| 1. |
- Amjadi, Golnaz, 1982-, et al.
(författare)
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Is industrial energy inefficiency transient or persistent? Evidence from Swedish manufacturing
- 2022
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 309
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Tidskriftsartikel (refereegranskat)abstract
- Energy inefficiency in production implies that the same level of goods and services could be produced using less energy. The potential energy inefficiency of a firm may be linked to long-term structural rigidities in the production process and/or systematic shortcomings in management (persistent inefficiency), or associated with temporary issues like misallocation of resources (transient inefficiency). Eliminating or mitigating different inefficiencies may require different policy measures. Studies measuring industrial energy inefficiency have mostly focused on overall inefficiencies and have paid little attention to distinctions between the types. The aim of this study was to assess whether energy inefficiency is transient and/or persistent in the Swedish manufacturing industry. I used a firm-level panel dataset covering fourteen industrial sectors from 1997 to 2008 and estimated a stochastic energy demand frontier model. The model included a four-component error term separating persistent and transient inefficiency from unobserved heterogeneity and random noise. I found that both transient and persistent energy inefficiencies exist in most sectors of the Swedish manufacturing industry. Overall, persistent energy inefficiency was larger than transient, but varied considerably in different manufacturing sectors. The results suggest that, generally, energy inefficiencies in the Swedish manufacturing industry were related to structural rigidities connected to technology and/or management practices.
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| 2. |
- Amuakwa-Mensah, Franklin, et al.
(författare)
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Role of banking sector performance in renewable energy consumption
- 2022
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 306, Part B
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Tidskriftsartikel (refereegranskat)abstract
- To secure future universal access to modern energy, large investments in renewable energy technology are required. This paper estimates the impact of five banking sector performance indicators (return on asset, market capitalisation, asset quality, managerial efficiency and financial stability) on renewable energy consumption for a global panel consisting of 124 countries. The study used two-step system-GMM panel model to handle potential endogeneity and serial correlation. The paper considers three homogenous subpanels which are constructed based on the income group classification (high-, middle-, and low-income countries). Generally, our results show that improved banking sector performance enhances renewable energy consumption, with heterogenous effect across income group classification. For high -income (HI) countries, an increase in bank size together with improved asset quality and managerial efficiency have positive effects on renewable energy consumption. For middle-income (MI) and low-income (LI) countries, a high return on asset, an increase in bank size and financial stability are positive determinants of renewable energy consumption. We also find heterogenous effect of banking performance indicators across various renewable energy consumption types. The results highlight the importance of a well-functioning bank sector to achieve the investment in renewable energy needed to meet future energy demand and simultaneous decrease CO2 emissions.
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| 3. |
- Andersson, Johanna, et al.
(författare)
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Study on reduced process temperature for energy optimisation in mesophilic digestion : A lab to full-scale study
- 2020
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 271
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Tidskriftsartikel (refereegranskat)abstract
- This unique study combined the lab and full scales to investigate how the anaerobic digestion process of sewage sludge was affected by a temperature change within the lower mesophilic temperature range (32–37.5 °C). Both scientific methane potential experiments and dewaterability studies were made to be used in changes of the operational conditions for a full-scale wastewater treatment plant. The aim of this study was to reduce anaerobic digestions heat consumption by altering the operational mesophilic temperature in first lab-scale experiments and then in continuation to full-scale conditions. The results of the study suggest that it is possible to save approximately 13% in heat energy demand each year by reducing the temperature for anaerobic digestion by 2.5 °C. At the lab scale, the effect of temperature on biogas production was tested in a batch experiment by measuring the biochemical methane production. At 32 °C, the production of biogas decreased by 11% compared to that at 37.5 °C. No significant difference was observed between 37.5 °C and 34.5 °C. The effect of temperature on sludge dewaterability was tested by measuring the capillary suction time, and no difference was detected between various temperatures. To confirm these results, the temperature in two full-scale digesters was gradually adjusted from 37.5 °C to 35 °C. No change was observed in biogas production, process stability, or dewaterability. The amount of heat energy that can be saved for the full-scale digesters was calculated using a heat balance. This study shows that it is possible to reduce the heat demand for AD in real applications without compromising the biogas production and process stability.
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| 4. |
- Aslani, Mohammad, et al.
(författare)
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Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment
- 2022
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 306
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Tidskriftsartikel (refereegranskat)abstract
- The considerable potential of rooftop photovoltaics (RPVs) for alleviating the high energy demand of cities has made them a proven technology in local energy networks. Identification of rooftop areas suitable for installing RPVs is of importance for energy planning. Having these suitable areas referred to as utilizable areas greatly assists in a reliable estimate of RPVs energy production. Within such a context, this research aims to propose a spatially detailed methodology that involves (a) automatic extraction of buildings footprint, (b) automatic segmentation of roof faces, and (c) automatic identification of utilizable areas of roof faces for solar infrastructure installation. Specifically, the innovations of this work are a new method for roof face segmentation and a new method for the identification of utilizable rooftop areas. The proposed methodology only requires digital surface models (DSMs) as input, and it is independent of other auxiliary spatial data to become more functional. A part of downtown Gothenburg composed of vegetation and high-rise buildings with complex shapes was selected to demonstrate the methodology performance. According to the experimental results, the proposed methodology has a high success rate in building extraction (about 95% correctness and completeness) and roof face segmentation (about 85% completeness and correctness). Additionally, the results suggest that the effects of roof occlusions and roof superstructures are satisfactorily considered in the identification of utilizable rooftop areas. Thus, the methodology is practically effective and relevant for the detailed RPVs assessments in arbitrary urban regions where only DSMs are accessible.
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| 5. |
- Bakhtiari, Hamed, et al.
(författare)
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Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation
- 2021
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 290
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Tidskriftsartikel (refereegranskat)abstract
- Due to the lack of available flexibility sources to cope with different uncertainties in the real-time operation of stand-alone renewable energy-based microgrids, the stochastic behavior of uncertainty sources needs to be included in the planning stage. Since there is a high association between some of the uncertainty sources, defining a proper time series to represent the behavior of each source of uncertainty is a challenging issue. Consequently, uncertainty sources should be modeled in such a way that the designed microgrid be able to cope with all scenarios from probability and impact viewpoints. This paper proposes a modified Metropolis–coupled Markov chain Monte Carlo (MC)3 simulation to predict the stochastic behavior of different uncertainty sources in the planning of a stand-alone renewable energy-based microgrid. Solar radiation, wind speed, the water flow of a river, load consumption, and electricity price have been considered as primary sources of uncertainty. A novel data classification method is introduced within the (MC)3 simulation to model the time-dependency and the association between different uncertainty sources. Moreover, a novel curve-fitting approach is proposed to improve the accuracy of representing the multimodal distribution functions, modeling the Markov chain states, and the long-term probability of uncertainty sources. The predicted representative time series with the proposed modified (MC)3 model is benchmarked against the retrospective model, the long-term historical data, and the simple Monte Carlo simulation model to capture the stochastic behavior of uncertainty sources. The results show that the proposed model represents the probability distribution function of each source of uncertainty, the continuity of samples, time dependency, the association between different uncertainty sources, short-term and long-term trends, and the seasonality of uncertainty sources. Finally, results confirm that the proposed modified (MC)3 can appropriately predict all scenarios with high probability and impact.
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| 6. |
- Bao, Minglei, et al.
(författare)
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Modeling and evaluating nodal resilience of multi-energy systems under windstorms
- 2020
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 270
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Tidskriftsartikel (refereegranskat)abstract
- With the growing frequency and extent of extreme weather events, the resilient operation of multi-energy systems (MESs) has drawn attention nowadays. However, there is little study on the methodology with a set of key indicators to quantify the resilience of MESs with the consideration of the impacts of extreme weather. To address the problem, this paper proposes a framework to evaluate the time-dependent resilience of MESs considering energy interactions during extreme weather events, such as windstorms. Firstly, the multi-phase performance curve is utilized to describe the response behavior of MESs at different phases under the impacts of windstorms. Secondly, a service-based optimal energy flow model is developed to minimize the consequences caused by windstorms through the coordination among different energy subsystems. In order to model the chaotic failures and restoration of components, the Monte-Carlo simulation technique is applied. Furthermore, nodal resilience metrics for different energy carriers are proposed to quantify the resilience in MESs. Numerical studies demonstrate the capability of the proposed technique to quantify the resilience of MESs under windstorms. The results show that the resilience performance level of MESs can differ in different regions with the impacts of windstorms. The findings can provide a useful reference for system operators to constitute targeted resilience improvement measures.
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| 7. |
- Bao, Z., et al.
(författare)
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An optimal charging scheduling model and algorithm for electric buses
- 2023
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Ingår i: Applied Energy. - : Elsevier Ltd. - 0306-2619 .- 1872-9118. ; 332
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Tidskriftsartikel (refereegranskat)abstract
- Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints. This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules, which can be efficiently addressed by a bi-criterion dynamic programming algorithm. A real-world regional electric bus fleet of 122 buses in Shanghai, China is selected for validating the effectiveness and practicability of the proposed charging scheduling model and algorithm. The optimization results numerically reveal the impacts of TOU tariffs, station load capacities, charging infrastructure configurations, and battery capacities on the bus system performance as well as individual recharging behaviors, and justify the superior solution efficiency of our algorithm against a state-of-the-art commercial solver.
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| 8. |
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| 9. |
- Bayati, Navid, et al.
(författare)
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Locating high-impedance faults in DC microgrid clusters using support vector machines
- 2022
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Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 308
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Tidskriftsartikel (refereegranskat)abstract
- With the increasing number of DC microgrids, DC microgrid clusters are emerging as a cost-effective solution. Therefore, due to the possible long distances between DC microgrids, once a fault occurs and is cleared, it should be located. Especially, locating high impedance faults (HIFs) is challenging. With communication-free fault locating methods, implementation costs can be reduced, and noise and delay of communication can be eliminated. In this paper, a novel localized fault location method using support vector machines (SVMs) is proposed for DC microgrid clusters. The purpose of this study is to facilitate the post fault conditions by locating the accurate place of the faults, even the challenging HIFs, by using the local measurements at one end of each line. The proposed scheme applies the faults, and fault features generated experimentally to the SVM, which is trained in Python for determining the fault location. The experimental test results prove that the proposed scheme is immune against disturbances, such as noise and bad calibration, and can efficiently and reliably estimate the location and resistance of faults with high accuracy.
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| 10. |
- Beckinghausen, A., et al.
(författare)
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From removal to recovery : An evaluation of nitrogen recovery techniques from wastewater
- 2020
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Ingår i: Applied Energy. - : Elsevier Ltd. - 0306-2619 .- 1872-9118. ; 263
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Tidskriftsartikel (refereegranskat)abstract
- Nitrogen recovery is the next step in the improvement of the wastewater treatment process, utilizing this important nutrient for fertilizers to decrease use of energy, petrochemicals, and impact on the environment. The majority of wastewater treatment plants currently employ methods to remove nitrogen which are energy intensive and have no additional benefits besides complying with effluent concentration limits. Instead, recovering nitrogen allows simultaneous treatment of wastewater while collecting a concentrated ammonia product, creating a circular economy solution. This review acts to compile current research regarding nitrogen recovery and compare different techniques' recovery efficiencies and energy requirements. One outcome of this review is that more than one third of the techniques reviewed had little comments around the energy question, and thus more research needs to take place as these recovery systems continue to evolve towards full scale implementation. Additionally, a basic economic analysis was completed to demonstrate potential investment opportunities to implement these technologies. From this investigation, gas permeable membrane technology has the potential to recover ammonia from wastewater using little energy and may provide a small income with the sale of the product. Other techniques such as vacuum membrane distillation with acid absorption need further validation to determine the energy costs, as the amount of heat recycling has a great impact on the overall energy and economic balances. Finally, a discussion of the misalignment of products from recovery techniques and fertilizers in use today highlights the lack of communication and information sharing between the research community and the end users.
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