Reliable Power Scheduling of an Emission-Free Ship: Multiobjective Deep Reinforcement Learning

Hasanvand, Saeed; Rafiei, Mehdi; Gheisarnejad, Meysam; Khooban, Mohammad‐Hassan

doi:10.1109/tte.2020.2983247

Cited by 71 publications

(32 citation statements)

References 43 publications

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“…The battery investment cost can be calculated like FCs with a small difference. In FCs, their life time is in the form of time, but for batteries, it is in the form of charge or discharge cycles [11] and [18]. Thus, by having the number of the cycles, batteries investment cost can be calculated as below:…”

Section: B Batterymentioning

confidence: 99%

“…This constraint guarantees that the loads are satisfied any time. This goal is modeled by equations (15), (18) and (23).…”

Section: ) Load Supplymentioning

confidence: 99%

“…On the other hand, a variety of methods have been developed to do the energy management issue in the available articles. In this regard, revolutionary algorithms [11], [12], dynamic programing [13], [14], fuzzy logic [8], model predictive control [4], [15], deep-learning methods [16]- [18], etc. have been applied on energy systems.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], a full mathematical model of governing constraints is extracted to automatically adjust the search area and improve the search ability of the utilized evolutionary optimizer. Simultaneous EM and component-sizing techniques have been proposed in [11], [13], [18] to consider investments cost in the optimization procedure. The proposed EM method in [18] aims to find the best components' sizes and energy supply plan by considering both cost and reliability factors.…”

Section: Introductionmentioning

confidence: 99%

“…Simultaneous EM and component-sizing techniques have been proposed in [11], [13], [18] to consider investments cost in the optimization procedure. The proposed EM method in [18] aims to find the best components' sizes and energy supply plan by considering both cost and reliability factors. In this regard, the summation of investment and operation costs and the Loss of Load Expectation index are taken as the main objectives.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Energy Management of an All-Electric City Bus With Wireless Power Transfer

et al. 2021

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Fuel cell-based hybrid electric vehicles are one of the most promising options to achieve zeroemission city buses. Efficient Energy Management (EM) plays a critical role to make such buses more efficient and practical. In this research, an available all-electric bus consisting of fuel cell (FC) and battery is considered and the efficiency of adding a Wireless Power Transfer (WPT) system to it is assessed. The proposed WPT system is only capable to receive energy in bus stations and use it to supply loads or charge the battery. To this end, the actual data of a city bus, its route and load profile were collected and utilized to ensure a realistic assessment. A full mathematical model of the energy system as well as the constraints governing the management issue is extracted and a Deep Deterministic Policy Gradient (DDPG) method is used to optimally manage the energy flows for the entire journey. All models are implemented in MATLAB software and the efficiency of the proposed system is investigated from economic and technical aspects. The results illustrate a high efficiency for the proposed WPT technique to be used in actual all-electric city buses.

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Section: B Batterymentioning

confidence: 99%

“…This constraint guarantees that the loads are satisfied any time. This goal is modeled by equations (15), (18) and (23).…”

Section: ) Load Supplymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Energy Management of an All-Electric City Bus With Wireless Power Transfer

et al. 2021

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Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

Cao

Geng

2023

Energy Science & Engineering

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The fuel cell system (FCS) is commonly combined with an energy storage system (ESS) for enhancing the performance of the ship. Consequently, the battery ESS size and power allocation strategy are critical for the hybrid energy system. This paper focuses on designing a method to solve these two problems. First, a battery degradation model is employed to assess the ESS lifetime. Subsequently, the sizing problem and the optimal power allocation are integrated into a cost‐minimization problem, which is solved by a double‐loop optimization approach. The inside loop utilizes the battery degradation model to calculate ESS lifetime. In the outside loop, a power allocation strategy based on the hybrid Particle Swarm Optimization algorithm and Gray Wolf Optimization algorithm is presented. Finally, the power allocation strategy is extended to real‐time implementation by the equivalent consumption minimization strategy (ECMS) and an improved ECMS is proposed to make the FCS operates near the maximal efficiency point. Compared with ECMS, the operating cost reduces by 0.26%. The result indicates that the proposed method can optimize the ESS size efficiently, and the power allocation strategy can assure the stable operation of the fuel cell ship.

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Time-delayed pith angle control of wind turbine systems-based Smith ultralocal model machine learning technique

Gheisarnejad

Khooban

2021

Control Strategy for Time-Delay Systems

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Reliable Power Scheduling of an Emission-Free Ship: Multiobjective Deep Reinforcement Learning

Cited by 71 publications

References 43 publications

Deep Learning-Based Energy Management of an All-Electric City Bus With Wireless Power Transfer

Deep Learning-Based Energy Management of an All-Electric City Bus With Wireless Power Transfer

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

Time-delayed pith angle control of wind turbine systems-based Smith ultralocal model machine learning technique

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