Showing papers on "Bidding published in 2011"

Integrity Data Attacks in Power Market Operations

Abstract: We study the economic impact of a potential class of integrity cyber attacks, named false data injection attacks, on electric power market operations. In particular, we show that with the knowledge of the transmission system topology, attackers may circumvent the bad data detection algorithms equipped in today's state estimator. This, in turn, may be leveraged by attackers for consistent financial arbitrage such as virtual bidding at selected pairs of nodes. This paper is a first attempt to formalize the economic impact of malicious data attacks on real-time market operations. We show how an attack could systematically construct a profitable attacking strategy, in the meantime being undetected by the system operator. Such a result is also valuable for the system operators to examine the potential economic loss due to such cyber attack. The potential impact of the false data injection attacks is illustrated on real-time market operations of the IEEE 14-bus system.

Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation

Abstract: This paper addresses the bidding problem faced by a virtual power plant (VPP) in a joint market of energy and spinning reserve service. The proposed bidding strategy is a non-equilibrium model based on the deterministic price-based unit commitment (PBUC) which takes the supply-demand balancing constraint and security constraints of VPP itself into account. The presented model creates a single operating profile from a composite of the parameters characterizing each distributed energy resources (DER), which is a component of VPP, and incorporates network constraints into its description of the capabilities of the portfolio. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and solved by genetic algorithm (GA).

Method, system and business model for a buyer's auction with near perfect information using the internet

Abstract: A methodology, system and business model are disclosed for facilitating a fully automated buyer's auction in which the major types of transaction costs are significantly reduced by providing the buyer and the sellers with near-perfect information about one another, including information about buyer preferences and competing sellers' offers. The system implements a buyer's auction with multidimensional bidding that minimizes market intelligence, search, bargaining and transaction execution costs and thus creates more competitive, frictionless markets. Buyers and sellers can efficiently conduct the buyer's auction within a unified environment, thereby minimizing buyer integration costs as well. The buyer's auction generates commercially marketable proprietary information and a revenue stream for the auctioneer providing such a service.

Bid Preference Programs and Participation in Highway Procurement Auctions

Abstract: We use data from highway procurement auctions subject to California's Small Business Preference program to study the effect of bid preferences on auction outcomes. Our analysis is based on an estimated model of firms' bidding and participation decisions, which allows us to evaluate the effects of current and alternative policy designs. We show that incorporating participation responses significantly alters the assessment of preferential treatment policies.

Procurement Contracting With Time Incentives: Theory and Evidence

Abstract: In public procurement, social welfare often depends on how quickly the good is delivered. A leading example is highway construction, where slow completion inflicts a negative externality on commuters. In response, highway departments award some contracts using scoring auctions, which give contractors explicit incentives for accelerated delivery. We characterize efficient design of these mechanisms. We then gather an extensive data set of highway projects awarded by the California Department of Transportation between 2003 and 2008. By comparing otherwise similar contracts, we show that where the scoring design was used, contracts were completed 30--40% faster and the welfare gains to commuters exceeded the increase in procurement costs. Using a structural model that endogenizes participation and bidding, we estimate that the counterfactual welfare gain from switching all contracts from the standard design to the efficient ApB design is nearly 22% of the total contract value ($1.14 billion). Copyright 2011, Oxford University Press.

Position Auctions with Consumer Search

Abstract: This paper examines a model in which advertisers bid for \sponsored-link" positions on a search engine. The value advertisers derive from each position is endogenized as coming from sales to a population of consumers who make rational inferences about rm qualities and search optimally. Consumer search strategies, equilibrium bidding, and the welfare benets of position auctions are analyzed. Implications for reserve prices and a number of other auction design questions are discussed.

Welfare guarantees for combinatorial auctions with item bidding

Abstract: We analyze the price of anarchy (POA) in a simple and practical non-truthful combinatorial auction when players have subadditive valuations for goods. We study the mechanism that sells every good in parallel with separate second-price auctions. We first prove that under a standard "no overbidding" assumption, for every subadditive valuation profile, every pure Nash equilibrium has welfare at least 50% of optimal --- i.e., the POA is at most 2. For the incomplete information setting, we prove that the POA with respect to Bayes-Nash equilibria is strictly larger than 2 --- an unusual separation from the full-information model --- and is at most 2 ln m, where m is the number of goods.

The Performance of the European Market for Corporate Control : Evidence from the 5th Takeover Wave

Abstract: This paper presents an in-depth analysis of the performance of large, medium-sized, and small corporate takeovers involving Continental European and UK firms during the fifth takeover wave. We find that takeovers are expected to create takeover synergies as their announcements trigger statistically significant abnormal returns of 9.13% for the target and of 0.53% for bidding firms. The characteristics of the target and bidding firms and of the bid itself are able to explain a significant part of these returns: (i) deal hostility increases the target's but decreases bidder's returns; (ii) the private status of the target is associated with higher bidder's returns; and (iii) an equity payment leads to a decrease in both bidder's and target's returns. The takeover wealth effect is however not limited to the bid announcement day but is also visible prior and subsequent to the bid. The analysis of pre-announcement returns reveals that hostile takeovers are largely anticipated and associated with a significant increase in the bidder's and target's share prices. Bidders that accumulate a toehold stake in the target experience higher post-announcement returns. A comparison of the UK and Continental European M&A markets reveals that: (i) the takeover returns of UK targets substantially exceed those of Continental European firms. (ii) The presence of a large shareholder in the bidding firm has a significantly positive effect on takeover returns in the UK and a negative one in Continental Europe. (iii) Weak investor protection and low disclosure in Continental Europe allow bidding firms to adopt takeover strategies enabling them to act opportunistically towards the target's incumbent shareholders.

Comparing open and Sealed Bid Auctions: Evidence from Timber Auctions*

Abstract: We study entry and bidding patterns in sealed bid and open auctions. Using data from the U.S. Forest Service timber auctions, we document a set of systematic effects: sealed bid auctions attract more small bidders, shift the allocation toward these bidders, and can also generate higher revenue. A private value auction model with endogenous participation can account for these qualitative effects of auction format. We estimate the model's parameters and show that it can explain the quantitative effects as well. We then use the model to assess bidder competitiveness, which has important consequences for auction design.

An Intelligent Auction Scheme for Smart Grid Market Using a Hybrid Immune Algorithm

Abstract: Smart Grid technology is recognized as a key component of the solution to challenges such as the increasing electric demand, an aging utility infrastructure and workforce, and the environmental impact of greenhouse gases produced during electric generation. This paper presents the application of a hybrid optimization algorithm for distributed energy resource (DER) management in Smart Grid operation. The approach emphasizes the advantages of using multiagent systems for profitable operation of a Smart Grid in the energy market. The trading strategy adopted for the auction process is a profit-maximizing adaptive bidding strategy based on risk and competitive equilibrium price prediction. The auctioneer manages the usage of DERs by receiving bids from buyers and asks from sellers. A hybrid-immune-system-based particle swarm optimization is used to minimize the fuel cost for generation assuming realistic market prices for power, distributed generator bids reflecting realistic operational costs, and load bids customized according to the consumers' priorities. The simulation results clearly indicate that the agent-based management is effective in coordinating the various DERs economically and profitably.

Auctions and bidding: A guide for computer scientists

Abstract: There is a veritable menagerie of auctions—single-dimensional, multi-dimensional, single-sided, double-sided, first-price, second-price, English, Dutch, Japanese, sealed-bid—and these have been extensively discussed and analyzed in the economics literature. The main purpose of this article is to survey this literature from a computer science perspective, primarily from the viewpoint of computer scientists who are interested in learning about auction theory, and to provide pointers into the economics literature for those who want a deeper technical understanding. In addition, since auctions are an increasingly important topic in computer science, we also look at work on auctions from the computer science literature. Overall, our aim is to identifying what both these bodies of work these tell us about creating electronic auctions.

Electricity Auctions: An Overview of Efficient Practices

Abstract: This report assesses the potential of electricity contract auctions as a procurement option for the World Bank's client countries. It focuses on the role of auctions of electricity contracts designed to expand and retain existing generation capacity. It is not meant to be a 'how-to' manual. Rather, it highlights some major issues and options that need to be taken into account when a country considers moving towards competitive electricity procurement through the introduction of electricity auctions. Auctions have played an important role in the effort to match supply and demand. Ever since the 1990s, the use of long-term contract auctions to procure new generation capacity, notably from private sector suppliers, has garnered increased affection from investors, governments, and multilateral agencies in general, as a means to achieve a competitive and transparent procurement process while providing certainty of supply for the medium to long term. However, the liberalization of electricity markets and the move from single-buyer procurement models increased the nature of the challenge facing system planners in their efforts to ensure an adequate and secure supply of electricity in the future at the best price. While auctions as general propositions are a means to match supply with demand in a cost-effective manner, they can also be and have been used to meet a variety of goals.

Analysis of Residential Demand Response and double-auction markets

Abstract: Demand response and dynamic pricing programs are expected to play increasing roles in the modern smart grid environment. While direct load control of end-use loads has existed for decades, price driven response programs are only beginning to be explored at the distribution level. These programs utilize a price signal as a means to control demand. Active markets allow customers to respond to fluctuations in wholesale electrical costs, but may not allow the utility to control demand. Transactive markets, utilizing distributed controllers and a centralized auction, can be used to create an interactive system which can limit demand at key times on a distribution system, decreasing congestion. With the current proliferation of computing and communication resources, the ability now exists to create transactive demand response programs at the residential level. With the combination of automated bidding and response strategies, coupled with education programs and customer response, emerging demand response programs have the ability to reduce utility demand and congestion in a more controlled manner. This paper will explore the effects of a residential double-auction market, utilizing transactive controllers, on the operation of an electric power distribution system.

A Dynamic Model of Sponsored Search Advertising

Abstract: Sponsored search advertising is ascendant---Forrester Research reports expenditures rose 28% in 2007 to $8.1 billion and will continue to rise at a 26% compound annual growth rate [VanBoskirk, S. 2007. U.S. interactive marketing forecast, 2007 to 2012. Forrester Research (October 10)], approaching half the level of television advertising and making sponsored search one of the major advertising trends to affect the marketing landscape. Yet little empirical research exists to explore how the interaction of various agents (searchers, advertisers, and the search engine) in keyword markets affects consumer welfare and firm profits. The dynamic structural model we propose serves as a foundation to explore these outcomes. We fit this model to a proprietary data set provided by an anonymous search engine. These data include consumer search and clicking behavior, advertiser bidding behavior, and search engine information such as keyword pricing and website design. With respect to advertisers, we find evidence of dynamic bidding behavior. Advertiser value for clicks on their links averages about 26 cents. Given the typical $22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.2%, well within common estimates of 1%--2% [Narcisse, E. 2007. Magid: Casual free to pay conversion rate too low. GameDaily.com (September 20)]. With respect to consumers, we find that frequent clickers place a greater emphasis on the position of the sponsored advertising link. We further find that about 10% of consumers do 90% of the clicks. We then conduct several policy simulations to illustrate the effects of changes in search engine policy. First, we find the search engine obtains revenue gains of 1% by sharing individual-level information with advertisers and enabling them to vary their bids by consumer segment. This also improves advertiser revenue by 6% and consumer welfare by 1.6%. Second, we find that a switch from a first-to second-price auction results in truth telling (advertiser bids rise to advertiser valuations). However, the second-price auction has little impact on search engine profits. Third, consumer search tools lead to a platform revenue increase of 2.9% and an increase of consumer welfare by 3.8%. However, these tools, by reducing advertising exposures, lower advertiser profits by 2.1%.

Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part II: Numerical Analysis

Abstract: This paper is to evaluate the presented model in part I for bidding strategy of virtual power plant (VPP) with centralized control in a joint market of energy and spinning reserve service. Two test VPPs are introduced and different scenarios are considered for markets prices. At first, the participation of VPP in only energy market is studied. Then, the spinning reserve market is taken into consideration and the bids of VPP in a joint market of energy and spinning reserve service is studied under different scenarios of markets prices and the results are analyzed. In all cases, the results show the effectiveness and the quality of the procedure and validate the proposed model.

Discrete Bids and Empirical Inference in Divisible Good Auctions

Abstract: I examine a model of a uniform price auction of a perfectly divisible good with private informa tion in which the bidders submit discrete bidpoints rather than continuous downward sloping demand functions. 1 characterize necessary conditions for equilibrium bidding. The characterization reveals a close relationship between bidding in multiunit auctions and oligopolistic behaviour. I demonstrate that a recently proposed indirect approach to the revenue comparisons of discriminatory and uniform price auc tions is not valid if bid functions have steps. In particular, bidders may bid above their marginal valuation in a uniform price auction. In order to demonstrate that discrete bidding can have important consequences for empirical analysis I use my model to examine a data set consisting of individual bids in uniform price treasury auctions of the Czech government. I propose an alternative method for evaluating the perfor mance of the employed mechanism. My results suggest that the uniform price auction performs well, both in terms of efficiency of the allocation and in terms of revenue maximization. I estimate that the em ployed mechanism failed to extract at most 3 basis points in terms of the annual yield of T-bills worth of expected surplus while implementing an allocation resulting in almost all the efficient surplus. Failing to account for discreteness of bids would in my application result in overestimating the unextracted revenue by more than 50%.

Understanding service uncertainties in industrial product–service system cost estimation

Abstract: The global transition towards service orientation is posing challenges in cost estimation for manufacturers driven by the uncertainties that arise at the bidding stage of long-lasting performance-based contracts (i.e. availability). Service uncertainty is driven by the quality of information flow and knowledge across a given service network; however, it commonly suffers from the unavailability of useful data to assist cost predictions. Currently, consideration of cost uncertainty for an industrial product–service system is lacking in literature. To fill this gap, this paper combines literature in service and uncertainty in cost estimation to propose the components of a service delivery system, classification of sources of uncertainty based on supply and demand and the suitable uncertainty modelling methods for service cost estimation. The paper categorises service uncertainties in a demand and supply approach, whilst also allocating the types of uncertainty into aleatory and epistemic to propose suitable uncertainty modelling approaches. For future research, various areas such as consideration of a holistic approach to account for service uncertainties and development of a framework to support inter-linkages across a service network are proposed.

Anticipation, Acquisitions and Bidder Returns: Industry Shocks and the Transfer of Information Across Rivals

Abstract: We document market anticipation of merger bids and that less anticipated bids earn significantly higher announcement returns. Subsequent bidders experience significant and positive returns surrounding initial industry bid announcements. These results suggest that announcement period returns underestimate the wealth effects of bidding. After recognizing anticipation, bidding activity is, on average, a significant wealth-creating event. Moreover, bidders pursuing public targets increase shareholder wealth and bidders in stock swaps do not lose. These results are in contrast to conventional wisdom. Our results shed light on the correct magnitude of acquisition returns and on information transfer throughout an industry surrounding an economic shock.

Statistical Modeling of Spot Instance Prices in Public Cloud Environments

Abstract: The surge in demand for utilizing public Cloud resources has introduced many trade-offs between price, performance and recently reliability. Amazon's Spot Instances (SIs) create a competitive bidding option for the public Cloud users at lower prices without providing reliability on services. It is generally believed that SIs reduce monetary cost to the Cloud users, however it appears from the literature that their characteristics have not been explored and reported. We believe that characterization of SIs is fundamental in the design of stochastic scheduling algorithms and fault tolerant mechanisms in public Cloud environments for spot market. In this paper, we have done a comprehensive analysis of SIs based on one year price history in four data centers of Amazon's EC2. For this purpose, we have analyzed all different types of SIs in terms of spot price and the inter-price time (time between price changes) and determined the time dynamics for spot price in hour-in-day and day-of-week. Moreover, we have proposed a statistical model that fits well these two data series. The results reveal that we are able to model spot price dynamics as well as the inter-price time of each SI by the mixture of Gaussians distribution with three or four components. The proposed model is validated through extensive simulations, which demonstrate that our model exhibits a good degree of accuracy under realistic working conditions.

Real-time bidding algorithms for performance-based display ad allocation

Abstract: We describe a real-time bidding algorithm for performance-based display ad allocation. A central issue in performance display advertising is matching campaigns to ad impressions, which can be formulated as a constrained optimization problem that maximizes revenue subject to constraints such as budget limits and inventory availability. The current practice is to solve the optimization problem offline at a tractable level of impression granularity (e.g., the page level), and to serve ads online based on the precomputed static delivery scheme. Although this offline approach takes a global view to achieve optimality, it fails to scale to ad allocation at the individual impression level. Therefore, we propose a real-time bidding algorithm that enables fine-grained impression valuation (e.g., targeting users with real-time conversion data), and adjusts value-based bids according to real-time constraint snapshots (e.g., budget consumption levels). Theoretically, we show that under a linear programming (LP) primal-dual formulation, the simple real-time bidding algorithm is indeed an online solver to the original primal problem by taking the optimal solution to the dual problem as input. In other words, the online algorithm guarantees the offline optimality given the same level of knowledge an offline optimization would have. Empirically, we develop and experiment with two real-time bid adjustment approaches to adapting to the non-stationary nature of the marketplace: one adjusts bids against real-time constraint satisfaction levels using control-theoretic methods, and the other adjusts bids also based on the statistically modeled historical bidding landscape. Finally, we show experimental results with real-world ad delivery data that support our theoretical conclusions.

A “Position Paradox” in Sponsored Search Auctions

Abstract: We study the bidding strategies of vertically differentiated firms that bid for sponsored search advertisement positions for a keyword at a search engine. We explicitly model how consumers navigate and click on sponsored links based on their knowledge and beliefs about firm qualities. Our model yields several interesting insights; a main counterintuitive result we focus on is the “position paradox.” The paradox is that a superior firm may bid lower than an inferior firm and obtain a position below it, yet it still obtains more clicks than the inferior firm. Under a pay-per-impression mechanism, the inferior firm wants to be at the top where more consumers click on its link, whereas the superior firm is better off by placing its link at a lower position because it pays a smaller advertising fee, but some consumers will still reach it in search of the higher-quality firm. Under a pay-per-click mechanism, the inferior firm has an even stronger incentive to be at the top because now it only has to pay for the consumers who do not know the firms' reputations and, therefore, can bid more aggressively. Interestingly, as the quality premium for the superior firm increases, and/or if more consumers know the identity of the superior firm, the incentive for the inferior firm to be at the top may increase. Contrary to conventional belief, we find that the search engine may have the incentive to overweight the inferior firm's bid and strategically create the position paradox to increase overall clicks by consumers. To validate our model, we analyze a data set from a popular Korean search engine firm and find that (i) a large proportion of auction outcomes in the data show the position paradox, and (ii) sharp predictions from our model are validated in the data.

Do Investors Learn from Experience? Evidence from Frequent IPO Investors

Abstract: We examine how experience affects the decisions of individual investors and institutions in IPO auctions to bid in subsequent auctions, and their bidding returns. We track bidding histories for all 31,476 individual investors and 1,232 institutional investors across all 84 IPO auctions during the period from 1995 to 2000 in Taiwan. For individual bidders, (1) high returns in previous IPO auctions increase the likelihood of participating in future auctions; (2) bidders' returns decrease as they participate in more auctions; (3) auction selection ability deteriorates with experience; and (4) those with greater experience bid more aggressively. These findings are consistent with naive reinforcement learning wherein individuals become unduly optimistic after receiving good returns. In sharp contrast, there is little sign that institutional investors exhibit such behavior. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Coordinated Trading of Wind and Thermal Energy

Abstract: Trading wind energy in short-term electricity markets has high associated risks due to the uncertainties in hourly available wind, energy prices, and imbalance penalties. Coordinated trading of wind and thermal energy is proposed to mitigate risks due to those uncertainties. The problem of wind-thermal coordinated trading is formulated as a mixed-integer stochastic linear program. The objective is to obtain the optimal trade-off bidding strategy that maximizes the total expected profits while controlling trading risks. For risk control, a weighted term of the conditional value at risk (CVaR) is included in the objective function. The CVaR aims to maximize the expected profits of the least profitable scenarios, thus improving trading risk control. A case study comparing coordinated with uncoordinated bidding strategies depending on the trader's risk attitude is included. Simulation results show that coordinated bidding can improve the expected profits while significantly improving the CVaR.

Opportunity Cost Bidding by Wind Generators in Forward Markets: Analytical Results

Abstract: Wind generation must trade in forward electricity markets based on imperfect forecasts of its output and real-time prices. When the real-time price differs for generators that are short and long, the optimal forward strategy must be based on the opportunity costs of charges and payments in real-time rather than a central estimate of wind output. We present analytical results for wind's optimal forward strategy. In the risk-neutral case, the optimal strategy is determined by the distribution of real-time available wind capacity, and the expected real-time prices conditioned on the forward price and wind out-turn; our approach is simpler and more computationally efficient than formulations requiring specification of full joint distributions or a large set of scenarios. Informative closed-form examples are derived for particular specifications of the wind-price dependence structure. In the usual case of uncertain forward prices, the optimal bidding strategy generally consists of a bid curve for wind power, rather than a fixed quantity bid. A discussion of the risk-averse problem is also provided. An analytical result is available for aversion to production volume risk; however, we doubt whether wind owners should be risk-averse with respect to the income from a single settlement period, given the large number of such periods in a year.

The 2007 Subprime Market Crisis Through the Lens of European Central Bank Auctions for Short-Term Funds

Abstract: We study European banks’ demand for short-term funds (liquidity) during the summer 2007 subprime market crisis. We use bidding data from the European Central Bank’s auctions for one-week loans, their main channel of monetary policy implementation. Our analysis provides a high-frequency, disaggregated perspective on the 2007 crisis, which was previously studied through comparisons of collateralized and uncollateralized interbank money market rates which do not capture the heterogeneous impact of the crisis on individual banks. Through a model of bidding, we show that banks’ bids reflect their cost of obtaining short-term funds elsewhere (e.g., in the interbank market) as well as a strategic response to other bidders. The strategic response is empirically important: while a naive interpretation of the raw bidding data may suggest that virtually all banks suffered an increase in the cost of short-term funding, we find that for about one third of the banks, the change in bidding behavior was simply a strategic response. We also find considerable heterogeneity in the short-term funding costs among banks: for over one third of the bidders, funding costs increased by more than 20 basis points, and funding costs vary widely with respect to the country-of-origin. Estimated funding costs of banks are also predictive of market - and accounting-based measures of bank performance, suggesting the external validity of our findings.

Strategyproof auctions for balancing social welfare and fairness in secondary spectrum markets

Abstract: Secondary spectrum access is emerging as a promising approach for mitigating the spectrum scarcity in wireless networks. Coordinated spectrum access for secondary users can be achieved using periodic spectrum auctions. Recent studies on such auction design mostly neglect the repeating nature of such auctions, and focus on greedily maximizing social welfare. Such auctions can cause subsets of users to experience starvation in the long run, reducing their incentive to continue participating in the auction. It is desirable to increase the diversity of users allocated spectrum in each auction round, so that a trade-off between social welfare and fairness is maintained. We study truthful mechanisms towards this objective, for both local and global fairness criteria. For local fairness, we introduce randomization into the auction design, such that each user is guaranteed a minimum probability of being assigned spectrum. Computing an optimal, interference-free spectrum allocation is NP-Hard; we present an approximate solution, and tailor a payment scheme to guarantee truthful bidding is a dominant strategy for all secondary users. For global fairness, we adopt the classic maxmin fairness criterion. We tailor another auction by applying linear programming techniques for striking the balance between social welfare and max-min fairness, and for finding feasible channel allocations. In particular, a pair of primal and dual linear programs are utilized to guide the probabilistic selection of feasible allocations towards a desired tradeoff in expectation.

Influence of Price Responsive Demand Shifting Bidding on Congestion and LMP in Pool-Based Day-Ahead Electricity Markets

Abstract: This paper investigates the influence of price responsive demand shifting bidding on congestion and locational marginal prices in pool-based day-ahead electricity markets The market dispatch problem of the pool-based day-ahead electricity market is formulated as to maximize the social welfare of market participants subject to operational constraints given by real and reactive power balance equations, and security constraints in the form of apparent power flow limits over the congested lines The social welfare objective function of the day-ahead market dispatch problem maximizes the benefit of distribution companies and other bulk consumers based on their price responsive demand shifting bids and minimizes the real and reactive power generation cost of generation companies The price responsive demand shifting bidding mechanism, which has been recently introduced in the literature, is able to shift the price responsive demand from the periods of high price to the periods of low price in day-ahead electricity markets The comparisons of the price responsive demand shifting bids with conventional price responsive and price taking bids are presented by solving hourly market dispatch problems on five-bus, IEEE 30-bus, realistic UP 75-bus Indian, and IEEE 118-bus systems for 24-h scheduling period It has been demonstrated that the proposed approach leads to reduction in congestion and locational marginal prices as compared to price responsive and price taking bids and meets the energy consumption targets of distribution companies/bulk consumers