Results 21 to 30 of about 291 (205)
Ain Shams Engineering Journal, 2018 This paper presents a comparison between four modern heuristic algorithms for optimal loss reduction of power distribution network equipped with renewable energy resources.
Amr M. Ibrahim, Rania A. Swief
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Frontiers in Energy Research, 2022 In this paper, the probabilistic model of the controllable distributed generation in active distribution network is developed and applied to the daily stochastic optimal dispatch.
Jianxi Wang +5 more
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Journal of Modern Power Systems and Clean Energy, 2017 The increasing adoption of gas-fired power plants directly strengthens the coupling between electric power and natural gas systems. Current industrial practice in optimal power flow for electric power systems has not taken the security constraints of gas
Guoqiang SUN +3 more
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Data clustering based probabilistic optimal power flow in power systems [PDF]
IET Generation, Transmission & Distribution, 2018 Ever increasing use of renewable energies beside other uncertain parameters in power systems makes it necessary to evaluate power system issues, probabilistically. One of the important studies in power systems operating is optimal power flow (OPF), which should be considered as probabilistic OPF (POPF).
Sadjad Galvani, Mahmoud Choogan
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Analytical reformulation of security constrained optimal power flow with probabilistic constraints [PDF]
2013 IEEE Grenoble Conference, 2013 ISBN:978-1-4673-5669 ...
Roald, Line +3 more
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Energies, 2013 This paper presents a novel probabilistic optimization algorithm for simultaneous active and reactive power dispatch in power systems with significant wind power integration.
Yong-Cheol Kang +3 more
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Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach [PDF]
2019 IEEE Power & Energy Society General Meeting (PESGM), 2019 Probabilistic optimal power flow (POPF) is an important analytical tool to ensure the secure and economic operation of power systems. POPF needs to solve enormous nonlinear and nonconvex optimization problems. The huge computational burden has become the major bottleneck for the practical application.
Yan Yang +4 more
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Stochastic variational inference for probabilistic optimal power flows
Electric Power Systems Research, 2021 Abstract This paper applies a generative deep learning model, namely a Variational Autoencoder, on probabilistic optimal power flows. The model utilizes Gaussian approximations in order to adequately represent the distributions of the results of a system under uncertainty. These approximations are realized by applying several techniques from Bayesian
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Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System
International Journal of Interactive Multimedia and Artificial Intelligence, 2018 This paper proposes a solution procedure for probabilistic load flow problem considering the optimal allocation of Static Var Compensator (SVC) in radial distribution systems.
Walaa Ahmed +4 more
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IEEE Access, 2021 The uncertainties of solar photovoltaics generation, electric vehicle charging demand, and home appliances load are the major challenge of energy management planning in the residential areas.
Chitchai Srithapon +4 more
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