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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Like the professionals, design students tend to avoid the complexity of the user context, and moral issues are largely overlooked. This inspired us to explore whether we could engage design students in thinking about moral issues by exploring different ethical frameworks in their designing. As a case environment we chose smart-grid product service combinations. In this paper we first discuss the ethical frameworks of four selected philosophers’: Plato, Rousseau, Kant, & Mill. Then we will describe the student design process, the resulting four smart grid service concepts and the user insights that came from a user evaluation. We discuss how this approach allowed the students to get insights in their own ethical stance and how they allowed users to reflect on possible futures. We also discuss how these ‘probing’ concepts were used within the larger smart grid project.
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Combining electric cars with utility services seems to be a natural fit and holds the promise to tackle various mobility as well as electricity challenges at the same time. So far no viable business model for vehicle-to-grid technology has emerged, raising the question which characteristics a vehicle-to-grid business model should have. Drawing on an exploratory study amongst 189 Dutch consumers this study seeks to understand consumer preferences in vehicle-to-grid business models using conjoint analysis, factor analysis and cluster analysis. The results suggest that consumers prefer private ownership of an EV and a bidirectional charger instead of community ownership of bidirectional charger, they prefer utility companies instead of car companies as the aggregator and they require home and public charging. The most salient attributes in a V2G business model seem to be functional rather than financial or social. The customer segment with the highest willingness to adopt V2G prefers functional attributes. Based on the findings, the study proposes a business model that incorporates the derived preferences
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The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.
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Author supplied: Abstract—The growing importance and impact of new technologies are changing many industries. This effect is especially noticeable in the manufacturing industry. This paper explores a practical implementation of a hybrid architecture for the newest generation of manufacturing systems. The papers starts with a proposition that envisions reconfigurable systems that work together autonomously to create Manufacturing as a Service (MaaS). It introduces a number of problems in this area and shows the requirements for an architecture that can be the main research platform to solve a number of these problems, including the need for safe and flexible system behaviour and the ability to reconfigure with limited interference to other systems within the manufacturing environment. The paper highlights the infrastructure and architecture itself that can support the requirements to solve the mentioned problems in the future. A concept system named Grid Manufacturing is then introduced that shows both the hardware and software systems to handle the challenges. The paper then moves towards the design of the architecture and introduces all systems involved, including the specific hardware platforms that will be controlled by the software platform called REXOS (Reconfigurable EQuipletS Operating System). The design choices are provided that show why it has become a hybrid platform that uses Java Agent Development Framework (JADE) and Robot Operating System (ROS). Finally, to validate REXOS, the performance is measured and discussed, which shows that REXOS can be used as a practical basis for more specific research for robust autonomous reconfigurable systems and application in industry 4.0. This paper shows practical examples of how to successfully combine several technologies that are meant to lead to a faster adoption and a better business case for autonomous and reconfigurable systems in industry.
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from the article: The demand for a wireless CO2 solution is ever increasing. One of the biggest problems with the majority of commercial available CO2 sensors is the high energy consumption which makes them unsuitable for battery operation. Possible candidates for CO2 sensing in a low power wireless application are very limited and show a problematic calibration process. This study focuses on one of those EMF candidates, which is a Ag4RbI5 based sensor. This EMF sensor is based on the potentiometric principle and consumes no energy. The EMF cell was studied in a chamber where humidity, temperature and CO2 level could be controlled. This study gives an detailed insight in the different drift properties of the potentiometric CO2 sensor and a method to amplify the sensors signal. Furthermore, a method to minimize the several types of drift is given. With this method the temperature drift can be decreased by a factor 10, making the sensor a possible candidate for a wireless CO2 sensor network.
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Vrijwel geruisloos is begin 2023 voor de kosten van het Belgische elektriciteitsnet voor kleinverbruikers een nieuw tariefsysteem ingevoerd. Huishoudens met een hoog piekverbruik betalen nu meer dan huishoudens die minder netcapaciteit benutten. Belgische huishoudens worden aldus gestimuleerd slim om te gaan met de schaarse netcapaciteit.
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Peer-to-peer (P2P) energy trading has been recognized as an important technology to increase the local self-consumption of photovoltaics in the local energy system. Different auction mechanisms and bidding strategies haven been investigated in previous studies. However, there has been no comparatively analysis on how different market structures influence the local energy system’s overall performance. This paper presents and compares two market structures, namely a centralized market and a decentralized market. Two pricing mechanisms in the centralized market and two bidding strategies in the decentralized market are developed. The results show that the centralized market leads to higher overall system self-consumption and profits. In the decentralized market, some electricity is directly sold to the grid due to unmatchable bids and asks. Bidding strategies based on the learning algorithm can achieve better performance compared to the random method.
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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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Energy policies are vital tools used by countries to regulate economic and social development as well as guarantee national security. To address the problems of fragmented policy objectives, conflicting tools, and overlapping initiatives, the internal logic and evolutionary trends of energy policies must be explored using the policy content. This study uses 38,277 energy policies as a database and summarizes the four energy policy objectives: clean, low-carbon, safe, and efficient. Using the TextCNN model to classify and deconstruct policies, the LDA + Word2vec theme conceptualization and similarity calculations were compared with the EISMD evolution framework to determine the energy policy theme evolution path. Results indicate that the density of energy policies has increased. Policies have become more comprehensive, barriers between objectives have gradually been broken, and low-carbon objectives have been strengthened. The evolution types are more diversified, evolution paths are more complicated, and the evolution types are often related to technology, industry, and market maturity. Traditional energy themes evolve through inheritance and merger; emerging technology and industry themes evolve through innovation, inheritance, and splitting. Moreover, this study provides a replicable analytical framework for the study of policy evolution in other sectors and evidence for optimizing energy policy design
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