scholarly journals Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194260-194268
Author(s):  
Bo Liu ◽  
Jiang Li ◽  
Haotian Ma ◽  
Yiying Liu
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Benders Decomposition ◽  
Generalized Benders Decomposition ◽  
Optimal Power ◽  
Decision Variables

scholarly journals Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 276
Author(s):  
Elkin D. Reyes ◽  
Sergio Rivera
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Functions ◽  
Optimal Power ◽  
Decision Variables ◽  
Metaheuristic Methods ◽  
Second Derivatives ◽  
Different Types ◽  
Derivatives Of

In an effort to quantify and manage uncertainties inside power systems with penetration of renewable energy, uncertainty costs have been defined and different uncertainty cost functions have been calculated for different types of generators and electric vehicles. This article seeks to use the uncertainty cost formulation to propose algorithms and solve the problem of optimal power flow extended to controllable renewable systems and controllable loads. In a previous study, the first and second derivatives of the uncertainty cost functions were calculated and now an analytical and heuristic algorithm of optimal power flow are used. To corroborate the analytical solution, the optimal power flow was solved by means of metaheuristic algorithms. Finally, it was found that analytical algorithms have a much higher performance than metaheuristic methods, especially as the number of decision variables in an optimization problem grows.

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