Reactive Power Optimization of Distribution Network With Distributed Generation Using Metamodel-based Global Optimization Method

Reactive power optimization of distribution network with distributed generation (DG) is an important technology to ensure network security and stability, improve power quality, and lower operation costs. The reactive power optimization control of distribution network with distributed generation should not only deal with the continuous control variables such as reactive power output of distributed generations and static reactive power compensation device (SVC), but also the number of capacitor banks, on-load voltage regulator (OLTC) taps and other discrete control variables. After considering the uncertainty of DGs and loads, and the number of switching times of discrete control variables, the model is formulated as a class of complex nonlinear mixed integer programming problems with much difficult to solve. In this paper, firstly, the method of probability scenarios was introduced to describe the uncertainty of DGs and loads, and a multi-stage method was proposed to solve the daily switching times constraint of capacitor banks and OLTCs. Then a new method for the optimal control of reactive power for distribution network with distributed generation using metamodel-based global optimization was presented. The method uses Latin hypercube to efficiently sample data points on the complex and computation intensive reactive power control objective function, and build a Kriging metamodel of it in a region that most likely contains the global optimum. The method then uses the metamodel to approximate the complex objective function, selects new promising sample points to improve the metamodel, and identify the optimum of the region, before switching to the next most promising region and eventually locating the global optimum. The new method can effectively reduce the number of evaluations of the complex objective function through network reactive power simulation and computation time, thus improving search efficiency of the network operation control optimization. Using the modified IEEE33 node and PG&E 69 node systems as test cases, the computational efficiency and robustness of this new method were compared with those of the conventional population based global optimization methods. Numerical results show the effectiveness and feasibility of the proposed new method. © 2018 Chin. Soc. for Elec. Eng.