The Netherlands is aiming for the roll-out of more solar PV. However like many densely populated countries, the country is running into issues of lack of space. Opportunities around infrastructural works like highways provide space without compromising the landscape. Examples of this double use are already developed and demonstrated, like for instance sound barriers and solar roads. New is the combination of solar PV with traffic barriers. This has a big potential since the Dutch main road network had 7.500 km of guiderail and the construction to put PV on is already there. In the MESH (Modular E cover for Solar Highways) project a consortium of knowledge institutes, a province and companies developed a prototype and tested it in a pilot. The consortium consists of TNO, Solliance (in which TNO is a partner, a high-end research institute for flexible thin film solar cells such as CIGS and Perovskite), Heijmans Infra (focusing mainly on the construction, improvement and maintenance of road infrastructure, including guiderails), DC Current (applying innovations with regard to power optimizers for the linear PV application), the Province of Noord-Holland (which acts as a leading customer) and the Amsterdam University of Applied Sciences (AUAS) as a knowledge institution that links education and research. In this project the theme Sustainable Energy Systems of AUAS is involved with both lecturers and student groups. In the project, Solliance investigated and developed the flexible thin film PV technology to be applied with a focus on shape and reliability. TNO and Heijmans developed a modular casing concept and a fastening system that allows quick installation on site. DC Current worked on the DC management with regard to voltage, electrical safety and minimizing failure in case of collision. At the end of the project, the partners in the consortium have validated knowledge about how to integrate PV into the guiderail and can start with the scaling up of the technology for commercial applications. In order to meet the various requirements for traffic safety on the one hand and generating electricity on the other hand, the Systems Engineering methodology was leading during the project. In the project we first built a small, but full scale prototype and invited safety experts to evaluate the design. With this feedback we made a redesign for the pilot. This pilot is placed on the highway as safety barrier and tested for a year. In a presentation at EU PVSEC18 [1] K.Sewalt reported on the design phase. This time we want to present the results of our test phase and give answers on our research questions.
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Installing photovoltaic panels (PV) on household rooftops can significantly contribute to mitigating anthropogenic climate change. The mitigation potential will be much higher when households would use PVs in a sustainable way, that is, if they match their electricity demand to their PVs electricity production, as to avoid using electricity from the grid. Whilst some have argued that owning PVs motivate households to use their PV in a sustainable way, others have argued that owning a PV does not result in load shifting, or that PV owners may even use more energy when their PV production is low. This paper addresses this critical issue, by examining to what extent PV owners are likely to shift their electricity demand to reduce the use of electricity from the grid. Extending previous studies, we analyse actual high frequency electricity use from the grid using smart meter data of households with and without PVs. Specifically, we employ generalized additive models to examine whether hourly net electricity use (i.e., the difference between electricity consumed from the grid and supplied back to the grid) of households with PVs is not only lower during times when PV production is high, but also when PV production low, compared to households without PVs. Results indicate that during times when PV production is high, net electricity use of households with PV is negative, suggesting they sent back excess electricity to the power grid. However, we found no difference in net electricity use during times when PV production is low. This suggests that installing PV does not promote sustainable PV use, and that the mitigation potential of PV installment can be enhanced by encouraging sustainable PV use
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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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More than 25!years after Moore’s first introduction of the public value concept in 995, the concept is now widely used, but its operationalization is still considered difficult. This paper presents the empirical results of a study analyzing the application of the public value concept in Higher Education Institutions, thereby focusing on how to account for public value. The paper shows how Dutch universities of applied sciences operationalize the concept ‘public value’, and how they report on the outcome achievements. The official strategy plans and annual reports for FY2016 through FY2018 of the ten largest institutions were used. While we find that all the institutions selected aim to deliver public value, they still use performance indicators that have a more narrow orientation, and are primarily focused on processes, outputs, and service delivery quality. However, we also observe that they use narratives to show the public value they created. In this way this paper contributes to the literature on public value accounting.
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Electrification of transportation, communication, working and living continues worldwide. Televisions, telephones, servers are an important part of everyday life. These loads and most sustainable sources as well, have one thing in common: Direct Current. The Dutch research and educational programme ‘DC – road to its full potential’ studies the impact of feeding these appliances from a DC grid. An improvement in energy efficiency is expected, other benefits are unknown and practical considerations are needed to come to a proper comparison with an AC grid. This paper starts with a brief introduction of the programme and its first stages. These stages encompass firstly the commissioning, selection and implementation of a safe and user friendly testing facility, to compare performance of domestic appliances when powered with AC and DC. Secondly, the relationship between the DC-testing facility and existing modeling and simulation assignments is explained. Thirdly, first results are discussed in a broad sense. An improved energy efficiency of 3% to 5% is already demonstrated for domestic appliances. That opens up questions for the performance of a domestic DC system as a whole. The paper then ends with proposed minor changes in the programme and guidelines for future projects. These changes encompass further studying of domestic appliances for product-development purposes, leaving less means for new and costly high-power testing facilities. Possible gains are 1) material and component savings 2) simpler and cheaper exteriors 3) stable and safe in-house infrastructure 4) whilst combined with local sustainable generation. That is the road ahead. 10.1109/DUE.2014.6827758
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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presentatie over managementcontrol in het creëren van maatschappelijke waarde
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Background: Knee and hip osteoarthritis (OA) among older adults account for substantial disability and extensive healthcare use. Effective pain coping strategies help to deal with OA. This study aims to determine the long-term relationship between pain coping style and the course of healthcare use in patients with knee and/or hip OA over 10 years. Methods: Baseline and 10-year follow-up data of 861 Dutch participants with early knee and/or hip OA from the Cohort Hip and Cohort Knee (CHECK) cohort were used. The amount of healthcare use (HCU) and pain coping style were measured. Generalized Estimating Equations were used, adjusted for relevant confounders. Results: At baseline, 86.5% of the patients had an active pain coping style. Having an active pain coping style was significantly (p = 0.022) associated with an increase of 16.5% (95% CI, 2.0–32.7) in the number of used healthcare services over 10 years. Conclusion: Patients with early knee and/or hip OA with an active pain coping style use significantly more different healthcare services over 10 years, as opposed to those with a passive pain coping style. Further research should focus on altered treatment (e.g., focus on self-management) in patients with an active coping style, to reduce HCU.
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The application of DC grids is gaining more attention in office applications. Especially since powering an office desk would not require a high power connection to the main AC grid but could be made sustainable using solar power and battery storage. This would result in fewer converters and further advanced grid utilization. In this paper, a sustainable desk power application is described that can be used for powering typical office appliances such as computers, lighting, and telephones. The desk will be powered by a solar panel and has a battery for energy storage. The applied DC grid includes droop control for power management and can either operate stand-alone or connected to other DC-desks to create a meshed-grid system. A dynamic DC nano-grid is made using multiple self-developed half-bridge circuit boards controlled by microcontrollers. This grid is monitored and controlled using a lightweight network protocol, allowing for online integration. Droop control is used to create dynamic power management, allowing automated control for power consumption and production. Digital control is used to regulate the power flow, and drive other applications, including batteries and solar panels. The practical demonstrative setup is a small-sized desktop with applications built into it, such as a lamp, wireless charging pad, and laptop charge point for devices up to 45W. User control is added in the form of an interactive remote wireless touch panel and power consumption is monitored and stored in the cloud. The paper includes a description of technical implementation as well as power consumption measurements.
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One potential renewable energy resource is green gas production throughanaerobic digestion (AD). However, only part of the biogas produced (up to50-60%) contains the combustible methane; the remainder are incombustiblegasses with the biggest being carbon dioxide. These gasses are often not usedand expelled in the atmosphere. Through the use of BIO-P2M where hydrogenis mixed with the remaining CO2 additional methane can be produced,increasing the yield and using the feedstocks more effectively. Within thisresearch the environmental sustainability and effectiveness of BIO-P2M isevaluated using the MEFA and aLCA method, expressed in; net green gasproduction, efficiency in (P)EROI, emissions in GWP100, and environmentalimpact in Ecopoints. The functional unit is set as a normal cubic meter ofGroningen quality natural gas. Results indicate a net improvement of allindicators when applying BIO-P2M in several configurations (in situ, ex situ).When allocating the production of renewable energy to the BIO-P2M systemenvironmental impacts for wind the results are still positive; however, whenusing solar PV as an energy source the environmental impact in Ecopointsexceeds that of the reference case of Groningen natural gas. An additionaloption for improving the indicators is optimization of the process. When usingBIO-P2M combined with heat and power unit for producing the internalelectricity and heat demands all indicators are improved substantially. On anational scale when utilizing al available waste materials for the BIO-P2Msystem around 1217 MNm3/a of green gas can be produced, which is 3% ofthe total yearly consumption in the Netherlands and around 60% more thanwhen using normal AD systems. Within the context BIO-P2M is an interestingoption for increasing green gas output and improving the overall sustainabilityof the AD process. However, the source of green electricity needs to be takeninto account and process optimization can ensure better environmentalperformance.
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