2019
DOI: 10.1016/j.enconman.2018.10.083
|View full text |Cite
|
Sign up to set email alerts
|

Multi-objective optimization and comparison framework for the design of Distributed Energy Systems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
26
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 99 publications
(27 citation statements)
references
References 22 publications
0
26
0
Order By: Relevance
“…A few words need to be said about the system's energy optimisation aspects. As far as the optimal design of distributed energy systems is concerned, elements such as annual costs or carbon emissions can be considered for drawing comparisons [63]. In addition, it becomes clear that there is plethora of methods and algorithms for the energy systems and their optimisation, but one needs to consider reliability, maintenance, as well as social aspects in optimization for making the system work [64].…”
Section: Economic Growth and Carbon-free Economymentioning
confidence: 99%
“…A few words need to be said about the system's energy optimisation aspects. As far as the optimal design of distributed energy systems is concerned, elements such as annual costs or carbon emissions can be considered for drawing comparisons [63]. In addition, it becomes clear that there is plethora of methods and algorithms for the energy systems and their optimisation, but one needs to consider reliability, maintenance, as well as social aspects in optimization for making the system work [64].…”
Section: Economic Growth and Carbon-free Economymentioning
confidence: 99%
“…This situation becomes clear when solar-based RETs are considered, such as photovoltaic panels and solar thermal collectors, because they consume an energy resource that has zero cost and zero emissions. Nevertheless, it is also interesting to analyze the various methods employed in the literature to determine the average CO 2 emission factors: the most common approach is to consider the electricity power mix of a region or a country, 31,[41][42][43][44][45][46] but Casisi et al 47 adopted the region's main thermoelectric plant, Wang et al 29 considered a coal power plant, and Conci et al 48 employed the average between the measured value in 2015 and the forecast value for 2050. Third, several studies disregard the effect of dynamic climatic conditions, such as hourly and seasonal variations in the ambient temperature and solar radiation, on the performance of solar-based RETs. 32 Second, to the best of the authors' knowledge, timebased electricity CO 2 emission factors have never been taken into account in energy systems optimization studies for buildings applications.…”
Section: Introductionmentioning
confidence: 99%
“…Even though it is true that sufficiently accurate data are difficult to obtain, all consulted energy systems optimization studies consider annual average values for the electricity CO 2 emissions, thus completely ignoring the dynamic interaction between the energy system and the electric grid as well as the potential benefits. Nevertheless, it is also interesting to analyze the various methods employed in the literature to determine the average CO 2 emission factors: the most common approach is to consider the electricity power mix of a region or a country, 31,[41][42][43][44][45][46] but Casisi et al 47 adopted the region's main thermoelectric plant, Wang et al 29 considered a coal power plant, and Conci et al 48 employed the average between the measured value in 2015 and the forecast value for 2050. Third, several studies disregard the effect of dynamic climatic conditions, such as hourly and seasonal variations in the ambient temperature and solar radiation, on the performance of solar-based RETs. A temporal and dynamic approach to the operation of solar-based RETs (eg, solar thermal collectors and photovoltaic panels) is needed to enhance the optimization procedure and the benefits that can be derived from their integration in energy systems.…”
Section: Introductionmentioning
confidence: 99%
“…References [13] and [14] present a new multistage and stochastic mathematical model, developed to support the decision making process of planning distribution network systems for integrating large-scale clean energy sources. A multi-objective mixed-integer linear programming framework is shown in [15], comparing two main methodologies of designing distributed energy systems using total annual cost and carbon emissions as objective functions. Reference [16] suggests novel cost and emission benefit allocation constraints inspired by cooperative Game theory to ensure that each involved building shares the benefit together.…”
Section: Introductionmentioning
confidence: 99%