Bid Price Research Articles

A DSO-based day-ahead market mechanism for optimal operational planning of active distribution network

Grid-edge distributed energy resources (DERs) present both operational challenges and opportunities to distribution system operators (DSO). This paper presents a day-ahead scheduling strategy for DER-rich active distribution networks to leverage operational flexibilities of various DERs for effective network management. The scheduling strategy is realized based on a DSO-centric day-ahead market model that incorporates a multi-period and two-stage optimal power flow (OPF) model to determine scheduling and pricing for network and non-network DERs. The uncertainties in day-ahead operation stemming from local generation, load consumption, and market prices are robustly considered for hedging the intermittency and forecasting uncertainties. A novel bid price function is proposed for local distributed solar PV generation aiming at maximizing participation in network service. A new reactive power management and pricing scheme is also presented for optimal scheduling of voltage support devices within distribution network. The proposed framework is implemented on IEEE-33 bus balanced test system and operational planning strategy is analyzed for overall cost-benefits of the system at various uncertainty levels. A 7-10 percent saving in cost of procurement for DSO can be accrued at various uncertainty levels, and a 10 percent increase in revenue for the PV generation can be observed.

Air pollution, bid price bias, and IPO underpricing

We examine the impact of air pollution on underpricing of initial public offerings (IPOs) and its underlying mechanisms. Our findings suggest that IPO underpricing increases when air pollution is more severe in cities where bidding investors are located. Mechanism tests show that air pollution influences investors' mood and cognition, leading them to make pessimistic bids and rely more on anchors. Additional tests document that the impact of air pollution on IPO underpricing is more salient when information asymmetry is more severe and when the IPO firm also faces severe air pollution. Our research contributes to the literature on the causal influence of air pollution on pricing efficiency in primary markets by linking air pollution to potential bias embedded in investors’ bid prices, which carries important implications for environmental regulations in emerging markets, given the economic cost of air pollution imposed on IPO firms.

The Relationship Below 80% of the Owner Estimate Price on Construction Projects to Project Performance

This study aims to analyze the causes of price quotes below 80% of the owner estimate price and how the relationship between the variable price quotes below 80% of the owner estimate price with project performance. This research was conducted by distributing questionnaires to service users involved in the implementation of projects with price quotes below 80% of the owner estimate price in Bali Province in the 2020-2022 fiscal year. The data from the distribution of questionnaires will be analyzed using the RII and SEM-PLS methods. Based on the RII data analysis, it is found that the dominant factors causing projects with price quotes below 80% of the owner estimate price are high competitiveness between service providers, service providers looking for small profits, service providers do not understand the basis of regulations related to the cost of taxes and required guarantees, service providers are unable to identify the risk of additional costs arising from the mismatch between the planned schedule and the realization and the factor that service providers are able to understand waste construction so that service providers can submit low bid prices. The relationship between price quotes below 80% of the owner estimate price and project performance has a positive and significant relationship. Price quotes as an independent variable are able to influence the dependent variable, namely project performance. Because price quotes below 80% of the owner estimate price and project performance have a positive and significant relationship, to improve project performance, namely in a way for service providers to think more about and take into account in detail before submitting a price quote so that during the implementation of project performance can be done optimally.

Second‐chance offers and buyer reputation systems: theory and evidence on auctions with default

AbstractWinners in online auctions frequently fail to complete purchases. Major auction platforms therefore allow “second‐chance” offers (the runner‐up bidder pays his own bid price) and let sellers leave negative feedback on buyers who default. We show theoretically that (i) all else equal, the availability of second‐chance offers reduces bids; (ii) sellers have no incentive to exclude bidders, even if they are nearly certain to default; (iii) buyer reputation systems reward bidders known to default with a positive probability. Our experiments show that the economic forces identified in the theoretical model are important enough to have predictive power for bidder behavior.

Rural household's willingness to pay for irrigation water use: An application of contingent valuation method, in the case of North Shewa zone, Ethiopia

AbstractThis study aimed to evaluate household's willingness to pay (WTOP) for irrigation water use (IWU) per timad (0.25 ha) of the irrigable land per year and to analyze determinants of WTOP for IWU at individual households based on the contingent valuation method (CVM). The primary data collected for this study was from 120 households using two‐stage sampling techniques such as simple random sampling and purposive from four peasant associations of the Efratana Gidim and Kewit districts by semistructured questionnaire. The secondary dataset was collected from several sources. The collected data were analyzed using descriptive, inferential, and Tobit econometric models. In addition, a seemingly unrelated bivariate probit (SUBP) model was applied to calculate the average WTOP for IWU per timad/year. The CVM survey indicated that from the total sample collected data, 80% of the households showed WTOP for IWU, while the remaining 20% did not show WTOP. The econometric result of the Tobit model showed that the education level of the household, household family size, Irrigation experience, total annual income, and credit utilization influenced farmers' WTOP positively and significantly, while only initial bid price affected the farmer negatively and significantly. Hence, the above variables should be given high emphasis in designing irrigation water‐related projects in the districts. The SUBP model result revealed that the household's average annual WTOP was 854.74 Ethiopia Birr/0.25 ha/year. The estimated MWTOP for IWU is a huge amount of money. Hence, the concerned body has to collect money from households, which has to be used in the development of irrigation systems in the study area. This study recommends that different stakeholders of government should collaborate in raising revenue from the local community and apply it for the desired community development purposes, particularly for food security enhancement as the districts are highly dependent on rainfed agriculture.

Improving Real-Time Bidding in Online Advertising using Markov Decision Processes and Machine Learning Techniques

Real-time bidding has emerged as an effective online advertising technique. With real-time bidding, advertisers can position ads per impression, enabling them to optimise ad campaigns by targeting specific audiences in real-time. This paper proposes a novel method for real-time bidding that combines deep learning and reinforcement learning techniques to enhance the efficiency and precision of the bidding process. In particular, the proposed method employs a deep neural network to predict auction details and market prices and a reinforcement learning algorithm to determine the optimal bid price. The model is trained using historical data from the i Pin You dataset and compared to cutting-edge real-time bidding algorithms. The outcomes demonstrate that the proposed method is preferable regarding cost-effectiveness and precision. In addition, the study investigates the influence of various model parameters on the performance of the proposed algorithm. It offers insights into the efficacy of the combined deep learning and reinforcement learning approach for real-time bidding. This study contributes to advancing techniques and offers a promising direction for future research.

Estimating the visitor’s willingness to pay towards biodiversity conservation at Kuala Lumpur Forest Eco Park, Malaysia, using the Contingent Valuation Method

Abstract. Musa F, Shahrudin INM. 2023. Estimating the visitor’s willingness to pay towards biodiversity conservation at Kuala Lumpur Forest Eco Park, Malaysia, using the Contingent Valuation Method. Biodiversitas 24: 3690-3699. The Kuala Lumpur Forest Eco Park is within the Bukit Nanas Forest Reserve, Malaysia's oldest and most biodiverse hill dipterocarp forest. The destruction of urban greenery in the heart of Kuala Lumpur has prompted stakeholders to conserve this forest as urban ecotourism areas for recreational, educational, and research activities. Due to the absence of a market, the economic value of biodiversity cannot be quantified in monetary terms from an economic perspective. Therefore, it will be difficult for stakeholders to justify the cost of conservation in their management in the future. This study's objective was to determine the economic value of biodiversity conservation at Kuala Lumpur Forest Eco Park by determining a visitor's Willingness to Pay (WTP) using the Contingent Valuation Method (CVM). A total of 250 questionnaires were distributed randomly to both international and domestic tourists to conduct this study. Five bid prices were chosen for domestic and international visitors based on the Logit Model's estimation of the visitor's WTP for biodiversity conservation. This study indicates that most visitors are willing to pay for biodiversity conservation influenced by religion, marital status, income, employment, and education. Furthermore, the results indicate that the WTP's mean and median per visit are MYR3.81 and MYR2.58 based on domestic visitor data. Consequently, this research may aid decision-makers in managing the financial strategy for biodiversity conservation activities. Thus, the conservation fee that visitors can accept based on their willingness to pay will further boost visitors' future visits to the Kuala Lumpur Forest Eco Park.

The professionalisation of the art trade in early nineteenth century London: Exploring the business model of Christie’s auction house

By 1800, the city of London had developed into a major hub for the art trade and numerous auction houses vied for market share in this competitive environment. Combining data from the Getty Provenance Index® and a unique set of journals preserved at the Christie’s archives in London which contain presale evaluations for paintings during the 1820s, this paper explores the business strategy of the auction house. We gauge the relationship between the estimates and the hammer price or highest bid in the subsequent public auction, and the reserve price when available. We argue that Christie’s developed the expertise and engendered the trust among sellers to secure consignments of desirable works of art, which were estimated at realistic and attractive levels that gave the company a competitive advantage in the booming London art market.

Grid investment and subsidy tradeoffs in renewable electricity auctions

Geographically neutral renewable energy auctions incentivize investors to locate their plants in optimal locations to minimize their bid prices, but this concentration might result in substantial grid investments. Ultimately, maximizing the social welfare for consumers requires minimizing the sum of renewable energy auction subsidies and grid investments. We quantify the trade-offs between grid investment costs and auction subsidies in geographically neutral and geographically diverse renewable energy auctions. We find that consumer costs are minimized in geographically diverse auctions, e.g., when renewables are not only located in the most optimal regions and, thus, when the auction subsidies are not minimized.

CVaR‐based method for optimizing the contract bidding strategy of PV power stations

AbstractWith the rapid development of the new power system, the proportion of renewable energy participating in the electricity market is constantly increasing, and the uncertainty of renewable energy power output and market clearing price brings risks to its participation in long‐term contract bidding. Therefore, a method of contract bidding involving bidding amount and bidding price of PV power station is proposed. Firstly, the multi‐order weighted Markov chain model is used to forecast the distribution of weather states in the trading cycle, and the fuzzy C‐means (FCM) method is used to cluster the joint samples of power output indicators and market clearing price indicators, and then Latin hypercube sampling is used to generate power output scenarios and market clearing price scenarios. Then, an optimization method for long‐term contract bidding strategy of PV power station considering conditional value at risk (CVaR) and transaction probability is established, and the model is divided into two stages and solved in an alternate iterative way. Finally, an example is used to verify that the constructed model enables PV power stations to effectively avoid risks and obtain more stable revenue.

The Use and Performance of Sealed Bid versus Oral Timber Auctions by State Forestry Agencies: A Minnesota Case Study

Abstract State governments are responsible for the stewardship and management of more than 28 million ha of forest land in the United States. This study presents the findings of an in-depth examination of the characteristics and performance of sealed bid and oral auctions using records from more than a thousand tracts offered for sale by the Minnesota Department of Natural Resources, which administers one of the nation’s most robust state timber sale programs. The study found that although tracts offered for sale through oral and sealed bid auctions sold at the same rate and are undifferentiated with respect to nearly all characteristics, sealed bid tracts generated significantly higher winning prices and additional revenue per hectare than tracts sold using oral auctions. Logging businesses were more likely to win sealed auctions than oral auctions, and their portfolio of purchased tracts contained a higher proportion acquired through sealed bid auctions than the portfolio of sealed bid tracts purchased by mills. The study adds to the limited body of literature on public agency timber auctions and is the first to provide a detailed analysis of the auction methods used by a state forest management agency. Study Implications: Public agencies use sealed bid and oral auctions to sell timber rights. This study of the nation’s largest state timber sale program found that the auction method used affects winning bid prices, the likelihood certain categories of bidders will win the auction, the allocation of timber to mills and logging businesses, and revenue generated for each hectare of timber sold. State timber sale program administrators may be able to use these findings to better understand how the choice of auction method affects the financial performance of their timber sale program as well as the allocation of timber among the major types of bidders.

Cooperative Power Bidding of Smart Community Grids With an Aggregator-Prosumer-Based Hierarchical Framework

The progressive development of electricity markets envisions the participation of smart community grids in energy trading, but it is still challenging to guarantee global optimality for uncertain power management of multiple entities under a hierarchical framework. To tackle the cooperative strategic bid-ding problem with uncertainties, this paper proposes a hierar-chical three-stage robust optimization (HTS-RO) method. With respect to the bilayer coordination framework involving an ag-gregator gathering multiple prosumers, a novel HTS-RO bidding model is firstly established relying on the canonical centralized two-stage RO model. In the first stage, the aggregator performs power bidding in the upper layer according to the derived power flow information of the shared buses from the lower-layer prosumers. Considering renewable-load uncertainties and pri-vacy preservation, each prosumer conducts its own robust scheduling via the second and third stages based on the bidding prices from the aggregator and submits its optimal results of the shared bus back to the aggregator. For the robust scheduling model of each prosumer, the second stage identifies the basic plans of the nominal scenario, and the third stage checks the fea-sibility of the basic plans under uncertainties. To solve the re-sulting intractable multi-level mixed-integer nonlinear pro-gramming, a tri-loop decomposition-based iterative algorithm is tailored to ensure that the distributed computation converges to the optimal solution with the overall profit, thereby achieving the equivalent split of the centralized optimization into a hierarchical manner. Numerical tests on generic smart community grids with multiple prosumers validate the applicability and superiority of the proposed HTS-RO method for power bidding.

Study on the electricity spot market trading mechanism considering the proportion of renewable energy consumption quota

The challenge of harmonizing the integration of renewable energy in market-driven transactions and assured accommodations presents a predicament in the development of China's electricity spot market. Moreover, as renewable energy penetration escalates, the issue of reserve undeliverability due to transmission congestion diminishes the power system's capacity to utilize renewable energy resources. To address this concern, this study introduces a secondary clearing mechanism for the electricity spot market, taking into account the proportion of renewable energy consumption quotas. Based on the first clearing, when renewable curtailment occurs, the bid pricing of abandoned power units undergoes flexible adjustment through the optimization of the price correction coefficient, followed by the execution of a secondary clearing utilizing the revised bidding information to fulfill the stipulations of the renewable energy consumption quota ratio. Drawing on the outcomes of the two-stage clearing, an incentive-compatible settlement compensation mechanism is proposed to preserve the impartiality of the market operator. The spot market clearing model accounts for the transmission safety margin, effectively mitigating the likelihood of transmission congestion, reserve inaccessibility, and renewable energy curtailment issues in real-time dispatching. Finally, a modified IEEE 30-bus system serves to substantiate the efficacy of the proposed market mechanism.

Estimation of Bid Price Ratio by Housing Type Using the APARCH Model

Estimation of Bid Price Ratio by Housing Type Using the APARCH Model

농지 경매 매각가율 결정요인 연구

In this study, an empirical analysis was conducted using a hedonic pricing model to determine the factors affecting the farmland auction sales price ratio in Hwaseong-City. The results show that the variables that have a positive(+) effect are road interface(others), successful bid price, number of bidders, and the variables that have a negative(-) effect are number of failed bids, planned management area(etc.), farm area were found to have an effect. The size of the influence on the farmland sales price ratio is in the order of the number of failed bids, road interface(others), planning management(etc.), farmland area, successful bid price, and number of bidders. The contributions of this study are that the farmland variables that form the value of farmland have been used to identify the factors that affect the farmland auction sales price ratio by utilizing the cases of farmland auctions that have won farmland auctions in the court auction, centering on Hwaseong-City. By dividing and analyzing the data, it provided reliable data for decision-making to the participants in the farmland auction.

Stock hyping before auction-style SEOs: Are primary market investors misled?

This study investigates the stock price pattern around seasoned equity offerings and the response of primary market investors to such a price pattern in an environment with weak investor protection. Using a sample of 1670 auction-style SEOs in China, we find stock prices exhibit an inverted-V pattern around SEO issue dates, increasing significantly before, peaking at, and then declining dramatically after issue dates. Further analysis shows that this price pattern can be explained by the managerial hyping hypothesis. With detailed information on investor bids submitted in the auctions, we find that investors' bid prices decrease while SEO discounts increase with pre-SEO price run-ups, indicating that primary market investors could identify SEO issuers' stock hyping tactic and react by submitting more conservative bid prices. We conclude that SEO issuers condition the market around SEO issue dates but this tactic does not help reduce issuers’ financing costs.

The Early Impact of GDPR Compliance on Display Advertising: The Case of an Ad Publisher

The European Union's General Data Protection Regulation (GDPR), with its explicit consent requirement, may restrict the use of personal data and shake the foundations of online advertising. The ad industry has predicted drastic loss of revenue from GDPR compliance and has been seeking alternative ways of targeting. Taking advantage of an event created by an ad publisher's request for explicit consent from users with European Union IP addresses, the authors find that for a publisher that uses a pay-per-click model, has the capacity to leverage both user behavior and web page content information for advertising, and observes high consent rates, GDPR compliance leads to modest negative effects on ad performance, bid prices, and ad revenue. The changes in ad metrics can be explained by temporal variations in consent rates. The impact is most pronounced for travel and financial services advertisers and least pronounced for retail and consumer packaged goods advertisers. The authors further find that web page context can compensate for the loss of access to users’ personal data, as the GDPR's negative impact is less pronounced when ads are posted on web pages presenting relevant content. The results suggest that publishers and advertisers should leverage targeting based on web page content after the GDPR’s rollout.

Adaptive RiskAware Bidding with Budget Constraint in Display Advertising

Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side plat- form (DSP) automatically provides bid price usually relying on the ad impression value estimation and the optimal bid price determination. However, the current bid strategy over- looks the randomness of the user behaviors (e.g., click) and the cost uncertainty caused by the auction competition. In this work, we propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learn- ing, which is the rst to simultaneously consider estimation uncertainty and the dynamic risk tendency of a DSP. Specif- ically, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a speci c state and market environment via a sequen- tial decision process. Additionally, we theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR). Consequently, we propose two instantiations to model risk tendency, includ- ing an expert knowledge-based formulation embracing three essential properties and an adaptive learning method based on self-supervised reinforcement learning. We conduct ex- periments on public datasets and show that the proposed framework achieves better performance in terms of the num- ber of clicks under di erent budget constraints 1.

A model for optimal bus routes packaging in a competitive contracting environment: A case study of Tehran

Transit authorities often prefer to outsource the bus routes to the private sector through a competitive tendering or negotiation procedure. A major challenge for decision-makers in this area is the optimal arrangement of routes among contracts which directly impacts the operating costs, labor costs, vehicle resources, level of competition between private operators, required subsidy, contract price, and system efficiency. Therefore, this study proposes a new model to find optimal route packages considering the parameters including package size, package revenue, the geographic distribution of routes, and terminal locations. The methodology relies on a genetic algorithm equipped with innovative methods, making it applicable to large-scale urban networks. We programmed both the model and the solution approach in C++ programming language and applied them to the city of Tehran as a real case study, which involved 169 routes and 2,215 bus fleets. In the main scenario, 10 packages were identified and compared with the current packages. The results demonstrated a 7.6% reduction in revenue deviation among the packages, indicating improved balance. Additionally, the routes within each package exhibited geographical concentration, aligning with the common terminal approach. By applying the proposed methodology to the bus network in Tehran, the study highlights the potential of the model in outsourcing bus routes to the private sector in similar real-world practices. It is worth noting that the Tehran Bus Organization has utilized the proposed packages derived from this study in their recent tender processes. Evaluation of the new packages' effectiveness demonstrated a notable decrease of 17% in average bid prices. This reduction directly corresponds to a decrease in operating costs and the required subsidy.

Consumers Willing to Pay for Freshwater-Grown Vegetables in District Faisalabad, Pakistan

Due to the scarcity of freshwater supplies, farmers have increasingly turned to wastewater for irrigation to grow crops. However, this practice poses risks as vegetables grown with wastewater may contain toxins and heavy metals that are harmful to human health, soil quality, and groundwater resources. Consequently, these vegetables can cause diseases, while freshwater-grown vegetables are more expensive due to the scarcity of water. This paper focuses on exploring consumers' willingness to pay for vegetables grown in freshwater and investigates factors such as age, income, education, bid price, risk perception, and health risk perception that influence their willingness to pay a premium price for these vegetables. The study was conducted in Faisalabad city, where 130 vegetable consumers were interviewed using a semi-structured questionnaire. The findings indicate that consumers highly value their health and are willing to pay a premium price for safe and healthy vegetables. This highlights the need for businesses and policymakers to meet the demand for safe vegetables by providing a consistent and reliable supply of treated water to farmers and educating them on the proper use of agrochemicals to mitigate health risks. Policymakers and other stakeholders can play a role in supporting vegetable farmers with these resources. This creates an opportunity for investors to establish businesses that meet the demand for safe and healthy vegetables in Faisalabad.