Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7418
Title: Collaborative Energy Management Optimization Toward a Green Energy Local Area Network
Authors: Qi, Jin 
Lai, Chunyuan 
Xu, Bin 
Sun, Yanfei 
Prof. LEUNG Kwong Sak 
Issue Date: Dec-2018
Publisher: IEEE
Source: IEEE Transactions on Industrial Informatics, 2018, vol. 14(12),8264729, pp. 5410-5418
Journal: IEEE Transactions on Industrial Informatics 
Abstract: Rapid economic development has been observed worldwide, which has caused environmental problems to worsen. Thus, the Energy Internet (EI), which accesses renewable energy and provides high-quality power services, has recently become a hot issue. As a subnet of the EI, an energy local area network (ELAN) consists of renewable power generation equipment, controllable distributed power generation equipment, storage systems, electric vehicles, and a large number of loads. Energy management is required for economic, environmental, and safety considerations. This paper proposes an energy management optimization model that addresses ELAN operations and includes pollution treatment fees; this model provides intelligent control of the charging and discharging of plug-in hybrid electric vehicles (PHEVs). This model achieves a nonlinear energy management optimization for an ELAN. To promote optimal performance, an improved comprehensive learning particle swarm optimization (CLPSO) algorithm is presented; it combines Tabu Search (TS) and CLPSO to avoid local optima. To verify the performance of our model, two experimental scenarios are built. The simulation results show that our energy management optimization model fulfills the optimal allocation of energy and that the PHEV intelligent charging/discharging strategy promotes economic benefits for the network.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7418
DOI: 10.1109/TII.2018.2796021
Appears in Collections:Applied Data Science - Publication

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