| 1. |
- Lin, H., et al.
(författare)
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Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model
- 2019
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 188
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Tidskriftsartikel (refereegranskat)abstract
- This paper developed an agent-based trip chain model (ABTCM) to study the distribution of electric vehicles (EVs) charging demand and its dynamic characteristics, including flexibility and uncertainty, at different types of location. Key parameters affecting charging demand include charging strategies, i.e. uncontrolled charging (UC) and off-peak charging (OPC), and EV supply equipment, including three levels of charging equipment. The results indicate that the distributions of charging demand are similar as the travel patterns, featured by traffic flow at each location. A discrete peak effect was found in revealing the relation between traffic flow and charging demand, and it results in the smallest equivalent daily charging demand and peak load at public locations. EV charging and vehicle-to-grid (V2G) flexibility were examined by instantaneous adjustable power and accumulative adjustable amount of electricity. The EVs at home locations have the largest charging and V2G flexibility under the UC strategy, except for a period of regular working time. The V2G flexibility at work and public locations is generally larger than charging flexibility. Due to the fast charging application, the uncertainties of charging demand at public locations are the highest in all locations. In addition, the OPC strategy mitigates the uncertainty of charging demand.
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| 2. |
- Lin, H., et al.
(författare)
-
Optimal planning of intra-city public charging stations
- 2022
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 238
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Tidskriftsartikel (refereegranskat)abstract
- Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.
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| 3. |
- Fu, Y., et al.
(författare)
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Effects of uncertainties on the capacity and operation of an integrated energy system
- 2021
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Ingår i: Sustainable Energy Technologies and Assessments. - : Elsevier Ltd. - 2213-1388 .- 2213-1396. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- Uncertainty is a common and critical problem for planning the capacity and operation of integrated energy systems (IESs). This study evaluates the effects of uncertainties on the capacity and operation of an IES. To this aim, system planning and operation with uncertainties are optimized by a two-stage stochastic programming model and compared with a referencing deterministic case. Specifically, the uncertainties of photovoltaic (PV) generation and energy demand are investigated. Regarding system capacity, a larger energy storage capacity is needed to accommodate a higher uncertainty. The superimposed uncertainties have a higher effect on system capacity than the sum of the effect of each uncertainty. The uncertainty of energy demand has a higher impact than the uncertainty of PV generation. Regarding system operation, the increase in operation cost is smaller than the increase in investment cost and total cost. In addition, the average flexibility provided by the energy storage increases with uncertainty and uncertainties affect the change rate for power charging/discharging of the electric energy storage. Regarding the effect on the grid, the uncertainties increase not only the magnitude of ramping-rate, but also the frequency of power-dispatch.
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| 4. |
- Lin, H., et al.
(författare)
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The impact of electric vehicle penetration and charging patterns on the management of energy hub : A multi-agent system simulation
- 2018
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Ingår i: Applied Energy. - : Elsevier Ltd. - 0306-2619 .- 1872-9118. ; 230, s. 189-206
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a multi-agent system (MAS) was developed to simulate the operation of an energy hub (EH) with different penetration rates (PRs) and various charging patterns of electric vehicle (EV). Three charging patterns, namely uncontrolled charging pattern (UCP), rapid charging pattern (RCP) and smart charging pattern (SCP), together with vehicle to grid (V2G), were simulated in the MAS. The EV penetration rates (EV-PRs), from 10% to 90% with a step of 20%, are considered in this study. Under the UCP, the peak load increases by 3.4–17.1% compared to the case without EVs, which is the reference case in this study. A main part of the increased electricity demand can be supplied by the gas turbine (GT) when the PR is lower, i.e. 71.7% under 10% PR and 37.4% under 50% PR. Under the SCP, the charging load of EVs is shifted to the valley period and thus the energy dispatch of the EH at 07:00–23:00 remain the same as that in the reference case. When V2G is considered, the electricity demand from the grid becomes the largest in all of the cases, e.g. the demand with 50% PR doubles the electricity demand in the reference case. However, the GT output decreases by 2.9–15.7% at 07:00–23:00 due to the effect of V2G. The variations in the EH's operation further raise the changes in energy cost, i.e. the electricity and cooling prices are lowered by 18.3% and 33.8% due to the availability of V2G and the heating and cooling prices increase by 3.5% and 4.3% under the UCP with the PR of 50%. Regarding the V2G capacity, near 39% of the EVs’ battery capacity can be discharged via V2G. In addition, the paper also produced a V2G potential line, which is an effective tool to provide the maximum potential of the EVs for peak shaving at any specific time.
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| 5. |
- Zhang, L., et al.
(författare)
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A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions
- 2023
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 119
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Tidskriftsartikel (refereegranskat)abstract
- Fault diagnosis of wind turbine gearboxes is crucial in ensuring wind farms’ reliability and safety. However, nonstationary working conditions, such as load change or speed regulation, may result in an accuracy deterioration of many existing fault diagnosis approaches. To overcome the issue, this research proposes a nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes using vibration signals. Concretely, we adopt Empirical Mode Decomposition (EMD) to decompose vibration signals into a series of Intrinsic Mode Functions (IMFs). Then, the multi-channel IMFs are fed into a 1D Convolutional Neural Network (CNN) for automatic feature learning and fault classification. Since EMD is a signal processing technique requiring no prior knowledge, the model architecture can be viewed as nearly end-to-end. The proposed approach was validated in a real-world dataset; it proved deep learning models have an overwhelming advantage in representation capacity over traditional shallow models. It also demonstrated that the introduction of EMD as a preprocessing step improves both the training efficiency and the generalization ability of a deep model, thus leading to a better fault diagnosis efficacy under variable working conditions.
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