arXiv research

Study finds CRPS learning doesn't improve day-ahead bidding profits despite better accuracy.

The paper analyzes profitable bidding strategies for BESS in day-ahead and intraday markets.

Bidders in day-ahead electricity markets want to sell/buy electricity when their bids generate positive surplus and not to take an action when the reverse holds. However, non-convexities in these markets cause conflicts between the actions that the bidders want to take and the actual market results. In this work, we in…

Proposes a virtual bidding strategy for electricity markets using stochastic control.

Transformer model forecasts electricity price spread for virtual bidding.

Grid-scale batteries' bid patterns in price uncertainty markets

Novel approach predicts long-term seasonal component of electricity prices for improved forecasting.

Optimizes bidding in hourly and quarter-hourly electricity markets to reduce price impact.

In this study, we focus on the market clearing problem of Turkish day-ahead electricity market. We propose a mathematical model by extending the variety of bid types for different price regions. The commercial solvers may not find any feasible solution for the proposed problem in some instances within the given time li…

This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for a merchant arbitrage fac…

Study compares two market clearing methods for European power markets.

Model predicts and optimizes trading of electricity price spreads across multiple zones.

Paper uses conformal prediction for solar power forecasting in electricity markets.

The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and…

Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.

The paper simplifies electricity market curves with less parameters.

Deep learning framework for bond and yield curve forecasting with no-arbitrage constraints.

A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.

In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market…

A new data-driven model forecasts electricity prices efficiently.

We consider a class of auctions (Lowest Unique Bid Auctions) that have achieved a considerable success on the Internet. Bids are made in cents (of euro) and every bidder can bid as many numbers as she wants. The lowest unique bid wins the auction. Every bid has a fixed cost, and once a participant makes a bid, she gets…

New ML method detects incomplete bid-rigging cartels.

A new relaxed framework for pricing illiquid derivatives using bid-ask spreads.

Algorithm learns to bid in auctions with shilling, masking real bids.

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a str…

BiCB combines traffic prediction and bidding optimization for live advertising.

Develops a new bidding system to maximize advertiser profit.

Optimizes bidding strategy for Maker Protocol auctions.

Optimizes bidding strategies for LinkedIn ads across multiple platforms.

This paper tackles optimal bidding strategies in adversarial first-price auctions.

THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.

New bid shading algorithm reduces costs by 55%.

Models of auctions or tendering processes are introduced. In every round of bidding the players select their bid from a probability distribution and whenever a bid is unsuccessful, it is discarded and replaced. For simple models, the probability distributions evolve to a stationary power law with the exponent dependent…

Deep network optimizes ad bidding for first-price auctions.

Optimizes RTB bidding without exploration, improving performance under various budgets.

The paper proposes a new method to forecast winning prices in real-time bidding.

In this paper a finite discrete time market with an arbitrary state space and bid-ask spreads is considered. The notion of an equivalent bid-ask martingale measure (EBAMM) is introduced and the fundamental theorem of asset pricing is proved using (EBAMM) as an equivalent condition for no-arbitrage. The Cox-Ross-Rubinst…

Statistical depth metrics help identify risky power grid scenarios.

Deep learning detects bid-rigging cartels with high accuracy.

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to …

Model predicts bid and ask price dynamics with spread-dependent intensities.

Quantum theory explains price dynamics in financial markets, capturing bid-ask spread and ergodicity.

Improved power arbitrage through domain-adapted reinforcement learning.

Paper uses reinforcement learning to optimize bid-ask spreads in OTC markets.

Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usuall…

This paper improves bidding price prediction for ancillary services markets, boosting revenues.

This work optimizes bid strategies for online auctions using measure-valued optimization.

Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clic…