propose an intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture—an effort towards optimizing the energy consumption profile of PHEVs users. Each micro-grid decides real-time dynamic price as home-price and roaming-price , depending on the supply-demand curve, to optimize its revenue. Consequently, two types of energy services are considered—home micro-grid energy, and foreign micro-grid energy. After designing the PHEVs’ mobility and battery models, the pricing policies for the home-price and the roaming-price are presented. A decision making process to implement a cost-effective charging and discharging method for PHEVs is also demonstrated based on the real-time price decided by the micro-grids. We evaluate and compare the results of distributed pricing policy with other existing centralized/distributed ones. Simulation results show that using the proposed architecture, the utility corresponding to the PHEVs increases by approximately $34$ percent over that of the existing ones for optimal charging of PHEVs.
TL;DR: In this article, the authors study a firm's optimal pricing policy under commitment, where customers arrive over time, are strategic in timing their purchases, and are heterogeneous along two dimensions: their valuation for the firm's product and their willingness to wait before purchasing or leaving.
Abstract: We study a firm's optimal pricing policy under commitment. The firm's objective is to maximize its long-term average revenue given a steady arrival of strategic customers. In particular, customers arrive over time, are strategic in timing their purchases, and are heterogeneous along two dimensions: their valuation for the firm's product and their willingness to wait before purchasing or leaving. The customers' patience and valuation may be correlated in an arbitrary fashion. For this general formulation, we prove that the firm may restrict attention to cyclic pricing policies, which have length, at most, twice the maximum willingness to wait of the customer population. To efficiently compute optimal policies, we develop a dynamic programming approach that uses a novel state space that is general, capable of handling arbitrary problem primitives, and that generalizes to finite horizon problems with nonstationary parameters. We analyze the class of monotone pricing policies and establish their suboptimality in general. Optimal policies are, in a typical scenario, characterized by nested sales, where the firm offers partial discounts throughout each cycle, offers a significant discount halfway through the cycle, and holds its largest discount at the end of the cycle. We further establish a form of equivalence between the problem of pricing for a stream of heterogeneous strategic customers and pricing for a pool of heterogeneous customers who may stockpile units of the product.
This paper was accepted by Yossi Aviv, operations management.
TL;DR: In this paper, the authors evaluated the performance of smart appliances in real life circumstances for various applications of demand response and found that the smart appliances were well received by the users and no response fatigue was observed.
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TL;DR: Model-assisted batch reinforcement learning is applied to the setting of building climate control subjected to dynamic pricing and it is found that within 10 to 20 days sensible policies are obtained that can be used for different outside temperature regimes.
Abstract: Driven by the opportunity to harvest the flexibility related to building climate control for demand response applications, this work presents a data-driven control approach building upon recent advancements in reinforcement learning. More specifically, model assisted batch reinforcement learning is applied to the setting of building climate control subjected to a dynamic pricing. The underlying sequential decision making problem is cast on a markov decision problem, after which the control algorithm is detailed. In this work, fitted Q-iteration is used to construct a policy from a batch of experimental tuples. In those regions of the state space where the experimental sample density is low, virtual support samples are added using an artificial neural network. Finally, the resulting policy is shaped using domain knowledge. The control approach has been evaluated quantitatively using a simulation and qualitatively in a living lab. From the quantitative analysis it has been found that the control approach converges in approximately 20 days to obtain a control policy with a performance within 90% of the mathematical optimum. The experimental analysis confirms that within 10 to 20 days sensible policies are obtained that can be used for different outside temperature regimes.
TL;DR: It is shown that by offering a menu of advance-purchase contracts that differ in when, and for how much, the product can be returned, a firm can more easily price discriminate between privately-informed consumers.
TL;DR: In this paper, a robust demand response control of commercial buildings for smart grid under load prediction uncertainty is proposed. But the authors do not consider the impacts of load prediction uncertainties on the performance of the control.
TL;DR: In this paper, the authors exploit the presence of drops in offered fares over time as an indicator of an active yield management intervention by two main European Low-Cost Carriers, and measure its effectiveness.
TL;DR: In this paper, the authors used a choice experiment approach to determine whether disclosing the environmental and system benefits of dynamic electricity tariffs to residential customers can increase adoption, and found that environmentally conscious respondents reduced their required discount to switch to dynamic tariffs around 10%.
TL;DR: In this paper, the joint dynamic pricing and inventory control policy for a stochastic inventory system with perishable products is considered, and the optimal joint pricing and production schedule is found by solving a Hamilton-Jacobi-Bellman (HJB) equation.
Abstract: This paper considers the joint dynamic pricing and inventory control policy for a stochastic inventory system with perishable products. The inventory system, with random disturbance, is modelled as a continuous-time stochastic differential equation. Combined dynamic pricing and production control, a stochastic dynamic optimisation problem that maximises the total discounted profit is developed. By applying the stochastic optimal control method, we formulate the problem of finding the optimal joint dynamic pricing and production schedule as the problem of solving a Hamilton–Jacobi–Bellman (HJB) equation. It is shown that the optimal dynamic pricing and production rate take the linear feedback form of the inventory level, which allows the decision-maker to effectively adjust the strategies as time evolves. In addition, to highlight the advantage of the joint dynamic pricing and production strategy, the case of the optimal production with static price is considered. Numerical examples are given to illustrate...
TL;DR: Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.
Abstract: Vehicle-to-grid (V2G) system with efficient demand response management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at electric vehicles (EVs). An incentivized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling-based auction. In the proposed approach, a number of aggregators sit on the first-level auction responsible to communicate with a group of EVs. EVs as bidders consider quality of energy (QoE) requirements, and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.
TL;DR: In this paper, a decentralized two-period supply chain is considered, where a manufacturer produces a product with benefits of cost learning, and sells it through a retailer facing a price-dependent demand.
Abstract: We consider a decentralized two-period supply chain in which a manufacturer produces a product with benefits of cost learning, and sells it through a retailer facing a price-dependent demand. The manufacturer's second-period production cost declines linearly in the first-period production, but with a random learning rate. The manufacturer may or may not have the inventory carryover option. We formulate the resulting problems as two-period Stackelberg games and obtain their feedback equilibrium solutions explicitly. We then examine the impact of mean learning rate and learning rate variability on the pricing strategies of the channel members, on the manufacturer's production decisions, and on the retailer's procurement decisions. We show that as the mean learning rate or the learning rate variability increases, the traditional double marginalization problem becomes more severe, leading to greater efficiency loss in the channel. We obtain revenue sharing contracts that can coordinate the dynamic supply chain. In particular, when the manufacturer may hold inventory, we identify two major drivers for inventory carryover: market growth and learning rate variability. Finally, we demonstrate the robustness of our results by examining a model in which cost learning takes place continuously
TL;DR: In this article, a fully distributed Demand-Side Management system for smart grid infrastructures, especially tailored to reduce the peak demand of residential users, is proposed, where energy tariffs are function of the overall power demand of customers.