Zobrazeno 1 - 10
of 202
pro vyhledávání: '"Xueshan Han"'
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 10, Pp 2006-2019 (2024)
Abstract The linear power flow (LPF) model is widely used in the optimization, operation, and control of distribution networks. These applications require the LPF model to be accurate, fast, and simple in order to simplify calculations as well as to
Externí odkaz:
https://doaj.org/article/ba161ad9c4064a6987a7879bf39c5ca1
Publikováno v:
Energies, Vol 17, Iss 14, p 3515 (2024)
With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model
Externí odkaz:
https://doaj.org/article/c4fcae4b8bf64257adcdac38920f6d8e
Publikováno v:
IEEE Access, Vol 11, Pp 15211-15228 (2023)
Achieving the coordinated optimization of tie-line reserve and energy storage on two timescales is a key issue in reserve scheduling for active distribution networks. To effectively manage the uncertainty related to renewable energy, we propose risk-
Externí odkaz:
https://doaj.org/article/1a73db55afe7436398fed44ceb07a77a
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 10, Iss 4, Pp 894-901 (2022)
The outage of power system equipment is one of the most important factors that affect the reliability and economy of power system. It is crucial to consider the influence of contingencies elaborately in planning problem. In this paper, a distribution
Externí odkaz:
https://doaj.org/article/047a2df58a134687a2604da663528793
Publikováno v:
IEEE Access, Vol 10, Pp 9357-9370 (2022)
To effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy in active distribution network, a day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement lea
Externí odkaz:
https://doaj.org/article/1c1e4af7129444c7bd939f54341dedea
Autor:
Aamer A. Shah, Almani A. Aftab, Xueshan Han, Mazhar Hussain Baloch, Mohamed Shaik Honnurvali, Sohaib Tahir Chauhdary
Publikováno v:
Energies, Vol 16, Iss 7, p 3295 (2023)
The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is cri
Externí odkaz:
https://doaj.org/article/371b66a12e8b4ca2a3db031a63947d01
Autor:
Aftab Ahmed Almani, Xueshan Han
Publikováno v:
Energies, Vol 16, Iss 5, p 2410 (2023)
Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allo
Externí odkaz:
https://doaj.org/article/710a41b100e04774b85ffd25e86b05e0
Publikováno v:
IEEE Access, Vol 9, Pp 31276-31286 (2021)
The coupled single-port circuit has been proposed for online voltage stability assessment based on wide-area measurements and grid equations. This circuit can explicitly reflect the influence of grid and load on voltage stability. However, it does no
Externí odkaz:
https://doaj.org/article/be0776695a8e4929bf50cbc809b3168d
Publikováno v:
IEEE Access, Vol 9, Pp 87196-87206 (2021)
This paper presents a prediction error-based power forecasting (PEBF) method for a Photovoltaic (PV) system, using Photovoltaics for Utility Scale Applications (PVUSA) model based grey box neural network (GBNN). First, the differential equation based
Externí odkaz:
https://doaj.org/article/beca14871c2146d2b47dc63b91201b78
Publikováno v:
Global Energy Interconnection, Vol 3, Iss 4, Pp 303-312 (2020)
Given the increasing uncertainties in power supply and load, this paper proposes the concept of power source and grid coordination uniformity planning. In this approach, the standard deviation of the transmission line load rate is considered as the u
Externí odkaz:
https://doaj.org/article/59ae6af892094aca87d713f191d78f8d