One of the most important considerations in winning a competitive bid is the determination of an optimum strategy developed by predicting the competitor's most probable actions. There may be some common factors for different contractors in establishing their bid prices, such as references for cost estimating, construction materials, site conditions, or labor prices. Those dependencies from past bids can be used to improve the strategy to predict future bids. By identifying the interrelationships between bidders with statistical correlations, this study provides an overview of how correlations among bidders influence the bidders winning probability. With data available for over 7,000 Michigan Department of Transportation highway projects that can be used to calculate correlations between the different contractors, a Monte Carlo simulation is used to generate correlated random variables and the probability of winning from the results of the simulation. The primary focus of this paper outlines the use of conditional probability for predicting the probability of winning to establish a contractor's strategy for remaining bids with their estimated bid price and known information about competitors from past data. If a contractor estimated his/her bid price to be lower than his/her average bid, a higher probability of winning would be achieved with competitors who have a low correlation with the contractor. Conversely, the lower probability of winning decreases as the contractor bid with highly correlated contractors when their bid price is estimated to be higher than the average bid.

The on-line auction has become an important issue in Taiwan, due to the intense competition between e-Bay and Yahoo! auction. However the relative researches in Taiwan hasn¡¦t analyzed the bidding strategies of on-line auction bidders in detail. Therefore after reviewing relative literature, the motivation of using on-line auction, the characteristics of personality, the experience of internet and the involvement of product were saw as independent variables to discuss their relationships with bidding strategies, including the time of entering, the increment of bid, the numbers of bid and the degree of insistence in this research. Discovering the main factors affecting the time of entering are the experience of using on-line auction and the rating of bidder; the degree of exocentric in personality can affect the increment of bid; the motivation of using on-line auction and the involvement of product can affect the numbers of bid and the degree of insistence. Hence we can understand these four strategies were affected by different factors, and the forming of entire bidding strategies is very complicated. Going a step further, these four strategies were used to proceed cluster analysis, dividing the bidders into three groups¡G1. amateur bidders; 2. snipers; 3. impulsive bidders.

The advancement of computer technology is playing an important role in almost all fields in the construction industry in the current era. It has become a tool for exchanging legal contract information, including bid data. In the traditional closed bidding system, the bidders were unaware of their competitors' bid quotes and had no opportunity available to make a counter an offer to the bid at a different level. However, in reverse auction bidding (RAB), contractors can track their competitors' bids and take the given opportunity to re-bid the projects at lower rates. Unlike traditional auctions, where buyers raise their purchasing prices to outbid competitors, reverse auctions permit buyers to purchase goods and services from suppliers who are encouraged to sell them at the lowest price. The benefit of the reverse auction bidding is either that the vendors are able to re-bid, or lower their bid multiple times. This is an example of transparent economic information. Van Vleet initiated the ongoing Reverse Auction Bidding study at Texas A&M University. Van Vleet had created a Microsoft Access database system and ASP web based user interface for RAB study. The methodology developed by van Vleet is still being used today, and this study has been extended into analyzing different personality types and the impact on the bidding system. In the previous studies conducted by different researchers in TAMU, the performance of participants in the RAB process along with their behavior are being observed with respect to their personality. Personality of each player is tested using the Keirsey Temperament Sorter (KTS) test. The previous study states that there appears to be a strong correlation between personality type and game performance. The first case study conducted by van Vleet involved five participants who had no prior experience in Reverse Auction Bidding. The number of participants has varied from three to ten participants. This research has been conducted on graduate students of the Construction Science Department of TAMU who have no prior experience in RAB. In continuation with the previous studies held in TAMU, the results show that there is an observable pattern in the bidding strategy of first time bidders while taking part in Reverse Auction Bidding.

The English online auction creates great business opportunities. However, it¡¦s different from traditional English auction in long bidding time, wide area, high risk, massive information, and low switching cost. It makes bidders easy to search information of the goods but difficult to win the auction. The bidders' transaction cost structure has changed from information phase to agreement phase. So, the bidders need more precise supporting tools to help them make decisions. The aim of this research is to investigate two-person optimal bidding strategies under three dimensions of the decision space: the sequence decision (bid firstly or later), the bid decision (bid or quit), and the jump decision (minimum increase or jump bid). It is assumed that there are two bidders competing in one auction. Variables associated with the auction system include the floor price and the minimum increase. Variables associated with the bidders include the private value of the bidding object, and the cost of bid per turn. Strategies were evaluated based on the following criteria: who wins, how many turns to win the bid, the deal price, the utility of the winner and the loser, the probability of winning, and the expected utility. The results indicate that every strategy has its advantages and disadvantages under certain conditions. This research provides guiding rules for the bidders to choose a better strategy.

<p>Unforeseen changes in production or consumption in power systems lead to changes in grid frequency. This can cause damages to the system, or to frequency sensitive equipment at the consumers. The system operator (SO) is the responsible for balancing production and consumption in the system. The regulating market is the market place where the SO can sell or purchase electricity in order to balance unforeseen events. Producers acting on the regulating market must be able to change their production levels fast (within minutes) when required. Hydropower is therefore suitable for trading on the regulating market because of its flexibility in power production. This thesis describes models that hydropower owners can use to generate optimal bidding strategies when the regulating market is considered.</p><p>When planning for trading on the market, the prices are not known. Therefore, the prices are considered as stochastic variables. The planning problems in this thesis are based on multi-stage stochastic optimization, where the uncertain power prices are represented by scenario trees. The scenario trees are generated by simulation of price scenarios, which is achieved by using a model based on ARIMA and Markov processes. Two optimization models are presented in this thesis:</p><p>* Model for generation of optimal bidding strategies for the regulating market.</p><p>* Model for generation of optimal bidding strategies for the spot market when trading on the regulating market is considered.</p><p>The described models are applied in a case study with real data from the Nordic power system.</p><p>Conclusions of the thesis are that the proposed approaches of modelling prices and generation of bidding strategies are possible to use, and that the models produces reasonable data when applied to real data.</p> / <p>Oväntade produktions- eller konsumtionsändringar i kraftsystem leder till ändringar i nätfrekvens. Detta kan orsaka skador på systemet eller på frekvenskänslig utrustning hos konsumenterna. Systemoperatören (SO) är den ansvarige för att balansera produktion och konsumtion i kraftsystemet. Till sin hjälp har SO reglermarknaden, som är den handelsplats där SO köper eller säljer el för att balansera oväntade händelser i systemet. Producenter som agerar på reglermarknaden måste snabbt (inom minuter) kunna ändra sina produktionsnivåer om så behövs. Vattenkraft är därför lämplig för handel på reglermarknaden på grund av dess flexibilitet i kraftproduktion. Denna avhandling beskriver modeller som vattenkraftägare kan använda för generering av optimala budstrategier då reglermarknaden beaktas.</p><p>När en producents planering för handel på marknaden utförs är marknadspriserna okända. Dessa är därför betraktade som stokastiska variabler. Planeringmodellerna som presenteras i denna avhandling är baserade på multi-periodisk stokastisk programmering, där de osäkra marknadspriserna är representerade av ett scenarieträd. Scenarierna i trädet genereras genom simulering av marknadspriser. En prismodell, baserad på ARIMA- och Markovprocesser, har därför utvecklats. Två olika optimeringsmodeller presenteras i denna avhandling:</p><p>* Model för generering av optimala budstrategier för reglermarknaden.</p><p>* Model för generering av optimala budstrategier för spotmarknaden då handel på reglermarknaden beaktas.</p><p>Modellerna tillämpas i en studie där data från den nordiska elmarknaden appliceras. Slutsatser i avhandlingen är att de föreslagna ansatserna för modellering av priser och generering av budstrategier är möjliga att anvÄanda, samt att modellerna producerar rimliga resultat när applicerade på verkliga data.</p>

The growing penetration of variable renewable energy has brought all-round challenges to the current power system, no matter for the grid infrastructure, market design or the power producers. The positive and negative externalities caused by the market participators are to be finer priced and a significant amount of flexibility is to be added to the system, in order to counter these problems and facilitate energy transition. The flexibility providers range from dispatchable sources such as thermal generators and gas turbines, interruptible data centers and electric vehicle clusters, to direct and indirect storage such as battery and hydrogen. The intermittency of variable renewable energies can be effectively managed combining with these flexibility providers, and the more expectable output can lead to the more expectable income.  This study investigated a wind-biogas-hydrogen hybrid system, and biogas plays the role of ramping the system up and down according to different situations. When there is a wind deficit, the biogas-to-power unit is activated to compensate for the gap; when there is a wind surplus, the extra power is fed to the biogas pyrolysis equipment and produces hydrogen. To figure out its performance in the power market, a Supply Function Equilibrium approach for the day-ahead market is adopted and extended to the active participation in the balancing market under a two-price settlement scheme. Selling the hydrogen, meanwhile, earns the system additional revenue from the hydrogen market. Given these settings, twelve scenarios of operation mode are proposed and their marginal costs are derived. Results show that the supply function of the system follows the price signal in the balancing market and as well is determined by the cost of activating the flexibility components. By aggregating the Karush-Kuhn-Tucker (KKT) conditions from all the scenarios, the bidding price that maximizes the system profit can thus be obtained.

Trading optimal wind power in energy and regulation market offers possibil-ities for increasing revenues as well as impacting security of the system in apositive way[33]. The bidding in both energy and regulation markets can bedone through stochastic optimization process of both markets.Stochastic optimization can be possible once the probabilistic forecst is avail-able through ensemble forecast methodology. For stochastic optimization, thepost-processing of the ensembles to generate quantiles that will be used in op-timization can be accomplished by employing different methodology. In thisstudy, we will concentrate on the impact of post-processing of ensembles onthe stochastic optimization.Generation of quantiles needed for stochastic optimization used herein formarket optimization will be the main focus of the investigation. The impactof price ratios between energy and reserve market will be also investigated toanalyse the impact of said ratios on the revenues. Furthermore this analysiswill be performed for both US and Swedish markets.

In electricity markets of modern electric power systems, many generation companies, as major market participants, aim to maximize their profits by supplying the electrical load in a competitive manner. This thesis is devoted to investigate the price based unit commitment problem which is used to optimize generation schedules of these companies in deregulated electricity markets. The solution algorithm developed is based on Dynamic Programming and Lagrange Relaxation methods and solves the optimization problem for a generation company having many generating units with different cost characteristics. Moreover, unit constraints including ramp-rate limits, minimum ON/OFF times, generation capacities of individual units and system constraints such as total energy limits, reserve requirements are taken into account in the problem formulation. The verification of the algorithm has been carried out by comparing the results of some sample cases with those in the literature. The effectiveness of the algorithm has been tested on several test systems. Finally, the possible utilization of the method by a generation company in Turkish Electricity Market to develop bidding strategies is also examined based on some case studies.

This thesis includes three self-contained projects: In the first project Bidding strategies and their impact on the auctioneer's revenue in combinatorial auctions, focusing on combinatorial auctions, we propose a simple and efficient model for evaluating the value of any bundle given limited information, design bidding strategies that efficiently select desirable bundles, and evaluate the performance of different bundling strategies under various market settings. In the second project Retailer shelf-space management with promotion effects, promotional investment effects are integrated with retail store assortment decisions and shelf space allocation. An optimization model for the category shelf-space allocation incorporating promotion effects is presented. Based on the proposed model, a category shelf space allocation framework with trade allowances is presented where a multi-player Retailer Stackelberg game is introduced to model the interactions between retailer and manufacturers. In the third project Supply-chain oriented robust parameter design, we introduce the game theoretical method, commonly used in supply-chain analysis to solve potential conflicts between manufacturers at various stages. These manufacturing chain partners collaboratively decide parameter design settings of the controllable factors to make the product less sensitive to process variations.

Unforeseen changes in production or consumption in power systems lead to changes in grid frequency. This can cause damages to the system, or to frequency sensitive equipment at the consumers. The system operator (SO) is the responsible for balancing production and consumption in the system. The regulating market is the market place where the SO can sell or purchase electricity in order to balance unforeseen events. Producers acting on the regulating market must be able to change their production levels fast (within minutes) when required. Hydropower is therefore suitable for trading on the regulating market because of its flexibility in power production. This thesis describes models that hydropower owners can use to generate optimal bidding strategies when the regulating market is considered. When planning for trading on the market, the prices are not known. Therefore, the prices are considered as stochastic variables. The planning problems in this thesis are based on multi-stage stochastic optimization, where the uncertain power prices are represented by scenario trees. The scenario trees are generated by simulation of price scenarios, which is achieved by using a model based on ARIMA and Markov processes. Two optimization models are presented in this thesis: * Model for generation of optimal bidding strategies for the regulating market. * Model for generation of optimal bidding strategies for the spot market when trading on the regulating market is considered. The described models are applied in a case study with real data from the Nordic power system. Conclusions of the thesis are that the proposed approaches of modelling prices and generation of bidding strategies are possible to use, and that the models produces reasonable data when applied to real data. / Oväntade produktions- eller konsumtionsändringar i kraftsystem leder till ändringar i nätfrekvens. Detta kan orsaka skador på systemet eller på frekvenskänslig utrustning hos konsumenterna. Systemoperatören (SO) är den ansvarige för att balansera produktion och konsumtion i kraftsystemet. Till sin hjälp har SO reglermarknaden, som är den handelsplats där SO köper eller säljer el för att balansera oväntade händelser i systemet. Producenter som agerar på reglermarknaden måste snabbt (inom minuter) kunna ändra sina produktionsnivåer om så behövs. Vattenkraft är därför lämplig för handel på reglermarknaden på grund av dess flexibilitet i kraftproduktion. Denna avhandling beskriver modeller som vattenkraftägare kan använda för generering av optimala budstrategier då reglermarknaden beaktas. När en producents planering för handel på marknaden utförs är marknadspriserna okända. Dessa är därför betraktade som stokastiska variabler. Planeringmodellerna som presenteras i denna avhandling är baserade på multi-periodisk stokastisk programmering, där de osäkra marknadspriserna är representerade av ett scenarieträd. Scenarierna i trädet genereras genom simulering av marknadspriser. En prismodell, baserad på ARIMA- och Markovprocesser, har därför utvecklats. Två olika optimeringsmodeller presenteras i denna avhandling: * Model för generering av optimala budstrategier för reglermarknaden. * Model för generering av optimala budstrategier för spotmarknaden då handel på reglermarknaden beaktas. Modellerna tillämpas i en studie där data från den nordiska elmarknaden appliceras. Slutsatser i avhandlingen är att de föreslagna ansatserna för modellering av priser och generering av budstrategier är möjliga att anvÄanda, samt att modellerna producerar rimliga resultat när applicerade på verkliga data. / QC 20101217