國立虎尾科技大學 |

Data Analytics in Power Markets

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data Analytics in Power Markets/ by Qixin Chen, Hongye Guo, Kedi Zheng, Yi Wang.
Author:	Chen, Qixin.
other author:	Guo, Hongye.
Description:	XIX, 284 p. 119 illus., 104 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Energy policy. -
Online resource:	https://doi.org/10.1007/978-981-16-4975-2
ISBN:	9789811649752

Data Analytics in Power Markets
Chen, Qixin.

Data Analytics in Power Markets[electronic resource] /by Qixin Chen, Hongye Guo, Kedi Zheng, Yi Wang. - 1st ed. 2021. - XIX, 284 p. 119 illus., 104 illus. in color.online resource.

Introduction to power market data and their characteristics -- Modeling load forecasting uncertainty using deep learning models -- Data-driven load data cleaning and its impacts on forecasting performance -- Generalized cost-oriented load forecasting in economic dispatch -- A monthly electricity consumption forecasting method -- Data-driven pattern extraction for analyzing market bidding behaviors -- Stochastic optimal offering based on probabilistic forecast on aggregated supply curves -- Power market simulation framework based on learning from individual offering strategy -- Deep inverse reinforcement learning for reward function identification in bidding models -- The subspace characteristics and congestion identification of LMP data -- Online transmission topology identification in LMP-based markets -- Day-ahead componential electricity price forecasting -- Quantifying the impact of price forecasting error on market bidding -- Virtual bidding and FTR speculation based on probabilistic LMP forecasting -- Abnormal detection of LMP scenario and data with deep neural networks.

This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.

ISBN: 9789811649752

Standard No.: 10.1007/978-981-16-4975-2doiSubjects--Topical Terms:

554736
Energy policy.

LC Class. No.: HD9502-9502.5

Dewey Class. No.: 333.79

Data Analytics in Power Markets
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520 $a This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.
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