Optimal control of investments for quality of supply improvement in electrical energy distribution networks

Automatica ◽  
2006 ◽  
Vol 42 (8) ◽  
pp. 1331-1336 ◽  
Author(s):  
A. Bemporad ◽  
D. Muñoz de la Peña ◽  
P. Piazzesi
Keyword(s):  
Optimal Control ◽  
Energy Distribution ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Quality Of Supply

scholarly journals A Survey of Fault Prediction and Location Methods in Electrical Energy Distribution Networks

Measurement ◽  
2021 ◽  
pp. 109947
Author(s):  
Rahman Dashti ◽  
Mohammad Daisy ◽  
Hamid Mirshekali ◽  
Hamid Reza Shaker ◽  
Mahmood Hosseini Aliabadi
Keyword(s):  
Energy Distribution ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Fault Prediction ◽  
Location Methods

scholarly journals Probabilistic Load Flow Algorithm of Distribution Networks with Distributed Generators and Electric Vehicles Integration

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4234 ◽  
Author(s):  
Zhou ◽  
Yang ◽  
Yang ◽  
Yang ◽  
Littler ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Energy Distribution ◽  
Flow Analysis ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Load Flow ◽  
Probabilistic Load Flow ◽  
Load Flow Analysis ◽  
Distributed Generators ◽  
Probabilistic Load

Probabilistic Load Flow (PLF) calculations are important tools for analysis of the steady-state operation of electrical energy networks, especially for electrical energy distribution networks with large-scale distributed generators (DGs) and electric vehicle (EV) integration. Traditional PLF has used the Cumulant Method (CM) and Latin Hypercube Sampling (LHS) method. However, traditional CM requires that each input variable be independent of one another, and the Cholesky decomposition adopted by the traditional LHS has limitations in that it is only applicable for positive definite matrices. To solve these problems, taking into account the Q-MCS theory of LHS, this paper proposes a CM PLF algorithm based on improved LHS (ILHS-CM). The cumulants of the input variables are obtained based on sampling results. The probability distribution of the output variables is obtained according to the Gram-Charlier series expansion. Moreover, DGs, such as wind turbines, photovoltaic (PV) arrays, and EVs integrated into the electrical energy distribution networks are comprehensively considered, including correlation analysis and dynamic load flow analysis for EV-coordinated charging. Four scenarios are analyzed based on the IEEE-30 node network, including with/without DGs and EVs, error analysis and performance evaluation of the proposed algorithm, correlation analysis of DGs and EVs, and dynamic load flow analysis with EV integration. The results presented in this paper demonstrate the effectiveness, accuracy, and practicability of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document