Cross-domain Pareto optimization of heterogeneous domains for the operation of smart cities
Ivelina Stoyanova and
Antonello Monti
Applied Energy, 2019, vol. 240, issue C, 534-548
Abstract:
This paper presents a tool for multi-objective cross-domain optimization of heterogenous energy and non-energy domains for urban areas in a comprehensive optimization approach. The multi-objective optimization concept facilitates the definition of any number of objective functions, versatile in their origin and properties, flexibly adaptable to area specifics or regional and communal priorities. The applied optimization for several domains at the lowest system level yields a comprehensive holistic solution optimal for the particular subsystem, as it is fitted for this subsystem according to its characteristics. The main advantage is the exploitation of cross-domain interactions and synergies and, therefore, the improved overall performance of the system. However, the cross-domain approach requires high-level modeling in order to overcome compatibility issues, which decreases the accuracy of domain-specific simulation and optimization compared to methods which apply detailed models. Based on the mathematical concept of multi-objective Pareto optimization, its adaptation, implementation and application in the context of Smart Cities are presented in detail. Several optimization formulations and methods are applied to the test scenario to demonstrate the concept, compare the results and evaluate the computational performance of the application. The ε-constraint method appears to be better suitable for heterogeneous domains, as the weighted sum method is highly sensitive to weight setting for heterogeneous objectives. The computational performance is very good in terms of computation time but meets numerical limitations for samples of around 500 buildings, leading to numerical infeasibility for samples of 1300 buildings. This is solved with the presented method for model aggregation. The tool is available on GitHub.
Keywords: Heterogeneous domains; Multi-objective; Optimization; Pareto; Smart cities (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919303009
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:240:y:2019:i:c:p:534-548
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2019.02.010
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().