Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153729
Title: Optimal evolutionary dispatch for integrated community energy systems considering uncertainties of renewable energy sources and internal loads
Authors: Liu, Xinghua
Xie, Shenghan
Geng, Chen
Yin, Jianning
Xiao, Gaoxi
Cao, Hui
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Liu, X., Xie, S., Geng, C., Yin, J., Xiao, G. & Cao, H. (2021). Optimal evolutionary dispatch for integrated community energy systems considering uncertainties of renewable energy sources and internal loads. Energies, 14(12), 3644-. https://dx.doi.org/10.3390/en14123644
Journal: Energies
Abstract: For the future development of integrated energy systems with high penetration of renewable energy, an integrated community energy systems (ICES) dispatch model is proposed including various renewable energy sources and energy conversion units. Energy coupling matrices of ICES based on traditional energy hub (EH) models are constructed. Uncertainties of long‐term forecast data of renewable energy sources and internal loads are depicted by multi‐interval uncertainty sets (MIUS). To cope with the impacts caused by uncertainties of renewable energy sources and internal loads, the whole dispatch process is divided into two stages. Considering various constraints of ICES, we solved the dispatch model through the improved particle swarm optimization (IPSO) algorithm in the first stage. The optimal evolutionary dispatch is then proposed in the second stage to overcome the evolution and errors of short‐term forecast data and obtain the optimal dispatch plan. The effectiveness of the proposed dispatch method is demonstrated using an example considering dramatic uncertainties. Compared with the traditional methods, the proposed dispatch method effectively reduces system operating costs and improves the environmental benefits, which helps to achieve a win‐win situation for both energy companies and users.
URI: https://hdl.handle.net/10356/153729
ISSN: 1996-1073
DOI: 10.3390/en14123644
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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