Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.
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The circular economy (CE) is heralded as reducing material use and emissions while providing more jobs and growth. We explored this narrative in a series of expert workshops, basing ourselves on theories, methods and findings from science fields such as global environmental input-output analysis, business modelling, industrial organisation, innovation sciences and transition studies. Our findings indicate that this dominant narrative suffers from at least three inconvenient truths. First, CE can lead to loss of GDP. Each doubling of product lifetimes will halve the related industrial production, while the required design changes may cost little. Second, the same mechanism can create losses of production jobs. This may not be compensated by extra maintenance, repair or refurbishing activities. Finally, ‘Product-as-a-Service’ business models supported by platform technologies are crucial for a CE transition. But by transforming consumers from owners to users, they lose independence and do not share in any value enhancement of assets (e.g., houses). As shown by Uber and AirBNB, platforms tend to concentrate power and value with providers, dramatically affecting the distribution of wealth. The real win-win potential of circularity is that the same societal welfare may be achieved with less production and fewer working hours, resulting in more leisure time. But it is perfectly possible that powerful platform providers capture most added value and channel that to their elite owners, at the expense of the purchasing power of ordinary people working fewer hours. Similar undesirable distributional effects may occur at the global scale: the service economies in the Global North may benefit from the additional repair and refurbishment activities, while economies in the Global South that are more oriented towards primary production will see these activities shrink. It is essential that CE research comes to grips with such effects. Furthermore, governance approaches mitigating unfair distribution of power and value are hence essential for a successful circularity transition.
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In wheelchair sports, there is an increasing need to monitor mechanical power in the field. When rolling resistance is known, inertial measurement units (IMUs) can be used to determine mechanical power. However, upper body (i.e., trunk) motion affects the mass distribution between the small front and large rear wheels, thus affecting rolling resistance. Therefore, drag tests – which are commonly used to estimate rolling resistance – may not be valid. The aim of this study was to investigate the influence of trunk motion on mechanical power estimates in hand-rim wheelchair propulsion by comparing instantaneous resistance-based power loss with drag test-based power loss. Experiments were performed with no, moderate and full trunk motion during wheelchair propulsion. During these experiments, power loss was determined based on 1) the instantaneous rolling resistance and 2) based on the rolling resistance determined from drag tests (thus neglecting the effects of trunk motion). Results showed that power loss values of the two methods were similar when no trunk motion was present (mean difference [MD] of 0.6 1.6 %). However, drag test-based power loss was underestimated up to −3.3 2.3 % MD when the extent of trunk motion increased (r = 0.85). To conclude, during wheelchair propulsion with active trunk motion, neglecting the effects of trunk motion leads to an underestimated mechanical power of 1 to 6 % when it is estimated with drag test values. Depending on the required accuracy and the amount of trunk motion in the target group, the influence of trunk motion on power estimates should be corrected for.
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Since the early work on defining and analyzing resilience in domains such as engineering, ecology and psychology, the concept has gained significant traction in many fields of research and practice. It has also become a very powerful justification for various policy goals in the water sector, evident in terms like flood resilience, river resilience, and water resilience. At the same time, a substantial body of literature has developed that questions the resilience concept's systems ontology, natural science roots and alleged conservatism, and criticizes resilience thinking for not addressing power issues. In this study, we review these critiques with the aim to develop a framework for power-sensitive resilience analysis. We build on the three faces of power to conceptualize the power to define resilience. We structure our discussion of the relevant literature into five questions that need to be reflected upon when applying the resilience concept to social–hydrological systems. These questions address: (a) resilience of what, (b) resilience at what scale, (c) resilience to what, (d) resilience for what purpose, and (e) resilience for whom; and the implications of the political choices involved in defining these parameters for resilience building or analysis. Explicitly considering these questions enables making political choices explicit in order to support negotiation or contestation on how resilience is defined and used.
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With the effects of climate change linked to the use of fossil fuels, as well as the prospect of their eventual depletion, becoming more noticeable, political establishment and society appear ready to switch towards using renewable energy. Solar power and wind power are considered to be the most significant source of global low-carbon energy supply. Wind energy continues to expand as it becomes cheaper and more technologically advanced. Yet, despite these expectations and developments, fossil fuels still comprise nine-tenths of the global commercial energy supply. In this article, the history, technology, and politics involved in the production and barriers to acceptance of wind energy will be explored. The central question is why, despite the problems associated with the use of fossil fuels, carbon dependency has not yet given way to the more ecologically benign forms of energy. Having briefly surveyed some literature on the role of political and corporate stakeholders, as well as theories relating to sociological and psychological factors responsible for the grassroots’ resistance (“not in my backyard” or NIMBYs) to renewable energy, the findings indicate that motivation for opposition to wind power varies. While the grassroots resistance is often fueled by the mistrust of the government, the governments’ reason for resisting renewable energy can be explained by their history of a close relationship with the industrial partners. This article develops an argument that understanding of various motivations for resistance at different stakeholder levels opens up space for better strategies for a successful energy transition. https://doi.org/10.30560/sdr.v1n1p11 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Ammonia is heavily used in agriculture as a fertilizer and in industry as a raw material for the production of various organic nitrogen compounds. Its high hydrogen content and its established infrastructure for both storage and distribution makes ammonia a prominent candidate for storing fluctuating renewable energy. The Haber-Bosch heterogenous reaction of hydrogen and nitrogen on an iron-based catalyst is used today at large scale ammonia production sites. The current industrial hydrogen production is dominated by fossil energy sources. The traditional Haber-Bosch process can become green and carbon-free if renewable electricity is used for hydrogen generation. However, a continuous operation of power to ammonia can be challenging with a fluctuating renewable energy source. Techno-economic models show that electrolysis and the hydrogen supply chain is the main dominating cost factor of power to ammonia.
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An important performance determinant in wheelchair sports is the power exchanged between the athletewheelchair combination and the environment, in short, mechanical power. Inertial measurement units (IMUs) might be used to estimate the exchanged mechanical power during wheelchair sports practice. However, to validly apply IMUs for mechanical power assessment in wheelchair sports, a well-founded and unambiguous theoretical framework is required that follows the dynamics of manual wheelchair propulsion. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to demonstrate the use of the IMU-based power estimates during wheelchair propulsion based on experimental data. Mechanical power during straight-line wheelchair propulsion on a treadmill was estimated using a wheel mounted IMU and was subsequently compared to optical motion capture data serving as a reference. IMU-based power was calculated from rolling resistance (estimated from drag tests) and change in kinetic energy (estimated using wheelchair velocity and wheelchair acceleration). The results reveal no significant difference between reference power values and the proposed IMU-based power (1.8% mean difference, N.S.). As the estimated rolling resistance shows a 0.9–1.7% underestimation, over time, IMU-based power will be slightly underestimated as well. To conclude, the theoretical framework and the resulting IMU model seems to provide acceptable estimates of mechanical power during straight-line wheelchair propulsion in wheelchair (sports) practice, and it is an important first step towards feasible power estimations in all wheelchair sports situations.
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Accurate assessment of rolling resistance is important for wheelchair propulsion analyses. However, the commonly used drag and deceleration tests are reported to underestimate rolling resistance up to 6% due to the (neglected) influence of trunk motion. The first aim of this study was to investigate the accuracy of using trunk and wheelchair kinematics to predict the intra-cyclical load distribution, more particularly front wheel loading, during hand-rim wheelchair propulsion. Secondly, the study compared the accuracy of rolling resistance determined from the predicted load distribution with the accuracy of drag test-based rolling resistance. Twenty-five able-bodied participants performed hand-rim wheelchair propulsion on a large motor-driven treadmill. During the treadmill sessions, front wheel load was assessed with load pins to determine the load distribution between the front and rear wheels. Accordingly, a machine learning model was trained to predict front wheel load from kinematic data. Based on two inertial sensors (attached to the trunk and wheelchair) and the machine learning model, front wheel load was predicted with a mean absolute error (MAE) of 3.8% (or 1.8 kg). Rolling resistance determined from the predicted load distribution (MAE: 0.9%, mean error (ME): 0.1%) was more accurate than drag test-based rolling resistance (MAE: 2.5%, ME: −1.3%).
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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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Residential electricity distribution grid capacity is based on the typical peak load of a house and the load simultaneity factor. Historically, these values have remained predictable, but this is expected to change due to increasing electric heating using heat pumps and rooftop solar panel electricity generation. It is currently unclear how this increase in electrification will impact household peak load and load simultaneity, and hence the required grid capacity of residential electricity distribution grids. To gain better insight, transformer and household load measurements were taken in an all-electric neighborhood over a period of three years. These measurements were analyzed to determine how heat pumps and solar panels will alter peak load and load simultaneity, and hence grid capacity requirements. The impacts of outdoor effective temperature and solar panel orientation were also analyzed. Moreover, the potential for smart grids to reduce grid capacity requirements was examined.
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