| 1. |
- Bian, Guodong, et al.
(författare)
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Detection and attribution of flood responses to precipitation change and urbanization : A case study in Qinhuai River Basin, Southeast China
- 2020
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Ingår i: Hydrology Research. - : IWA Publishing. - 1998-9563 .- 0029-1277 .- 2224-7955. ; 51:2, s. 351-365
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Tidskriftsartikel (refereegranskat)abstract
- Both flood magnitude and frequency might change under the changing environment. In this study, a procedure combining statistical methods, flood frequency analysis and attribution analysis was proposed to investigate the response of floods to urbanization and precipitation change in the Qinhuai River Basin, an urbanized basin located in Southeast China, over the period from 1986 to 2013. The Mann–Kendall test was employed to detect the gradual trend of the annual maximum streamflow and the peaks over threshold series. The frequency analysis was applied to estimate the changes in the magnitude and frequency of floods between the baseline period (1986–2001) and urbanization period (2002–2013). An attribution analysis was proposed to separate the effects of precipitation change and urbanization on flood sizes between the two periods. Results showed that: (1) there are significant increasing trends in medium and small flood series according to the Mann–Kendall test; (2) the mean and threshold values of flood series in the urbanization period were larger than those in the baseline period, while the standard deviation, coefficient of variation and coefficient of skewness of flood series were both higher during the baseline period than those during the urbanization period; (3) the flood magnitude was higher during the urbanization period than that during the baseline period at the same return period. The relative changes in magnitude were larger for small floods than for big floods from the baseline period to the urbanization period; (4) the contributions of urbanization on floods appeared to amplify with the decreasing return period, while the effects of precipitation diminish. The procedure presented in this study could be useful to detect the changes of floods in the changing environment and conduct the attribution analysis of flood series. The findings of this study are beneficial to further understanding interactions between flood behavior and the drivers, thereby improving flood management in urbanized basins.
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| 2. |
- Guo, Guodong, et al.
(författare)
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Safe multi-agent deep reinforcement learning for real-time decentralized control of inverter based renewable energy resources considering communication delay
- 2023
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 349
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Tidskriftsartikel (refereegranskat)abstract
- The increasing penetration of distributed renewable energy resources brings a great challenge for real-time voltage security of distribution grids. The paper proposes a safe multi-agent deep reinforcement learning (MADRL) algorithm for real-time control of inverter-based Volt-Var control (VVC) in distribution grids considering communication delay to minimize the network power loss, while maintaining the nodal voltages in a safe range. The multi-agent VVC is modeled as a constrained Markov game, which is solved by the MADRL algorithm. In the training stage, the safety projection is added to the combined policy to analytically solve an action correction formulation to promote more efficient and safe exploration. In the real-time decision-making stage, a state synchronization block is designed to impute the data under the latest timestamp as the input of the agents deployed in a distributed manner, to avoid instability caused by communication delay. The simulation results show that the proposed algorithm performs well in safe exploration, and also achieves better performance under communication delay.
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| 3. |
- Jiang, Tao, et al.
(författare)
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Guest Editorial : Special issue on flexible and resilient urban energy systems
- 2023
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Ingår i: International Journal of Electrical Power & Energy Systems. - : Elsevier BV. - 0142-0615 .- 1879-3517. ; 154, s. 109439-
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Forskningsöversikt (refereegranskat)abstract
- This guest editorial summarizes the topics and the papers selected for the Special Issue on Flexible and Resilient Urban Energy Systems. After rigorous reviewing process, 21 papers are accepted for publication. These 21 accepted papers cover various aspects of urban energy systems and are distributed as following: situational awareness of urban energy systems (2 papers), quantification metrics of flexibility and resilience of urban energy systems (3 papers), vulnerability modeling of urban energy systems under various extreme events (3 papers), planning of flexible and resilient urban energy systems (4 papers), robust and resilient operation and control of urban energy systems (4 papers), recovery and restoration strategy of urban energy systems (2 papers), and coordination and interoperability of interconnected energy systems (3 papers). The Guest Editorial Board hopes this Special Issue can provide a valuable information for future research and advancements in the field of flexible and resilient urban energy systems.
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| 4. |
- Li, Hansen, et al.
(författare)
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Chan-Chuang and resistance exercise for drug rehabilitation : a randomized controlled trial among Chinese male methamphetamine users
- 2023
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Ingår i: Frontiers In Public Health. - : World Academic Publishing Co., Limited. - 2296-2565. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To examine the health benefits of Chan-Chuang and resistance exercise.METHODS: We deployed an 8-week randomized controlled trial, in which 76 male methamphetamine users were allocated to control (n = 25), Chan-Chuang (n = 26), and residence exercise groups (n = 25). Our primary outcomes were drug craving, mental wellbeing, sleep quality, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP). Our secondary outcomes were body mass index (BMI), vital capacity, grip strength, balance, and vertical jump.RESULTS: Chan-Chuang exercise resulted in reduced HR, DBP, and MAP, along with improvements in vital capacity, grip strength, and balance compared to the control group. Resistance exercise reduced SBP and MAP, and also improved vital capacity, grip strength, balance, and vertical jump.CONCLUSION: These findings may support the role of Chan-Chuang and resistance exercise in maintaining the physical fitness of methamphetamine users at mandatory detention centers.
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| 5. |
- Xu, Guodong, et al.
(författare)
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Reducing Energy Loss and Morphology Optimization Manipulated by Molecular Geometry Engineering for Hetero-junction Organic Solar Cells
- 2020
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Ingår i: Chinese journal of chemistry. - : WILEY-V C H VERLAG GMBH. - 1001-604X .- 1614-7065. ; 38:12, s. 1553-1559
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Tidskriftsartikel (refereegranskat)abstract
- A Summary of main observation and conclusion Molecular geometry engineering is an effective strategy to control the micromorphology and molecular energy level in organic photovoltaics (OPVs). Two novel copolymers based on alkylsilyl- and chloride-functionalized benzodithiophene (BDT) were designed and synthesized for wide bandgap copolymer donor materials in OPVs. It was found that the two copolymers exhibited distinctly different properties in active layer when blended with fullerene-free acceptor IT-4F. The chloride-functionalized copolymer PBDTCl-TZ with deeper molecular energy level and better coplanar structure induced more ordered aggregation in blend film. Thus, the device based on PBDTCl-TZ exhibits better energy alignment with IT-4F and smaller radiative recombination. Furthermore, the non-radiative recombination of PBDTCl-TZ:IT-4F based device is about 45 mV lower than the PBDTSi-TZ:IT-4F based device, contributing to a lower energy loss (E-loss), and a higher open-circut voltage (V-OC). As a result, the devices based on the blend of PBDTCl-TZ:IT-4F exhibit a high power conversion efficiency (PCE) of up to 12.2% with a highV(OC)of 0.837 V, higher than that of PBDTSi-TZ:IT-4F, of which the PCE is 11.2% with a V-OC of 0.781 V.
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| 6. |
- Zhang, Mengfan, et al.
(författare)
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Data Driven Decentralized Control of Inverter based Renewable Energy Sources using Safe Guaranteed Multi-Agent Deep Reinforcement Learning
- 2024
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Ingår i: IEEE Transactions on Sustainable Energy. - 1949-3029 .- 1949-3037. ; 15:2, s. 1288-1299
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Tidskriftsartikel (refereegranskat)abstract
- The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposedalgorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications
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| 7. |
- Zhang, Mengfan, et al.
(författare)
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Data Driven Decentralized Control of Inverter Based Renewable Energy Sources Using Safe Guaranteed Multi-Agent Deep Reinforcement Learning
- 2024
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Ingår i: IEEE Transactions on Sustainable Energy. - : Institute of Electrical and Electronics Engineers (IEEE). - 1949-3029 .- 1949-3037. ; 15:2, s. 1288-1299
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Tidskriftsartikel (refereegranskat)abstract
- The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposed algorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications.
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| 8. |
- Zhang, Mengfan, et al.
(författare)
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DNN Assisted Projection based Deep Reinforcement Learning for Safe Control of Distribution Grids
- 2024
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Ingår i: IEEE Transactions on Power Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8950 .- 1558-0679. ; 39:4, s. 5687-5698
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Tidskriftsartikel (refereegranskat)abstract
- Deep reinforcement learning (DRL) is a promising solution for voltage control of distribution grids with high penetration of inverter-based renewable energy sources (RESs). Yet, when adopting the DRL-based control method, the safe and optimal operation of the system cannot be guaranteed at the same time, as the conventional DRL agent is not designed to solve the hard constraint problem. To address this challenge, this paper proposes a deep neural network (DNN) assisted projection based DRL method for safe control of distribution grids. First, a finite iteration projection algorithm is proposed to guarantee hard constraints by converting a non-convex optimization problem into a finite iteration problem. Next, a DNN assisted projection method is proposed to accelerate the calculation of projection and achieve the practical implementation of hard constraints in DRL problem. Finally, a DNN Projection embedded twin-delayed deep deterministic policy gradient (DPe-TD3) method is proposed to achieve optimal operation of distribution grids with guaranteed 100% safety of the distribution grid. The safety of the DRL training is guaranteed via the embedded Projection DNN in TD3 with participation in gradient return process, which could smoothly and effectively project the DRL agent actions into the feasible area, thus guaranteeing the safety of data driven control and the optimal operation at the same time. The case studies and comparisons are conducted in the IEEE 33 bus system to show the effectiveness of the proposed method.
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| 9. |
- Zhang, Mengfan, et al.
(författare)
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Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System
- 2022
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Ingår i: 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728193885
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Konferensbidrag (refereegranskat)abstract
- With the large integration of renewables, the traditional power system becomes more sustainable and effective. Yet, the fluctuation and uncertainties of renewables have led to large challenges to the voltage stability in distribution power systems. This paper proposes a multi-agent deep reinforcement learning method to address the issue. The voltage control issue of the distribution system is modeled as the Markov Decision Process, while each grid-connected interface inverter of renewables is modeled as a deep neural network (DNN) based agent. With the designed reward function, the agents will interact with and seek for the optimal coordinated voltage-var control strategy. The offline-trained agents will execute online in a decentralized way to guarantee the voltage stability of the distribution without any extra communication. The proposed method can effectively achieve a communication-free and accurate voltage-var control of the distribution system under the uncertainties of renewables. The case study based on IEEE 33-bus system is demonstrated to validate the method.
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