Reserve Price Research Articles

Auctioning Long-Term Projects under Financial Constraints

Abstract We consider a procurement auction for the provision of a basic service to which an add-on must later be appended. Potential providers are symmetric, have private information on their cost for the basic service and the winning firm must also implement the add-on. To finance value-enhancing activities related to the add-on, this firm may need extra funding by outside financiers. Non-verifiable effort related to these activities creates a moral hazard problem which makes the firm’s payoff function for the second period concave in returns over the relevant range. Concavity has two effects. First, it makes it more attractive to backload payments to facilitate information revelation. Second, uncertainty on the cost of the add-on introduces a background risk which requires a risk premium. In this context, we characterize the optimal intertemporal structure of payments to the winning firm, equilibrium bidding behavior and reserve prices for a first-price auction.

Optimal scheduling of virtual power plants with reversible solid oxide cells in the electricity market

Virtual power plants (VPP) can act as an independent entity to aggregate different distributed resources to participate in the electricity market, which is an inevitable trend in building green and low-carbon power systems. To investigate the beneficial effect of bi-directional electric-hydrogen conversion on market trading and optimal scheduling of VPP, this paper presents an optimal scheduling method for VPP with reversible solid oxide cells (RSOC) in the electricity market. Firstly, according to the Butler-Volmer equation and Faraday law, the relationship between the hydrogen flow rate and the electric power is established, and the equivalent physical models of the two RSOC working modes are developed based on the relationship. Then, the optimal scheduling model of the VPP participating in the energy and reserve joint market is proposed based on the two-stage robust optimization theory. The system operator reserve demand is used to describe the uncertainty of the transactions between VPP and reserve market. Finally, the column and constraint generation (C&CG) algorithm is used to solve the two-stage robust scheduling model. The simulation results show that RSOC can effectively promote renewable energy consumption and improve the VPP operating profit. Where, the operating profit can be increased by 46.5 % and the wind power consumption rate can be increased by 3.4 %, compared with the non-hydrogen VPP. Furthermore, compared with the traditional conversion equipment, RSOC can increase the VPP operating profit by 2.3 % and the wind power consumption rate by 1.85 %.

Chaotic Sea-Horse Optimizer for Profit-Based Unit Commitment to Maximize the Profit of GENCOs in Restructured Power System Considering Reserve Allocation

Objectives: The primary objective of this research work is to solve the Profit Based Unit Commitment (PBUC) problem in Power Generation Companies (GENCOs) in the competitive Electricity market. The energy and spinning reserve allocation are considered to maximize the profit of power generation companies. Methods: A new and recently developed optimization technique of Chaotic Sea-Horse Optimizer (CSHO) is planned for an optimal solution PBUC problem. The control parameters of CSHO are the number of Population is 50, the Chaotic map parameter is 4, the dimensions are 10, and the maximum number of iterations is 100 are considered to achieve the global optimal solutions. The competitive study with standard golden approaches of the LR method and other soft computing techniques such as GA, PSO, etc. are considered for performance evaluations of CSHO. The proposed CSHO is mainly based on the natural behavior of Sea-Horse in the ocean. The MATLAB 15.0 platform is utilized for the expected problem. Findings: The CSHO was tested on a standard IEEE-39 bus system (10 generators and 24 hours) with reserve allocation. The CSHO technique improves the total profit of GENCOs with various test loads and reserve demand. The simulation results like Optimal UC schedule, power generation of thermal plants, fuel cost, startup cost, revenue, and profit are displayed. The percentage of total profit of 29.76% is improved when compared with the mathematical approach of the LR method with 102 second time period. Novelty: The CSHO has currently developed an effective algorithm that easily reaches the global optimal solutions. The CSHO mimics the movement, hunting, and breeding behavior of sea-horses in nature. Implementation of chaotic maps helps to improve the convergence speed of the proposed method. Keywords: Deregulation, Profit Based Unit Commitment, Energy and Reserve Allocation, Cost Minimization, Profit of GENCOs, Chaotic Sea-Horse Optimizer

A novel frequency constrained unit commitment considering VSC-HVDC's frequency support in asynchronous interconnected system under renewable energy Source's uncertainty

The increasing share of renewable energy source (RES) poses a challenge to the frequency security of the power system. Frequency constrained unit commitment (FCUC) serves as an effective measure to address this challenge at the operational level. This paper introduces a novel FCUC model applicable to the asynchronous interconnected system connected by voltage source converter based HVDC (VSCHVDC), fully taking into account the frequency support capability of VSCHVDC in order to reduce the demand for synchronous generators' inertia and reserve while ensuring frequency security, thereby lowering operating costs. Additionally, the uncertainty of RES is also considered. The constraint expressions for three frequency indicators are derived based on a system frequency response model that includes frequency support from VSCHVDC. The proposed model optimizes unit commitment, generation and reserve dispatch and VSCHVDC transmission power and frequency response parameters, while addressing RES uncertainty using the distributionally robust chance constrained approach. In response to the highly nonlinear characteristics of the maximum frequency derivation (MFD) constraints, we propose an intelligent sampling-based support vector machine to convexify the MFD constraints and introduce a two-stage decomposition algorithm for solving the model. The effectiveness of the proposed model is demonstrated based on a modified IEEE RTS-79 system.

Sensitivity of water reallocation performance assessments to water use data

The lack of detailed and reliable data on the estimates of water use has been a key limitation in informing sustainable, equitable and efficient water reallocations in the agricultural sector. Conventional water use data have been commonly obtained from surveys or agronomic models, which have limitations on accurately reflecting the actual water use. This paper integrates cutting-edge satellite-based water use data with an ensemble of four Calibrated Mathematical Programming Models (CMPM) (one Positive Multi-Attribute Utility Programming model, one Weighted Goal Programming model, and two Positive Mathematical Programming models) to assess and compare the performance of water reallocations under satellite-based versus conventional water use estimates. We apply these methods to the water-stressed Mancha Oriental Aquifer (MOA) in central Spain, where we simulate the impacts of a hypothetical temporary water reacquisition policy in 2017, the last dry year in record. We find that water use estimates obtained with conventional approaches (which range between 4916 m3/ha and 4510 m3/ha, on average) are 13–24 % lower than satellite-based estimates (5577 m3/ha on average) during the dry year. Moreover, the water reacquisition simulation using the CMPM ensemble shows that the reserve prices (25–66 % higher) and buyback costs (26–67 % higher) derived from conventional water use data approaches are consistently and significantly higher than those derived from satellite-based water use estimates for all the elements of the ensemble, suggesting that a policy informed with satellite-based data could significantly reduce the costs of the reallocation.

Allocation problem in cross-platform ride-hail integration

We consider a ride-hail system in which a third-party integrator receives ride requests and allocates them to ride service platforms. The ride allocation problem (RAP) is modeled as a Stackelberg game. The integrator, as the leader, chooses the allocation that maximizes its profit, by pricing the rides such that no platform (i.e., follower) can find a more profitable allocation. In pursuit of self-interest, the integrator may refuse to match as many rides as the platforms are willing to serve, thereby injecting an artificial scarcity into the system. To protect the platforms from over exploitation, an exogenous reserve price is introduced to bound their per capita profit from below. We formulate RAP as a bilevel pricing problem, and convert it to a single-level problem by dualizing the lower level. When artificial scarcity is eliminated and all reserve prices are set to zero, we prove the single-level problem can be turned into a mixed integer-linear program that equals its linear relaxation, thus becoming polynomially solvable. Moreover, this version of RAP is shown to be related to cooperative assignment games. Numerical experiments confirm that artificial scarcity negatively affects matching productivity and social welfare. The integrator is favored to take most profits, and leveraging artificial scarcity strengthens its dominance. Moreover, the tighter the supply, the more the integrator benefit from artificial scarcity. The reserve price helps redistribute benefits from the integrator to the platforms. However, demanding an excessively large reserve price may depress the platforms’ profits, while undermining system efficiency.

Regulating data sales: The role of data selling mechanisms

We analyze the effects of different data selling mechanisms of a monopolistic Data Broker (DB) who sells consumer data to firms in a downstream market with free entry, where data can be used for consumer price discrimination. We consider three possible data selling mechanisms, namely auctions with and without reserve prices, and Take-It-Or-Leave-It offers, which exhibit decreasing levels of DB’s bargaining power towards firms. We highlight the emergence of an entry barrier effect in the downstream market, regardless of the data selling mechanism. Moreover, we show that the auction-based selling mechanisms, and particularly the auction with reserve prices, induce the DB to sell the lowest quantity of data, implying the lowest level of consumer surplus. Conversely, under TIOLI, the DB floods the market for data, selling to all firms data partitions that overlap over subsets of consumers. Imposing the sale of non-overlapping partitions to all firms would improve consumer surplus and welfare.

Coupons versus Rebates: An Economic–Mathematical Comparative Analysis with Policy Implications

It is very important to understand how promotions offered by sellers to consumers influence consumer and seller behaviors because it helps us study how consumers make their purchase decisions and how sellers determine their sale strategies. Unlike past studies which utilized the classical price discrimination dilemma and focused on sellers’ aspect to divide consumers into two types (higher/lower reservation prices), we focused on the consumers’ perspective and applied neoclassical consumer choice theory to develop a two-period mathematical utility-maximization model to study how different promotions influence consumer and seller behaviors. This mathematical study uncovered several key findings: (1) coupons offer consumers a greater discount compared to rebates with identical discount rates; (2) impatient consumers exhibit a preference for coupons over rebates; (3) coupons generate higher sales than rebates; and (4) sellers may adopt the coupon policy for low-ticket products and the rebate policy for high-ticket products.

On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design

Market designers often only have partial information about the environment and prefer simple mechanisms that are robust to the underlying uncertainty. In “On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design,” Anunrojwong, Balseiro, and Besbes study the fundamental problem of selling an item to n buyers, when only an upper bound on values is known and the seller minimizes worst-case regret. They establish that the same mechanism is robustly optimal across a wide range of environments for distributions of values: i.i.d., mixtures of i.i.d., exchangeable, and affiliated (the last two capturing positive dependence). Moreover, this robust mechanism is a second-price auction with random reserve price depending on n. Without positive dependence, the problem reduces to the one-buyer case. This result supports the wide use of second-price auctions in practice and allows them to quantify the robust value of competition in auctions.

Digital Assets and the Exorbitant Dollar Privilege

This paper analyzes how the rise of digital assets, particularly Stablecoins, affects the US dollar's dominance in global financial markets. It discusses whether Stablecoins, backed by US debt or other assets such as cryptocurrencies, could replace traditional US safe assets. On the one hand, Stablecoins could increase the demand for dollar reserves, strengthening the international role of the dollar. On the other hand, if Stablecoins are backed by non-dollar reserves, the global demand for dollars may decline, potentially reducing its international role.

Resource Adequacy through Operating Reserve Demand Curves: Design Options and their Impact on the Market Equilibrium

Operating reserve demand curves (ORDCs) have become part of the electricity market design in several power systems. They improve the security of supply through enhanced peak prices that occur already when the system is running low on operating reserves, before an actual shortfall occurs. Previous research, however, suggests that the ORDC’s impact on resource adequacy would be thwarted by the merit order effect. Hence, we propose a methodology to model the investment in markets with ORDC, which specifically captures the interaction with renewable deployment. A stylized power system setting is used to determine the market equilibrium at different stages of decarbonization, and compared to a conventional energy-only market. Classical ORDCs consistently increase reliability by attracting additional investments. This effect can be amplified by “shifting” the ORDC, increasing the willingness to pay for balancing reserves. Our results suggest that perfect reliability can be achieved with only moderate cost increases.

Dynamic Demand Estimation in Auction Markets

Abstract We study demand estimation in a large auction market. In our model, a dynamically evolving population of buyers with unit demand and heterogeneous and privately known preferences for a finite set of differentiated products compete in a sequence of auctions that occur in discrete time. We define an empirically tractable equilibrium concept in which bidders behave as though they are competing with the stationary distribution of opposing bids, characterize bidding strategies, and prove existence of equilibrium. Having developed this demand system, we prove that it is non-parametrically identified from panel data. We extend the model to consider a random coefficients demand system akin to workhorse demand models in industrial organization, and show that this too is non-parametrically identified. We apply the model to estimate demand and show how large sellers can exercise market power by using persistent reserve price policies, which induce higher bids and, therefore, revenues. Our analysis highlights the importance of both dynamic bidding strategies and panel data sample selection issues when analysing these markets.

Assessment of renewable power generation applied in homestay hotels: Energy and cost-benefit considering dynamic occupancy rates and reservation prices

Increasing the revenue from hotel operations and decreasing the stress on utility grids from hotel energy consumption are two priorities for the modern tourism industry. Dynamic occupancy rate and hotel reservation prices influence their operations revenue, while occupancy rate also impacts real hotel energy consumption. Renewable power generation as an effective measure can benefit hotels not only by decreasing electricity purchases but also increasing some extra income from selling electricity generated from renewable sources. This study investigated the effects of renewable power generation on energy and the cost-benefit performance of hotel operation prices under different occupancy rates and reservation prices. A two-stage energy and cost-benefit performance assessment and a novel comprehensive cost-benefit assessment model were developed to examine these research issues. The impact of the renewable power generation system (i.e., a photovoltaic-battery system installed on the building rooftop) was considered on the hotel energy and cost-benefit performance. It was found that the novel cost-benefit assessment model accurately quantified the overall hotel profit by calculating its energy consumption cost with the PV-battery system under different occupancy rates and reservation prices. Three typical homestay hotels located in Sanya were tested and assessed. The results showed that PV-battery systems effectively decreased the amount of electricity purchased by hotels. Besides, the renewable energy penetration was above 57.98% in various cases. The overall hotel profit increased by a maximum of 31.4% since renewable power generation was used under different occupancy rates and hotel reservation prices.

Optimal auction design with aftermarket Cournot competition

We study an optimal information/mechanism design problem for selling an object to a number of asymmetric, privately informed bidders in which the winning bidder competes with a third party under differentiated Cournot competition afterwards. We show how to decompose the problem into two sub-problems: Bayesian persuasion and standard mechanism design. Full disclosure of the winner's marginal cost emerges as the unique optimal information policy, combined with allocating the auctioned object to the bidder with the highest “virtual surplus.” Bidders' signaling incentives increase the seller's expected revenue and lower discriminatory reserve prices in the optimal mechanism, improving ex post efficiency, measured by the probability of no-sale.

Potential Revenue from Reserve Market Participation in Wind Power- and Solar Power-Dominated Electricity Grids: The Near-Term, Mid-Term, and Long-Term

As electricity systems become increasingly dominated by variable inverter-based generation (such as wind and solar photovoltaics (PV)), additional sources of variability appear, while the share of dispatchable thermal power plants decreases. Maintaining a stable grid frequency requires new sources of inertia to slow the frequency changes, as well as new sources of reserve power to counter the imbalances. This work uses a linear optimization modeling approach to analyze the reserve market of the electricity system during the transition to a carbon-free system with high shares of variable electricity generation. The modeling is performed for three regional contexts and with varying degrees of available flexibility technologies. As for the reserve supply, batteries, electrolyzers, electric boilers and heat pumps for district heating, curtailed wind and solar power, and hydropower and thermal power plants are all included. The results indicate that of these suppliers of reserves, the introduction of grid-scale batteries into the system drastically reduces the reserve market size. Only early in the transition is revenue from the reserve market greater than 5% of the total revenue for any technology. While the demand for reserves increases as the share of solar PV and wind power increases, the modeling reveals that access to flexible loads, storage units, and emulated inertia from wind power also increases. Depending on the choice of flexibility measures available, demand-side participation could play a major role in minimizing the cost of grid stability in the future.

Study of Oil and Gas Prospecting Techniques: A Review

Consumption of petroleum products continues to grow steadily. In 2020, oil consumption numbers surpassed exploration's, indicating a possible market imbalance. That said, the demand for new oil reserves becomes essential. Prospecting techniques for these commodities are used to have greater accuracy before drilling a well. Different Geological, Geophysical, and Geochemical methods are applied with different principles in their search, with Seismic being the most widely used technique. Sniffers have gained ground among geochemical methods, and new technologies are emerging. Despite many methods, none is conclusive and should be used in conjunction with others, whether in the prospecting area or for leak monitoring.

Sponsored search advertising and inventory replenishment: a decision support framework for an online retailer

AbstractSponsored search advertising has steadily emerged as one of the most popular advertising tools in online retail. Customers prefer search results that appear on the top to those that appear lower and are willing to pay more for products/brands that appear higher on the search. Sponsored search has a higher conversion efficiency and impacts demand more endogenously through the ranking on the search page than traditional advertising. Online retailers (e-tailers) invest aggressively in bidding to ensure they are ranked high on the search pages. The dynamic nature of sponsored search entails a higher degree of inventory readiness, and e-tailers must dovetail their sponsored search advertising strategy to drive traffic with the level of inventory to avoid consumer disappointments due to stockouts. Extant research has not delved into this critical aspect of sponsored search advertising. We endeavor to solve this business problem for an e-tailer in a dynamic stochastic setting and provide a multi-threshold decision support framework based on different inventory levels. The policy identifies inventory levels: (i) at which a retailer should not place an order, (ii) her desired level of inventory, and (iii) a ceiling up to which no bids are placed. The e-tailer can use our proposed framework to derive an inventory based sponsored search advertising campaign that ensures synchronization between bids and inventory and increases profits. Our results show that customers’ sensitivity to the website’s search rank and variation in reservation price impact the e-tailer's inventory and sponsored search bidding decisions.

E-Auction-A Web Based Electronic Auctioning System

Abstract: E-auction is a web-based electronic auctioning system that enables users to participate in auctions online. The system provides a plat- form for sellers to post their products for auction and for buyers tobid on them. The e-auction system is designed to be user-friendly and easily accessible, with features that allow for secure transactions and real-time bidding .The e-auction system provides several features for both buyers and sellers. For sellers, the system allows them to create auction listings with product descriptions, images, and starting bids. They can also set reserve prices, which are the minimum prices at which they are willing to sell their products. Sellers can monitor the progress of their auctions and accept or reject bids as they come in. The e-auction system also includes security features to protect against fraud and unauthorized access. The sys- tem uses SSL encryption to secure all communications between theclient and server, and user authentication is required for access to the system. The system also logs all transactions and provides audittrails for monitoring and investigation purposes. In conclusion, the e-auction system is a powerful tool for buyers and sellers alike, providing a secure and user-friendly platform for online auctions. Its features and capabilities make it an ideal solution for businesses andindividuals looking to buy or sell products online.

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).

Implementation in undominated strategies with applications to auction design, public good provision and matching

This paper considers implementation in undominated strategies by finite mechanisms, where multiple outcomes may be implemented at a single state of the world. We establish a sufficient condition for implementation applicable in a general environment with private values. We apply it to three well-known environments and obtain strikingly permissive results. In the single-object auction, the second-price auction with a reserve price can be outperformed in terms of revenue. In the public good provision problem, the Vickrey–Clarke–Groves mechanism can be outperformed from the viewpoint of a designer who wishes to minimise deficit subject to efficiency. In the two-sided matching environment where preferences on one side of the market are private information, the social choice correspondence that outputs all stable matchings at every preference profile, is implementable.