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.

Presents comprehensive data analytic methods in power markets Introduces modeling, forecasting, pattern extraction, and related application in power markets Provides case studies on real market datasets

Autorentext
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).

Inhalt
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. <p