Hybrid Energy Storage System (HESS) have the potential to offer better flexibility to a grid than any single energy storage solution. However, sizing a HESS is challenging, as the required capacity, power and ramp rates for a given application are difficult to derive. This paper proposes a method for splitting a given load profile into several storage technology independent sub-profiles, such that each of the sub-profiles leads to its own requirements. This method can be used to gain preliminary insight into HESS requirements before a choice is made for specific storage technologies. To test the method, a household case is investigated using the derived methodology, and storage requirements are found, which can then be used to derive concrete storage technologies for the HESS of the household. Adding a HESS to the household case reduces the maximum import power from the connected grid by approximately 7000 W and the maximum exported power to the connected grid by approximately 1000 W. It is concluded that the method is particularly suitable for data sets with a high granularity and many data points.
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It is of utmost importance to collect organic waste from households as a separate waste stream. If collected separately, it could be used optimally to produce compost and biogas, it would not pollute fractions of materials that can be recovered from residual waste streams and it would not deteriorate the quality of some materials in residual waste (e.g. paper). In rural areas with separate organic waste collection systems, large quantities of organic waste are recovered. However, in the larger cities, only a small fraction of organic waste is recovered. In general, citizens dot not have space to store organic waste without nuisances of smell and/or flies. As this has been the cause of low organic waste collection rates, collection schemes have been cut, which created a further negative impact. Hence, additional efforts are required. There are some options to improve the organic waste recovery within the current system. Collection schemes might be improved, waste containers might be adapted to better suit the needs, and additional underground organic waste containers might be installed in residential neighbourhoods. There are persistent stories that separate organic waste collection makes no sense as the collectors just mix all municipal solid waste after collection, and incinerate it. Such stories might be fuelled by the practice that batches of contaminated organic waste are indeed incinerated. Trust in the system is important. Food waste is often regarded as unrein. Users might hate to store food waste in their kitchen that could attract insects, or the household pets. Hence, there is a challenge for socio-psychological research. This might also be supported by technology, e.g. organic waste storage devices and measures to improve waste separation in apartment buildings, such as separate chutes for waste fractions. Several cities have experimented with systems that collect organic wastes by the sewage system. By using a grinder, kitchen waste can be flushed into the sewage system, which in general produces biogas by the fermentation of sewage sludge. This is only a good option if the sewage is separated from the city drainage system, otherwise it might create water pollution. Another option might be to use grinders, that store the organic waste in a tank. This tank could be emptied regularly by a collection truck. Clearly, the preferred option depends on local conditions and culture. Besides, the density of the area, the type of sewage system and its biogas production, and the facilities that are already in place for organic waste collection are important parameters. In the paper, we will discuss the costs and benefits of future organic waste options and by discussing The Hague as an example.
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The concept of the Daily Urban System (DUS) has gained relevance over the past decades as the entity to examine and explain the functionality of the urban landscape. Daily Urban Systems are usually defined and measured by the strength of commuter or shopper flows between the nodes of the system. It is important to realize that these Daily Urban Systems are the accumulated pattern of individuals making frequent, recurring trips to other localities than their own. Understanding the microeconomic decisions behind these spatial interactions will help in assessing the functional and spatial structure of DUS. In this paper is explored how, based on Dutch empirical data, the individual household’s spatial interactions shape the daily urban system and how the destination of these interactions correlates with personal and spatial variables and motives for interaction. The results show that the occurrence of non-local spatial interactions can be explained by the size-based Christallerian hierarchy of the localities of residence, but that it is the regional population – or market potential – that explains and moderates the sorting of households and the intensity and direction of their spatial interactions in the DUS, matching agglomeration theory.
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This study explores how households interact with smart systems for energy usage, providing insights into the field's trends, themes and evolution through a bibliometric analysis of 547 relevant literature from 2015 to 2025. Our findings discover: (1) Research activity has grown over the past decade, with leading journals recognizing several productive authors. Increased collaboration and interdisciplinary work are expected to expand; (2) Key research hotspots, identified through keyword co-occurrence, with two (exploration and development) stages, highlighting the interplay between technological, economic, environmental, and behavioral factors within the field; (3) Future research should place greater emphasis on understanding how emerging technologies interact with human, with a deeper understanding of users. Beyond the individual perspective, social dimensions also demand investigation. Finally, research should also aim to support policy development. To conclude, this study contributes to a broader perspective of this topic and highlights directions for future research development.
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The intermittency of renewable energy technologies requires adequate storage technologies. Hydrogen systems consisting of electrolysers, storage tanks, and fuel cells can be implemented as well as batteries. The requirements of the hydrogen purification unit is missing from literature. We measured the same for a 4.5 kW PEM electrolyser to be 0.8 kW for 10 min.A simulation to hybridize the hydrogen system, including its purification unit, with lithium-ion batteries for energy storage is presented; the batteries also support the electrolyser. We simulated a scenario for operating a Dutch household off-electric-grid using solar and wind electricity to find the capacities and costs of the components of the system.Although the energy use of the purification unit is small, it influences the operation of the system, affecting the sizing of the components. The battery as a fast response efficient secondary storage system increases the ability of the electrolyser to start up.
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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Objective We examined whether the role of maternal education in children's unhealthy snacking diet is moderated by other socio-economic indicators. Methods Participants were selected from the Amsterdam Born Children and their Development cohort, a large ongoing community-based birth cohort. Validated Food Frequency Questionnaires (FFQ) (n = 2782) were filled in by mothers of children aged 5.7±0.5yrs. Based on these FFQs, a snacking dietary pattern was derived using Principal Component Analysis. Socio-economic indicators were: maternal and paternal education (low, middle, high; based on the highest education completed) household finance (low, high; based on ability to save money) and neighbourhood SES (composite score including educational level, household income and employment status of residents per postal code). Cross-sectional multivariable linear regression analysis was used to assess the association and possible moderation of maternal education and other socio-economic indicators on the snacking pattern score. Analyses were adjusted for children's age, sex and ethnicity. Results Low maternal education (B 0.95, 95% CI 0.83;1.06), low paternal education (B 0.36, 95% CI 0.20;0.52), lower household finance (B 0.18, 95% CI 0.11;0.26) and neighbourhood SES (B -0.09, 95% CI -0.11;-0.06) were independently associated with higher snacking pattern scores (p<0.001). The association between maternal education and the snacking pattern score was somewhat moderated by household finance (p = 0.089) but remained strong. Children from middle-high educated mothers (B 0.44, 95% CI 0.35;0.52) had higher snacking pattern scores when household finance was low (B 0.49, 95% CI 0.33;0.65). Conclusions All socio-economic indicators were associated with increased risk of unhealthy dietary patterns in young children, with low maternal education conferring the highest risk. Yet, within the group of middle-high educated mothers, lower household finance was an extra risk factor for unhealthy dietary patterns. Intervention strategies should therefore focus on lower educated mothers and middle-high educated mothers with insufficient levels of household finance.
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Digitalization is the core component of future development in the 4.0 industrial era. It represents a powerful mechanism for enhancing the sustainable competitiveness of economies worldwide. Diverse triggering effects shape future digitalization trends. Thus, the main research goal in this study is to use sustainable competitiveness pillars (such as social, economic, environmental and energy) to evaluate international digitalization development. The proposed empirical model generates comprehensive knowledge of the sustainable competitiveness-digitalization nexus. For that purpose, a nonlinear regression has been applied on gathered annual data that consist of 33 European countries, ranging from 2010 to 2016. The dataset has been deployed using Bernoulli’s binominal distribution to derive training and testing samples and the entire analysis has been adjusted in that context. The empirical findings of artificial neural networks (ANN) suggest strong effects of the economic and energy use indicators on the digitalization progress. Nonlinear regression and ANN model summary report valuable results with a high degree of coefficient of determination (R2>0.9 for all models). Research findings state that the digitalization process is multidimensional and cannot be evaluated as an isolated phenomenon without incorporating other relevant factors that emerge in the environment. Indicators report the consumption of electrical energy in industry and households and GDP per capita to achieve the strongest effect.
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This applied research is an attempt to analyse the effectiveness of milk marketing and facilitate developing a sustainable milk value chain for dairy farmer’s groups in Punakha district. Both quantitative and qualitative methods of survey, key informant interviews and focus group discussion were used as research strategies to obtain relevant information. The survey was conducted using both open and closed-ended structured questionnaire in seven subdistricts of Barp, Dzomi, Guma, Kabisa, Shelnga-Bjemi, Talog and Toedwang. A total of 60 respondents; 30 existing milk suppliers and 30 non-milk suppliers were drawn using a simple random sampling technique. One-to-one interviews were conducted following semi-structured questions with eight key informants in the chain. One focus group interview was conducted with the existing dairy farmer groups representatives to triangulate and discover in-depth information about the situation of the milk value chain in the district. The survey data was analysed using the Statistical Package for Social Sciences software version 20. A method of grounded theory design was used to analyse the qualitative data of interviews and focus group discussion. Value chain mapping was employed for assessing the operational situation of the current milk chain. The mean cost of milk production was estimated at Nu.27.53 per litre and the maximum expenses were incurred in animal feeds which were estimated to be 46.34% of the total cost of milk production. In this study, milk producers had the highest share of added value and profit which were estimated at 45.45% and 44.85% respectively. Limited information and coordination amongst stakeholders have contributed to slow progression in the formal milk market. The finding reveals that 90% of nondairy farmer groups respondents were interested in joining formal milk marketing. The average morning milk available for supply from this group would be 4.41 ± 3.07 litres daily by each household. The study also found that 50% of the respondents were interested in supplying evening milk with an average of 4.43 ± 2.25 litres per day per household. Based on the result of this study, it was concluded that there are possibilities of expanding the milk value chain in the district. However, there is a need to enhance consistent milk supply through a quality-based milk payment system, access to reasonable input supplies, and facilitate strong multi-stakeholder processes along the milk value chain.
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