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 mast...
详细信息
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.
With the advancement of energy storage technologies, installing an energy storage system (ESS) in a distribution network has become a new solution to accommodate more and more distributed renewable generations. In thi...
详细信息
With the advancement of energy storage technologies, installing an energy storage system (ESS) in a distribution network has become a new solution to accommodate more and more distributed renewable generations. In this study, the optimal allocation of distributed ESSs is studied to maximise the benefit of the distribution system operator. The ESS allocation problem is divided into two stages: the mixed integer investment problem as the first stage and the optimal operation problems considering daily charging/discharging schedule of ESSs as the second stage. To tackle the uncertainties of distributed generation output and base load, typical days (scenarios) are firstly obtained by the clustering method and thereafter the operation problems include a number of scenarios, each with the corresponding possibility. Then each second stage problem is relaxed to a second-order cone programming model. To efficiently solve the whole problem considering multiple scenarios, the generalised Benders decomposition (gbd) algorithm is adopted, which is further accelerated by relaxing and rebinding integer constraints. Numerical experiments are conducted on a 17-bus test system to demonstrate the effectiveness of the proposed method. Additionally, comparisons between different algorithms are performed to verify the merits of the proposed acceleration method with respect to the original gbd and the branch-and-bound algorithm.
暂无评论