This book is written on the basis of the condensation of practice and research results in teaching and research of power system pricing mechanism for more than ten years. Starting from the background of China's dual-carbon target, the book introduces theories and methods in many fields, such as electric power economics, management science, and optimization, and systematically introduces the basic ideas, principles, and methods of the new power system pricing mechanism under the demand-side management, and carries out a number of simulation experiments to verify and analyze the effectiveness and practicality of the pricing model. The research involves a variety of pricing models and algorithms, such as the social welfare maximization model and derivative models, Lagrangian dual decomposition and gradient projection pricing methods, KKT conditions and complementary theory pricing methods, distributed solution algorithms for game and two-layer models, machine learning pricing methods, and price adjustment mechanisms based on demand response. The preparation of this book focuses on the guidance of theory to practice, interpreting theoretical knowledge with the research results of actual scientific research topics, which helps readers to better understand and master the relevant knowledge, and at the same time, focusing on the hierarchical structure of knowledge to satisfy the learning requirements of readers at different levels and with different professional backgrounds. It provides a practical reference book for undergraduates and postgraduates of related majors and researchers engaged in research engaged in related fields.

Li Junxiang is a professor and doctoral supervisor at the Business School, University of Shanghai for Science and Technology. He also is director of the Smart Emergency Logistics Management Laboratory, director of Shanghai Nonlinear Science Research Association, deputy secretary‑general of Shanghai Mechanical Engineering Society, expert of the Think‑tank of Shanghai Blockchain Technology Association, expert of Shanghai Energy‑Saving and Environmental‑Protection Industry, and member of the expert committee of Viser Expert Pool (VE). He obtained his bachelor’s degree from Shandong University, master and doctoral degrees from Dalian University of Technology. He completed postdoctoral research at Tongji University. He worked as a visiting scholar at the Hong Kong University of Science and Technology, the Newcastle University in UK, and Curtin University in Australia. In recent years, his research has focused on Smart Grid, Supply Chain Management, Operational Management, Operations Research, Quality Management, and Optimization. He has published more than 200 academic papers, over 50 of which are indexed in the SCI, and led and participated in numerous national and provincial‑ministerial research projects, including projects supported by the National Natural Science Foundation of China (NSFC).

Gao Yan is a professor at Business School, University of Shanghai for Science and Technology. His research focuses on nonsmooth optimization theory and its applications. In recent years, his work has centered on demand side management in power systems, with an emphasis on real-time pricing mechanisms, employing advanced optimization theory and reinforcement learning approaches. He currently serves as a member of the Executive Council of the Society for Management Science and Engineering (China), Executive Vice President of the Shanghai Society for Systems Engineering, and Vice Dean of the Shanghai Academy of Systems Science. He has previously served as Executive Dean of the Business School at the University of Shanghai for Science and Technology and as a member of the Discipline Evaluation Group (System Science) of the State Council of China. He has received the National Outstanding Teacher Award, the Fok Ying-Tung Young Teacher Award, and the IBM Faculty Award. Prof. Gao has published over 300 peer-reviewed papers, and his research has been supported by the National Natural Science Foundation of China and the IBM SUR (Shared University Research) program.