Power dynamic allocation strategy for urban rail hybrid energy storage system based on iterative learning control

Wang, Xin; Luo, Yingbing; Qin, Bin; Guo, Lingzhong

doi:10.1016/j.energy.2022.123263

Cited by 23 publications

(11 citation statements)

References 12 publications

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“…Based on the propulsion power models, various power management methods for land-based microgrids can be used for AESs, such as frequency decomposition control based on power feedforward control [22], rule-based voltage/power control [23], and adaptive power allocation based on state feedback [24]. Most of these methods use proportional integral (PI) control and filter-based methods as a baseline control, which can also be used for basic control of HESSs in AESs.…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic power management for all‐electric ships based on data‐driven propulsion power modelling

Luo

Fang

Niu

et al. 2023

IET Electric Power Appl

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Among all types of onboard load demands in all‐electric ships (AESs), the propulsion power predominates (usually >70%), and a large‐scale hybrid energy storage system (HESS) tends to be installed to provide multi‐timescale flexibility. A two‐part dynamic power management method is therefore proposed consisting of a novel multi‐scenario propulsion power model, which models the impacts of floating conditions separately from other uncertain factors, and a three‐layer dynamic allocation strategy based on feedforward control to coordinate the main/auxiliary generators and the HESS. Three case studies are adopted to verify the validity of the proposed method, including a real‐time simulation experiment in RT‐Lab. The proposed method has three advantages: (1) the proposed multi‐scenario propulsion power model accounts for power fluctuations brought by floating conditions, which greatly alleviates the required regulating flexibility for AESs; (2) the proposed three‐layer dynamic power allocation strategy better shares the power demand on the main/auxiliary generators and the HESS, which reduces battery power fluctuations and prevents the overdischarging/overcharging of HESS; and (3) the RT‐Lab experiment proves that the proposed method can be used in real‐time applications for AESs.

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Section: Literature Surveymentioning

confidence: 99%

Dynamic power management for all‐electric ships based on data‐driven propulsion power modelling

Luo

Fang

Niu

et al. 2023

IET Electric Power Appl

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“…Since propulsion power contributes to the largest part of onboard load demand, and the power mismatch between load and generation sides will cause bus voltage sags in AESs, which requires proper allocation strategies for HESS. Various power management methods for land‐based microgrids can be used for AESs as references, such as frequency decomposition control [22], rule‐based voltage/power control [23] and optimise‐based control [24]. However, two specific characteristics of AESs should be fully considered: (1) the coordinated control of large‐scale multiple power resources and (2) the mitigation of high‐frequency and low‐frequency power fluctuations in different timescales from propulsion load.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical robust shipboard hybrid energy storage sizing with three‐layer power allocation

Luo

Fang

Khan

et al. 2023

IET Electrical Syst in Trans

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Hybrid energy storage systems (HESSs) have gradually been viewed as essential energy/ power buffers to balance the generation and load sides of fully electrified ships. To resolve the balance issue of HESS under multiple power resources, that is, shipboard diesel generators and fuel cells (FCs), this study proposes a robust sizing method implemented with a power allocation strategy. The proposed method is hierarchically formulated as two sequential sub-problems: (1) a robust programming to determine the power/energy capacities of HESS under the maximal power demand scenario and (2) a control framework to fulfil the power allocation under multiple power resources. A ship case with one diesel engine and one FC is studied to show the validity of the proposed method. The simulation results show that the integration of HESS facilitates the power supply of critical propulsion loads. Compared with no HESS, HESS integration can reduce the deviation of direct current bus voltage sag by 56% and reduce the power fluctuations of the main engine and FC by 7.3% and 55.9%, respectively.

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“…Standby power supply is usually set in production and operation companies. Because there are many domestic communication base stations (BSs), the reserve storage is relatively abundant 4,5 . How to connect ES into the PS, alleviate the pressure of peak power consumption and reduce the system cost has great research significance.…”

Section: Introductionmentioning

confidence: 99%

“…Because there are many domestic communication base stations (BSs), the reserve storage is relatively abundant. 4,5 How to connect ES into the PS, alleviate the pressure of peak power consumption and reduce the system cost has great research significance. Some scholars combine genetic algorithm with bare bone particle swarm optimization (BBPSO) to improve the convergence effect in flexible job shop scheduling.…”

Section: Introductionmentioning

confidence: 99%

Research on adaptive dispatching of power system considering reserve energy storage and cost

Wang,

Liu

et al. 2023

Adv Control Appl

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The power system (PS) has the problem of grid connection of energy storage (ES) system. When the ES of the communication base station (BS) is associated with the power grid, relevant control strategies are formulated to schedule the base station energy storage (BSES). The total cost required during the scheduling period is determined using the lease income model. In the dispatching process, the BSES is applied to the peak load shifting (PLS) dispatching and economic dispatching of the PS. It is optimized by particle swarm optimization (PSO) algorithm and improved bare bone particle swarm optimization (BBPSO) algorithm. The constructed rental income model is used to calculate the total cost required during the scheduling period. In the dispatching, the BSES is applied to the PLS dispatching and economic dispatching of the PS. This model is optimized by PSO algorithm and improved BBPSO algorithm. The findings indicate that the BSES has good PLS capability. The larger the BS is, the more obvious the charging and discharging situation is. When the time is 4 h, the output load of 150,000 BSES is 486.67 MW, 341.14 MW more than that of 100,000 BSs. The discharge depth affects the lease cost, and the best discharge depth is 0.4. At this discharge depth, the larger the BS scale is, the greater the costs. In improving the performance of BBPSO algorithm, the model has the minimum convergence iteration of 15, with the best convergence effect. In the economic dispatching of PS, the total cost of accessing 200,000 BSs to store energy is 846.4658 million per year, which saves 367.4591 million. The suggested approach can effectively lower PS costs and increase stability.

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Power dynamic allocation strategy for urban rail hybrid energy storage system based on iterative learning control

Cited by 23 publications

References 12 publications

Dynamic power management for all‐electric ships based on data‐driven propulsion power modelling

Dynamic power management for all‐electric ships based on data‐driven propulsion power modelling

Hierarchical robust shipboard hybrid energy storage sizing with three‐layer power allocation

Research on adaptive dispatching of power system considering reserve energy storage and cost

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