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Energy Management in Homes and Residential Microgrids

: Short-Term Scheduling and Long-Term Planning

家庭和住宅微电网的能源管理

图书详细信息
  • 中图分类号: TM
  • 语种: ENG
  • 出版信息: Elsevier Science_RM 2023 387页
  • EISBN: 9780443237294
  • PISBN-P: 9780443237287
  • 原文访问地址:

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内容简介

Energy Management in Homes and Residential Microgrids: Short-Term Scheduling and Long-Term Planning provides an in-depth exploration of Home Energy Management Systems (HEMS), with a focus on practical applications for both short- and long-term models. Through this guide, readers will learn how to create efficient systems that facilitate the integration of renewable energy into the grid and simultaneously manage end-users' energy consumption. The short-term operation of Home Energy Management Systems is analyzed through various lenses, including renewable energy integration, energy storage integration, uncertainty in parameters, off-grid operation, outages and events, resilience, electric vehicle integration, and battery swapping strategy. The modelling of these topics is explained with step-by-step instructions, and the parameters and implications are thoroughly discussed. Additionally, the book offers insight into the long-term expansion planning for residential microgrids, providing a detailed examination of dynamic modeling, control, and stability of these small-scale energy systems. Throughout the book, simple and advanced examples are provided, and each example comes with numerical data, detailed formulation, modelling, and simulation. Energy Management in Homes and Residential Microgrids: Short-Term Scheduling and Long-Term Planning is a valuable reference and learning tool for students, researchers, and engineers working on short-term and long-term energy management systems in homes and residential microgrids.Explains how to model all systems as mixed integer linear programming in GAMS software alongside step-by-step instructionsOffers numerous examples for each topic discussed, using both simple and advanced conceptsAccounts for problems by providing solutions to practical situations and real-world conditions for both short-term and long-term models

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