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Abstract In recent years, Distributed Energy Systems (DESs) have been recognized as a good option for sustainable development of future energy systems. With growing environmental concerns, design optimization of DESs through economic assessments only is not sufficient. To achieve long-run sustainability of energy supply, the key idea of this paper is to investigate exergy assessments in DES design optimization to attain rational use of energy resources while considering energy qualities of supply and demand. By using low-temperature sources for low-quality thermal demand, the waste of high-quality energy can be reduced, and the overall exergy efficiency can be increased. Based on a pre-established superstructure, the aim is to determine numbers and sizes of energy devices in the DES and the corresponding operation strategies. A multi-objective linear problem is formulated to reduce the total annual cost and increase the overall exergy efficiency. The Pareto frontier is found to provide different design options for planners based on economic and sustainability priorities, through minimizing a weighted-sum of the total annual cost and primary exergy input, by using branch-and-cut. Numerical results demonstrate that different optimized DES configurations can be found according to the two objectives. Moreover, results also show that the total annual cost and primary exergy input are reduced by 20% - 30% as compared with conventional energy supply systems.

Abstract In recent years, Distributed Energy Systems (DESs) have been recognized as a good option for sustainable development of future energy systems. With growing environmental concerns, design optimization of DESs through economic assessments only is not sufficient. To achieve long-run sustainability of energy supply, the key idea of this paper is to investigate exergy assessments in DES design optimization to attain rational use of energy resources while considering energy qualities of supply and demand. By using low-temperature sources for low-quality thermal demand, the waste of high-quality energy can be reduced, and the overall exergy efficiency can be increased. Based on a pre-established superstructure, the aim is to determine numbers and sizes of energy devices in the DES and the corresponding operation strategies. A multi-objective linear problem is formulated to reduce the total annual cost and increase the overall exergy efficiency. The Pareto frontier is found to provide different design options for planners based on economic and sustainability priorities, through minimizing a weighted-sum of the total annual cost and primary exergy input, by using branch-and-cut. Numerical results demonstrate that different optimized DES configurations can be found according to the two objectives. Moreover, results also show that the total annual cost and primary exergy input are reduced by 20% - 30% as compared with conventional energy supply systems.

Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%.

Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%.

AbstractTropical forests are being deforested worldwide, and the remaining fragments are suffering from biomass and biodiversity erosion. Quantifying this erosion is challenging because ground data on tropical biodiversity and biomass are often sparse. Here, we use an unprecedented dataset of 1819 field surveys covering the entire Atlantic Forest biodiversity hotspot. We show that 83−85% of the surveys presented losses in forest biomass and tree species richness, functional traits, and conservation value. On average, forest fragments have 25−32% less biomass, 23−31% fewer species, and 33, 36, and 42% fewer individuals of late-successional, large-seeded, and endemic species, respectively. Biodiversity and biomass erosion are lower inside strictly protected conservation units, particularly in large ones. We estimate that biomass erosion across the Atlantic Forest remnants is equivalent to the loss of 55−70 thousand km2of forests or US$2.3−2.6 billion in carbon credits. These figures have direct implications on mechanisms of climate change mitigation.

AbstractTropical forests are being deforested worldwide, and the remaining fragments are suffering from biomass and biodiversity erosion. Quantifying this erosion is challenging because ground data on tropical biodiversity and biomass are often sparse. Here, we use an unprecedented dataset of 1819 field surveys covering the entire Atlantic Forest biodiversity hotspot. We show that 83−85% of the surveys presented losses in forest biomass and tree species richness, functional traits, and conservation value. On average, forest fragments have 25−32% less biomass, 23−31% fewer species, and 33, 36, and 42% fewer individuals of late-successional, large-seeded, and endemic species, respectively. Biodiversity and biomass erosion are lower inside strictly protected conservation units, particularly in large ones. We estimate that biomass erosion across the Atlantic Forest remnants is equivalent to the loss of 55−70 thousand km2of forests or US$2.3−2.6 billion in carbon credits. These figures have direct implications on mechanisms of climate change mitigation.