Multi-objective Smart Distribution Network Operation Considering Demand Response Programming

2021 ◽  
Author(s):  
Mohammad Ali Lasemi ◽  
Foad Najafi ◽  
Ahmad Arabkoohsar
Keyword(s):  
Demand Response ◽  
Distribution Network ◽  
Multi Objective ◽  
Network Operation ◽  
Smart Distribution Network ◽  
Response Programming

Smart distribution network operation: Emerging techniques and standards

2011 ◽  
Author(s):  
G. A. Taylor ◽  
M. R. Irving ◽  
N. Nusrat ◽  
R. Liao ◽  
S. Panchadcharam
Keyword(s):  
Distribution Network ◽  
Network Operation ◽  
Smart Distribution Network

scholarly journals Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4856
Author(s):  
Masoud Zahedi Vahid ◽  
Ziad M. Ali ◽  
Ebrahim Seifi Najmi ◽  
Abdollah Ahmadi ◽  
Foad H. Gandoman ◽  
...  
Keyword(s):  
Power Generation ◽  
Optimal Allocation ◽  
Distribution Network ◽  
Energy Losses ◽  
Voltage Profile ◽  
Distributed Power Generation ◽  
Load Demand ◽  
Network Operation ◽  
Smart Distribution Network ◽  
Manta Ray

In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.

Novel algorithm for aggregated demand response strategy for smart distribution network

2013 ◽  
Author(s):  
Muhammad Babar ◽  
T. P. Imthias Ahamed ◽  
Aqueel Shah ◽  
Essam A. Al-Ammar ◽  
Nazar H. Malik
Keyword(s):  
Demand Response ◽  
Distribution Network ◽  
Response Strategy ◽  
Smart Distribution Network ◽  
Novel Algorithm
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