Journal ArticleDOI
A Data-Driven Pattern Extraction Method for Analyzing Bidding Behaviors in Power Markets
TLDR
A data-driven analysis framework for bidding behavior is proposed in which a data standardization processing method is proposed that addresses the particularities of the bidding data and provides a fundamental dataset for further market analyses.Abstract:
Myriad studies have been conducted on bidding behaviors following a worldwide restructuring of the electric power market. The common theme in such studies involves idealized and theoretical economic assumptions. However, practical bidding behavior could deviate from that based on theoretical assumptions, which would undoubtedly limit the effectiveness and practicality of the prevalent market-based studies. To analyze the actual bidding behavior in power markets, this paper proposes a data-driven analysis framework for bidding behavior in which a data standardization processing method is proposed that addresses the particularities of the bidding data and provides a fundamental dataset for further market analyses. Then, an adaptive clustering method for bidding behavior is developed that applies the ${K}$ -medoids method and the Wasserstein distance measurement to extract the generators’ bidding patterns from a massive dataset. An empirical analysis of the bidding behavior is conducted on actual data from the Australian energy market. The typical bidding patterns are extracted, and the bidding behaviors are further analyzed.read more
Citations
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Proceedings ArticleDOI
Agent-based modeling in electrical energy markets using dynamic Bayesian networks
TL;DR: An agent-based model to address the problem of short-term strategic bidding of conventional generation companies (GenCos) in a power pool is presented and it is shown that the agents will be able to predict and adapt to approximate Nash equilibrium of the market through time using local reasoning and incomplete publicly available data.
Journal ArticleDOI
Deep Inverse Reinforcement Learning for Objective Function Identification in Bidding Models
TL;DR: This paper proposes a data-driven bidding objective function identification framework with three procedures, and a deep inverse reinforcement learning method that is based on maximum entropy is introduced to identify individual reward functions, whose high-dimensional nonlinearity could be saved in multilayer perceptions (MLPs).
Journal ArticleDOI
Bidding behaviors of GENCOs under bounded rationality with renewable energy
TL;DR: In this article , the authors proposed a novel bidding behavior model for GENCOs under bounded rationality with renewable energy to overcome the problems of strong assumptions, where all GENCO's observed bidding behaviors are proved to deviate from the simulated perfectly rational ones.
Journal ArticleDOI
Bidding strategy evolution analysis based on multi-task inverse reinforcement learning
TL;DR: In this paper , a multi-task inverse reinforcement learning-based analysis framework is proposed to identify several bidding objectives adopted by the participant in different time periods and label the adopted objective in each day, according to historical bidding records and market status.
Journal ArticleDOI
Multi-Market Bidding Behavior Analysis of Energy Storage System Based on Inverse Reinforcement Learning
TL;DR: In this paper , a novel inverse RL (IRL) based framework was proposed to identify the bidding decision objective function of ESS in coupled multi-market through their historical bidding records and operation status.
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Understanding strategic bidding in multi‐unit auctions: a case study of the Texas electricity spot market
Ali Hortacsu,Steven L. Puller +1 more
TL;DR: In this article, the authors examine the bidding behavior of firms in the Texas electricity spot market, where bidders submit hourly supply schedules to sell power, and characterize an equilibrium model of bidding and use detailed firm-level data on bids and marginal costs to compare actual bidding behavior to theoretical benchmarks.
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Household Energy Consumption Segmentation Using Hourly Data
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Strategic bidding of transmission-constrained GENCOs with incomplete information
Tao Li,Mohammad Shahidehpour +1 more
TL;DR: In this article, a method for analyzing the competition among transmission-constrained generating companies (GENCOs) with incomplete information is presented. But the authors do not consider the impact of transfer capability on GENCOs' bidding strategies.