An Efficient and Cost-Effective Power Scheduling in Zero-Emission Ferry Ships

Letafat, Armin; Rafiei, Mehdi; Ardeshiri, Masoud; Sheikh, Morteza; Banaei, Mohsen; Boudjadar, Jalil; Khooban, Mohammad Hassan

doi:10.1155/2020/6487873

Cited by 24 publications

(13 citation statements)

References 35 publications

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“…In other words, if a malicious hacker can intrude to the communication channels, the data taken by the estimator and consequently the results of the energy market will be affected. But, this situation can be grossly more vulnerable in the 8 energy market based on the peer to peer structure compared to the centralized one owing to more communication ways [46]. To overcome this issue, we want to develop the effective IPS-RL method based detection scheme for the energy market, carrying out on the peer to peer framework.…”

Section: Proposed Peer-to-peer Energy Trading Formulationmentioning

confidence: 99%

A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment

et al. 2021

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Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peerto-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the nearreality simulations., m Value of the average and variance. cThe injected malicious data.Attack time.

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Section: Proposed Peer-to-peer Energy Trading Formulationmentioning

confidence: 99%

A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment

et al. 2021

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“…As it has been mentioned before, FCs cannot rapidly respond to the sudden changes in the loads, due to their low dynamic response [24]. So, it is necessary to join them with another resource of energy with ability to apply a rapid change to its output (batteries in this case).…”

Section: A Fuel Cellmentioning

confidence: 99%

“…This constraint prevents over-charging and over-discharging by using m  and M  in equations (16), (19), (24) and (25).…”

Section: ) Soc Rangementioning

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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“…In order to deal with this issue, the output power is presumed to be equal to 10% of normal output power. Also, the limits of the consumed hydrogen mass can be defined such as the constraint (46) [22], [23].…”

Section: ) Electrical Vehiclesmentioning

confidence: 99%

Towards Energy Management Negotiation Between Distributed AC/DC Networks

Mohamed

Abdullah²,

Al-Sumaiti

et al. 2020

IEEE Access

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The ongoing development of technology has changed the nature of electric networks in the field of electric power generation. In the same vein, the generation units and demands are getting close by the integration of distributed resources within the distribution system with the aim of bringing more advantages to the system. Considering the promising concept of hybrid networks, which comprises different distributed resources, as a supportive element for the power system, this paper mainly focuses on modeling a distributed hybrid network, through which both the AC and DC loads can be supplied within the hybrid network. This network is operated in a distributed way where no independent operator is assumed for the operation. The primal-dual method of multipliers (PDMM) as an effective distributed method handles the operation of this network. So far, this method has only been applied to the AC grid or DC grid separately, while the presented method is modified for the operation of the hybrid network. Convergence speed and preciseness are mentioned as the advantages of this method which dominates the alternating direction method of multipliers (ADMM). In real cases, there are some errors in the output power of the renewable resources. Aiming to make the presented PDMM method more applicable, the uncertainty is also modelled using the Unscented Transform (UT) approach, as a way of uncertainty modelling which has shown attractive features, especially the capability of correlation modelling; therefore, it is more preferable for uncertainty modeling in many cases compared to the other methods. The proposed work is implemented on a smart island and the authenticity of this work is proved by comparing the performance of the proposed distributed approach with the centralized method.

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An Efficient and Cost-Effective Power Scheduling in Zero-Emission Ferry Ships

Cited by 24 publications

References 35 publications

A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment

A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment

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

Towards Energy Management Negotiation Between Distributed AC/DC Networks

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