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ISSN:
2315-4462 (Print); 2373-3594 (Online)
Abbreviated Title:
Int. J Smart Grid Clean Energy
Frequency:
4 issues per year
Editor-in-Chief:
Prof. Danny Sutanto
DOI:
10.12720/sgce
APC:
500 USD
Indexed by:
Inspec (IET),
CNKI
, Crossref, Google Scholar,
etc
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Editor-in-Chief
Prof. Danny Sutanto
University of Wollongong, Australia
I am very excited to serve as the first Editor-in-Chief of the Journal of Smart Grid and Clean Energy (IJSGCE)and hope that the publication can enrich the readers’ experience .... [
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What's New
2024-03-28
March 28th, 2024 News! Vol. 13, No. 1 has been published online!
2024-01-04
IJSGCE will adopt Article-by-Article Work Flow. For the quarterly journal, each issue will be released at the end of the issue month.
2023-10-09
October 9th, 2023 News! Vol. 11, No. 4 has been published online!
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2019
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Vol. 8, No. 3, May 2019
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Multiple time-scale optimization for the dispatch of integrated energy system based on model predictive control
Author(s): Yajie Huang
a*
, Junyong Wu
a
,Xingyan Niu
b
, Shiqiao Gao
b
a
Electrical Engineering Department,Beijing Jiaotong University,Beijing,100044,China
b
R&D China Center, Électricité de France,Beijing,100005,China
International Journal of Smart Grid and Clean Energy
, vol. 8, no. 3, May 2019: pp. 263-270
ISSN: 2315-4462 (Print)
ISSN: 2373-3594 (Online)
Digital Object Identifier: 10.12720/sgce.8.3.263-270
Abstract
: With the development of society and economy, energy shortage is increasingly serious. How to improve the utilization rate of energy is main problem at present. The integrated energy system, is to comprehensively plan, coordinately control, intelligently dispatch and multiply interact with multiple energy flows such as cold, heat, power and gas, which has broken the existing models of individual plan, design and operation of each energy supply system, and becomes a new model of future energy utilization. In this paper, a multiple time-scale cooperative optimization is proposed based on model predictive control, which is for the control optimization of energy storage, cold storage and heat storage to fulfill the minimization of total operation cost of integrated energy system based on the charge mode of maximum demand. The implementation of cost optimization has 3 steps which are month-in-advance off-line planning, day-ahead dispatch and short-term online MPC control. This optimization strategy not only reduces the power imbalance of control system caused by the error of model prediction of renewable energy and load, but also ensures the overall optimization efficiency of system. A park in Beijing is taken as an example to analyze and verify the effectiveness of the proposed model and algorithm.
Keywords
: integrated energy system, model predictive control, coordinated optimization, multiple time-scale control strategy of energy storage
Full Paper.pdf
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