Bidding

About: Bidding is a research topic. Over the lifetime, 15371 publications have been published within this topic receiving 294233 citations. The topic is also known as: competitive bidding.

Coordinated Bidding Strategy of Wind Farms and Power-to-Gas Facilities Using a Cooperative Game Approach

Abstract: Power-to-gas (P2G) technology is an effective way to provide additional flexibility to electricity systems with high penetration of renewable energy. Effective P2G bidding strategies in electricity markets can bring in arbitrage opportunities. This article proposes a coordinated bidding strategy for wind farms and P2G facilities using a cooperative game approach. A coordination framework for optimizing the profit of wind farms and P2G facilities is presented, in which wind power participates in day-ahead, real-time and reserve markets, and the P2G facilities participate in day-ahead and reserve markets for arbitrage. Wind farms strategically purchase reserve to avoid high imbalance penalties. As a coalition, wind farms can provide energy to P2G facilities and receive reserve capacity in return. The coordinated bidding strategy is modeled on stochastic programming to maximize the expected joint profit of wind farms and P2G facilities. Nucleolus and Shapley-value based methods are proposed to allocate profits to each player in the coalition. Numerical results based on real world market data demonstrate that the proposed bidding strategy can increase the profits of both wind farms and P2G facilities. Strategically purchasing reserve can reduce the imbalance penalties of wind farms, and participating in reserve markets of P2G facilities can increase profit.

Achieving Performance and Availability Guarantees with Spot Instances

Abstract: In the Infrastructure-as-a-Service (IaaS) cloud computing market, spot instances refer to virtual servers that are rented via an auction. Spot instances allow IaaS providers to sell spare capacity while enabling IaaS users to acquire virtual servers at a lower price than the regular market price (also called "on demand" price). Users bid for spot instances at their chosen limit price. Based on the bids and the available capacity, the IaaS provider sets a clearing price. A bidder acquires their requested spot instances if their bid is above the clearing price. However, these spot instances may be terminated by the IaaS provider impromptu if the auction's clearing price goes above the user's limit price. In this context, this paper addresses the following question: Can spot instances be used to run paid web services while achieving performance and availability guaran-tees? The paper examines the problem faced by a Software-as-a-Service (SaaS) provider who rents spot instances from an IaaS provider and uses them to provide a web service on behalf of a paying customer. The SaaS provider incurs a monetary cost for renting computing resources from the IaaS provider, while charging its customer for executing web service transactions and paying penalties to the customer for failing to meet performance and availability objectives. To address this problem, the paper proposes a bidding scheme and server allocation policies designed to optimize the average revenue earned by the SaaS provider per time unit. Experimental results show that the proposed approach delivers higher revenue to the SaaS provider than an alternative approach where the SaaS provider runs the web service using "on demand" instances. The paper also shows that the server allocation policies seamlessly adapt to varying market conditions, traffic conditions, penalty levels and transaction fees.

Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium

Abstract: In online advertising markets, advertisers often purchase ad placements through bidding in repeated auctions based on realized viewer information. We study how budget-constrained advertisers may bid in the presence of competition, when there is uncertainty about future bidding opportunities as well as competitors' heterogenous preferences and budgets. We formulate this problem as a sequential game of incomplete information, where bidders know neither their own valuation distribution, nor the budgets and valuation distributions of their competitors. We introduce a family of dynamic bidding strategies we refer to as "adaptive pacing" strategies, in which advertisers adjust their bids throughout the campaign according to the sample path of observed expenditures. We analyze the performance of this class of strategies under different assumptions on competitors' behavior. Under arbitrary competitors' bids, we establish through matching lower and upper bounds the asymptotic optimality of this class of strategies as the number of auctions grows large. When adopted by all the bidders, the dynamics converge to a tractable and meaningful steady state. Moreover, we show that these strategies constitute an approximate Nash equilibrium in dynamic strategies: The benefit of unilaterally deviating to other strategies, including ones with access to complete information, becomes negligible as the number of auctions and competitors grows large. This establishes a connection between regret minimization and market stability, by which advertisers can essentially follow equilibrium bidding strategies that also ensure the best performance that can be guaranteed off-equilibrium.