Browsing by Author "Li, Shuangqi"

Now showing 1 - 7 of 7
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    Ageing mitigation for battery energy storage system in electric vehicles
    (IEEE, 2022-09-27) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Li, Jianwei; Huo, Da; Cheng, Shuang
    Battery energy storage systems (BESS) have been extensively investigated to improve the efficiency, economy, and stability of modern power systems and electric vehicles (EVs). However, it is still challenging to widely deploy BESS in commercial and industrial applications due to the concerns of battery aging. This paper proposes an integrated battery life loss modeling and anti-aging energy management (IBLEM) method for improving the total economy of BESS in EVs. The quantification of BESS aging cost is realized by a multifactorial battery life loss quantification model established by capturing aging characteristics from cell acceleration aging tests.Meanwhile, a charging event analysis method is proposed to deploy the built life loss model in vehicle BESS management. Two BESS active anti-aging vehicle energy management models: vehicle to grid (V2G) scheduling and plug-in hybrid electric vehicle (PHEV) power distribution, are further designed, where the battery life loss quantification model is used to generate the aging cost feedback signals. The performance of the developed method is validated on a V2G peak-shaving simulation system and a hybrid electric vehicle. The work in this paper presents a practical solution to quantify and mitigate battery aging costs by optimizing energy management strategies and thus can further promote transportation electrification.
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    Linearizing battery degradation for health-aware vehicle energy management
    (IEEE, 2023-09) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Huo, Da; Li, Jianwei; Cheng, Shuang
    The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.
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    Online battery-protective vehicle to grid behavior management
    (Elsevier, 2022-01-03) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Huo, Da; Zeng, Xianwu; Pei, Xiaoze; Cheng, Shuang; Li, Jianwei
    With the popularization of electric vehicles, vehicle-to-grid (V2G) has become an indispensable technology to improve grid economy and reliability. However, battery aging should be mitigated while providing V2G services so as to protect customer benefits and mobilize their positivity. Conventional battery anti-aging V2G scheduling methods mainly offline operates and can hardly be deployed online in hardware equipment. This paper proposes a novel online battery anti-aging V2G scheduling method based on a novel two-stage parameter calibration framework. In the first stage, the V2G scheduling is modeled as an optimization problem, where the objective is to reduce grid peak-valley difference and mitigate battery aging. The online deployment of the developed optimization-based V2G scheduling is realized by a rule-based V2G coordinator in the second stage, and a novel parameter calibration method is developed to adjust controller hyper-parameters. With the parameter calibration process, the global optimality and real-time performance of V2G strategies can be simultaneously realized. The effectiveness of the proposed methodologies is verified on a practical UK distribution network. Simulation results indicate that it can effectively mitigate battery aging in providing V2G services while guaranteeing algorithm real-time performance.
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    Reactive power optimization in integrated electricity and gas systems
    (IEEE, 2020-05-28) Zhao, Pengfei; Gu, Chenghong; Xiang, Yue; Zhang, Xin; Shen, Yichen; Li, Shuangqi
    Volt/VAR optimization (VVO) is one important operation in distribution systems to maintain acceptable voltage profiles. However, the high penetration of renewable generation poses severe challenges to VVO, leading to voltage deviation and fluctuation. This is further complicated by the growing coupling between the electricity and natural gas systems. To resolve the unacceptable voltage deviation under energy system interdependency, this article proposes a cooptimization of VVO for an integrated electricity and gas system (IEGS) with uncertain renewable generation. A two-stage data-driven distributionally robust optimization is developed to model the coordinated optimization problem, which determines the two-stage VVO and operation schemes with dispatch and corrective adjustment through active power regulation and reactive power support in both day-ahead and real-time stage. A semidefinite programming is reformulated to ensure the tractability and the proposed problem is solved by a constraint generation framework. Simulation studies are conducted on a 33-bus-6-node IEGS. Case studies demonstrate that the interdependency between electricity and gas systems reduces the significant operation cost and voltage rise. It, thus, can benefit integrated system operators with a powerful operation tool to manage the systems with fewer costs but integrate more renewable energy while maintaining the high supply quality.
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    Socially governed energy hub trading enabled by blockchain-based transactions
    (IEEE, 2023-09-05) Zhao, Pengfei; Li, Shuangqi; Cao, Zhidong; Hu, Paul Jen-Hwa; Gu, Chenghong; Yan, Xiaohe; Huo, Da; Luo, Tianyi; Wang, Zikang
    Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers’ decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchain-based trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs.
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    Two-stage co-optimization for utility-social systems with social-aware P2P trading
    (IEEE, 2022-08-30) Zhao, Pengfei; Li, Shuangqi; Hu, Paul Jen-Hwa; Cao, Zhidong; Gu, Chenghong; Yan, Xiaohe; Huo, Da; Hernando-Gil, Ignacio
    Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model’s effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.
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    Unmanned aerial vehicles versus smart grids
    (Institution of Engineering and Technology (IET), 2025-01) Pengfei Zhao, Alexis; Li, Shuangqi; Huo, Da; Alhazmi, Mohannad
    The increasing threat of unmanned aerial vehicles (UAVs) to smart grid infrastructures poses critical challenges to energy systems security. This study examines smart grid vulnerabilities to UAV‐based attacks and proposes a novel optimisation framework to enhance grid resilience. Employing a multi‐objective optimisation approach using the Non‐dominated Sorting Genetic Algorithm III (NSGA‐III) and a game‐theoretic Stackelberg model, the research captures the strategic interplay between UAV operators and grid defenders. Key contributions include the development of a multi‐objective optimisation framework, integration of adversarial game theory, incorporation of dynamic environmental conditions, and generation of Pareto‐optimal solutions for strategic defence planning. This research makes four pivotal contributions: (a) the design of a comprehensive multi‐objective optimisation framework tailored for UAV strike optimisation, (b) the integration of game‐theoretic principles to model adversarial behaviours, (c) the inclusion of dynamic environmental factors to improve solution robustness, and (d) the application of NSGA‐III to generate trade‐off solutions, equipping decision‐makers with diverse strategies to enhance grid resilience. By addressing an urgent and timely challenge, this work offers practical guidance for fortifying smart grid infrastructures against emerging UAV threats in increasingly complex operational environments.