A two-stage stochastic programming model for the optimal design of distributed energy systems

► The optimal design of distributed energy systems under uncertainty is studied. ► A stochastic model is developed using genetic algorithm and Monte Carlo method. ► The proposed system possesses inherent robustness under uncertainty. ► The inherent robustness is due to energy storage facilities and...

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Bibliographic Details
Published in:Applied energy Vol. 103; pp. 135 - 144
Main Authors: Zhou, Zhe, Zhang, Jianyun, Liu, Pei, Li, Zheng, Georgiadis, Michael C., Pistikopoulos, Efstratios N.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.03.2013
Elsevier
Subjects:
algorithms
Applied sciences
Design engineering
Distributed energy system
Economic data
ecosystem services
Electric energy
Energy
Energy economics
Exact sciences and technology
General, economic and professional studies
Genetic algorithm
Mathematical analysis
Mathematical models
Methodology. Modelling
Modelling
Monte Carlo method
Optimization
planning
Programming
Stochastic programming
Strategy
supply balance
system optimization
The Monte Carlo method
Uncertainty
Uncertainty
Distributed energy system
The Monte Carlo method
Genetic algorithm
Stochastic programming
Electricity
Electric power production
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:► The optimal design of distributed energy systems under uncertainty is studied. ► A stochastic model is developed using genetic algorithm and Monte Carlo method. ► The proposed system possesses inherent robustness under uncertainty. ► The inherent robustness is due to energy storage facilities and grid connection. A distributed energy system is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. The optimal design of such a complex system under energy demand and supply uncertainty poses significant challenges in terms of both modelling and corresponding solution strategies. This paper proposes a two-stage stochastic programming model for the optimal design of distributed energy systems. A two-stage decomposition based solution strategy is used to solve the optimization problem with genetic algorithm performing the search on the first stage variables and a Monte Carlo method dealing with uncertainty in the second stage. The model is applied to the planning of a distributed energy system in a hotel. Detailed computational results are presented and compared with those generated by a deterministic model. The impacts of demand and supply uncertainty on the optimal design of distributed energy systems are systematically investigated using proposed modelling framework and solution approach.
Bibliography:http://dx.doi.org/10.1016/j.apenergy.2012.09.019
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2012.09.019