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

Zhe Zhou, Jianyun Zhang, Pei Liu, Zheng Li, Michael C. Georgiadis and Efstratios N. Pistikopoulos

Applied Energy, 2013, vol. 103, issue C, 135-144

Abstract: 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.

Keywords: Stochastic programming; Distributed energy system; Uncertainty; Genetic algorithm; The Monte Carlo method (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (86)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:103:y:2013:i:c:p:135-144

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DOI: 10.1016/j.apenergy.2012.09.019

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