
Data Analytics in Power Markets
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
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.
More details
Other editions
Additional editions

Persons
Qixin Chen , IEEE Senior Member, Tenured Associate Professor of Department of Electrical Engineering in Tsinghua University, Chair of IEEE working group on load aggregation; Associate Director for Energy Internet Research Institute, Tsinghua University
Research interests: Power market, low carbon electricity technology, power system.
Honors:
- National Youth Top-notch Talent Support Program, Ministry of Science and Technology, China (2018)
- National Science Fund for Distinguished Young Scholars (2016);
- Research Fund for Distinguished Young Scholars, Fok Ying-Tong Education Foundation (2015);
- Beijing New-Star Plan for Young Scholars, Scientific Committee of Beijing City Government (2015);
-Young Scientist Honor (40 under the Age of 40), by World Economic Forum Summer Davos (2013);-Top 35 Young Innovator under the Age of 35 (TR 35), by MIT Technology Review (2012);
-FirstRunner-up, Young Scientist Award, by ProSper.Net, Scopus and Elsevier (2011);
-Nominee Honor, National Excellent 100 Doctoral Dissertation, Ministry of Education, China (2013);
-Paper Author, China's top 5000 scientific journal papers (F5000) (2012/2013/2016);
-Annual Award for Publishing a One-Hundred Most Influential Chinese Scholar Paper (2012).
Hongye Guo , postdoc research fellow at Department of Electrical Engineering in Tsinghua University. Visiting scholar of Stanford University in 2018. Visiting scholar of Illinois Institute of Technology in 2019.
Research interests: Power market, game theory, energy economics, machine learning.
Honors:
- "Shuimu" Tsinghua Scholar (2020);
- Best PhD Dissertation of Tsinghua University (2020);
- Outstanding Young Researcher, Department of Electrical Engineering, Tsinghua University (2020);- Doctoral National Scholarship (2019);
- Integrated Excellence Scholarships, Tsinghua University (2018);
Kedi Zheng, PhD student of Department of Electrical Engineering in Tsinghua University.
Research interests: Power market, locational marginal price (LMP) theory, electricity forecasting.
Honors:
- Integrated Excellence Scholarships, Tsinghua University (2018/2020)
- Outstanding Graduate Award, City of Beijing (2017);
- Excellent Graduate Award, Tsinghua University (2017);
Yi Wang , Assistant Professor of Department of Electrical and Electronic Engineering in the University of Hong Kong, Editor of International Transactions on Electrical Energy Systems, Youth Associate Editor of CSEE Journal of Power & Energy Systems, Secretary of IEEE working group on load aggregation.
Research interests: Load forecasting, demand response, machine learning for smart grid, multiple energy systems.
Honors:
-Siebel Scholar Award;-IEEE Transactions on Smart Grid Best Reviewer (2018/2017);
-IEEE Transactions on Power Systems Outstanding Reviewer (2018/2016);
-Fellowships for Future Scholars, Tsinghua University (2014);
-Tsinghua Science & Technology Best Paper Awards;
-Doctoral National Scholarship (2016/2017/2018).
Content
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use the free software Adobe Reader, Adobe Digital Editions, or any other PDF viewer of your choice (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or another reading app for eBooks, e.g., PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.