Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations

2018 ◽  
Vol 231 ◽  
pp. 615-627 ◽  
Author(s):  
Jing Kang ◽  
Shengwei Wang
2020 ◽  
Vol 29 (9) ◽  
pp. 1214-1226 ◽  
Author(s):  
Chaoqun Zhuang ◽  
Shengwei Wang

Strict and simultaneous space temperature and humidity controls are often required in many applications, such as hospitals, laboratories, cleanrooms for pharmaceutical and semiconductor manufacturing. The energy intensity in such applications can be up to 100 times than typical office buildings, mainly due to the improper system design and control. Although some uncertainty-based design methods have been developed for air-conditioning systems, most of the existing systems are designed based on a certain ventilation mode while neglecting the life-cycle performance of the components. This study, therefore, proposes a robust optimal design method for cleanroom air-conditioning systems, considering the uncertainties in design parameters for inputs and operation strategies as well as the life-cycle performance of components. An adaptive full-range decoupled ventilation strategy, which incorporated five operation modes, was adopted in the design optimization. Two maintenance modes were adopted and compared to consider the flexibility of maintenance. The proposed design method has been implemented and validated in the design optimization of an existing air-conditioning system. The results showed that, compared with the conventional design, up to 54% reduction of life-cycle costs and superior satisfaction of services could be achieved by using the proposed method.


2021 ◽  
pp. 1-27
Author(s):  
Jian Zhang ◽  
Heejin Cho ◽  
Pedro Mago

Abstract Off-grid concepts for homes and buildings have been a fast-growing trend worldwide in the last few years because of the rapidly dropping cost of renewable energy systems and their self-sufficient nature. Off-grid homes/buildings can be enabled with various energy generation and storage technologies, however, design optimization and integration issues have not been explored sufficiently. This paper applies a multi-objective genetic algorithm (MOGA) optimization to obtain an optimal design of integrated distributed energy systems for off-grid homes in various climate regions. Distributed energy systems consisting of renewable and non-renewable power generation technologies with energy storage are employed to enable off-grid homes/buildings and meet required building electricity demands. In this study, the building types under investigation are residential homes. Multiple distributed energy resources are considered such as combined heat and power systems (CHP), solar photovoltaic (PV), solar thermal collector (STC), wind turbine (WT), as well as battery energy storage (BES) and thermal energy storage (TES). Among those technologies, CHP, PV, and WT are used to generate electricity, which satisfies the building's electric load, including electricity consumed for space heating and cooling. Solar thermal energy and waste heat recovered from CHP are used to partly supply the building's thermal load. Excess electricity and thermal energy can be stored in the BES and TES for later use. The MOGA is applied to determine the best combination of DERs and each component's size to reduce the system cost and carbon dioxide emission for different locations. Results show that the proposed optimization method can be effectively and widely applied to design integrated distributed energy systems for off-grid homes resulting in an optimal design and operation based on a trade-off between economic and environmental performance.


2019 ◽  
Vol 218 ◽  
pp. 782-795 ◽  
Author(s):  
Xiaokai Xing ◽  
Yamin Yan ◽  
Haoran Zhang ◽  
Yin Long ◽  
Yufei Wang ◽  
...  

Energy ◽  
2012 ◽  
Vol 44 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Eugenia D. Mehleri ◽  
Haralambos Sarimveis ◽  
Nikolaos C. Markatos ◽  
Lazaros G. Papageorgiou

2019 ◽  
Vol 158 ◽  
pp. 3152-3157 ◽  
Author(s):  
Lingshi Wang ◽  
Fu Xiao ◽  
Borui Cui ◽  
Maomao Hu ◽  
Tao Lu

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