A hierarchical genetic algorithm and mixed-integer linear programming-based stochastic optimization of the configuration of integrated trigeneration energy systems

Facing the growing pressure of climate change and environmental protection, integrated energy systems (IESs), which comprise different energy sources, have become promising candidates for future energy systems. However, the capacity configuration of each source remains challenging due to the various...

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Bibliographic Details
Published in:	Clean technologies and environmental policy Vol. 23; no. 6; pp. 1913 - 1927
Main Authors:	Zhang, Yisong, Jiang, Jingjing, Zhang, Xian, Sun, Li
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021 Springer Nature B.V
Subjects:	Algorithms Candidates Climate change Configurations Cooling Cost reduction Costs Couplings Earth and Environmental Science Energy Energy sources Environment Environmental Economics Environmental Engineering/Biotechnology Environmental policy Environmental protection Genetic algorithms Genetics Heat storage Heating Hierarchies Industrial and Production Engineering Industrial Chemistry/Chemical Engineering Integer programming Integrated energy systems Linear programming Mathematical analysis Mixed integer Monte Carlo simulation Operating costs Optimization Original Paper Randomness Renewable resources Resource availability Sampling Storage Sustainable Development Stochastic optimization Genetic algorithm IES Economic evaluation MILP problem
ISSN:	1618-954X, 1618-9558
Online Access:	Get full text
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