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Showing papers on "Bidding published in 2014"


Journal ArticleDOI
TL;DR: An optimal day-ahead price-based power scheduling problem for a community-scale microgrid (MG) is studied and the great benefits in exploiting the building thermal dynamics and the flexibility of the proposed scheduling method in achieving different practical design tradeoffs are presented.
Abstract: In this paper, we study an optimal day-ahead price-based power scheduling problem for a community-scale microgrid (MG). The proposed optimization framework aims to balance between maximizing the expected benefit of the MG in the deregulated electricity market and minimizing the MG operation cost considering users' thermal comfort requirements and other system constraints. The power scheduling and bidding problem is formulated as a two-stage stochastic program where various system uncertainties are captured by using the Monte Carlo simulation approach. Our formulation is novel in that it can exploit the thermal dynamic characteristics of buildings to compensate for the variable and intermittent nature of renewable energy resources and enables us to achieve desirable tradeoffs for different conflicting design objectives. Extensive numerical results are presented to demonstrate the great benefits in exploiting the building thermal dynamics and the flexibility of the proposed scheduling method in achieving different practical design tradeoffs. We also investigate the impacts of different design and system parameters on the curtailment of renewable energy resources and the optimal expected profit of the MG.

337 citations


Journal ArticleDOI
TL;DR: The authors showed that renegotiation imposes significant adaptation costs and reduced form regressions suggest that bidders respond strategically to contractual incompleteness and that adaptation costs are an important determinant of their bids.
Abstract: Procurement contracts are often renegotiated because of changes that are required after their execution Using highway paving contracts we show that renegotiation imposes significant adaptation costs Reduced form regressions suggest that bidders respond strategically to contractual incompleteness and that adaptation costs are an important determinant of their bids A structural empirical model compares adaptation costs to bidder markups and shows that adaptation costs account for 75–14 percent of the winning bid Markups from private information and market power, the focus of much of the auctions literature, are much smaller by comparison Implications for government procurement are discussed (JEL D44, D82, D86, H57, L13, L74, R42)

305 citations


Journal ArticleDOI
TL;DR: In this paper, the authors measure the pass-through of emissions costs to electricity prices and explore its determinants, showing that emissions costs are almost fully passed-through to the electricity prices.
Abstract: We measure the pass-through of emissions costs to electricity prices and explore its determinants. We perform both reduced-form and structural estimations based on optimal bidding in this market. Using rich micro-level data, we estimate the channels aecting pass-through in a exible manner, with minimal functional form assumptions. Contrary to many studies in the general pass-through literature, we nd that emissions costs are almost fully passed-through to electricity prices. Since electricity is traded through high-frequency auctions for highly inelastic demand, rms have weak incentives to adjust markups after the cost shock. Furthermore, the

297 citations


Proceedings ArticleDOI
24 Aug 2014
TL;DR: In this paper, the authors study bid optimisation for real-time bidding (RTB) based display advertising and derive simple bidding functions that can be calculated in real time; their finding shows that the optimal bid has a non-linear relationship with the impression level evaluation such as the click-through rate and the conversion rate, which are estimated in realtime from the impression-level features.
Abstract: In this paper we study bid optimisation for real-time bidding (RTB) based display advertising. RTB allows advertisers to bid on a display ad impression in real time when it is being generated. It goes beyond contextual advertising by motivating the bidding focused on user data and it is different from the sponsored search auction where the bid price is associated with keywords. For the demand side, a fundamental technical challenge is to automate the bidding process based on the budget, the campaign objective and various information gathered in runtime and in history. In this paper, the programmatic bidding is cast as a functional optimisation problem. Under certain dependency assumptions, we derive simple bidding functions that can be calculated in real time; our finding shows that the optimal bid has a non-linear relationship with the impression level evaluation such as the click-through rate and the conversion rate, which are estimated in real time from the impression level features. This is different from previous work that is mainly focused on a linear bidding function. Our mathematical derivation suggests that optimal bidding strategies should try to bid more impressions rather than focus on a small set of high valued impressions because according to the current RTB market data, compared to the higher evaluated impressions, the lower evaluated ones are more cost effective and the chances of winning them are relatively higher. Aside from the theoretical insights, offline experiments on a real dataset and online experiments on a production RTB system verify the effectiveness of our proposed optimal bidding strategies and the functional optimisation framework.

263 citations


Proceedings ArticleDOI
16 Jun 2014
TL;DR: This paper proposes that prediction-based pricing is an appealing market design, and shows that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue, and provides analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction- based pricing.
Abstract: Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.

189 citations


Journal ArticleDOI
TL;DR: The effectiveness and cost-effectiveness of two main types of instruments (feed-in tariffs and quotas with tradable green certificates) have usually been compared in the literature on renewable electricity promotion as discussed by the authors.
Abstract: The effectiveness and cost-effectiveness of two main types of instruments (feed-in tariffs and quotas with tradable green certificates) have usually been compared in the literature on renewable electricity promotion. Due to negative past experiences with a third instrument (auctions), this instrument has been broadly dismissed in academics and, until recently, also in policy practice. However, and based on an in-depth review of experiences with auction schemes for renewable electricity around the world, this paper argues that some of the problems with auctions in the past can be mitigated with the appropriate design elements and that, indeed, auctions can play an important role in the future implementation of renewable electricity support instruments around the world. The paper provides a proposal for the coherent integration of several design elements.

170 citations


Journal ArticleDOI
TL;DR: The effectiveness and excellence of proposed stochastic bidding strategy of microgrid in a joint day-ahead market of energy and spinning reserve service is proven by comparing simulation results with traditional deterministic bidding strategy.

162 citations


Journal ArticleDOI
15 Jul 2014-Energy
TL;DR: In this article, an analysis of the German balancing mechanism illustrates that DR is undermined by three mechanism design aspects: minimum bidding volume, minimum bid duration and binding up and down bids.

139 citations


Journal ArticleDOI
TL;DR: A Nordic power producer who engages in the day-ahead spot market and the hour-ahead balancing market is considered and the gain from coordinated bidding is quantified, and the performance of alternative bidding strategies used in practice is assessed.

118 citations


Journal ArticleDOI
TL;DR: In this paper, a hierarchical demand response (DR) bidding framework was proposed in the day-ahead energy markets, which integrates customer DR preferences and characteristics in the ISO's market clearing process, where load aggregators submitted aggregated DR offers to the ISO which would centrally optimize final decisions on aggregators' DR contributions in wholesale markets.
Abstract: This paper presents a hierarchical demand response (DR) bidding framework in the day-ahead energy markets which integrates customer DR preferences and characteristics in the ISO's market clearing process. In the proposed framework, load aggregators submit aggregated DR offers to the ISO which would centrally optimize final decisions on aggregators' DR contributions in wholesale markets. The hourly load reduction strategies include load shifting and curtailment and the use of onsite generation and energy storage systems. The ISO applies mixed-integer linear programming (MILP) to the solution of the proposed DR model in the day-ahead market clearing problem. The proposed model is implemented using a 6-bus system and the IEEE-RTS, and several studies are conducted to demonstrate the merits of the proposed DR model.

110 citations


Journal ArticleDOI
TL;DR: In a competitive environment with bid-based markets, power generation companies desire to develop bidding strategies that maximize their revenue as mentioned in this paper, where the agent is a price-maker hydro-electric producer.
Abstract: In a competitive environment with bid-based markets, power generation companies desire to develop bidding strategies that maximize their revenue In this paper we ask: What approaches and methodologies have been used to model the bidding problem for hydro-electric producers? We present the problem's developments over time and, through reviewing different variants of the problem, progressively build to the case in which the agent is a price-maker hydro-electric producer In each variant of the bidding problem, we examine how the approaches used to solve it may or may not be applicable to other variants Last, for the price-maker hydro-electric producer's bidding problem, we recognize the most recent developments and illuminate a path for future efforts

Journal ArticleDOI
TL;DR: In this article, the economic fundamentals that govern market design and behavior in German balancing power markets are analyzed and the role of the scoring and the settlement rule as key elements of the market design.

Posted Content
TL;DR: The research problem of bid optimisation in RTB and the simple yet comprehensive evaluation protocol are introduced and a series of benchmark experiments are also conducted, including both click-through rate (CTR) estimation and bid Optimisation.
Abstract: Being an emerging paradigm for display advertising, Real-Time Bidding (RTB) drives the focus of the bidding strategy from context to users' interest by computing a bid for each impression in real time. The data mining work and particularly the bidding strategy development becomes crucial in this performance-driven business. However, researchers in computational advertising area have been suffering from lack of publicly available benchmark datasets, which are essential to compare different algorithms and systems. Fortunately, a leading Chinese advertising technology company iPinYou decided to release the dataset used in its global RTB algorithm competition in 2013. The dataset includes logs of ad auctions, bids, impressions, clicks, and final conversions. These logs reflect the market environment as well as form a complete path of users' responses from advertisers' perspective. This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. Thus, they are valuable for reproducible research and understanding the whole RTB ecosystem. In this paper, we first provide the detailed statistical analysis of this dataset. Then we introduce the research problem of bid optimisation in RTB and the simple yet comprehensive evaluation protocol. Besides, a series of benchmark experiments are also conducted, including both click-through rate (CTR) estimation and bid optimisation.

Proceedings ArticleDOI
08 Oct 2014
TL;DR: In this article, the authors discussed the current market practice of RTB advertising, presented the key roles and typical business processes in RTB markets, and summarized the current research progresses in the existing literature.
Abstract: Real-time bidding (RTB) is an emerging and promising business model for online computational advertising in the age of big data. Based on analysis of massive amounts of Cookie data generated by Internet users, RTB advertising has the potential of identifying in real-time the characteristic and interest of the target audience in each ad impression, automatically delivering best-matched ads, and optimizing their prices via auction-based programmatic buying scheme. RTB has significantly changed online advertising, evolving from the traditional pattern of "media buying" and "ad-slot buying" to "targetaudience buying", and is expected to be the standard business model for online advertising in the future. In this paper, we discussed the current market practice of RTB advertising, presented the key roles and typical business processes in RTB markets, and summarized the current research progresses in the existing literature. The aim of this paper is to provide useful reference and guidance for future works.

Journal ArticleDOI
Mar Reguant1
TL;DR: In this paper, the authors extend multi-unit auction estimation techniques to a setting in which firms can express cost complementarities over time, and show that accounting for startup costs can provide a natural correction for these markup biases.
Abstract: I extend multi-unit auction estimation techniques to a setting in which firms can express cost complementarities over time. In the context of electricity markets, I show how the auction structure and bidding data can be used to estimate these complementarities, which in these markets arise due to startup costs. I find that startup costs are substantial and that taking them into account helps better explain firm bidding strategies and production patterns. As in other dynamic settings, I find that startup costs limit the ability of firms to change production over time, exacerbating fluctuations in market prices. These fluctuations can induce estimates of market power that ignore dynamic costs to overstate markup volatility, with predicted markups that can be even negative in periods of low demand. I show how accounting for startup costs can provide a natural correction for these markup biases.

Journal ArticleDOI
TL;DR: This article investigated Japanese consumers' willingness to pay for Marine Stewardship Council (MSC) ecolabelled seafood using a sealed bid, second price auction and found that there is a statistically significant premium of about 20 per cent for MSC-ecolabelled salmon over non-labeled salmon when consumers are provided information on both the status of global fish stocks and the purpose of the MSC program.
Abstract: This paper investigates Japanese consumers' willingness to pay for Marine Stewardship Council (MSC) ecolabelled seafood using a sealed bid, second price auction. Participants in an experiment in Tokyo were provided varying degrees of information about the status of world and Japanese fisheries and the MSC program in sequential rounds of bidding on ecolabelled and nonlabelled salmon products. A random-effects tobit regression shows that there is a statistically significant premium of about 20 per cent for MSC-ecolabelled salmon over nonlabelled salmon when consumers are provided information on both the status of global fish stocks and the purpose of the MSC program. This premium arises from a combination of an increased willingness to pay for labelled products and a decreased willingness to pay for unlabelled products. However, in the absence of experimenter-provided information, or when provided information about the purpose of the MSC program alone without concurrent information about the need for the MSC program, there is no statistically significant premium.

Proceedings ArticleDOI
Shuai Yuan1, Jun Wang1, Bowei Chen1, Peter Mason, Sam Seljan 
24 Aug 2014
TL;DR: This paper empirically examines several commonly adopted algorithms for setting up a reserve price and suggests the proposed game theory based OneShot algorithm performed the best and the superiority is significant in most cases.
Abstract: In this paper, we report the first empirical study and live test of the reserve price optimisation problem in the context of Real-Time Bidding (RTB) display advertising from an operational environment. A reserve price is the minimum that the auctioneer would accept from bidders in auctions, and in a second price auction it could potentially uplift the auctioneer's revenue by charging winners the reserve price instead of the second highest bids. As such it has been used for sponsored search and been well studied in that context. However, comparing with sponsored search and contextual advertising, this problem in the RTB context is less understood yet more critical for publishers because 1) bidders have to submit a bid for each individual impression, which mostly is associated with user data that is subject to change over time. This, coupled with practical constraints such as the budget, campaigns' life time, etc. makes the theoretical result from optimal auction theory not necessarily applicable and a further empirical study is required to confirm its optimality from the real-world system; 2) in RTB an advertiser is facing nearly unlimited supply and the auction is almost done in "last second", which encourages spending less on the high cost ad placements. This could imply the loss of bid volume over time if a correct reserve price is not in place. In this paper we empirically examine several commonly adopted algorithms for setting up a reserve price. We report our results of a large scale online experiment in a production platform. The results suggest the our proposed game theory based OneShot algorithm performed the best and the superiority is significant in most cases.

Journal ArticleDOI
TL;DR: Test results on the IEEE 24-bus and 118-bus systems show that the PTDF formulation of the transmission constraints is computationally far more efficient than the Nodal formulation.

BookDOI
01 Jan 2014
TL;DR: Game theory has been applied to a growing list of practical problems, from antitrust analysis to monetary policy; from the design of auction institutions to the structuring of incentives within firms; from patent races to dispute resolution.
Abstract: Game theory has been applied to a growing list of practical problems, from antitrust analysis to monetary policy; from the design of auction institutions to the structuring of incentives within firms; from patent races to dispute resolution. The purpose of Game Theory and Business Applications is to show how game theory can be used to model and analyze business decisions. The contents of this revised edition contain a wide variety of business functions – from accounting to operations, from marketing to strategy to organizational design. In addition, specific application areas include market competition, law and economics, bargaining and dispute resolution, and competitive bidding. All of these applications involve competitive decision settings, specifically situations where a number of economic agents in pursuit of their own self-interests and in accordance with the institutional “rules of the game” take actions that together affect all of their fortunes. As this volume demonstrates, game theory provides a compelling guide for analyzing business decisions and strategies.

Journal ArticleDOI
TL;DR: The proposed mixed-integer linear programming (MILP) model is to minimize the total operation cost while incorporating explicit LMP formulations and non-negative LMP requirements into the network-constrained unit commitment (NCUC) problem, which are derived from the Karush-Kuhn-Tucker optimality conditions of the economic dispatch (ED) problem.
Abstract: Environmental issues in power systems operation lead to a rapid deployment of renewable wind generations. Wind generation is usually given the highest priority by assigning zero or negative energy bidding prices in the day-ahead power market, in order to effectively utilize available wind energy. However, when congestions occur, negative wind bidding prices would aggravate negative locational marginal prices (LMPs) in certain locations. The paper determines the proper amount of demand response (DR) load to be shifted from peak hours to off peaks under the Independent System Operator's (ISO) direct load control, for alleviating transmission congestions and enhancing the utilization of wind generation. The proposed mixed-integer linear programming (MILP) model is to minimize the total operation cost while incorporating explicit LMP formulations and non-negative LMP requirements into the network-constrained unit commitment (NCUC) problem, which are derived from the Karush-Kuhn-Tucker (KKT) optimality conditions of the economic dispatch (ED) problem. Numerical case studies illustrate the effectiveness of the proposed model.

DissertationDOI
18 Sep 2014
TL;DR: TheBOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.
Abstract: Negotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators. Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent. There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies. To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted. The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios. We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components. In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions. Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies. Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature. The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance. Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other. Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies. We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model. Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent.

Journal ArticleDOI
TL;DR: This paper has proposed a day-ahead demand-side bidding approach to realize the desired demand response and the demand- side bidding problem is mathematically formulated and solved.
Abstract: By offering appropriate incentives, electricity users can be incentivized to alter their power consumption patterns so as to achieve the electricity supplier's intended profile. In this paper we have proposed a day-ahead demand-side bidding approach to realize the desired demand response. In our mechanism, customers submit their day-ahead electricity demands to the utility company through a secure communication infrastructure. The electricity supplier then generates the customers' hourly demand profile by aggregation of the individual requests. This information then forms the basis of procuring power in the wholesale market as well as developing a dynamic electricity pricing scheme. However, as expected, the actual electricity supply and the actual electricity consumption may not match. This could be due to the inability of the utility company to secure enough power at the wholesale market or due to changes in consumer power requirements. In the event the demand outstrips the supply, the customers willingly offer to shed some of their flexible appliances' loads but bid for prices at which they would like to pay for the curtailed load. We assume that customers are rational but selfish and are only concerned with maximizing their own benefits. In this study, this demand- side bidding problem is mathematically formulated and solved. Simulation studies are also carried out to verify the effectiveness of the proposed method.

Journal ArticleDOI
TL;DR: In this paper, the optimal day-ahead bidding strategy for a wind power producer operating in an electricity market with high wind penetration was studied for a single generator operating in a generalized electricity market, with minimal assumptions about the structure of the production, bidding or consumption of electricity.

Proceedings ArticleDOI
24 Aug 2014
TL;DR: This paper releases the first publicly available dataset on RTB display advertising, which includes logs of ad biddings, impressions, clicks, and final conversions from iPinYou's global RTB algorithm competition in 2013.
Abstract: RTB (Real Time Bidding) is one of the most exciting developments in computational advertising in recent years. It drives transparency and efficiency in the display advertising ecosystem and facilitates the healthy growth of the display advertising industry. It enables advertisers to deliver the right message to the right person at the right time, publishers to better monetize their content by leveraging their website audience, and consumers to view relevant information through personalized ads. However, researchers in computational advertising area have been suffering from lack of publicly available datasets. iPinYou organizes a three-season global RTB algorithm competition in 2013. For each season, there is offline stage and online stage. On the offline stage, iPinYou releases a dataset for model training and reserves a dataset for testing. The dataset includes logs of ad biddings, impressions, clicks, and final conversions. After the whole competition ends, iPinYou organizes and releases all these three datasets for public use. These datasets can support experiments of some important research problems such as bid optimization and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. In this paper, we give descriptions of these datasets to further boost the interests of computational advertising research community using this dataset.

Journal ArticleDOI
TL;DR: This paper analyzes the typical MG market policies and investigates how these policies can be converted in such a way that one can use commercial power system software for MG power market study.
Abstract: For a microgrid (MG) to participate in a real-time and demand-side bidding market, high-level control strategies aiming at optimizing the operation of the MG are necessary. One of the difficulties for research of a competitive MG power market is the absence of efficient computational tools. Although many commercial power system simulators are available, these power system simulators are usually not directly applicable to solve the optimal power dispatch problem for an MG power market and to perform MG power-flow study. This paper analyzes the typical MG market policies and investigates how these policies can be converted in such a way that one can use commercial power system software for MG power market study. The paper also develops a mechanism suitable for the power-flow study of an MG containing inverter-interfaced distributed energy sources. The extensive simulation analyses are conducted for grid-tied and islanded operations of a benchmark MG network.

Journal ArticleDOI
TL;DR: This paper proposes a distributed demand-side management method intended for smart grid users with load prediction capabilities, who possibly employ dispatchable energy generation and storage devices, and devise a distributed, iterative algorithm converging to the variational solutions of the GNEP.
Abstract: The envisioned smart grid aims at improving the interaction between the supply- and the demand-side of the electricity network, creating unprecedented possibilities for optimizing the energy usage at different levels of the grid. In this paper, we propose a distributed demand-side management (DSM) method intended for smart grid users with load prediction capabilities, who possibly employ dispatchable energy generation and storage devices. These users participate in the day-ahead market and are interested in deriving the bidding, production, and storage strategies that jointly minimize their expected monetary expense. The resulting day-ahead grid optimization is formulated as a generalized Nash equilibrium problem (GNEP), which includes global constraints that couple the users' strategies. Building on the theory of variational inequalities, we study the main properties of the GNEP and devise a distributed, iterative algorithm converging to the variational solutions of the GNEP. Additionally, users can exploit the reduced uncertainty about their energy consumption and renewable generation at the time of dispatch. We thus present a complementary DSM procedure that allows them to perform some unilateral adjustments on their generation and storage strategies so as to reduce the impact of their real-time deviations with respect to the amount of energy negotiated in the day-ahead. Finally, numerical results in realistic scenarios are reported to corroborate the proposed DSM technique.

Journal ArticleDOI
TL;DR: In this article, the authors proposed a minimax regret approach for a market participant to obtain an optimal bidding strategy and the corresponding self-scheduled generation plans based on the confidence intervals of price forecasts rather than point estimators.
Abstract: The electricity price volatility brings challenges to bidding strategies in the electricity markets. In this paper, we propose a minimax regret approach for a market participant to obtain an optimal bidding strategy and the corresponding self-scheduled generation plans. Motivated by recently proposed robust optimization approaches, our approach relies on the confidence intervals of price forecasts rather than point estimators. We reformulate the minimax regret model as a mixed-integer linear program (MILP), and solve it by the Benders' decomposition algorithm. Moreover, we design a bidding strategy based on the price forecast confidence intervals to generate the offer curve. Finally, we numerically test the minimax regret approach, in comparison with the robust optimization approach, on three types of thermal generators by using real electricity price data from PJM to verify the effectiveness of our proposed approach.

Journal ArticleDOI
TL;DR: The authors analyzes the time series and cross-sectional patterns of bidding wars for houses and shows that bidding war incidence is greater during macroeconomic and housing booms, and also considers other potential contributing factors, including buyer irrationality, the use of the Internet in home purchases and land use regulation.
Abstract: This article analyzes the time series and cross-sectional patterns of bidding wars for houses. Bidding wars were once rare, a fairly constant 3–4% of transactions. This led to treating list price as a ceiling in empirical and theoretical research on housing. The bidding war share roughly tripled between 1995 and 2005, rising to more than 30% in some markets. The share fell during the subsequent bust, but it remains approximately twice as high as previously. The article shows bidding war incidence to be greater during macroeconomic and housing booms. The article also considers other potential contributing factors, including buyer irrationality, the use of the Internet in home purchases and land use regulation.

Journal ArticleDOI
TL;DR: In this article, the authors proposed a game theoretic bidding strategy for wind power producers to maximize their expected payoff or profit through the proposed Nash equilibrium based bidding strategy, which is evaluated on two realistic case studies considering different technical constraints.

Proceedings ArticleDOI
27 Jul 2014
TL;DR: In this article, the real-time price forecasting in New York electricity market through random forest is studied. And the model can adjust to the latest forecasting condition, i.e. the latest climatic, seasonal and market condition, by updating the random forest parameters with new observations.
Abstract: This paper mainly focuses on the real-time price forecasting in New York electricity market through random forest. Accurate forecasting is regarded as the most practical way to win power bid in today's highly competitive electricity market. Comparing with existing price forecasting methods, random forest, as a newly introduced method, will provide a price probability distribution, which will allow the users to estimate the risks of their bidding strategy and also making the results helpful for later industrial using. Furthermore, the model can adjust to the latest forecasting condition, i.e. the latest climatic, seasonal and market condition, by updating the random forest parameters with new observations. This adaptability avoids the model failure in a climatic or economic condition different from the training set. A case study in New York HUD VL area is presented to evaluate the proposed model.