Abstract
Modern power systems rely on renewable energy sources and distributed generation systems more than ever before; the combination of those two along with advanced energy storage systems contributed widely to the development of microgrids (MGs). One of the significant technical challenges in MG applications is to improve the power quality of the system subjected to unknown disturbances. Hence innovative control strategies are vital to cope with the problem. In this paper, an innovative online intelligent energy storage-based controller is proposed to improve the power quality of a MG system; in particular, voltage and frequency regulation at steady state conditions are targeted. The MG system under consideration in this paper consists of two distributed generators, a diesel synchronous generator, and a photovoltaic power system integrated with a battery energy storage system. The proposed control approach is based on hybrid differential evolution optimization (DEO) and artificial neural networks (ANNs). The controller parameters have been optimized under several operating conditions. The obtained input and output patterns are consequently used to train the ANNs in order to perform an online tuning for the controller parameters. Finally, the proposed DEO-ANN methodology has been evaluated under random disturbances, and its performance is compared with a benchmark controller.
Highlights
Electric power systems have transformed significantly since distributed energy resources (DER), renewable energy sources (RES), intensively emerged into the distribution networks.Many reasons related to environmental issues, governmental policies and economic aspects have motivated the increased use of RES, such as photovoltaic systems (PV), wind turbines (WT) and small hydropower turbines (HT) [1]
Owing to the aforementioned analysis, this paper presents a novel online intelligent control approach based on hybrid differential evolution optimization (DEO) and artificial neural networks (ANN)
The main theme of this paper is to study the behavior of the MG system under transients
Summary
Electric power systems have transformed significantly since distributed energy resources (DER), renewable energy sources (RES), intensively emerged into the distribution networks. In Reference [35], a BESS-based controller was proposed to improve the MG power quality by restoring the system voltage and frequency during transients. In Reference [37], an active and reactive power control strategy based on BESS and SCESS was proposed to restore MG system frequency and voltage during contingencies. To enhance the dynamic system response under abnormal conditions These control methods showed an effective dynamic performance, but they did not contribute significantly to the improvement of the MG power quality, and they involved complex calculations and long processes. The idea is to utilize complementary benefits of both strategies to perform an optimal online tuning of the BESS-based PI controller parameters so as to improve the MG system power quality.
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