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.

Electric Vehicle Power Trading Mechanism Based on Blockchain and Smart Contract in V2G Network

Abstract: In order to realize peer-to-peer (P2P) transactions between electric vehicles (EVs) in vehicle-to-grid (V2G) networks, we propose an EV power trading model based on blockchain and smart contract. Firstly, based on the blockchain and smart contract technology, a decentralized power trading model is proposed to realize the information equivalence and transparent openness of power trading. Then, considering the randomness and uncertainty of EV charging and discharging, the EV trading parties use the reverse auction mechanism based on dynamic pricing strategy to complete the transaction matching, which can not only improve the profit of the less competitive power seller, but also it can reduce the cost of the electricity purchaser. Finally, in order to verify the feasibility of our proposed scheme, V2G's EV power trading smart contract was designed, and the smart contract was released to Ethereum and simulated experiments were carried out. The effectiveness of the proposed scheme is verified by simulation experiments and comparison with traditional power trading schemes.

Joint Dynamic Pricing and Order Fulfillment for E-commerce Retailers

Abstract: We consider an e-commerce retailer (e-tailer) who sells a catalog of products to customers from different regions during a finite selling season and fulfills orders through multiple fulfillment centers. The e-tailer faces a joint pricing and fulfillment (JPF) optimization problem: at the beginning of each period, the e-tailer needs to jointly decide the price for each product and how to fulfill an incoming order (i.e., from which warehouse to ship the order). The objective of the e-tailer is to maximize its total expected profits defined as total expected revenues minus total expected shipping costs. (All other costs are fixed in this problem.) The exact optimal policy for JPF is difficult to solve; so, we propose two heuristic controls that have provably good performance compared to reasonable benchmarks. Our first heuristic control directly uses the solution of a deterministic approximation of JPF as its control parameters. Our second heuristic control improves the first one by adaptively adjusting the ...

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.