作者：
Huang, ShengWu, QiuweiGuo, YifeiRong, FeiTech Univ Denmark
Dept Elect Engn Ctr Elect Power & Energy Elektrovej 325 DK-2800 Lyngby Denmark Shandong Univ
Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Shandong Peoples R China Hunan Univ
Coll Elect & Informat Engn Elect Engn Changsha 410082 Hunan Peoples R China
An optimal active power control scheme based on model predictive control (MPC) is proposed for a doubly-fed induction generator (DFIG)-based wind farm equipped with distributed energy storage systems (esss). A two-sta...
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An optimal active power control scheme based on model predictive control (MPC) is proposed for a doubly-fed induction generator (DFIG)-based wind farm equipped with distributed energy storage systems (esss). A two-stage optimal control scheme is proposed. In the first stage, the power reference of each WT and the total power command of esss are generated, aiming to reduce the fatigue load of WTs by minimizing variations of thrust force and shaft torque. In the second stage, the charge/discharge power of esss are optimized to achieve the fair power sharing and maximize the capacity margin. An MPC based optimization problem is formulated for the constrained multiple input and multiple output (MIMO) wind farm system. The dynamics of converters and WTs are taken into account by the MPC. With the proposed control scheme, the active power references are optimized between WTs and esss according to their local wind conditions. Fatigue loads of WTs are reduced efficiently by coordinating the DFIG-based WTs and distributed esss. A wind farm with 10 DFIG-based WTs was used to validate the control performance of the proposed optimal active power control scheme.
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...
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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.
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