Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 13, Issue 3, Pages 1802-1818Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2022.3172004
Keywords
Planning; Renewable energy sources; Internet of Things; Uncertainty; Natural gas; Mathematical models; Load modeling; Energy hubs; multi-energy systems; operation; and planning optimization; communication and control
Categories
Funding
- National Natural Science Foundation of China [51977166]
- Natural Science Foundation of Shaanxi Province [2021GXLH-Z-059]
- Science and Technological Project of Northwest Branch of State Grid Corporation of China [SGNW0000DKQT2100172]
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This article provides a comprehensive review of the utilization of renewable energy resources, focusing on energy hub modeling and optimization algorithms. It also discusses the IoT-based energy hub control structure and corresponding management methods.
The increasing concerns with adverse environmental issues have led to the proliferation of renewable energy resources (RESs), which have been expanded more recently to multi-energy systems (MESs) in various parts of the world. MES can improve energy efficiency and reduce carbon emission by co-optimizing multiple forms of energy, including electricity, natural gas, heating, cooling, etc., which provide a promising approach to carbon neutrality. Energy hub (EH) is an efficient framework for MES modeling and management, where various energy carriers are optimally converted, utilized, and stored for satisfying certain sociopolitical and socioeconomic mandates. This paper presents a comprehensive review of available EH optimization and control studies. First, we introduce basic concepts and EH modeling methods. Then, we conduct a systematic review of optimization methods, as well as state-of-the-art solution algorithms for EH planning, operation, and trading. Furthermore, we analyze an internet of things (IoT) based EH control structure and review the corresponding state estimation, communication, and control methods for managing large EH data sets. Finally, we present and discuss several research topics for future research.
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