Optimal probabilistic reliable hybrid allocation for system reconfiguration applying WT/PV and reclosures

Swief, R.A.; El-Amary, Noha H.

doi:10.1016/j.asej.2019.09.010

Cited by 22 publications

(15 citation statements)

References 36 publications

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“…the seagull optimization algorithm, the modified farmland fertility algorithm [20], the crow search algorithm [21], the whale optimization algorithm, the gravitational search algorithm [22], and finally the multiverse optimization algorithm [23].…”

Section: B Literature Reviewmentioning

confidence: 99%

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

Çetinbaş¹,

Tamyürek²,

Demirtaş

2022

IEEE Access

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Section: B Literature Reviewmentioning

confidence: 99%

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

Çetinbaş¹,

Tamyürek²,

Demirtaş

2022

IEEE Access

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“…Many studies have been presented for different multi objective problems. Application of participating RERs has been investigated for improving reliability, reducing losses, reducing production cost, and reducing emission costs in [2][3][4]. High RER penetration comes with operational challenges due to their high level of intermittency.…”

Section: Pgmentioning

confidence: 99%

“…High RER penetration comes with operational challenges due to their high level of intermittency. With large scale PHEV integration, Vehicle to Grid (V2G) services can be used to cover the RER volatility based on pre-schedule table which is based on the SOC [5][6][7].Many studies in power system nowadays consider the probability effect [8][9][10].…”

Section: Pgmentioning

confidence: 99%

“…Gravitational Search Algorithm is another heuristic-based optimization technique that has been implemented for scheduling, sizing and citing of DGs [15][16][17][18][19][20]. Compared to the previously reported work which only considers either RERs [10] or PHEVs [13] in the scheduling problem, this paper augments the RER with PHEVs, together with the impact of weekly variation of loads, RER profile and SOC of PHEVs. As such, this paper presents a comprehensive unit commitment model compared to previous work.…”

Section: Pgmentioning

confidence: 99%

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Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

2020

Self Cite

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This paper introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB ® Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.

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“…For example, Yang Li proposed a two-stage optimization method for optimal distributed generation planning considering the integration of energy storage in [18]. [19] introduces an optimal probabilistic study based on multi objective problem integrating the stochastic behavior of the renewable resources to improve system performance properties. [20] presents a multiobjective approach to maximise the loadability of distribution networks by simultaneous reconfiguration and optimal allocation of distributed energy resources using a comprehensive teaching-learningbased optimisation algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Improved Beetle Swarm Algorithm Based on Social Learning for a Game Model of Multiobjective Distribution Network Reconfiguration

Chen

Wang

et al. 2020

IEEE Access

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With the increased distributed generation (DG) and electric vehicle (EV) load penetration in distribution networks, it is more difficult to ensure the safe and economic operation of the distribution networks because of the great volatility and randomness of DG and EV loads. In this paper, the uncertainties of wind power, photovoltaics, conventional loads, and EV loads are considered. Photovoltaics and conventional loads are related to solar radiation, and they are subtracted to form the net load. Then, the Wasserstein distance is used to divide the scene, and K-means clustering is used to reduce the scene, so the reconstruction analysis is carried out in the limited typical scene. In addition, to minimize network loss, load balance and maximum voltage deviation, a multiobjective game reconstruction model of the distribution network is established under the condition of satisfying network constraints. Moreover, the social beetle swarm optimization algorithm considering two social behaviors is adopted to solve the complex problem. Finally, the simulation results are verified on the standard IEEE-33 system and IEEE-118 system. The results show that the proposed strategy and algorithm can effectively reduce the network loss, improve the node voltage level and ensure that the load is not overloaded.

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Optimal probabilistic reliable hybrid allocation for system reconfiguration applying WT/PV and reclosures

Cited by 22 publications

References 36 publications

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

An Improved Beetle Swarm Algorithm Based on Social Learning for a Game Model of Multiobjective Distribution Network Reconfiguration

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