Abstract
With a large number of distributed generators (DGs) and electrical vehicles (EVs) integrated into the power distribution system, the complexity of distribution system operation is increased, which arises to higher requirements for online reactive power optimization. This paper proposes two methods for online reactive power optimization, a scene-matching method based on Random Matrix (RM) features and a deep learning method based on Deep Belief Network (DBN). Firstly, utilizing the operation and ambient Big Data (BD) of the distribution system, we construct the high-dimension Random Matrices and extract 57 state features for the subsequent scene-matching and DBN training. Secondly, the feature-based scene-matching method is proposed. Furtherly, to effectively deal with the uncertainty of DGs and to avoid the performance deterioration of the scene-matching method under a new unknown scene, the DBN-based model is constructed and trained, with the former features as the inputs and the conventional reactive power control solutions as the outputs. This DBN model can learn the nonlinear complicated relationship between the system features and the reactive power control solutions. Finally, the comprehensive case studies have been conducted on the modified IEEE-37 nodes active distribution system, and the performances of the proposed two methods are compared with the conventional method. The results show that the DBN-based method possesses the better performance than the others, and it can reduce the network losses and node voltage deviations obviously, even under the new unknown and unmatched scenes. It does not depend on the distribution system model and parameters anymore and can provide online decision-making more quickly. The discussions of the two methods under different DG penetrations and the historical data volume were given, verifying the adaptability, robustness and generalization ability of the DBN-based method.
Highlights
The reactive power optimization of the distribution network can reduce the power losses and improve the voltage quality and the economical operation of a distribution network [1,2,3,4]
The results show that the Deep Belief Network (DBN) model trained and obtained under the low distributed generators (DGs) penetration is still available for the reactive power optimization demands in the higher DG penetration scenario
The performance of loss reducing of the DBN method is similar to the conventional method, The results show that the DBN model trained and obtained under the low DG penetration is still available for the reactive power optimization demands in the higher DG penetration scenario
Summary
The reactive power optimization of the distribution network can reduce the power losses and improve the voltage quality and the economical operation of a distribution network [1,2,3,4]. [5,6], the reactive power optimization can realize reasonable distribution of reactive power in the distribution network and reduce the power losses and node voltage deviations. The algorithm is a key issue of the reactive power optimization of the distribution network [7]. The conventional optimization methods are dependent on the model and parameters of the distribution network, which have obvious disadvantages of poor convergence and stability. Some of the heuristic intelligent algorithms have been widely developed for reactive power optimization [10,11,12,13], and these methods can process the discrete variables accurately, the initial values of these algorithms are adopted randomly, which may extend the computation time, and tend to fall into local minima
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