Abstract
The dynamic optimal power flow (DOPF) is a mixed-integer nonlinear programming problem. This article builds a DOPF model with discrete and continuous variables, and then proposes the iterative method based on the master and sub-problems obtained from the generalized Benders decomposition (GBD). Firstly, the power output of conventional generators and the reactive power of the wind farm are modeled as the continuous decision variables, and the transformer taps ratio is built as a discrete decision variable. Secondly, the objective function is to minimize the total power generation cost and network loss. Thirdly, the DOPF problem is decomposed into the master problem and sub-problems by fixing a complex variable, which reduces the complexity of DOPF. Then, the proposed algorithm is used to solve the master and sub-problems. Finally, simulation results show that the proposed method has advantages in terms of reducing computational time and enhancing accuracy.
Highlights
Dynamic optimal power flow (DOPF) is one of the key problems in power system economic dispatch and safe operation [1]
The innovation of this study includes: i). it analyzes the characteristics of the continuous variables and the discrete variable, and decomposes the DOPF into the main problem with discrete decision variables and the subproblems with continuous decision variables by generalized Benders decomposition (GBD) to solve the optimization problem with mixed decision variables. ii). the relaxation variables and Benders constraints are used to decrease the scale of the programming model and even improve the computation efficiency in the iterative solution processes
2) DISCRETE DECISION VARIABLES There are on-load tap changers in the power system, and the transformer ratio is used as a discrete decision variable in this DOPF model
Summary
Dynamic optimal power flow (DOPF) is one of the key problems in power system economic dispatch and safe operation [1]. Reference [7] developed a DOPF algorithm based on the nonlinear interior point method, which considered time separation and time-related constraints in a single optimization problem. Reference [8] proposed a modified honey bee mating optimization to solve the DOPF problem, which can deal with more equality and inequality constraints, such as operation limits, valve-point loading, and line flows. B Liu et al.: Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables Based on Generalized Benders decomposition (GBD) [22]. This paper applies the generalized Benders decomposition algorithm to solve DOPF with continuous and discrete decision variables. The relaxation variables and Benders constraints are used to decrease the scale of the programming model and even improve the computation efficiency in the iterative solution processes.
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