This book aims to work out the distributed economic operation in smart grids in a systematic way, which ranges from model-based to model-free perspectives. The main contributions of this book can be summarized into three folds. First, we investigate the fundamental economic operation problems in smart grids from model-based perspective. Specifically, these problems can be modeled as deterministic optimization models, and we propose some distributed optimization algorithms by integrating the multi-agent consensus theory and optimization techniques to achieve the distributed coordination of various generation units and loads. Second, due to the randomness of the large-scale renewable energies and the flexibility of the loads, we further address these economic operation problems from a model-free perspective, and we propose learning-based approaches to address the uncertainty and randomness. At last, we extend the idea of model-based and model-free algorithms to plug-in electric vehicles (PEVs) charging/discharging scheduling problem, the key challenge of which involves multiple objectives simultaneously while the behavior of PEVs and the electricity price are intrinsically random. This book presents several recent theoretical findings on distributed economic operation in smart grids from model-based and model-free perspectives. By systematically integrating novel ideas, fresh insights, and rigorous results, this book provides a base for further theoretical research on distributed economic operation in smart grids. It can be a reference for graduates and researchers to study the operation and management in smart grids. Some prerequisites for reading this book include optimization theory, matrix theory, game theory, reinforcement learning, etc.
This book presents for the first time a detailed and comprehensive interpretation of Zhongguancun, China’s first national self-dependent innovation demonstration zone. Explored in the book are examples of world-class, leading enterprises in fields, such as the Internet, big data, artificial intelligence, green and low-carbon, modern supply chain and high-end service. According to some data, the rate of contribution to the economic increase of Beijing made by Zhongguancun rose to 36.8% in 2015 from 17.9% in 2010. More specifically, in 2015, nearly 40% of the economic increase in Beijing was contributed by Zhongguancun Science Park. By 2017, Zhongguancun fostered 650 gazelle enterprises and 70 unicorn companies. The book also uniquely provides readers with a panoramic interpretation of the environment for innovation and entrepreneurship in Zhongguancun. It is mainly divided into three parts: History of Zhongguancun, Data of Zhongguancun, Cases of Zhongguancun and Policies of Zhongguancun. Through the depiction of history, data, cases and policy, this book clarifies that in most cases, enterprises in Zhongguancun become successes by following such a road characterized by starting from scratch and by relying on science and technology innovation and expanding from small to big by virtue of the capital market. ““Zhongguancun Model: Driving the Dual Engines of Science & Technology and Capital” deepens the reader’s understanding of the new economy development in China and is essential reading for business/management researchers and practitioners, economists, IT specialists, and IT policy makers around the world.
This book aims to work out the distributed economic operation in smart grids in a systematic way, which ranges from model-based to model-free perspectives. The main contributions of this book can be summarized into three folds. First, we investigate the fundamental economic operation problems in smart grids from model-based perspective. Specifically, these problems can be modeled as deterministic optimization models, and we propose some distributed optimization algorithms by integrating the multi-agent consensus theory and optimization techniques to achieve the distributed coordination of various generation units and loads. Second, due to the randomness of the large-scale renewable energies and the flexibility of the loads, we further address these economic operation problems from a model-free perspective, and we propose learning-based approaches to address the uncertainty and randomness. At last, we extend the idea of model-based and model-free algorithms to plug-in electric vehicles (PEVs) charging/discharging scheduling problem, the key challenge of which involves multiple objectives simultaneously while the behavior of PEVs and the electricity price are intrinsically random. This book presents several recent theoretical findings on distributed economic operation in smart grids from model-based and model-free perspectives. By systematically integrating novel ideas, fresh insights, and rigorous results, this book provides a base for further theoretical research on distributed economic operation in smart grids. It can be a reference for graduates and researchers to study the operation and management in smart grids. Some prerequisites for reading this book include optimization theory, matrix theory, game theory, reinforcement learning, etc.
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