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Tao Qian
Xi'an China 710049

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Journal article
Published: 13 October 2020 in IEEE Transactions on Sustainable Energy
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To overcome the topographic limitations of pumped hydro storage (PHS) system, novel gravity energy storage (GES) technologies are developing. In this paper, a pioneering work on the modeling and scheduling of a GES system is conducted. The GES system stores and releases energy by lifting and lowering concrete bricks. Since the working principle of the GES system is similar to that of a PHS plant, a double-tower based model of the GES system is established. In addition, a day-ahead scheduling model of the GES system is proposed to test the operation of such system. Specifically, the uncertainty of market price is considered with a data-driven distributionally robust optimization method. Some linearization and reformulation techniques are presented to transform the problem into a mixed integer linear programming (MILP) problem which can be solved easily. Numerical experiments indicate that the proposed model can effectively characterize the operation of the GES system. Concrete bricks are lifted up during low-price period and descend under gravity during high-price period to gain benefits. Additionally, the round trip efficiency considerably influences the total profits.

ACS Style

Xiong Wu; Nailiang Li; Xiuli Wang; Yi Kuang; Wencheng Zhao; Tao Qian; Hongyang Zhao; Jiatao Hu. Day-Ahead Scheduling of a Gravity Energy Storage System Considering the Uncertainty. IEEE Transactions on Sustainable Energy 2020, 12, 1020 -1031.

AMA Style

Xiong Wu, Nailiang Li, Xiuli Wang, Yi Kuang, Wencheng Zhao, Tao Qian, Hongyang Zhao, Jiatao Hu. Day-Ahead Scheduling of a Gravity Energy Storage System Considering the Uncertainty. IEEE Transactions on Sustainable Energy. 2020; 12 (2):1020-1031.

Chicago/Turabian Style

Xiong Wu; Nailiang Li; Xiuli Wang; Yi Kuang; Wencheng Zhao; Tao Qian; Hongyang Zhao; Jiatao Hu. 2020. "Day-Ahead Scheduling of a Gravity Energy Storage System Considering the Uncertainty." IEEE Transactions on Sustainable Energy 12, no. 2: 1020-1031.

Journal article
Published: 31 August 2020 in IEEE Transactions on Intelligent Transportation Systems
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The extreme fast charging (XFC) technology helps to reduce refueling time, alleviate mile anxiety, extend driving range and finally promote the popularity of electric vehicles (EVs). However, it would also pose great challenges on the power grid infrastructure especially distribution networks, due to the large-scale and intermittent power demand. This paper proposes a coordinated planning method for power distribution networks and XFC EV charging stations, with the on-site batteries considered. Firstly, considering the traffic flow pattern, the operation of XFC stations is analyzed on both energy and power demand. Secondly, the coordinated planning model is developed to satisfy the time-varying XFC load, with both transportation and electricity constraints considered. In addition, the on-site batteries are introduced to flatten the XFC energy used and supplement its power supply. The case studies have verified the effectiveness of the proposed method. The influence of XFC on the distribution networks and the effects of the on-site storage are also studied.

ACS Style

Chengcheng Shao; Tao Qian; Yanan Wang; Xifan Wang. Coordinated Planning of Extreme Fast Charging Stations and Power Distribution Networks Considering On-Site Storage. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 493 -504.

AMA Style

Chengcheng Shao, Tao Qian, Yanan Wang, Xifan Wang. Coordinated Planning of Extreme Fast Charging Stations and Power Distribution Networks Considering On-Site Storage. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (1):493-504.

Chicago/Turabian Style

Chengcheng Shao; Tao Qian; Yanan Wang; Xifan Wang. 2020. "Coordinated Planning of Extreme Fast Charging Stations and Power Distribution Networks Considering On-Site Storage." IEEE Transactions on Intelligent Transportation Systems 22, no. 1: 493-504.

Journal article
Published: 28 January 2020 in IEEE Transactions on Smart Grid
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Electric power and transportation networks become increasingly coupled through electric vehicles (EV) charging station (EVCS) as the penetration of EVs continues to grow. In this paper, we propose a holistic framework to enhance the operation of coordinated electric power distribution network (PDN) and urban transportation network (UTN) via EV charging services. Under this framework, a bi-level model is formulated to optimally determine EVCS charging service fees (CSF) for guiding EV charging behaviors and minimizing the total social cost. At the upper level, PDN with wind power generation is formulated as a second-order cone problem (SOCP) where CSF is determined. Given the settings calculated at the upper level, the lower level problem is described as a traffic assignment problem (TAP) which is subject to the user equilibrium (UE) principle and captures the individual rationality of single EV owners in UTN. The uncertainties in wind power output and origin-destination (O-D) traffic demands are considered in the proposed model and a deep reinforcement learning (DRL)-based solution framework is developed to decouple and approximately solve the stochastic bi-level problem. Both gradient-based and gradient-free training algorithms are implemented in this paper and the respective results are compared. The case studies on a 5-node system, 24-node Sioux-Falls system and real-world Xi’an city in China are conducted to verify the effectiveness of the proposed model, which demonstrates the enhanced operation of coordinated PDN and UTN networks by reducing the traffic congestion and improving the integration of renewable energy.

ACS Style

Tao Qian; Chengcheng Shao; Xuliang Li; Xiuli Wang; Mohammad Shahidehpour. Enhanced Coordinated Operations of Electric Power and Transportation Networks via EV Charging Services. IEEE Transactions on Smart Grid 2020, 11, 3019 -3030.

AMA Style

Tao Qian, Chengcheng Shao, Xuliang Li, Xiuli Wang, Mohammad Shahidehpour. Enhanced Coordinated Operations of Electric Power and Transportation Networks via EV Charging Services. IEEE Transactions on Smart Grid. 2020; 11 (4):3019-3030.

Chicago/Turabian Style

Tao Qian; Chengcheng Shao; Xuliang Li; Xiuli Wang; Mohammad Shahidehpour. 2020. "Enhanced Coordinated Operations of Electric Power and Transportation Networks via EV Charging Services." IEEE Transactions on Smart Grid 11, no. 4: 3019-3030.

Journal article
Published: 20 September 2019 in IEEE Transactions on Smart Grid
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A coordinated operation of smart grid (SG) and intelligent transportation system (ITS) provides electric vehicle (EV) owners with a myriad of power and transportation network data for EV charging navigation. However, the optimal charging navigation would be a challenging task owing to the randomness of traffic conditions, charging prices and waiting time at EV charging station (EVCS). In this paper, we propose a deep reinforcement learning (DRL)-based EV charging navigation, aiming at minimizing the total travel time and the charging cost at EVCS. First, we utilize the deterministic shortest charging route model (DSCRM) to extract feature states out of collected stochastic data and then formulate EV charging navigation as a Markov Decision Process (MDP) with an unknown transition probability. The proposed DRL-based approach will approximate the solution, which can adaptively learn the optimal strategy without any prior knowledge of uncertainties. Case studies are carried out within a practical zone in Xi’an city, China. Numerous experimental results verity the effectiveness of the proposed approach and illustrate its adaptation to EV driver preferences. The coordination effect of SG and ITS on reducing the waiting time and the charging cost in EV charging navigations is also analyzed.

ACS Style

Tao Qian; Chengcheng Shao; Xiuli Wang; Mohammad Shahidehpour. Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System. IEEE Transactions on Smart Grid 2019, 11, 1714 -1723.

AMA Style

Tao Qian, Chengcheng Shao, Xiuli Wang, Mohammad Shahidehpour. Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System. IEEE Transactions on Smart Grid. 2019; 11 (2):1714-1723.

Chicago/Turabian Style

Tao Qian; Chengcheng Shao; Xiuli Wang; Mohammad Shahidehpour. 2019. "Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System." IEEE Transactions on Smart Grid 11, no. 2: 1714-1723.

Journal article
Published: 26 July 2019 in Sustainability
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The requirement for energy sustainability drives the development of integrated energy distribution systems (IEDSs). In this paper, considering the coordination of district multi-energy systems (DMESs), a hierarchical management strategy is proposed to enhance IEDS resilience. The proposed strategy is divided into three modes: the normal operation mode, the preventive operation mode and the resilient operation mode. In the normal operation mode, the objective of DEMSs is to minimize the operation costs. In the preventive operation mode, the objective of DEMSs is to maximize the stored energy for mitigating outage. The resilient operation mode consists of two stages. DMESs schedule their available resources and broadcast excess generation capacities or unserved loads to neighboring DMESs through the cyber communication network in the first stage. In the second stage, DMESs interchange electricity and natural gas with each other through the physical common bus for global optimization. A consensus algorithm was applied to determine the allocated proportions of exported or imported electricity and natural gas for each DMES in a distributed way. An IEDS including five DMESs was used as a test system. The results of the case studies demonstrate the effectiveness of the proposed hierarchical management strategy and algorithm.

ACS Style

Shixiong Qi; Xiuli Wang; Xue Li; Tao Qian; Qiwen Zhang. Enhancing Integrated Energy Distribution System Resilience through a Hierarchical Management Strategy in District Multi-Energy Systems. Sustainability 2019, 11, 4048 .

AMA Style

Shixiong Qi, Xiuli Wang, Xue Li, Tao Qian, Qiwen Zhang. Enhancing Integrated Energy Distribution System Resilience through a Hierarchical Management Strategy in District Multi-Energy Systems. Sustainability. 2019; 11 (15):4048.

Chicago/Turabian Style

Shixiong Qi; Xiuli Wang; Xue Li; Tao Qian; Qiwen Zhang. 2019. "Enhancing Integrated Energy Distribution System Resilience through a Hierarchical Management Strategy in District Multi-Energy Systems." Sustainability 11, no. 15: 4048.

Journal article
Published: 24 October 2018 in Sustainability
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The requirement for energy sustainability drives the development of renewable energy technologies and gas-fired power generation. The increasing installation of gas-fired units significantly intensifies the interdependency between the electricity system and natural gas system. The joint scheduling of electricity and natural gas systems has become an attractive option for improving energy efficiency. This paper proposes a robust day-ahead scheduling model for electricity and natural gas system, which minimizes the total cost including fuel cost, spinning reserve cost and cost of operational risk while ensuring the feasibility for all scenarios within the uncertainty set. Different from the conventional robust optimization with predefined uncertainty set, a new approach with risk-averse adjustable uncertainty set is proposed in this paper to mitigate the conservatism. Furthermore, the Wasserstein–Moment metric is applied to construct ambiguity sets for computing operational risk. The proposed scheduling model is solved by the column-and-constraint generation method. The effectiveness of the proposed approach is tested on a 6-bus test system and a 118-bus system.

ACS Style

Li Yao; Xiuli Wang; Tao Qian; Shixiong Qi; Chengzhi Zhu. Robust Day-Ahead Scheduling of Electricity and Natural Gas Systems via a Risk-Averse Adjustable Uncertainty Set Approach. Sustainability 2018, 10, 3848 .

AMA Style

Li Yao, Xiuli Wang, Tao Qian, Shixiong Qi, Chengzhi Zhu. Robust Day-Ahead Scheduling of Electricity and Natural Gas Systems via a Risk-Averse Adjustable Uncertainty Set Approach. Sustainability. 2018; 10 (11):3848.

Chicago/Turabian Style

Li Yao; Xiuli Wang; Tao Qian; Shixiong Qi; Chengzhi Zhu. 2018. "Robust Day-Ahead Scheduling of Electricity and Natural Gas Systems via a Risk-Averse Adjustable Uncertainty Set Approach." Sustainability 10, no. 11: 3848.