Environmental Impact Evaluation of Distributed Renewable Energy System Based on Life Cycle Assessment and Fuzzy Rough Sets

Li, Chengzhou; Wang, Ningling; Zhang, Hongyuan; Liu, Qingxin; Chai, Youguo; Shen, Xinmin; Yang, Zhiping; Yang, Yongping

doi:10.3390/en12214214

Cited by 27 publications

(15 citation statements)

References 47 publications

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“…In the previous studies on renewable energy sources, reference was mainly made to their technical [42], ecological [43] economical [44] and social [45] aspects. These works concentrate, among others, on analyses regarding the development of the renewable energy market [46][47][48][49], technologies related to the production of energy from RES [50] as well as on the socio-environmental consequences of using energy from these sources [51,52].…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing Similarities between the European Union Countries in Terms of the Structure and Volume of Energy Production from Renewable Energy Sources

2020

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The European Union (EU) countries, as one of the most economically developed regions in the world, are taking increasingly decisive actions to reduce the emission of harmful substances into the natural environment. This can be exemplified by a new climate strategy referred to as “The European Green Deal”. Its basic assumption is that the EU countries will have achieved climate neutrality by 2050. To do so, it is necessary to make an energy transition involving the widest possible use of renewable energy sources (RES) for energy production. However, activities in this area should be preceded by analyses due to the large diversity of the EU countries in terms of economic development, the number of inhabitants and their wealth as well as geographical location and area. The results of such analyses should support the implementation of adopted strategies. In order to assess the current state of the energy sector in the EU and indicate future directions of activities, research was carried out to analyze the structure and volume of energy production from RES in the EU countries. The aim of the study was to divide the EU countries into similar groups by the structure and volume of energy production from RES. This production was compared with the number of inhabitants of each EU country, its area and the value of Gross Domestic Product (GDP). This approach allows a new and broader view of the structure of energy production from RES and creates an opportunity to take into account additional factors when developing and implementing new climate strategies. The k-means algorithm was used for the analysis. The presented analyses and obtained results constitute a new approach to studying the diversified energy market in the EU. The results should be used for the development of a common energy and climate policy and economic integration of the EU countries.

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Section: Literature Reviewmentioning

confidence: 99%

Analyzing Similarities between the European Union Countries in Terms of the Structure and Volume of Energy Production from Renewable Energy Sources

2020

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“…The combination of unsupervised convolutional neural networks and fuzzy-rough C-mean was used effectively for clustering of large-scale image dataset in [18]. The environmental impact of a renewable energy system was estimated using fuzzy rough sets in [19]. The interval-valued fuzzy-rough based Delphi method was applied for evaluating the siting criteria of offshore wind farms in [20].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Fuzzy-Rough Cognitive Networks

Harmati

2021

Symmetry

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Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for FRCNS, only a very limited number of studies discuss the theoretical issues of these models. In this paper, we examine the behaviour of FRCNs viewing them as discrete dynamical systems. It will be shown that their mathematical properties highly depend on the size of the network, i.e., there are structural differences between the long-term behaviour of FRCN models of different size, which may influence the performance of these modelling tools.

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“…Life cycle assessment (LCA) accounts for these requirements and is increasingly implemented into decision-making procedures inside and outside the energy sector (cf. e.g., [17][18][19][20][21]). The need for an a priori integration of life cycle assessment (LCA) into energy optimisation modelling is recurrently mentioned in literature and frameworks, as well models have been developed by different authors.…”

Section: Introductionmentioning

confidence: 99%

LAEND: A Model for Multi-Objective Investment Optimisation of Residential Quarters Considering Costs and Environmental Impacts

et al. 2020

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Renewable energy systems are especially challenging both in terms of planning and operation. Energy system models that take into account not only the costs but also a wide range of environmental impacts support holistic planning. In this way, burden-shifting caused by greenhouse gas mitigation can be identified and minimised at an early stage. The Life cycle Assessment based ENergy Decision support tool LAEND combines a multi-criteria optimising tool for energy system modelling and an integrated environmental assessment for the analysis of decentral systems. By a single or multi-objective optimisation, considering costs, environmental impact indicators as well as weighted impact indicator sets, the model enables the determination of optimal investment planning and dispatch of the analysed energy system. The application of LAEND to an exemplary residential quarter shows the benefit of the model regarding the identification of conflicting goals and of a system that compensates for the different objectives. The observed shift of environmental impacts from the use phase to the production phase of the renewable electricity generators points further to the importance of the integration of the entire life cycle.Energies 2020, 13, 614 2 of 22 detailed consideration. On the community level, less technical, administrative and economic resources for sustainable energy projects are available which hinders a successful transition. Decision support at the community level, therefore gains importance [8].With regard to climate impacts, renewable energies are often considered carbon-free in general and to emit less or no emissions to the air in the use phase (besides the burning of biomass) [9]. But the utilisation of renewable energies like e.g., wind and photovoltaics, lead to new conflicts of goals only partly implicated by storage needs: use of resources, especially the exploitation of rare earth materials, toxicities, land use and the loss of biodiversity might increase [10][11][12][13]. Additionally, the environmental impacts shift from the use phase (key emission phase of fossil energies) to the manufacturing phase of the energy converters (key emission phase of renewable energies and storages) [14].Thus, decision support considering the environmental impact of decentral energy systems gains importance and needs to cope with additional requirements in terms of:

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Environmental Impact Evaluation of Distributed Renewable Energy System Based on Life Cycle Assessment and Fuzzy Rough Sets

Cited by 27 publications

References 47 publications

Analyzing Similarities between the European Union Countries in Terms of the Structure and Volume of Energy Production from Renewable Energy Sources

Analyzing Similarities between the European Union Countries in Terms of the Structure and Volume of Energy Production from Renewable Energy Sources

Dynamics of Fuzzy-Rough Cognitive Networks

LAEND: A Model for Multi-Objective Investment Optimisation of Residential Quarters Considering Costs and Environmental Impacts

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