Droop control is used for power management in DC grids. Based on the level of the DC grid voltage, the amount of power regulated to or from the appliance is regulated such, that power management is possible. The Universal 4 Leg is a laboratory setup for studying the functionality of a grid manager for power management. It has four independent outputs that can be regulated with pulse width modulation to control the power flow between the DC grid and for example, a rechargeable battery, solar panel or any passive load like lighting or heating.
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Electrification of residential areas is increasingly common. Major areas of development include rooftop solar panels, electric vehicles and heat pumps. However, existing grid components may have insufficient network capacity to accommodate the resulting electricity flows. Battery energy storage (BES) can be used to prevent transformer overloading resulting from electrification. Ideally, BES should be sized and placed such that it can prevent overloading with a minimum amount of storage capacity, but it is unclear how load characteristics affect BES capacity requirements. This study investigated how load simultaneity affects the minimum BES capacity required to prevent transformer overloading, comparing a central with a distributed BES layout. It was found that as simultaneity increases, distributed storage requires relatively less capacity than central storage. This is likely due to the reduced ability of central BES to share capacity between connections as simultaneity increases, and the ability of distributed BES to better reduce transportation losses.
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The main question in this PhD thesis is: How can Business Rules Management be configured and valued in organizations? A BRM problem space framework is proposed, existing of service systems, as a solution to the BRM problems. In total 94 vendor documents and approximately 32 hours of semi-structured interviews were analyzed. This analysis revealed nine individual service systems, in casu elicitation, design, verification, validation, deployment, execution, monitor, audit, and version. In the second part of this dissertation, BRM is positioned in relation to BPM (Business Process Management) by means of a literature study. An extension study was conducted: a qualitative study on a list of business rules formulated by a consulting organization based on the Committee of Sponsoring Organizations of the Treadway Commission risk framework. (from the summary of the Thesis p. 165)
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Standard mass-production is a well-known manufacturing concept. To make small quantities or even single items of a product according to user specifications at an affordable price, alternative agile production paradigms should be investigated and developed. The system presented in this article is based on a grid of cheap reconfigurable production units, called equiplets. A grid of these equiplets is capable to produce a variety of different products in parallel at an affordable price. The underlying agent-based software for this system is responsible for the agile manufacturing. An important aspect of this type of manufacturing is the transport of the products along the available equiplets. This transport of the products from equiplet to equiplet is quite different from standard production. Every product can have its own unique path along the equiplets. In this article several topologies are discussed and investigated. Also, the planning and scheduling in relation to the transport constraints is subject of this study. Some possibilities of realization are discussed and simulations are used to generate results with the focus on efficiency and usability for different topologies and layouts of the grid and its internal transport system. Closely related with this problem is the scheduling of the production in the grid. A discussion about the maximum achievable load on the production grid and its relation with the transport system is also included.
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Lemma. This article argues that for Knowledge Management it is not important how knowledge is defined but how it is conceptualized.
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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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The need for increasing further the penetration of Renewable Energy Sources (RESs) is demanding a change in the way distribution grids are managed. In particular, the RESs intermittent and stochastic nature is finding in Battery Energy Storage (BES) systems its most immediate countermeasure. This work presents a reality-based assessment and comparison of the impact of three different BES technologies on distribution grids with high RES penetration, namely Li-ion, Zn-Air and Redox Flow. To this end, a benchmark distribution grid with real prosumers’ generation and load profiles is considered, with the RES penetration purposely scaled up in such a way as to violate the grid operational limits. Then, further to the BES(s) placement on the most affected grid location(s), the impact of the three BES types is assessed considering two Use Cases: 1) Voltage & Congestion Management and 2) Peak Shaving & Energy shifting. Assessment is conducted by evaluating a set of technical Key Performance Indicators (KPIs), together with a simplified economic analysis.
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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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Background: Osteoarthritis (OA) is a chronic disease primarily affecting older adults, mainly impacting the hip and knee joints. The increasing prevalence of OA contributes to rising healthcare demands and costs. Current OA treatment guidelines emphasize the importance of self-management education and guidance, particularly in promoting physical activity and weight management. In addition, improving sleep is crucial for managing OA. Developing effective self-management interventions necessitates a comprehensive understanding of the factors that facilitate these behaviors. Especially for changing health behaviors, it is important to focus on psychosocial factors. Therefore, this systematic review aimed to identify the psychosocial factors associated with physical activity, weight management, and sleep in adults with hip and/or knee OA. Methods: Five databases (PubMed, Embase, CINAHL, PyschINFO, Web of Science) were searched for observational studies reporting statistics on the association between psychosocial determinants and physical activity, weight management, or sleep in people with OA. The methodological quality was assessed using the Quality Assessment Tool for Observational Studies of the National Heart, Lung, and Blood Institute. After screening 5,812 articles, 31 studies were included for analysis. Results: The results showed that intention, self-efficacy, and willpower beliefs were positively associated with physical activity. Kinesiophobia, pain catastrophizing and pain-related fear were negatively associated with physical activity. Depressive symptoms, negative affect, pain catastrophizing, and low willpower beliefs were associated with poor weight management. Anxiety, depression, pain anxiety, and post-traumatic stress disorder were related to poor sleep behavior. Conclusions This review enhances the understanding of the psychosocial factors underlying physical activity, weight management and sleep in OA. These insights are valuable for developing tailored behavior change interventions aimed at improving physical activity, weight management and sleep in patients with hip and/or knee OA.
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from the article: "Abstract The way in which construction logistics is organised has considerable impact on production flow, transportation efficiency, greenhouse gas emissions and congestion, particularly in urban areas such as city centres. In cities such as London and Amsterdam municipalities have issued new legislation and stricter conditions for vehicles to be able to access cities and city centres in particular. Considerate clients, public as well private, have started developing tender policies to encourage contractors to reduce the environmental impact of construction projects. This paper reports on an ongoing research project applying and assessing developments in the field of construction logistics in the Netherlands. The cases include contractors and third party logistics providers applying consolidation centres and dedicated software solutions to increase transportation efficiency. The case show various results of JIT logistics management applied to urban construction projects leading to higher transportation efficiencies, and reduced environmental impact and increased production efficiency on site. The data collections included to-site en on-site observations, measurement and interviews. The research has shown considerable reductions of vehicles to deliver goods and to transport workers to site. In addition the research has shown increased production flow and less waste such as inventory, waiting and unnecessary motion on site."
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