Showing papers on "Bidding published in 2018"
TL;DR: A cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment was shown to increase the profits of service providers and infrastructure suppliers simultaneously.
Abstract: Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.
311 citations
TL;DR: In this paper, a stochastic formulation of a storage owner's arbitrage profit maximization problem under uncertainty in day-ahead and real-time market prices is proposed, which helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage.
Abstract: Electricity markets must match real-time supply and demand of electricity. With increasing penetration of renewable resources, it is important that this balancing is done effectively, considering the high uncertainty of wind and solar energy. Storing electrical energy can make the grid more reliable and efficient and energy storage is proposed as a complement to highly variable renewable energy sources. However, for investments in energy storage to increase, participating in the market must become economically viable for owners. This paper proposes a stochastic formulation of a storage owner's arbitrage profit maximization problem under uncertainty in day-ahead and real-time market prices. The proposed model helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage. Case study results on realistic market price data show that the novel stochastic bidding approach does significantly better than the deterministic benchmark.
166 citations
TL;DR: Experimental results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best and the feasibility and practicality of electricity price prediction is confirmed.
Abstract: Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.
131 citations
17 Oct 2018
TL;DR: The results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.
Abstract: Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.
124 citations
TL;DR: A hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy and a k-step power consumption forecasting strategy is shown to promote the proposed framework for real-world application usage.
Abstract: Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.
122 citations
TL;DR: A novel bidding strategy for PEVs offering V2G by including the projected battery degradation cost to integrate them into microgrid operation and two energy management strategies are proposed for inclusion of V1G into the micro grid operation based on the forecast accuracy on energy supply and demand, and market prices.
Abstract: In modern electric power systems, plug-in electric vehicle (PEV) with vehicle-to-grid (V2G) potential are becoming reliable and flexible resources for energy balancing under varying energy supply and demand scenarios. In this evolving paradigm, designing energy management strategies for feasible and cost-effective utilisation of V2G is one of the several challenges faced by the utility operators and regulators. This paper proposes two energy management strategies to effectively utilize V2G potential of PEVs in managing energy imbalances in grid-connected microgrids. The contributions of this paper are in twofold. First, it proposes a novel bidding strategy for PEVs offering V2G by including the projected battery degradation cost to integrate them into microgrid operation. Second, two energy management strategies are proposed for inclusion of V2G into the microgrid operation based on the forecast accuracy on energy supply and demand, and market prices. The proposed V2G integration strategies are implemented using a multi-agent system developed in Java agent development framework and applied to a microgrid case study system. The simulation results and their analysis show that V2G can be used to maximum depth of discharge levels if the electricity price variation is high and battery cost of PEVs is low.
122 citations
TL;DR: In this article, an optimal control policy and an optimal bidding policy based on realistic market settings and an accurate battery aging model are proposed to solve the problem of battery aging in performance-based frequency regulation markets.
Abstract: Battery participants in performance-based frequency regulation markets must consider the cost of battery aging in their operating strategies to maximize market profits. In this paper, we solve this problem by proposing an optimal control policy and an optimal bidding policy based on realistic market settings and an accurate battery aging model. The proposed control policy has a threshold structure and achieves near-optimal performance with respect to an offline controller that has complete future information. The proposed bidding policy considers the optimal control policy to maximize market profits while satisfying the market performance requirement through a chance-constraint. It factors the value of performance and supports a tradeoff between higher profits and a lower risk of violating performance requirements. We demonstrate the optimality of both policies using simulations. A case study based on the PJM Interconnection LLC (PJM) regulation market shows that the approach is effective at maximizing operating profits.
114 citations
TL;DR: A comprehensive optimal bidding strategy for an energy hub is modeled and takes advantages of multi-inputs vector of energy hub to submit the optimal bids including electricity selling/buying and optimizes the cost.
113 citations
TL;DR: Results show that the DRX market participation can improve the VPP's energy management, and can be solved efficiently by the scenario-based optimization approach.
Abstract: This paper presents a mathematical model for the energy bidding problem of a virtual power plant (VPP) that participates in the regular electricity market and the intraday demand response exchange (DRX) market. Different system uncertainties due to the intermittent renewable energy sources, retail customers’ demand, and electricity prices are considered in the model. The DRX market enables a VPP to purchase demand response services, which can be treated as “virtual energy resources,” from several demand response providers to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. This could increase the expected profit and the renewable energy utilization of the VPP. The overall energy bidding problem is modeled as a three-stage stochastic program, which can be solved efficiently by the scenario-based optimization approach. Extensive numerical results show that the DRX market participation can improve the VPP's energy management.
105 citations
TL;DR: A scenario-based stochastic decision-making model is proposed to determine the optimal strategy for the operation of integrated natural gas generating unit (NGG) and power-to-gas conversion (P2G) facilities in energy and regulation markets.
Abstract: A scenario-based stochastic decision-making model is proposed in this paper to determine the optimal strategy for the operation of integrated natural gas generating unit (NGG) and power-to-gas conversion (P2G) facilities in energy and regulation markets. Using the proposed strategy, the coordination of NGG and P2G facilities will provide a higher market payoff than that of independent NGG and P2G participation. The market price uncertainty is simulated in multiple scenarios using the Latin hypercube sampling method and the conditional value-at-risk strategy is adopted for evaluating the financial risks introduced by price uncertainties. The optimal bidding strategy is developed for both P2G and NGG operations and the Shapley-value method is employed to allocate the market payoff among NGG and P2G facilities. A case study which is based on the Pennsylvania, New Jersey, and Maryland market data is employed to verify the effectiveness of the proposed model and examine the characteristics of the proposed bidding strategy for the optimal operation of integrated NGG and P2G facilities.
105 citations
TL;DR: In this article, a hierarchical control system has been developed to optimize, plan, and control the charging, market bidding, and response to grid system operator control of the EVs in the Los Angeles Air Force Base Electric Vehicle Demonstration.
TL;DR: In this article, the authors evaluate the utility of storage assets given different electricity system configurations, market paradigms, and management schemes using a production cost model and find that storage utilization rates are sensitive to the storage assets' market bidding structure and ownership paradigm, particularly in inflexible electricity systems.
TL;DR: A hybrid method that combines similar and recent day-based selection, correlation and wavelet analysis in a pre-processing stage is presented, revealing positive forecasting results in comparison with other state-of-the-art methods.
TL;DR: This paper proposes a methodology for optimal bidding for a flexibility aggregator participating in three sequential markets, formulate the decision models as multi-stage stochastic programs and generate scenarios for the possible realizations of prices.
TL;DR: In this article, the minimum and maximum amounts of electricity market prices are considered instead of the forecasted prices which allows formulating a collection of robust mixed-integer linear programming problem problems.
TL;DR: This paper proposes bidding strategies and compensation mechanisms for a load aggregator (LA) that implements the direct thermostat control (DTC) program and two compensation policies are proposed and compared.
Abstract: This paper proposes bidding strategies and compensation mechanisms for a load aggregator (LA) that implements the direct thermostat control (DTC) program. By joining the DTC program, residential consumers allow their house temperature to vary within pre-specified zones at pre-specified hours and be controlled by the LA. They also receive compensation from the LA. The heating/cooling (H/C) load control model is integrated into a two-stage bidding framework. Demand-price bidding curves are derived for the day-ahead market to address price uncertainties. Demand-only bids are derived for the real-time (RT) market. The flexibility of H/C loads is exploited in RT to address uncertainties in thermal-related house characteristics as well as uncertainties in weather and non-H/C load. Two compensation policies are proposed and compared. A case study verifies the proposed method.
17 Oct 2018
TL;DR: This paper forms budget constrained bidding as a Markov Decision Process and proposes a model-free reinforcement learning framework to resolve the optimization problem, and employs a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively.
Abstract: Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.
TL;DR: In this article, an information gap decision theory (IGDT) was proposed to obtain the bidding strategy of a renewable-based microgrid (MG) operator to supply the local load with the lowest cost from the alternative energy sources containing upstream grid, micro-turbines (MTs), renewable energy sources (RESs), photovoltaic (PV) systems and wind-turines (WT)) and energy storage system (ESS).
19 Jul 2018
TL;DR: In this article, a reinforcement learning (RL) solution was proposed for handling the complex dynamic environment in sponsored search auction, named SS-RTB, where the state transition probabilities vary between two days.
Abstract: Bidding optimization is one of the most critical problems in online advertising. Sponsored search (SS) auction, due to the randomness of user query behavior and platform nature, usually adopts keyword-level bidding strategies. In contrast, the display advertising (DA), as a relatively simpler scenario for auction, has taken advantage of real-time bidding (RTB) to boost the performance for advertisers. In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB. SS-RTB has a much more complex dynamic environment, due to stochastic user query behavior and more complex bidding policies based on multiple keywords of an ad. Most previous methods for DA cannot be applied. We propose a reinforcement learning (RL) solution for handling the complex dynamic environment. Although some RL methods have been proposed for online advertising, they all fail to address the "environment changing'' problem: the state transition probabilities vary between two days. Motivated by the observation that auction sequences of two days share similar transition patterns at a proper aggregation level, we formulate a robust MDP model at hour-aggregation level of the auction data and propose a control-by-model framework for SS-RTB. Rather than generating bid prices directly, we decide a bidding model for impressions of each hour and perform real-time bidding accordingly. We also extend the method to handle the multi-agent problem. We deployed the SS-RTB system in the e-commerce search auction platform of Alibaba. Empirical experiments of offline evaluation and online A/B test demonstrate the effectiveness of our method.
13 Apr 2018
TL;DR: The blockchain technology with low transaction cost is used to develop the smart contract of public bid and sealed bid, which can ensure the bill secure, private, non-reputability and inalterability owing to all the transactions are recorded in the same but decentralized ledgers.
Abstract: Because of the popularity of the Internet, the integration services have gradually changed people daily life, such as e-commerce activities on transactions, transportation and so on. The E-auction, one of the popular e-commerce activities, allows bidders to directly bid the products over the Internet. As for sealed bid, the extra transaction cost is required for the intermediaries because the third-party is the important role between the buyers and the sellers help to trade both during the auction. In addition, it never guarantees whether the third-party is trust. To resolve the problems, the blockchain technology with low transaction cost is used to develop the smart contract of public bid and sealed bid. The smart contract, proposed in 1990 and implements via Ethereum platform, can ensure the bill secure, private, non-reputability and inalterability owing to all the transactions are recorded in the same but decentralized ledgers. The smart contract is composed of the address of Auctioneer, the start auction time, deadline, the address of current winner, the current highest price. In the experiments, the accounts are created through Ethereum wallet. In miner stage, the MinerGate is used in miner stage for obtaining money to pay the transaction fee. At recorder stage, the nodes of blockchain are synchronized to generate smart contract.
TL;DR: In this paper, the authors present new models for evaluating flexible resources in two-settlement electricity markets (day-ahead and real-time) with uncertain net loads (demand minus wind).
Abstract: Part one of this two-part paper presents new models for evaluating flexible resources in two-settlement electricity markets (day-ahead and real-time) with uncertain net loads (demand minus wind). Physical resources include wind together with fast- and slow-start demand response and thermal generators. We also model financial participants (virtual bidders). Wind is stochastic, represented by a set of scenarios. The two-settlement system is modeled as a two-stage process in which the first stage involves unit commitment and tentative scheduling, while the second stage adjusts flexible resources to resolve imbalances. The value of various flexible resources is evaluated through four two-settlement models: 1) an equilibrium model in which each player independently schedules its generation or purchases to maximize expected profit; 2) a benchmark (expected system cost minimization); 3) a sequential equilibrium model in which the independent system operator first optimizes against a deterministic wind power forecast; and 4) an extended sequential equilibrium model with self-scheduling by profit-maximizing slow-start generators. A tight convexified unit commitment allows for demonstration of certain equivalencies of the four models. We show how virtual bidding enhances market performance, since, together with self-scheduling by slow-start generators, it can help a deterministic day-ahead market to choose the most efficient unit commitment.
TL;DR: The simulation results of a realistic case of microgrids from Guizhou Province, China, validate that the proposed peer-to-peer energy trading mechanism is capable of raising themicrogrids’ profits and renewable energy source utilization.
Abstract: Networked microgrids are emerging for coordinating distributed energy resources in distribution networks in the future Energy Internet, for which developing an efficient energy market model is crucial for facilitating multi-directional trading among microgrids. In this paper, a peer-to-peer energy trading mechanism is presented using non-cooperative bidding among microgrids. Multidimensional willingness, including time pressure and counter behavior for mimicking the personalized behaviors of microgrids, was taken into account in the design of the bidding strategy. Under a parallel trading framework based on a blockchain, the proposed multidimensional willingness bidding strategy turns out to be able to make rational decisions with sufficient flexibility in the bidding process. The simulation results of a realistic case of microgrids from Guizhou Province, China, validate that the proposed peer-to-peer energy trading mechanism is capable of raising the microgrids’ profits and renewable energy source utilization.
TL;DR: A new framework for energy scheduling of an active distribution network based on the concept of technical virtual power plant (TVPP), considering operational constraints of distribution network is proposed, and the proposed bilevel problem is transformed into mixed integer linear programming problem.
Abstract: Regarding the potentials of activating commercial consumers in demand response programs, this paper proposes a new framework for energy scheduling of an active distribution network based on the concept of technical virtual power plant (TVPP), considering operational constraints of distribution network. The TVPP enables presence of commercial buildings and other distributed energy resources in day-ahead (DA) electricity market, as a price maker agent. In this regard, a bilevel optimization framework is designed to optimize the bidding strategy of TVPP in the DA market with the main goal of maximizing TVPP profit. The upper-level problem maximizes the TVPP profit, while in the lower level, market is cleared from independent system operator viewpoint. Using Karush—Kuhn–Tucker optimality conditions and strong duality theory, the proposed bilevel problem is transformed into mixed integer linear programming problem. Implementing the model on the Roy Billinton Test System (RBTS) test system demonstrates the applicability of the proposed model.
TL;DR: In this article, a short-term planning model is developed to determine the bidding curves on a day-ahead market for a price-maker retailer with flexible power demand, which concerns the interactions between the spot price and the flexible demand.
Abstract: This study develops a short-term planning model to determine the bidding curves on a day-ahead market for a price-maker retailer with flexible power demand. It concerns the interactions between the spot price and the flexible demand. Both conditional value-at-risk and volume deviation risk are taken into account. A numerical study using the data from the Nordic electricity market is performed to investigate the influence of risk factors on the retailer's profit, risk levels, average spot price, and total consumption. Three types of price elasticity are compared to show that the retailer can benefit from the flexibility in demand side in some cases. The flexibility also leads to lower spot prices so that the customers in real-time price-based demand response can face a lower electricity price for per-unit power consumption.
TL;DR: This paper proposes Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts, and shows that such a joint optimization would largely increase the campaign effectiveness and the profit.
Abstract: Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine , a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.
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24 Feb 2018TL;DR: In this paper, the authors examined the changes in the value of ownership claims associated with corporate acquisitions and used the observed value changes to address the degree of competition in the market for corporate acquisitions, concluding that the market is competitive on the basis of the abnormal stock price changes of bidding firms, the time series behavior of the market value of target firms, and the proportion of gains that accrue to target and bidding firms.
Abstract: Several studies of mergers and tender offers examine the changes in the value of ownership claims associated with corporate acquisitions and use the observed value changes to address the degree of competition in the market for corporate acquisitions. These studies conclude that the takeover market is competitive on the basis of the abnormal stock price changes of bidding firms, the time series behavior of the market value of target firms, and the proportion of gains that accrue to target and bidding firms. Unfortunately, none of these tests are sufficient to conclude that the takeover market is competitive. A competitive acquisition market implies that the potential gain to unsuccessful bidders at the successful offer price is nonpositive. This implication is tested using data on tender offers in which there are multiple bidders. The results appear to be consistent with competition in the market for corporate acquisitions.
TL;DR: A new variant of Grey Wolf Optimizer named as Intelligent GreyWolf Optimizer (IGWO) is proposed, which is observed that profit obtained from IGWO is more from OGWO, GWO and PSO for both a single trading hour and a trading day.
TL;DR: In this paper, the authors developed a complete European day-ahead market model in GAMS, formulating it as a Mix Integer Quadratic Constraint Problem (MIQCP) and iterative procedure, to mitigate the nonconvexity of electricity prices across Europe due to the “fill or kill” condition of block, complex and Prezzo Unico Nazionale (PUN) orders.
01 Apr 2018
TL;DR: This work explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC), and in this solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction.
Abstract: Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.
TL;DR: The authors analyzed bidding data from uniform price auctions of US Treasury bills and notes between July 2009 and October 2013 and estimated a structural model of bidding that takes into account informational asymmetries introduced by the bidding system employed by the US Treasury.
Abstract: We analyze bidding data from uniform price auctions of US Treasury bills and notes between July 2009 and October 2013. Primary dealers consistently bid higher yields compared to direct and indirect bidders. We estimate a structural model of bidding that takes into account informational asymmetries introduced by the bidding system employed by the US Treasury. While primary dealers' estimated willingness-to-pay is higher than direct and indirect bidders’, their ability to bid-shade is even higher, leading to higher yield/lower price bids. Total bidder surplus averaged to about three basis points across the sample period along with efficiency losses around two basis points. (JEL D44, E63, H63)