Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends
Electric power transmission networks; Optimization; Renewable energy resources; Scheduling; Uncertainty analysis; Computational performance; Electrical power generation; Integration of renewable energies; Operating constraints; Renewable energy generation; Stochastic generation; Stochastic optimisat...
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my.uniten.dspace-250102023-05-29T15:30:19Z Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends Tan W.-S. Shaaban M. Ab Kadir M.Z.A. 55336912400 57189064979 25947297000 Electric power transmission networks; Optimization; Renewable energy resources; Scheduling; Uncertainty analysis; Computational performance; Electrical power generation; Integration of renewable energies; Operating constraints; Renewable energy generation; Stochastic generation; Stochastic optimisation; Uncertainty modelling; Stochastic systems One of the most intricate optimisation problems in power systems is generation scheduling. It determines the schedule and dispatch of electrical power generation to meet the load demand under various technical and operating constraints. Generation scheduling is a vivid problem, particularly in recent years, due to the aggressive integration of renewable energy, with stochastic nature, into power grids. As the literature size has swollen substantially in the past several years, this study critically looks into uncertainty modelling and the formulation of various techniques that were implemented in stochastic optimisation-based generation scheduling. The strengths and weaknesses of existing methods are fully exposed. Market operation policies that significantly affect the scheduling of renewable energy generation in different timescales are elaborated. Potential applications and future trends in terms of modelling, computational performance, and incorporation of flexibility and resilience notions are thoroughly discussed. � The Institution of Engineering and Technology 2019. Final 2023-05-29T07:30:19Z 2023-05-29T07:30:19Z 2019 Article 10.1049/iet-gtd.2018.6331 2-s2.0-85066735689 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066735689&doi=10.1049%2fiet-gtd.2018.6331&partnerID=40&md5=dcb8ec1c79043ff3bef52ab0be5d6b45 https://irepository.uniten.edu.my/handle/123456789/25010 13 9 1467 1480 All Open Access, Green Institution of Engineering and Technology Scopus |
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Electric power transmission networks; Optimization; Renewable energy resources; Scheduling; Uncertainty analysis; Computational performance; Electrical power generation; Integration of renewable energies; Operating constraints; Renewable energy generation; Stochastic generation; Stochastic optimisation; Uncertainty modelling; Stochastic systems |
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55336912400 |
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55336912400 Tan W.-S. Shaaban M. Ab Kadir M.Z.A. |
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Tan W.-S. Shaaban M. Ab Kadir M.Z.A. |
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Tan W.-S. Shaaban M. Ab Kadir M.Z.A. Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends |
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Tan W.-S. |
title |
Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends |
title_short |
Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends |
title_full |
Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends |
title_fullStr |
Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends |
title_full_unstemmed |
Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends |
title_sort |
stochastic generation scheduling with variable renewable generation: methods, applications, and future trends |
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Institution of Engineering and Technology |
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2023 |
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1806426659332554752 |