Simulation studies of resilient communication network architecture for monitoring and control wind power farms

Hussain, Shahid; Kim, Young-Chon

doi:10.1109/icact.2015.7224877

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Unnormalized values are appropriate when actual values provide key context to stakeholders. Unnormalized availability measures included connectivity of network topology [166], functional edges [118], housing capacity [119].Unnormalized productivity measures included gas supplied [131], power supplied [167], flow rate of petroleum [160].Unnormalized efficacy measures included hospital patient wait time [137], [138], networking end-to-end delay [168], networking throughput [139]- [142],…”

Section: Baselines Targets and Normalizationmentioning

confidence: 99%

Infrastructure resilience curves: Performance measures and summary metrics

Poulin

Kane

2021

Reliability Engineering & System Safety

114

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Section: Baselines Targets and Normalizationmentioning

confidence: 99%

Infrastructure resilience curves: Performance measures and summary metrics

Poulin

Kane

2021

Reliability Engineering & System Safety

114

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“…Other investigations into Offshore wind farm communication networks, were focused on the internetwork of wireless sensor motes [1]. A series of investigations reported by the same research team have also contributed to building a resilient network to support offshore wind farms [10] [11] which mainly focused on wired network topologies with simulation investigations developed in [10] where a single point-to-point microwave link between the wind farm and the control centre was setup.…”

Section: Related Workmentioning

confidence: 99%

Point-to-Point Channel Modelling Within Offshore Wind Farms

Mbogo

Peng

Tang

2018

Advances in Intelligent Systems and Computing

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From the perspective of several measurement campaigns in the offshore environment, it has been reported that the sea surface reflections are the main source of deep fading. We present a novel solution to this problem, by investigating the analytical implications of the propagation model which best fits the offshore channel characteristics. We also present a novel and yet simple implementation of receiver diversity which can mitigate the fading caused by sea surface reflections and ensure that the link is always steady even under extreme turbulent conditions.

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“…Therefore, compared to the TOU system, the real-time prices (i.e., often update every 15 min) are more economical for grid operators and EV owners [9]. However, the charging control algorithm requires inputs, such as driving habits (i.e., arrival and departure time), battery characteristics (i.e., battery capacity and state-of-charge (SoC), and electricity market price using a communication network [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Call the subroutine Charge_Control (i.e., Algorithm 2). First, it validates constraints defined by Equations ( 10) and (11). Then, it checks the optimal charging steps, the decision variable, and the energy requirements and thereby controls the charging process of EVs according to their optimal schedules.…”

mentioning

confidence: 99%

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

et al. 2022

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The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

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Simulation studies of resilient communication network architecture for monitoring and control wind power farms

Cited by 7 publications

References 10 publications

Infrastructure resilience curves: Performance measures and summary metrics

Infrastructure resilience curves: Performance measures and summary metrics

Point-to-Point Channel Modelling Within Offshore Wind Farms

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

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