机构:
Mohammed V Univ, Fac Sci, Lab Condensed Matter & Interdisciplinary Sci, Rabat, MoroccoMohammed V Univ, Fac Sci, Lab Condensed Matter & Interdisciplinary Sci, Rabat, Morocco
Benchrifa, Rachid
[1
]
Chaouch, Mohamed
论文数: 0引用数: 0
h-index: 0
机构:
Qatar Univ, Coll Arts & Sci, Dept Math Stat & Phys, Program Stat, Doha, QatarMohammed V Univ, Fac Sci, Lab Condensed Matter & Interdisciplinary Sci, Rabat, Morocco
Chaouch, Mohamed
[3
]
机构:
[1] Mohammed V Univ, Fac Sci, Lab Condensed Matter & Interdisciplinary Sci, Rabat, Morocco
Costs;
Rotors;
Poles and towers;
Optimization;
Wind speed;
Power system stability;
Distribution networks;
Artificial neural networks;
Radial basis function networks;
Wind turbines;
Artificial neural network;
NPV;
optimization;
radial distribution network;
wind energy;
wind turbine;
OPTIMIZATION;
ALLOCATION;
ALGORITHM;
ENERGY;
SPEED;
D O I:
10.1109/ACCESS.2023.3324884
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
This paper aims to present an economic decision-making model for determining the optimal wind turbine (WT) design for different bus nodes in a Radial Distribution Network (RDN) based on the wind potential of the studied site and grid capability. The main objective function in the optimization problem of this study is the maximization of the Net Present Value (NPV) of wind energy incomes subject to the WT geometrical design variables, including the rotor diameter and Tower Height; and under the RDN constraints to maintain the power system stability. Adequate placements among the different bus nodes for WT installation are those with the maximum NPV value. Furthermore, the intermittent characteristic of wind energy leads to the use of an Artificial Neural Network (ANN) in wind speed forecasting for good estimation of the generated wind energy. The effectiveness of the proposed model was validated using IEEE 9 and IEEE 33 Bus RDNs. The results demonstrate that the WT design determination is not related to the power of the wind potential but mostly to the capability of the connected RDN.