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

被引：238
|
作者：
Zhou, Zhe [1 ]
Zhang, Jianyun [1 ]
Liu, Pei [1 ]
Li, Zheng [1 ]
Georgiadis, Michael C. [2 ]
Pistikopoulos, Efstratios N. [3 ]
机构：
[1] Tsinghua Univ, Dept Thermal Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
[2] Aristotle Univ Thessaloniki, Dept Chem Engn, Thessaloniki 54124, Greece
[3] Univ London Imperial Coll Sci Technol & Med, Dept Chem Engn, CPSE, London SW7 2AZ, England
来源：
APPLIED ENERGY | 2013年 / 103卷
关键词：
Stochastic programming; Distributed energy system; Uncertainty; Genetic algorithm; The Monte Carlo method; MONTE-CARLO-SIMULATION; GLOBAL SOLAR-RADIATION; TRIGENERATION SYSTEMS; SENSITIVITY-ANALYSIS; CCHP SYSTEM; OPTIMIZATION; ALGORITHM; UNCERTAINTY; DEMAND; COGENERATION;
D O I：
10.1016/j.apenergy.2012.09.019
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：135 / 144
页数：10
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