Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Memari, Mehran; Karimi, Alì; Hashemi‐Dezaki, Hamed

doi:10.1002/2050-7038.12902

Cited by 23 publications

(19 citation statements)

References 42 publications

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“…In formula (),

{P}_{g,t}

represents the price of traditional ES. The expected energy not supplied (EENS) is used to determine the amount of electricity that the BS can store to participate in the scheduling 15–17 . EENS can be solved by the equivalent continuous load curve, and the calculation method is shown in formula ().…”

Section: Research On Adaptive Ps Dispatching Based On Improved Bbpso ...mentioning

confidence: 99%

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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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“…In formula (),

{P}_{g,t}

Section: Research On Adaptive Ps Dispatching Based On Improved Bbpso ...mentioning

confidence: 99%

“…For the BSES participating in the economic dispatching optimization of PS, the model is established. The relevant scheduling cost objective function F is shown in formula (16).…”

Section: Bses Pls Dispatching and Ps Economic Dispatchingmentioning

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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“…Accordingly, several research works have tried to develop fast and accurate non-MCS approaches. The scenario reduction algorithms have been used to raise the speed of reliability calculations in [29]. Physical uncertainties have been examined by Memari et al [29] with various clustering algorithms, while the CPIs were not concerned.…”

Section: Introductionmentioning

confidence: 99%

“…The scenario reduction algorithms have been used to raise the speed of reliability calculations in [29]. Physical uncertainties have been examined by Memari et al [29] with various clustering algorithms, while the CPIs were not concerned. Hariri et al [30] presented an analytical method for evaluating the smart grids' reliability considering DERs' probabilistic behaviours.…”

Section: Introductionmentioning

confidence: 99%

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Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Aslani

Hashemi‐Dezaki

Ketabi

2022

IET Renewable Power Gen

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The reliability of smart micro‐grids (SMGs), as a cyber‐physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)‐based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber‐physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS‐based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS‐based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber‐physical interdependencies.

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Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

Ehteshami

Hashemi‐Dezaki

Javadi

2022

Energy Science & Engineering

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Several studies have been reported for optimal operation of electrical railway systems (ERSs). However, the stochastic energy management of ERSs, including renewable energy resources (RERs), has received less attention. The RERs’ uncertainties might affect the ERSs. On the other hand, the calculation time of the Monte Carlo simulation (MCS)‐based approaches is an essential challenge, which should be solved, particularly in real‐time decisions and recursive optimization problems. Thus, it is crucial to study the ERSs' stochastic behaviors and uncertainties, including RERs. This paper tries to overcome the discussed concerns and challenges by proposing a novel ERS’s optimal stochastic energy management using clustering algorithms. In this paper, the backward scenario reduction algorithm has been used. In addition, regenerative braking energy (RBE) and energy management systems (ESSs) have been studied. Studying the impacts of changes in the number of passengers on the optimized operation of ERSs and investigating the interaction between the utility grid and the ERS are other contributions of this research. The proposed method is applied to an actual test system of Tehran Urban and Suburban Railway Operation Company (TUSROC). Test results are validated by comparing with available MCS‐based methods. Simulation results illustrate the accuracy and computation time advantages of the proposed method. Simulation results illustrate that <0.6% inaccuracy appears in the proposed method, while it would be 500 times faster than MCS‐based ones. The comparative test results show the advantages of the introduced method.

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Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Cited by 23 publications

References 42 publications

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

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

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

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