In manufacturing of organic electronics, inkjet printing as an alternative technique for depositing materials is becoming increasingly important. Aside to the ink formulations challenges, improving the resolution of the printed patterns is a major goal. In this study we will discuss a newly developed technique to selectively modify the substrate surface energy using plasma treatment as a means to achieve this goal. First, we look at the effects of the μPlasma treatment on the surface energy for a selection of plastic films. Second, we investigated the effects of the μPlasma treatment on the wetting behaviour of inkjet printed droplets to determine the resolution of the μPlasma printing technique. We found that the surface energy for all tested films increased significantly reaching a maximum after 3-5 repetitions. Subsequently the surface energy decreased in the following 8-10 days after treatment, finally stabilizing at a surface energy roughly halfway between the surface energy of the untreated film and the maximum obtained surface energy. When μPlasma printing lines, an improved wetting abillity of inkjet printed materials on the plasma treated areas was found. The minimal achieved μPlasma printed line was found to be 1 mm wide. For future application it is important to increase the resolution of the plasma print process. This is crucial for combining plasma treatment with inkjet print technology as a means to obtain higher print resolutions.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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Airports represent the major bottleneck in the air traffic management system with increasing traffic density. Enhanced levels of automation and coordination of surface operations are imperative to reduce congestion and to improve efficiency. This paper addresses the problem of comparing different control strategies on the airport surface to investigate their impacts and benefits. We propose an optimization approach to solve in a unified manner the coordinated surface operations problem on network models of an actual hub airport. Controlled pushback time, taxi reroutes and controlled holding time (waiting time at runway threshold for departures and time spent in runway crossing queues for arrivals) are considered as decisions to optimize the ground movement problem. Three major aspects are discussed:1) benefits of incorporating taxi reroutes on the airport performance metrics; 2) priority of arrivals and departures in runway crossings; 3) tradeoffs between controlled pushback and controlled holding time for departures. A preliminary study case is conducted in a model based on operations of Paris Charles De-Gaulle airport under the most frequently used configuration. Airport is modeled using a node-link network structure. Alternate taxi routes are constructed based on surface surveillance records with respect to current procedural factors. A representative peak-hour traffic scenario is generated using historical data. The effectiveness of the proposed optimization methods is investigated.
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This century, greenhouse gas emissions such as carbon dioxide, methane and nitrogen oxides must be significantly reduced. Greenhouse gases absorb and emit infrared radiation that contributes to global warming, which can lead to irreversible negative consequences for humans and the environment. Greenhouse gases are caused by the burning of fossil fuels such as crude oil, coal, and natural gas, but livestock farming, and agriculture are also to blame. In addition, deforestation contributes to more greenhouse gases. Of the natural greenhouse gases, water vapor is the main cause of the greenhouse effect, accounting for 90%. The remaining 10% is caused from high to low by carbon dioxide, methane, nitrogen oxides, chlorofluorocarbons, and ozone. In addition, there are industrial greenhouse gases such as fluorinated hydrocarbons, sulphurhexafluoride and nitrogen trifluoride that contribute to the greenhouse effect too. Greenhouse gases are a major cause of climate change, with far-reaching consequences for the welfare of humans and animals. In some regions, extreme weather events like rainfall are more common, while others are associated with more extreme heat waves and droughts. Sea level rise caused by melting ice and an increase in forest fires are undesirable effects of climate change. Countries in low lying areas fear that sea level rise will force their populations to move to the higher lying areas. Climate change is affecting the entire world. An estimated 30-40% o f the carbon dioxide released by the combustion of fossil fuels dissolves into the surface water resulting in an increased concentration of hydrogen ions. This causes the seawater to become more acidic, resulting in a decreasing of carbonate ions. Carbonate ions are an important building block for forming and maintaining calcium carbonate structures of organisms such as oysters, mussels, sea urchins, shallow water corals, deep sea corals and calcareous plankton.
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The government of Ukraine has adopted the Renewable Energy Directive (RED) with clear goals and a roadmap to facilitate its energy transition towards renewable sources. This is done because of both climate concerns as well as reasons related to Ukraine’s foreign policy which led the government to decide that Ukraine should work more on its own energy independence. Currently the percentage of renewable energy sources in Ukraine is among the lowest of the entire Europe and there is only slow development in terms of the growth of the sector, even though there is a lot of available biomass, given the large and flat surface of the country with a well-developed agricultural sector. As in most countries in the world, there is a quite intensive and well-developed debate in Ukraine about the energy sector, energy usage and the necessary transition towards more renewable types of energy. One of the consequences of it is that Ukraine is one of the partner countries in the Paris agreement and committed itself to reducing the amount of greenhouse gas emissions in the future. That means that a transformation towards renewable energy is needed, even though currently in Ukraine only a low percentage of energy is generated by sustainable sources. The general picture is that in Ukraine the development of the renewable energy sector is going not as fast as could have been. In other words, there are several barriers present that hinder the energy transition. One of the issues behind such a barrier may be a limited access to technology, or problems with legislation or other issues which may be unknown so far, but certainly relevant for foreign investors. The Ukrainian government adopted the so-called Renewable Energy Directive (RED), set goals for the energy transition and support the transition itself. In some areas progress was made, for example in the growing number of biomass fired boilers, but still Ukraine remains one of the European countries with the lowest percentage of renewable energy production. Therefore, in order to identify currently existing barriers and help to find possible applications of new technologies in Ukraine, the Dutch Enterprise Agency (Rijksdienst voor Ondernemerschap) commissioned this study. It was done within the framework of the Partners in Business on Bioenergy program. The focus of this study is on analysing the renewable energy sector, with special attention for biomass, in the form of biomass-based heating and biomass for biofuels. Of course, other parts of the renewable energy sector such as solar and wind energy are also taken into consideration. The second part consists of a case study to determine the business case for direct processing of sugar beets with Betaprocess as a possible application of biomass to biofuel production in Ukraine. The third study is aiming at determining the amount of biomass that can safely be taken from the fields, without negatively affecting the fertility of the soil. These sub-studies mentioned in the previous paragraph offer a better understanding of the renewable energy market in general and biomass/biofuel applications in particular. This study sheds light on several important questions that entrepreneurs and/or other foreign investors may have about investing in Ukraine. Even though it is well-known that doing business in Ukraine is challenging, it is also very important to have a clear picture of the opportunities that this country offers, within the limits that nature sets, in order to avoid negative consequences like soil degradation. The objective of this report is to find out about which opportunities and barriers exist in the Ukrainian transition towards renewable energy generation, to calculate the profitability of new biomass-processing technologies as well as finding out limitations of biomass usage.
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Airports and surrounding airspaces are limited in terms of capacity and represent the major bottleneck in the air traffic management system. This paper proposes a two level model to tackle the integrated optimization problem of arrival, departure, and surface operations. The macroscopic level considers the terminal airspace management for arrivals and departures and airport capacity management, while the microscopic level optimizes surface operations and departure runway scheduling. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on real-world case studies of Paris Charles De-Gaulle airport, show the benefits of this integrated approach.
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Blue-green roofs have been utilized and studied for their enhanced water storage capacity compared to conventional roofs or extensive green roofs. Nonetheless, research about the thermal effect of blue-green roofs is lacking. The goal of this research is to study the thermal effect of blue-green roofs in order to assess their potential for shielding the indoor environment from outdoor temperature extremes (cold- and heat-waves). In this field study, we examined the differences between blue-green roofs and conventional gravel roofs from the perspective of the roof surface temperatures and the indoor temperatures in the city of Amsterdam for late 20th century buildings. Temperature sensor (iButtons) values indicate that outside surface temperatures for blue-green roofs are lower in summer and fluctuate less during the whole year than temperatures of conventional roofs. Results show that for three warm periods during summer in 2021 surface substrate temperatures peaked on average 5°C higher for gravel roofs than for blue-green roofs. Second, during both warm and cold periods, the temperature inside the water crate layer was more stable than the roof surface temperatures. During a cold period in winter, minimum water crate layer temperatures remained 3.0 o C higher than other outdoor surface temperatures. Finally, also the variation of the indoor temperature fluctuations of locations with and without blue-green roofs have been studied. Locations with blue-green roofs are less sensitive to outside air temperature changes, as daily temperature fluctuations (standard deviations) were systematically lower compared to conventional roofs for both warm and cold periods.
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Lectorale redeboekje naar aanleiding van de intrede in het lectoraat Systeemintegratie in de energietransitie
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To achieve the “well below 2 degrees” targets, a new ecosystem needs to be defined where citizens become more active, co-managing with relevant stakeholders, the government, and third parties. This means moving from the traditional concept of citizens-as-consumers towards energy citizenship. Positive Energy Districts (PEDs) will be the test-bed area where this transformation will take place through social, technological, and governance innovation. This paper focuses on benefits and barriers towards energy citizenships and gathers a diverse set of experiences for the definition of PEDs and Local Energy Markets from the Horizon2020 Smart Cities and Communities projects: Making City, Pocityf, and Atelier.
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Citizen participation in local renewable energy projects is often promoted as many suppose it to be a panacea for the difficulties that are involved in the energy transition process. Quite evidently, it is not; there is a wide variety of visions, ideologies and interests related to an ‘energy transition’. Such a variety is actually a precondition for a stakeholder participation process, as stakeholder participation only makes sense if there is ‘something at stake’. Conflicting viewpoints, interests and debates are the essence of participation. The success of stakeholder participation implies that these differences are acknowledged, and discussed, and that this has created mutual understanding among stakeholders. It does not necessarily create ‘acceptance’. Renewable energy projects often give rise to local conflict. The successful implementation of local renewable energy systems depends on the support of the local social fabric. While at one hand decisions to construct wind turbines in specific regions trigger local resistance, the opposite also occurs! Solar parks sometimes create a similar variation: Various communities try to prevent the construction of solar parks in their vicinity, while other communities proudly present their parks. Altogether, local renewable energy initiatives create a rather chaotic picture, if regarded from the perspective of government planning. However, if we regard the successes, it appears the top down initiatives are most successful in areas with a weak social fabric, like industrial areas, or rather recently reclaimed land. Deeply rooted communities, virtually only have successful renewable energy projects that are more or less bottom up initiatives. This paper will first sketch why participation is important, and present a categorisation of processes and procedures that could be applied. It also sketches a number of myths and paradoxes that might occur in participation processes. ‘Compensating’ individuals and/or communities to accept wind turbines or solar parks is not sufficient to gain ‘acceptance’. A basic feature of many debates on local renewable energy projects is about ‘fairness’. The implication is that decision-making is neither on pros and cons of various renewable energy technologies as such, nor on what citizens are obliged to accept, but on a fair distribution of costs and benefits. Such discussions on fairness cannot be short cut by referring to legal rules, scientific evidence, or to standard financial compensations. History plays a role as old feelings of being disadvantaged, both at individual and at group level, might re-emerge in such debates. The paper will provide an overview of various local controversies on renewable energy initiatives in the Netherlands. It will argue that an open citizen participation process can be organized to work towards fair decisions, and that citizens should not be addressed as greedy subjects, trying to optimise their own private interests, but as responsible persons.
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