On the nature of the heat transfer feasibility constraint in the optimal synthesis/design of complex energy systems

Energy ◽  
2012 ◽  
Vol 41 (1) ◽  
pp. 236-243 ◽  
Author(s):  
Andrea Toffolo ◽  
Andrea Lazzaretto ◽  
Michael R. von Spakovsky
2018 ◽  
Vol 140 (11) ◽  
Author(s):  
A. Toffolo ◽  
S. Rech ◽  
A. Lazzaretto

The fundamental challenge in the synthesis/design optimization of energy systems is the definition of system configuration and design parameters. The traditional way to operate is to follow the previous experience, starting from the existing design solutions. A more advanced strategy consists in the preliminary identification of a superstructure that should include all the possible solutions to the synthesis/design optimization problem and in the selection of the system configuration starting from this superstructure through a design parameter optimization. This top–down approach cannot guarantee that all possible configurations could be predicted in advance and that all the configurations derived from the superstructure are feasible. To solve the general problem of the synthesis/design of complex energy systems, a new bottom–up methodology has been recently proposed by the authors, based on the original idea that the fundamental nucleus in the construction of any energy system configuration is the elementary thermodynamic cycle, composed only by the compression, heat transfer with hot and cold sources and expansion processes. So, any configuration can be built by generating, according to a rigorous set of rules, all the combinations of the elementary thermodynamic cycles operated by different working fluids that can be identified within the system, and selecting the best resulting configuration through an optimization procedure. In this paper, the main concepts and features of the methodology are deeply investigated to show, through different applications, how an artificial intelligence can generate system configurations of various complexity using preset logical rules without any “ad hoc” expertise.


2018 ◽  
Vol 6 (6) ◽  
pp. 7960-7968 ◽  
Author(s):  
Małgorzata Musiał ◽  
Michał Kuczak ◽  
Anna Mrozek-Wilczkiewicz ◽  
Robert Musiol ◽  
Edward Zorębski ◽  
...  

Author(s):  
M. A. Ancona ◽  
M. Bianchi ◽  
L. Branchini ◽  
A. De Pascale ◽  
F. Melino ◽  
...  

Abstract In order to increase the exploitation of the renewable energy sources, the diffusion of the distributed generation systems is grown, leading to an increase in the complexity of the electrical, thermal, cooling and fuel energy distribution networks. With the main purpose of improving the overall energy conversion efficiency and reducing the greenhouse gas emissions associated to fossil fuel based production systems, the design and the management of these complex energy grids play a key role. In this context, an in-house developed software, called COMBO, presented and validated in the Part I of this study, has been applied to a case study in order to define the optimal scheduling of each generation system connected to a complex energy network. The software is based on a non-heuristic technique which considers all the possible combination of solutions, elaborating the optimal scheduling for each energy system by minimizing an objective function based on the evaluation of the total energy production cost and energy systems environmental impact. In particular, the software COMBO is applied to a case study represented by an existing small-scale complex energy network, with the main objective of optimizing the energy production mix and the complex energy networks yearly operation depending on the energy demand of the users. The electrical, thermal and cooling needs of the users are satisfied with a centralized energy production, by means of internal combustion engines, natural gas boilers, heat pumps, compression and absorption chillers. The optimal energy systems operation evaluated by the software COMBO will be compared to a Reference Case, representative of the current energy systems set-up, in order to highlight the environmental and economic benefits achievable with the proposed strategy.


2020 ◽  
Vol 20 (5) ◽  
pp. 04020041 ◽  
Author(s):  
Klementyna A. Gawecka ◽  
David M. G. Taborda ◽  
David M. Potts ◽  
Eleonora Sailer ◽  
Wenjie Cui ◽  
...  

1998 ◽  
Vol 156 ◽  
pp. 935-951 ◽  
Author(s):  
Vaclav Smil

Recent writings on China's achievements during the last quarter of the 20th century stress, almost without exception, the enormity of change. But, for both universal and particular reasons, this survey of the country's energy resources and uses will stress continuity as much as change. Taking the inertia of complex energy systems as the key universal given, the most important particular explanation lies in peculiarities of China's resource endowment.


2003 ◽  
Vol 125 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Moncef Krarti

An overview of commonly used methodologies based on the artificial intelligence approach is provided with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. A description of selected applications to building energy systems of AI approaches is outlined. In particular, methods using the artificial intelligence approach for the following applications are discussed: Prediction energy use for one building or a set of buildings (served by one utility), Modeling of building envelope heat transfer, Controlling central plants in buildings, and Fault detection and diagnostics for building energy systems.


Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero

This paper presents a summary of our most recent advances in Thermoeconomic Diagnosis, developed during the last three years [1–3], and how they can be integrated in a zooming strategy oriented towards the operational diagnosis of complex systems. In fact, this paper can be considered a continuation of the work presented at the International Conference ECOS’99 [4–6] in which the concepts of malfunction (intrinsic and induced) and dysfunction [7] were analyzed in detail. These concepts greatly facilitate and simplify the analysis, the understanding and the quantification of how the presence of an anomaly, or malfunction, affects the behavior of the other plant devices and of the whole system. However, what remains unresolved is the so-called inverse problem of diagnosing [3], i.e. given two states of the plant (actual and reference operating conditions), find the causes of deviation of the actual conditions with respect to the reference conditions. The present paper tackles this problem and describes significant advances in addressing how to locate the actual causes of malfunctions, based on the application of procedures for filtering induced effects that hide the real causes of degradation. In this paper a progressive zooming thermoeconomic diagnosis procedure, which allows one to concentrate the analysis in an ever more specific zone is described and applied to a combined cycle. In an accompanying paper (part 2 [8]) the accuracy of the diagnosis results is discussed, depending on choice of the thermoeconomic model.


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