Bidding

About: Bidding is a research topic. Over the lifetime, 15371 publications have been published within this topic receiving 294233 citations. The topic is also known as: competitive bidding.

Second‐Price Auctions with Asymmetric Payoffs: An Experimental Investigation

Abstract: A series of two-player, second-price common-value auctions are reported. In symmetric auctions, bidders suffer from a winner's curse. In asymmetric auctions in which one bidder has a private value advantage, the effect on bids and prices is proportional rather than explosive (the prediction of Nash equilibrium bidding theory). Although advantaged bidders are close to making best responses to disadvantaged bidders, the latter bid much more aggressively than in equilibrium, thereby earning negative average profits. Experienced bidders consistently bid closer to the Nash equilibrium than inexperienced bidders, although these adjustments towards equilibrium are small and at times uneven.

The Competitive Effect in Bonus Bidding: New Evidence

Abstract: One of the major unsettled questions in the study of competitive bidding concerns the impact of additional competition in auctions on the optimal bid levels of competing firms. Numerous theoretical and simulation studies suggest an inverse relationship between the expected number of competitors and the bid level of a particular firm in sealed bid auctions involving objects of uncertain value. The few empirical studies that have been done contradict this assertion. In this article, we address this issue by pointing out a serious statistical defect in previous empirical work and by reestimating a bid level equation by using a more appropriate technique. New evidence is provided which reconciles the differences between previous empirical results and the major predictions of the widely accepted bidding theory models. The new results presented here support the conclusions of the theoretical studies.

Day-ahead market bidding for a Nordic hydropower producer: taking the Elbas market into account

Abstract: In many power markets around the world the energy generation decisions result from two-sided auctions in which producing and consuming agents submit their price-quantity bids. The determination of optimal bids in power markets is a complicated task that has to be undertaken every day. In the present work, we propose an optimization model for a price-taker hydropower producer in Nord Pool that takes into account the uncertainty in market prices and both production and physical trading aspects. The day-ahead bidding takes place a day before the actual operation and energy delivery. After this round of bidding, but before actual operation, some adjustments in the dispatched power (accepted bids) have to be done, due to uncertainty in prices, inflow and load. Such adjustments can be done in the Elbas market, which allows for trading physical electricity up to one hour before the operation hour. This paper uses stochastic programming to determine the optimal bidding strategy and the impact of the possibility to participate in the Elbas. ARMAX and GARCH techniques are used to generate realistic market price scenarios taking into account both day-ahead price and Elbas price uncertainty. The results show that considering Elbas when bidding in the day-ahead market does not significantly impact neither the profit nor the recommended bids of a typical hydro producer.

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Abstract: This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

Bidding for energy supply with request for service

Abstract: An auction service is provided that stimulates competition between energy suppliers (i.e., electric power or natural gas). A bidding moderator (Moderator) receives bids from the competing suppliers of the rate each is willing to charge to particular end users for estimated quantities of electric power or gas supply (separate auctions). Each supplier receives competing bids from the Moderator and has the opportunity to adjust its own bids down or up, depending on whether it wants to encourage or discourage additional energy delivery commitments in a particular geographic area or to a particular customer group. Each supplier's bids can also be changed to reflect each supplier's capacity utilization. Appropriate billing arrangements are also disclosed.