Titlebook: Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing; Rui Xie,Wei Wei Book 2024 The Editor(s) (

确定方向

Sizing Renewable, Transmission, and Energy Storage in Low-Carbon Power Systemsst-case carbon emission expectation and distributionally robust risk of load shedding are transformed into linear models. This makes the proposed sizing model a matter of solving mixed-integer linear programming problems. This chapter introduces the work in Xie et al. (Energy 263:125653, 2023), (.).

heterogeneous

Book 2024ties and applies them to tackle the critical challenge of energy storage sizing in renewable-integrated power systems, providing readers with an efficient and reliable approach to analyze and design real-world energy systems with uncertainties... ..Covering a diverse range of topics, this book start

neologism

Introduction,es. Then the key concepts of optimization in the uncertain environment are summarized, including the basic ideas of stochastic programming, robust optimization, and distributionally robust optimization. The chapter then shifts to the critical role of ES in power systems, analyzing the diverse usages

INCUR

Preliminaryn, the basics of probability and risk measures are summarized. Subsequently, optimization theories are reviewed, including convex optimization, conic programs and duality, multi-objective optimization, stochastic programming, and robust optimization. Some results are stated without proof and referen

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-Divergence Distributionally Robust Optimization.-divergence to the empirical probability distribution is within a given threshold. An overview of .-divergence is presented first to give a sense of what the .-divergence ambiguity set is, including the definitions, important properties, and the relationships between different types of .-divergence

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Sizing Renewable and Energy Storage in Fully Renewable Stand-Alone Microgridsbjective optimization model to minimize load shedding Shortfall risk and total investment cost, taking into account the uncertainty of renewable energy generation and load. To account for the inaccuracy of empirical probability distributions, an ambiguity set of probability distributions is construc

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