| 1. |
- 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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| 2. |
- Liu, L., et al.
(författare)
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Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model
- 2022
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 247
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Tidskriftsartikel (refereegranskat)abstract
- Extreme electricity prices occur with a higher frequency and a larger magnitude in recent years. Accurate forecasting of the occurrence of extreme prices is of great concern to market operators and participants. This paper aims to forecast the occurrence probability of day-ahead extremely low and high electricity prices and investigate the relative importance of different influencing variables. The data obtained from the Australian National Electricity Market (NEM) were employed, including historical prices (one day before and one week before), reserve capacity, load demand, variable renewable energy (VRE) proportion and interconnector flow. A Multivariate Logistic Regression (MLgR) model was proposed, which showed good forecasting capability in terms of model fitness and classification accuracy with different thresholds of extreme prices. In addition, the performance of the MLgR model was verified by comparing with two other models, i.e., Multi-Layer Perceptron (MLP) and Radical Basis Function (RBF) neural network. Relative importance analysis was performed to quantify of the contribution of the variables. The proposed method enriches the theories of electricity price forecast and advances the understanding of the dynamics of extreme prices. By applying the model in practice, it will contribute to promoting the management of operation and establishment of a robust energy market.
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| 3. |
- Adhikari, Subash, et al.
(författare)
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A high-stringency blueprint of the human proteome
- 2020
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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Forskningsöversikt (refereegranskat)abstract
- The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.
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| 4. |
- 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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| 5. |
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| 6. |
- Huang, Q., et al.
(författare)
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Assessment of the arbitrage by a compressed CO2 energy storage system-based on dynamic characteristics
- 2024
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Ingår i: Journal of Energy Storage. - : Elsevier Ltd. - 2352-152X .- 2352-1538. ; 95
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Tidskriftsartikel (refereegranskat)abstract
- Fluctuations in electricity price create arbitrage opportunities for compressed CO2 energy storage (CCES) systems. However, previous studies often neglected the dynamic characteristics of CCES systems, leading to inaccurate assessments. This paper addresses this gap by evaluating the CCES system arbitrage considering its dynamic characteristics. We introduce a novel indicator, state of charge (SOC), into a mixed-integer linear programming (MILP) optimization model to capture the dynamics. Utilizing real electricity prices, the model optimizes the CCES operation strategy for a maximum profit. The results demonstrate that a CCES system with a 267 MWh capacity could achieve a total income of 22.5 MEUR in 2022, with a net present value (NPV) of 258.1 MEUR over 35 years, a payback time of 2 years, and an average round-trip efficiency (ARTE) of 77.0 %. Sensitivity analysis reveals that the sizes of the compressor, the expander, and the high-pressure gas tank significantly impact the arbitrage potential. In contrast, the steady-state model-based results demonstrate that the CCES system could yield a higher NPV of 573.7 MEUR, a shorter payback time of 1 year, and a higher ARTE of 87.0 %. This emphasizes the pivotal importance of integrating dynamic characteristics into the design and assessment of CCES systems for arbitrage assessment.
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| 7. |
- 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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| 8. |
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| 9. |
- Liu, L., et al.
(författare)
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Evaluating the benefits of Integrating Floating Photovoltaic and Pumped Storage Power System
- 2019
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Ingår i: Energy Conversion and Management. - : Elsevier Ltd. - 0196-8904 .- 1879-2227. ; 194, s. 173-185
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Tidskriftsartikel (refereegranskat)abstract
- Floating Photovoltaic systems have developed very fast in recent years. Compared to individual Floating Photovoltaic systems, further advantages, such as grid connectivity and energy storage, can be obtained when Floating Photovoltaic operates collaboratively with Pumped Storage Power Systems. This paper proposed an Integrated Floating Photovoltaic-Pumped Storage Power System and quantitatively assessed the potential of the integrated system in electricity generation and conservation of water and land resource. The study developed a coordinated operation model for the Integrated Floating Photovoltaic-Pumped Storage Power System, which employed a dual-objective optimization, namely to maximize the benefits of electricity generation and to minimize the energy imbalance at the same time. The dual-objective optimization was solved using the genetic algorithm method. Other benefits of the Integrated Floating Photovoltaic-Pumped Storage Power System, namely conservation of water and land resource, were also assessed. The proposed methodology was applied to a 2 GW Floating Photovoltaic farm and a 1 GW Pumped Storage Power System. Results indicated that the Integrated Floating Photovoltaic-Pumped Storage Power System has a great potential for gaining the benefits of electricity generation (9112.74 MWh in a typical sunny day averagely) and reducing energy imbalance (23.06 MW aggregately in one day). The coordinated operation provides the possibility to achieve a higher generation benefits without affecting the reliability of the grid, while the optimization method plays a key role of efficient coordination. In addition, the system would help to save 20.16 km 2 land and 19.06 million m 3 water a year due to the reduction in evaporation loss. The synthetic benefits greatly improve the economic and environmental feasibility of photovoltaic systems in reality.
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| 10. |
- Liu, L., et al.
(författare)
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Forecasting Power Output of Photovoltaic System Using A BP Network Method
- 2017
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Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; 142, s. 780-786
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Tidskriftsartikel (refereegranskat)abstract
- The characteristics of intermittent and stochastic of solar energy has brought great challenges to power grid system in terms of operation and regulation. Power forecasting is an important factor for optimal schedule of power grid system and assessing the working performance of PV systems. In order to forecast the power output of a PV system located in Ashland at 24-hour-ahead for higher efficiency, a back propagation (BP) neural network model is proposed. Before designing the model, correlation analysis is done to investigate the relationship between power output and solar irradiance and ambient temperature, which are key parameters affecting the power output of PV systems. Based on a correlation analysis, the model admitted the following input parameters: hourly solar radiation intensity, the highest, the lowest daily and the average daily temperature, and hourly power output of the PV system. The output of the model is the forecasted PV power output 24 hours ahead. Based on the datasets, the neural network is trained to improve its accuracy. The best performance is obtained with the BP neural network structure of 28-20-11. The analysis of the error indicator MAPE shows that the proposed model has great accuracy and efficiency for forecasting the power output of photovoltaic systems.
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